Electronic copy available at: http://ssrn.com/abstract=1097131

Emotion and Finance

-

An Interdisciplinary Approach to the Impact of Emotions on

Financial Decision Making

Christoph Merkle*

02/28/2007

Abstract

The paper explores the influences of emotions on human decision making, especially in

context of finance. It reviews the psychological, neuroscientific and neuroeconomic evidence

about the topic and shows which applications these insights have found in behavioral finance

and the new subject of neurofinance. Several descriptive and formal approaches are

introduced and their usefulness is discussed.

* University of Mannheim, Graduate School of Economic and Social Sciences (GESS),

Center for Doctoral Studies in Business, contact: chmerkle@mail.uni-mannheim.de

Electronic copy available at: http://ssrn.com/abstract=1097131

I

Contents

Contents I

List of Tables and Figures III

Abbreviations III

1. Introduction 1

2. Rationality and its Economic Implementation 3

2.1. Rationality in economics, philosophy and psychology 3

2.2. Expected utility theory and Bayesian inference 6

2.3. Investor behavior and efficient market hypothesis 8

3. Emotions – Their Meaning, Function and Impact 9

3.1. Attempt of a definition 9

3.2. The function and impact of emotion 11

3.3. The neglect of emotion in economic theory 12

4. Psychology and Cognitive Science 13

4.1. The treatment of emotions in psychology 13

4.2. The two-system view of intuition and reasoning 16

4.3. Heuristics and biases reorganized 18

4.4. The affect heuristic 20

4.5. Risk 22

4.6. Psychological modeling 24

4.6.1. Introduction 24

4.6.2. The valence-based approach 25

4.6.3. Models of experienced emotions 26

II

5. Brain Science and Neuroeconomics 29

5.1. The neuroscience of emotion 29

5.1.1 Brain systems and circuitry involved in emotion 29

5.1.2 The hypothesis of somatic markers 32

5.1.3 Emotion, cognition, consciousness and decision making 34

5.1.4 Once again: Two-system view 35

5.2. Neuroeconomics 37

5.2.1. What is and does neuroeconomics? 37

5.2.2. Implications from (affective) neuroscience for economics 38

5.2.3. Risk 40

5.2.4. The reward system in the brain 41

5.2.5. A second chance for expected utility? 43

6. What Can and Does Finance Make of it? 45

6.1. Financial markets and emotion 45

6.1.1. Behavioral finance and the marketplace 45

6.1.2. A market model of emotional and rational investors 48

6.2. The individual investor and emotion 49

6.2.1. Investor behavior emotionally understood 49

6.2.2. Behavioral portfolio theory 52

6.2.3. The affect heuristic – an implementation 53

6.3. Neurofinance 55

6.3.1. A new science and what it reveals 55

6.3.2. Two examples of research in neurofinance 57

7. Conclusion 59

Bibliography IV

III

List of Tables and Figures

Table 1 – Models of human decision making 3 Table 2 – Affective conditions 11 Figure 1 – Elements in the decision process 15 Table 3 – Two systems 16 Table 4 – Organization of heuristics and biases 19 Figure 2 – Risk-as-Feelings hypothesis 23 Figure 3 – The human brain 30 Figure 4 – Reward system of the brain 42

Abbreviations

ANS – autonomous nervous system ch. – chapter cp. – compare bk. – book ed./eds. – editor/editors e.g. – for example (exempli gratia) EMH – efficient market hypothesis EUT – expected utility theory f. – following page ff. – following pages fMRI – functional magnetic resonance imaging ibid. – at the same place (ibidem) no. – number p. – page pp. – pages pt. – part PET – positron emission tomography SCR – skin conductance response SEP – subjective expected pleasure sec. – section vol. – volume

1

1. Introduction

Interdisciplinarity is on the rise as research reaches the limits of separate subjects. For a

long time though it was discredited to grasp beyond the realm of ones own discipline

having only a rough knowledge in other domains.1 Consequently the information flow

between disciplines falls short scientific necessity.2 The approach chosen here is to

compile expertise from psychology and neuroscience to make it productive for econo-

mic purpose. This shall not be confused with scientific imperialism.3

It is an elementary academic argument that a science can never be more secure than the

foundations it is build on. If a controversy exists what should constitute these founda-

tions, the conclusions drawn of and the models constructed on them will be controver-

sial itself.4 This is particularly true for economics and finance, where the human factor

plays a role. The human nature seems to be somewhat unreliable and unpredictable. Yet

how to build economic models on it , that are meaningful and coherent?

The rationality assumption forgoes this problem by formulating criteria based on formal

logic and probability calculus. Though everyone agrees that people have and employ

reason to reach their goals, it is just as obvious that there is another concept of human

response to their environment. This concept is emotion.

It has been tried to attribute all “mistakes” people make in experiments or in real life to

limitations of their reasoning capacity.5 This is convenient as it leaves rationality as the

basic principle of human behavior untouched. However, humans seem to manage the

various demands of life not through brute information processing power, but through a

decision making mechanism of which emotions are an integral part.6 It is consequent

then to allow for emotions influencing decision behavior and to treat reason and

emotion more symmetrically in economic theory.7

But it takes too narrow a view to reduce emotions to their application as a decision

making tool. Emotions give life a meaning and without them we would have no reason

for living.8 This mutual function represents the complexity of emotions when it comes

to evaluating them for analytic purposes.

1 Cp. Nissani (1997), p. 203. 2 Cp. Gigerenzer/Selten (2001), p.10. The authors give a striking example of parallel research of the same matter in psychology and economics. 3 See Elster (1999), p. 415, for a discussion. 4 Cp. Savage (1954), p. 1. 5 Cp. Romer (2000), p. 442. A strategy leading to extreme assumptions, consider e.g. (one-shot) ultima-tum or dictator game, which is fairly easy to understand. 6 Cp. Gigerenzer/Selten (2001), p. 192. 7 Cp. Romer (2000), p.443. 8 Cp. Elster (1999), p. 403, who simply states: “Emotions are the stuff of live”.

2

Primary aim of this article is to give a survey of the various efforts to explain emotion

and its impact on human behavior. Most papers addressing this topic take in a limited

perspective, either economically, psychologically or neuroscientificly. Here principal

insights of different sciences will be highlighted.

Next to these merely theoretic considerations a further focus lies on specific applica-

tions in financial context. Finance is a field that has embraced the notion of rationality

most readily, but also makes rapid progress in incorporating alternative suggestions.

The creation of behavioral finance already in the eighties shows that psychological

findings fall on fertile ground.

The remainder of the article is organized as follows: Section 2 deals with the perception

of rationality in economics, psychology and philosophy and tries to elicit some notable

differences. Expected utility theory and Bayesian inference are briefly sketched as

corner stones of economic rationality implementation and assessed in their usefulness

for economic modeling. On financial markets the presence of rationality results in

efficient markets, a hypotheses that has dominated financial theory for decades and will

be discussed in the light of obviously existing irrationality.

Section 3 tries to define the term emotion and to distinguish it from other affective

constructs. Moreover it will be argued what may be the causes of the long lasting

neglect of emotions in economic theory.

Section 4 is devoted to the psychological aspects of emotion and behavior and structures

the vast literature concerning heuristics and biases concentrating on those, which are

emotionally induced. Among them the affect heuristic stands out as an explanation for

the emotional appraisal of stimuli. It is based on the two-system view of intuition and

reasoning, which serves to explain how the decision process is mentally organized. An

assessment of several approaches in psychological modeling closes the chapter.

Section 5 investigates the complex of brain science and neuroeconomics starting with a

general description of the interaction of rationality and emotion in the human brain,

which is not restricted to economic issues. As a particular theory the hypothesis of

somatic markers is introduced, which is based on interdependencies between body and

brain. Neuroeconomics as a relatively new science is addressed in the following sub-

section giving a survey of present results and future perspectives. The reward system is

introduced and hereby explained how brain-internal goals are formed. Furthermore it is

asked, how economic concepts like utility or risk are represented in the brain.

Finally section 6 deals with progresses of behavioral finance in incorporating emotions

and corresponding psychological findings into existing financial theory. This is done on

3

aggregated market level and for the individual investor, providing for an exemplary

model in each case. The section also comprises an overview over early findings of

neurofinance and presents two experiments, which assess brain activity within financial

decision making tasks.

The concluding section will point out some connections and similarities and rounds out

the picture of emotion and financial markets.

The different approaches modeling human decision making that will be of interest here

are displayed in Table 1.9 Theoretical rationality judges behavior only by its outcomes,

regardless how they were achieved. Procedural rationality examines the strategies and

rules underlying behavior. Neuroscience goes even one step further to analyze the very

origin of decision making.

Level of Model Object of Model Type of Model

Neural cognition Biological equipment Neural networks Procedural rationality Individual behavior Rules and heuristics

Theoretical rationality Decision outcomes Formal logic and probability calculus

Table 1

2. Rationality and its Economic Implementation

2.1. Rationality in economics, philosophy and psychology

On the same time we tend to agree with Aristotle, man being a rational animal, and with

Freud’s view that humans are irrational.10 What are the truths behind these statements?

What distinguishes man from other animals might rather be the capacity to be rational,

not meaning acting rational all the time.11 On the other hand sometimes not complying

with a norm of rationality does not cause us to be irrational.12

Speaking of a norm of rationality indicates the normative character of the concept. It is

necessary to take in a (to a certain degree) normative perspective, as a pure description

of behavior does not carry on very far.13 A normative formulation of rationality requi-

res general principles that are both intuitive and coherent.

Economics therefore resorts to rules of logic and calculus as these seem to provide a

reliable and durable basis for further research. The elements of this perception vary

9 Taken from Sadrieh et al. (2001), p. 91. Slightly adapted. 10 Cp. Stein (1996), p. 2. 11 Cp. Nozik (1993), p. xi. 12 Cp. ibid., p. 98, Evans/Over/Manktelow (1993), p. 185, Stein (1996), pp. 7ff. Stein emphasizes, that irrationality consists of a systematic divergence from the norm. 13 It is argued in the literature how to reconcile the descriptive and normative view on rationality. Gigerenzer/Selten (2001) suggest “bounded rationality”, Stein (1996) a “naturalized picture of rationality”.

4

from a narrow view including just transitivity and completeness of preferences to a

wider concept comprised of expected utility theory and statistic probability calculus,

prominently Bayesian inference.14 A common feature is the procedural or instrumental

notion of rationality, preferences and goals do not underlie an assessment. Instead the

process is investigated, how to reach rational decisions given the preferences.15 This

involves reasoning in accordance with the determined principles without restrictions

concerning computational power or time. Naturally economists realize that these condi-

tions are not likely to hold.16 However, this does not cause any harm to the concept of

rationality, most often failures are attributed to performance errors.17

The careful reader might have noticed a seeming contradiction between table 1 and

what was just said about the procedural character of economic rationality. But the table

indicates how judgments about rationality are accomplished and these are in fact

rendered by the outcomes. Interestingly someone reaching the rational decision by

chance is perceived as rational.18

Philosophy has partly accepted economic theory of rationality as dominant normative

view.19 However, philosophers tend to avoid a strict axiomatic formulation of princi-

ples, expressing the requirements verbally. Some evaluations are close to the economic

concept stating that rationality consists of three optimizing operations: an action chosen

must be optimal given the preferences and beliefs of the agent, the beliefs themselves

optimal given the available information and finally the optimal amount of resources

must be allocated to the acquisition of information.20 Others define rationality in a

sphere rather inaccessible for economic thinking.21 Mostly an instrumental perspective

of rationality is supported as it provides an intuitive and powerful access to the question,

whereas more substantive approaches run into the difficulty of justification.22 This

confirms the economic view that goals, desires and preferences are inherently subjective

and their content does not underlie rational standards.

14 Cp. Arrow (1986), p. S390 and Rabin (1998) p. 11. For a more practical oriented view on rationality see Eisenführ/Weber (2003), pp. 4ff. 15 Cp. Nozick (1993), p. 64. This is object of a famous criticism by Hume (1958), bk. 2, pt. 3, sec. III.: “It is not contrary to reason to prefer the destruction of the whole world to the scratching of my finger.” 16 Cp. for example Arrow (1986), p. S385. 17 A performance error implies that individuals miss to act rationally for reasons such as distraction, weariness, not enough effort etc. In this case the rationality hypothesis can be maintained, whereas competence errors meaning not to have full understanding which principles to apply would favour irrationality. See Stein (1996), ch. 2 for a discussion. 18 This is labelled „theoretical rationality“ here following the terminology of Max Weber (see Kahlberg [1980]). Sadrieh et al. (2001) use the expression “substantive rationality”, which is a bit misleading. 19 Cp. Nozick (1993), p. 41. 20 Cp. Elster (1999), p. 285. 21 Cp. for example de Sousa (1997), pp. 262ff. who states six principles of rationality. 22 Cp. Nozick (1993), p. 133ff.

5

A common feature is a more careful use of the term noting its incompleteness and the

potential unattainability of a correct and complete set of principles.23 Furthermore it is

widely acknowledged that the concept undergoes modifications. The detection of

systematic flaws should lead to a more adequate formulation of the standards of

rationality.24 By contrast the economic perception appears rather static.

The divide between reason and emotion is still rather sharp in philosophy, due to the

historic discourse dominated by thinkers such as Plato and Descartes. However, the

rationality of emotions is increasingly discussed and will likely alter the traditional

picture.25

A similar trend can be observed in psychology where the notion of rationality is already

wider and includes many phenomena beyond economic rationality.26 Economists often

finds themselves in a position to defend the thesis of humans being rational. The attitude

of psychologists usually is much more neutral.27 This enables to consider a variety of

influences on human mind, beliefs and behavior. Instead of conceptional modeling the

attention is turned to an analysis of thinking processes.28 Psychologists are nonetheless

familiar with theories of utility, probability and decision making. The main difference to

traditional economics might be seen in a rationality concept taking into account the

limitations in available knowledge and computational capacity.29 Thus it is more closely

oriented at real-life conditions and circumstances.

Precedingly the contrast to traditional economics has been emphasized, because modern

economic theory is en route towards a more realistic picture of human behavior. There

are basically to ways of achieving such a shift. On the one hand there is no principle

implying that an economic theory must be based on rationality.30 Hence one is free to

suggest an alternative concept, formulate assumptions and create appropriate models.

On the other hand the notion of rationality might be modified itself, probably a bit

towards the psychological one.31

The purpose here is neither to answer the question whether humans are rational or not,

nor to determine which perception of rationality is true. The aim is rather to find a more

23 Cp. Stein (1996), p. 7 and Nozick (1993), p. 46. 24 Cp. Nozick (1993), p. xiii. 25 Cp. Elster (1999) and de Sousa (1997), who undertake an examination of the rationality of emotions. 26 Cp. Simon (1986), p. S209. 27 Cp. ibid. 28 Cp. Gigerenzer/Selten (2001), p. 3. 29 Cp. Simon (1986), p. S211. 30 Cp. Arrow (1986), p. S385. 31 It is not quite evident how to classify e.g. “bounded rationality” within this description. The term itself suggests the former. This is confirmed by Gigerenzer/Selten (2001), p. 4, introducing it as a “competing notion”. The latter approach might be represented by a “naturalized picture of rationality” as proposed by Stein (1996), p. 255. See also footnote 13.

6

suitable basis for economic modeling. Rationality as understood by economists has

worked well as a working hypothesis, but the gap between normative theory and

empiric results should be addressed. Seen evolutionary it would be recommended not to

postulate principles or axioms, but to analyze decision behavior with regard to its

success in natural selection.32 The applied rules and heuristics should then be judged in

terms of their adaptive significance in evolution.33 Moreover it might be necessary to

abandon global consistency in favor of a more contextual view.34

Some of the aforementioned propositions for a further refinement of the rationality con-

cept can be transferred to the treatment of emotions. A first step is certainly to observe

and explain emotionally induced behavior in experiments or in real life. Secondly it has

to be determined to what extent current models can cover the findings. This will be

discussed in the following section for expected utility theory and Bayesian inference.

Their importance and relevance results from comprising two aspects of rationality in

standard economic theory: rationality of decision and rationality of belief.

2.2. Expected utility theory and Bayesian inference

Many economist argue that expected utility theory (EUT) is a model flexible enough to

incorporate whatever conceivable.35 Their claim is attached to the merely procedural

nature of EUT.

This is not the place to provide a thorough description of expected utility theory or

Bayes’ theorem.36 Yet it will be necessary to introduce some aspects to allow an assess-

ment of - for instance - the statement above.

In fact the term utility is rather unspecified and may include almost anything that

contributes to the well-being or satisfaction of a person.37 Hence it would be feasible to

integrate emotion into EUT, if it is possible to express emotional preferences coherently

in terms of costs and benefits, which are comparable to the other elements of utility.38

The last sentence also allows some conclusions, where potential problems might arise

from. To begin with expressing preferences is by no means easy, particularly when it

32 Cp. Camerer (1998), p. 175, who calls the common practice a form of “creationism”. 33 Cp. Romer (2000), p. 443. See also Cosmides/Tooby (1991). 34 Cp. Simon (1986), p. 210. 35 E.g. Hammond (1988). 36 For this purpose consult von Neumann/Morgenstern (1953) or Savage (1954). 37 Simon (1986), pp. S209ff., criticizes EUT for lacking premises about preferences or the content of the utility function. Von Neumann/Morgenstern (1953), p. 9, defend the concept by stating “the notion of utility is raised above the status of a tautology by such economic theories as make use of it and the results of which can be compared with experience or at least with common sense.” 38 Actually Jeremy Bentham’s construct of utility [1789] included emotions prominently, a notion that later disappeared. Cp. Loewenstein (2000), p. 426.

7

comes to emotions.39 This applies likewise to the measurement of utility for example

performed by comparison of lotteries.40 Some authors argue that at most an ordinal

measurement of emotion is feasible and that some emotions lend themselves to no

comparison at all.41 However, neuroscientific methods will probably offer new oppor-

tunities of physiological measurement.

Beyond the technical problems occurring in a cost-benefit analysis of emotions there are

further objections against this treatment. The influence of emotion on decision behavior

is not transparent and therefore cannot reasonably be included as costs or benefits.42

Emotions may have a dual role within the process of decision. They shape the reward

parameters and affect the ability to make choices.43 A focus exclusively on the former

neglects a major effect of emotions.

Due to this account of expected utility theory some authors propose to switch to a more

appropriate model.44 In any case it should have become obvious that emotions cannot

easily be integrated within EUT.

In contrast to EUT, which is a construction to explain individual economic behavior,

laws of statistics, notably Bayes’ theorem, are objectively true or at least mathematically

provable. Hence it is well founded to accept Bayesian updating as a normative principle

to adjust probabilities in the event of new information. However, it sets high standards

for information processing. Evidence is sparse that human beings could perform the

extensive computations necessary in decision situations with large numbers of cues.45

Simple and psychologically plausible decision rules are just as well consistent with the

empiric data and might not be inferior.46

Besides the concept of human beliefs works quite differently to that of statistic probabi-

lities. The assignment of probabilities to each and every statement or future state of the

world or a combination of these is an overwhelming task.47 Beliefs act as a simplifier

and allow to neglect a great deal of information. But beliefs (and subjective probabili-

ties as well) are subject to emotions and this has to be considered.

39 Cp. Evans/Over/Manktelow (1993), p. 181. For a practical approach to elicit preferences see Eisenführ/ Weber (2003). 40 For a description of the procedure see von Neumann/Morgenstern (1953), pp. 18ff., or more practically oriented Eisenführ/Weber (2003). The latter propose some alternatives as well. 41 Cp. Elster (1999), p. 279. 42 Cp. Fesseler (2001), pp. 207-209 43 Cp. Elster (1999), p. 413. See Nozick (1993), p. 55, for a similar argument. 44 For example Camerer (1998), p. 166, or Selten (2001), p. 14. It should be noted that both authors see emotion as one reason among others for a change. 45 Cp. Gigerenzer (2001), p. 45, or Camerer (1998), p. 171. 46 Cp. ibid. 47 Cp. Nozick (1993), p. 96.

8

2.3. Investor behavior and efficient market hypothesis

Traditional finance was inspired by the image of the rational investor. Portfolio theory

proposed by Markowitz is based on a mean-variance optimization that can be formula-

ted by means of a utility function.48 For the time being there is no reason to assume that

the general observations made before do not apply to individual decision behavior on

financial markets. The exclusive presence of rational investors would even lead to an

intrinsic dilemma, leaving no room for trading at all.49 Today it is widely accepted that

there is irrationality in the market though disputed if systematic or random.

The consequences for the market as a whole are not as apparent and therefore efficient

market hypothesis (EMH) shall be discussed here in greater detail. EMH represents the

assumption that all available information is fully reflected in market prices.50 It is not

required that market participants are rational one by one.51 This has produced the

perception that EMH can be maintained even in the light of individual deviation from

rationality. Advocates claim that within the arbitrary number of anomalies many work

in opposite directions and thus will cancel out in equilibrium.52 Furthermore anomalies

are not stable with regard to time or measurement approach.53

Indeed there exists a huge number of biases causing either under- or overreaction. But it

would be rash to infer them to cancel out. The observed patterns generally derive from

common roots, and as people share similar heuristics, one should expect aligned react-

ions.54 That some anomalies disappear over time is also a weak criticism, as others are

now well established; and generally to demand regularity of irrational behavior seems

peculiar.55 Some weight carries the objection that wealth is directed to wise investors,

because these perform better, become richer and will finally control the market. This

process of learning though is slow and periled by a variety of psychological factors.56

The key forces to attain efficiency, like arbitrage, turn out to be much weaker and more

limited than supposed.57 Moreover the presumption that financial markets are domina-

ted by professional investors coming close to rationality and ruling price formation

might well be rejected. Behavioral finance has emerged as an alternative to explain

48 Cp. Markowitz (1952), pp. 90/91. 49 Except for liquidity reasons. Cp. Arrow (1986), p. S389. 50 Cp. Fama (1970), pp. 383/384. 51 Cp. Shleifer (2000), p. 3. 52 Cp. Fama (1998), p. 284. 53 Cp. Fama (1998), pp. 287/88. 54 Cp. Hirshleifer (2001), pp. 1536ff. 55 Cp. Shiller (2003), p. 102. As far as methodology is concerned, it is (naturally) possible to explain some anomalies ex post by well adjusted asset pricing models [Cp. Fama (1998), p.287]. 56 See Hirshleifer (2001), pp. 1538ff., for a description. 57 Cp. Shleifer (2000), p. 2.

9

pricing and market behavior.58 Within this theory emotions certainly have their place

although they still deserve some more attention.

What is missing yet is a general behavioral conception of asset price formation. Today

most models are designed to explain individual anomalies and biases. On this account

Fama argues that EMH can “only be replaced by a specific model of price formation,

itself potentially rejectable by empirical tests.”59 It is vague, whether such a model is yet

to come, but the task definitely presents a challenge to behavioral finance.

3. Emotions – Their Meaning, Function und Impact

“Reason is, and ought only to be the slave of the passions, and can never

pretend to any other office than to serve and obey them.” David Hume60

3.1. Attempt of a definition

The term emotion has already been introduced without having provided a clear defini-

tion. Previously it was stated implicitly that emotions stand in a certain antagonism to

reason. This dualistic view can be traced back to Aristotle, who sees emotions as a

component of the non-rational part of the soul. His understanding of emotions is that

people by undergoing change, accompanied by pain or pleasure, reach different judg-

ments.61 This emotional influence on judgment was perceived a negative one almost

ever since. Adam Smith may be considered as an example writing:

“The man who acts according to the rules of perfect prudence, of strict justice, and of proper benevolence, may be said to be perfectly virtuous. But the most perfect knowledge of those rules will not alone enable him to act in this manner: his own passions are very apt to mislead him; sometimes to drive him and sometimes to seduce him to violate all the rules which he himself, in all his sober and cool hours, approves of.”62

In modern times both the dualistic picture of reason and emotion and the negative view

of the latter are challenged. Some psychological and neuroscientific evidence will be

presented in sections 4 and 5. But still some economists favor a segregation of decision

mechanisms based on emotions or on thoughts respectively.63

Returning to a definition of emotion, there exists little agreement what exactly is an

emotion and how to distinguish it from other affective constructs. Mostly emotion is

58 Cp. ibid. 59 Fama (1998), p. 284. Obviously Fama here refers to Popper, however it remains questionable if EMH itself constitutes a potentially refutable theory. This is negated e.g. by Lo/MacKinley (1999), pp. 6/7, who argue that what can be tested is only a joint hypotheses of EMH and several auxiliary assumptions. 60 Cp. Hume (1958), bk. 2, pt. 3, sect. III. 61 Cp. Aristotle (1991), p. 121. 62 Cp. Smith (1759), pt. VI, sec. III. 63 For example Romer (2000), p. 439.

10

characterized by a number of representative attributes. Frijda offers some helpful and

comprehensive criteria, which shall be outlined here64:

1. Qualitative feel:

A quite obvious feature of emotions is what they feel like. One can assign a separate

qualitative feeling to each emotion, and probably related central nervous system patterns

will be identified.

2. Antecedents:

In contrast to drives or bodily reactions emotions are preceded by an evaluative process.

Generally accepted this comprises the perception and assessment of a stimulus, more

controversial is whether these antecedents are cognitive.65

3. Intentional objects:

Emotions are attached to persons, events or objects, they carry a situational meaning

beyond a mere stimulus. One envies someone or regrets something determinant. This

attribute is closely related to the (cognitive) antecedents.

4. Physiological arousal and expressions

Emotional states are accompanied by hormonal changes and changes in the autonomic

nervous system. Among these physiological phenomena are observable expressions like

alteration of skin color, bodily posture, voice etc.

5. Hedonic quality / valence

To a certain degree emotions are pleasant or painful. This feature, termed valence, has

gained some attention by economists, as it offers a possibility to align emotions on a

scale and integrate them into a utility function.

6. Action tendencies

Emotions generate action tendencies, meaning a readiness to execute an action related

to the undergone feeling. For example shame goes along with a tendency to hide. This

feature is central for the behavioral interpretation of emotion.

Reviewing Aristotle’s and other definitions one gains confidence that the presented

criteria cover the relevant aspects of emotion.66 To qualify as an emotion does not

necessarily require to fulfill all conditions. Some emotions may lack hedonic quality,

situational meaning or action tendencies.

64 For the following cp. Frijda (1986) and Elster (1998/1999) for a similar summary. 65 LeDoux (1999), pp. 17/22/64 , repeatedly stresses the unconscious character of many emotional responses, but this need not to contradict a cognitive appraisal (cp. Lazarus 1991), see also section 4. 66 E.g. to cite an economist, a definition of Romer (2000), p. 439, that “feelings represent all possible mechanisms that produce an hedonic state, that is, a pleasant or unpleasant sensation that is accessible to conscious thought” is well represented by cognitive antecedents and hedonic quality.

11

For economic purposes it will be useful to adopt a pragmatic and therefore relatively

wide notion of the term emotion including feeling and affect, although some scientists

insist on a differentiation.67 It is not meant though to dilute the account given before.

Affective conditions State Disposition

Object Emotion Attitude / Sentiment

Objectless Mood Temperament Table 2

68

The table shows a distinction between emotions and other affective constructs along

two dimensions. Having an object corresponds to the criterion of intentional objects

discussed before. Dispositions are more enduring than states, they often last for month,

years or lifelong and contrary to acute states they are mostly latent and unexpressed.69

3.2. The function and impact of emotion

Some functions of emotions can be derived from the description given before. Emotion

involves action readiness and certainly a purpose of the emotion “fear” is to generate the

action “fight” or “flight”.70 The physiological changes coming along represent another

function by altering body chemistry in favor of induced action.71

It will later be discussed by which means emotion interferes with cognitive activity, for

the time being it can be recorded that this interplay is also a function of emotion.

Connected with this issue and the action tendencies is the role of emotion regarding

behavior.

Next to these intrapersonal functions one should not disregard the social functions

consisting of informing others about the expresser’s intentions and motives and recei-

ving similar information.72 Additionally most objects of emotions are of interpersonal

nature.73 Having in mind the significance of emotion for a meaningful life one could

also propose a reward function of emotion.

This thought is inspired by looking at the impact of emotion on decision behavior, a

question of course intimately connected with the functional perspective. In decision

making context emotions present themselves a reward by their hedonic value, and they

affect the relative salience or weight of other costs and benefits.74 But their influence

67 Feeling is commonly understood as conscious emotional experience. Cp. e.g. LeDoux (1999), p. 268. 68 Taken from Clore/Gasper (2000), p. 17, slightly adapted. 69 Clore/Gasper (2000), pp. 17/18. 70 Cp. Elster (1998), p. 51. 71 Cp. Ekman/Davidson (1994), p. 137. The authors present a survey of the answers given by several capacities of the field to twelve questions concerning emotions. Cp. also Rolls (1999), pp. 67-70. 72 Cp. Ekman/Davidson (1994), p. 139. 73 Cp. ibid. 74 Cp. Fesseler (2001), p. 193, Elster (1999), p. 413.

12

sets in earlier by constraining available choices and affecting resources spent on

searching for alternatives.75 Emotions may heighten or prolong relevant stimuli and may

influence the recall of information from the memory.76 And more generally, linked to

the mentioned social function, emotions inform an individual of its position and

relationship to the world and shape its beliefs.77

These comments were made in anticipation of the sections to come, where particularly

the effects on decision behavior will be addressed in detail.

3.3. The neglect of emotion in economic theory

The quotation of Adam Smith presented in 3.1. displays an awareness of emotional

influence on human behavior. Later on this insight seems to vanish although one can

trace some emotional references in works of eminent economists.78 The deathblow may

have come with the already cited work of von Neumann and Morgenstern, who initiated

an era of economic analysis based on rational decision making.

The appeal to emotions, if any, was addressed to specific issues rather than to incorpo-

rate it generally into economic theory.79 But this neglect was not limited to economics,

it also applies to psychology, cognitive science, neuroscience and philosophy.80 There

was actually no scientific field for economists to borrow from, enforced by a focus of

emotion theorists on causes and emergence of emotions rather than on their effects on

behavior and decisions.81

Yet this is not the whole story, as nowadays several alternative theories (e.g. to EUT)

exist, if not directly based on emotions though at least behaviorally funded. There is a

certain reluctance to switch in the light of the far reaching consequences. The most

weight carries the argument of the lacking agreement on which theory is superior.82 In

contrast the excuse that existing theories are well understood and easily applied is less

valuable.83 Even if some of them provide a good approximation to reality, they shall be

replaced if better approximations are available.84

75 Cp. Damasio (1995), p. 264, Cp. Tooby/Cosmides (1990), pp. 408ff. 76 Cp. Fessler (2001), p. 193. 77 Ibid. For a synopsis of the emotional influence on beliefs see Frijda/Manstead/Bem (2000). 78 Cp. Hirshleifer (2001), p. 1533, who mentions Fisher and Keynes. 79 Cp. Elster (1998), p. 47. 80 Cp. Solomon (2003), p. 1, for philosophy, LeDoux (1999), pp. 11/20/42, for neuroscience and cognitive science, and Zajonc (1998), p. 594, for psychology. 81 See Elster (1998), pp. 47/48, who offers the cited survey of Ekman/Davidson (1994) as one example. 82 Cp. Camerer (1998), p. 166, who proposes “cumulative prospect theory”, which is indeed very popular within the behavioral economics community. 83 Cp. Camerer (1998), p. 166. 84 Ibid. See also McFadden (1999), p. 76, who admits the prevailing model to be convenient and success-ful, but false.

13

Finally another point should be made: Even the behavioral theories (in economics and

finance) have concentrated mainly on cognitive aspects. Therefore despite the progress

in these fields the neglect of emotion is not yet eliminated and will not be, if there is no

shift in attention. The complexity and contextuality of emotions surely hamper the

advancement in this area.

4. Psychology and Cognitive Science

“Le cœur a sa raison, que la raison ne connaît pas” Blaise Pascal

4.1. The treatment of emotions in psychology

In a discourse about emotion and decision making it seems natural to resort to psycho-

logy, because it explores systematically human judgment, behavior and well-being and

thus can give economists some important insights.85 The account given before on the

meaning, impact and function of emotion draws heavily on these insights.

Traditionally there have been three main theories to explain emotional experience.86

Central theory sees emotion as a subjective feeling induced by brain activity and

followed by a response. Feedback theory reverses this interrelation stating that emotions

are elicited as a feedback on bodily sensations.87 Cognitive perspective finally considers

emotion to be a product of the cognitive evaluation of a stimulus or event.

The latter raises the question of the relationship between emotion and cognition. This is

vital for economic modeling, as when existing cognitive models shall be enriched by

emotion this should be done with some knowledge about their interaction. The standard

view in this context is (cognitive) appraisal theory. Appraisal is an assessment of the

significance of a stimulus for personal goals and well-being and, if relevant in terms of

gains or losses, generates an emotion.88 As a “general purpose theory”89 this resembles

the notion of antecedents presented before. But mostly appraisal theory is thought more

specifically connected with the cognitive perspective on emotion. The strongest position

then is to deny the existence of emotion without cognition and see it always as a

response to cognitive activity.90 Appraisal thus is necessary and sufficient for emotion.91

The interplay is dominated by cognition giving rise to emotion, but also in contrary

85 Cp. Rabin (1998), p. 11 and McFadden (1999), p. 75. 86 For more details see Frijda (1986) or LeDoux (1996). 87 Also known as James-Lange theory after William James and Carl Lange. 88 Cp. Lazarus (1991), p. 354. 89 LeDoux (1996), p. 52. 90 This view is put forward e.g. by Lazarus (1991), p.353. 91 Cp. ibid.

14

direction emotions influence appraisals.92 It should be noted that the concept of

appraisal does not require conscious computation.93

It is argued that appraisal theory goes astray in not addressing the relationship between

cognition and emotion, but between cognition and cognitive evaluation of emotion.94

Such introspection involves higher levels of cognition from the start, which may be

unnecessary for the onset of emotion.95 A more general critique of cognitive models is

that in explaining emotion exclusively by thoughts the distinct character of feelings is

lost.96

Alternatively one can imagine emotional reactions independent and without the partici-

pation of a cognitive appraisal.97 In this case emotional responses to stimuli occur that

may be rationalized only afterwards.98 Emotion and cognition are then regarded as sepa-

rate mental functions that process information independently and can influence each

other in a variety of ways.99

Despite their opposed propositions both theories must not be incompatible. They mainly

differ in their concept of cognition. If it is restricted to conscious computing, affective

reactions may well proceed without it, if it contains everything that generates meaning,

emotion will need it as an antecedent.

What the approaches have in common and what is probably more relevant from a

behavioral perspective, is the strong interaction of both mental modes. Seldom if ever

cognition operates without affective influence. Hereby “feelings act as a selective atten-

tional filter for incoming stimuli”.100 The strong immediate experience of emotion may

lead to a crowding out of other goals.101 Secondly emotion influences the retrieval of

information and knowledge from memory.102 Positive feelings tend to facilitate the

processing of information concerning success, negative feelings concerning failure.103

Attention is focused on aspects of a situation that are consistent with the prevailing

emotion, what may result in different estimations of probabilities for certain events or a

92 Cp. Loewenstein et al. (2001), p. 280. 93 Cp. Lazarus (1991), pp. 361ff. 94 Cp. Zajonc (1998), p. 619. 95 Cp. LeDoux (1996), p. 64. 96 Cp. ibid., p. 38. 97 Cp. Zajonc (1998), p. 597. 98 Cp. Zajonc (1980), p. 155. He writes : “Quite often ‘I decided in favor of X’ is no more than ‘I liked X’. Most of the time, information collected about alternatives serves us less for making a decision than for justifying it afterwards” 99 Cp. Zajonc (1980), p. 151 and LeDoux p. 69. This account is related to the two-system view that will be presented in 4.2. 100 Wright/Bower (1992), p. 277 and Fessler (2001), p. 193. 101 Cp. Loewenstein (1996), p. 272 102 Cp. Wright/Bower (1992), p. 276f. and Frijda (1986), p. 121. 103 Cp. Frijda (1986), p. 123.

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different rating of an alternative’s global attractiveness.104 This effect could have vital

consequences in financial analysis.

Figure 1105

Thirdly feelings affect the availability of mental constructs and decision strategies.106

This includes various influences on reaching judgments and a reduction of the available

choice set. Figure 1 shows the interrelations seen by an economist.107 Attitude is defined

similar to the explanation given in 3.1., perceptions and beliefs represent the cognitive

appraisal of a stimulus (here information). Preferences are understood in an economic

sense, whereas the usage of motives is a bit vague.108 Black arrows indicate the standard

economic view of the decision process: only cognitively evaluated information and

preferences enter into the decision process. Allowing for affective constructs, the num-

ber of factors and their impacts (gray arrows) increases significantly. However, recal-

ling what was said before, the draft is far from being complete. If one favors appraisal

theory, then an arrow should lead from perception to affect, following Zajonc one could

also imagine a direct link between stimulus and affect. Moreover the relationship of

affect and preferences should be added for emotion influencing the relative desirability

of different preferences.109 To reach an exhaustive illustration of the relations is not the

target here, as cognition, emotion and behavior form an intricate web.

The transition from mental processing to choice or behavior deserves some more atten-

tion. Some regard emotion as functional in management of action and as bridging

104 Cp. Wright/Bower (1992), p. 278. 105 Taken from McFadden (1999), p. 74. 106 Cp. Wright/Bower (1992), p. 277. Consider also the statements made in 3.2. 107 The descriptions given by McFadden (1999), pp.74ff., are interpreted within the terminology introdu-ced before. It presents a good example to what degree psychological insights have penetrated economics. 108 Probably it can be interpreted similar to motivation in Lazarus (1991). 109 Cp. Loewenstein (1996), p. 273.

16

device from mere thinking to acting.110 Emotion has the capacity to discriminate

between harmful or beneficial consequences for the individual and to respond adaptive-

ly.111 It may even have primacy over cognition and be responsible for a majority of

human decisions.112 Behavior is considered emotional, when it results from action

tendency and serves a relational purpose to the felt emotion.113 In its strong influence on

behavior feeling is occasionally experienced unwanted and destructive, yet inescapable;

therefore people often apply sophisticated tactics to override it.114 This awareness stands

in contrast to the common interpretation of one’s own actions as deliberate and under

volitional control.115

4.2. The two-system view of intuition and reasoning

Based on the last statement one can infer that there is indeed a deliberate mental mode

in processing information, next to a rather automatic one. Many evidence exists for the

behavioral mechanism to consist of two simultaneously active, parallel systems.116

These two systems are labeled differently, experiential vs. rational system, intuition vs.

reasoning, associative vs. rule-based system or simply system one and two.117 The

distinction is closely related to that of emotion and cognition, but emphasizes more the

procedural way information is evaluated and responses are produced. There is agree-

ment however that the intuitive or experiential system has an affective basis or is emo-

tionally driven.118 It is assumed to work by images, associations and experiences.

System 1 (intuitive) System 2 (reflective)

Automatic Controlled

Effortless Effortful

Associative Deductive

Rapid, parallel Slow, serial

Process opaque Self-aware

Process characteristics

Skilled action Rule application

Affective Neutral Causal propensities Statistics

Concrete, specific Abstract

Content on which processes act

Prototypes Sets Table 3

110 Cp. Frijda/Manstead/Bem (2000), pp. 3f. For neurological evidence see Damasio (1995). 111 Cp. Zajonc (1998), p. 592. Similar in Lazarus (1991), p. 362, however with implicit cognition. 112 Cp. Zajonc (1980), pp. 154ff. 113 Cp. Frijda (1986), p. 95. 114 Cp. Loewenstein et al. (2001), p. 269. 115 Cp. Loewenstein et al. (2001), p. 276, consider also Zajonc (1980), p. 155, and what was said about ex-post rationalization. 116 Cp. Leventhal (1982), p. 123 or Zajonc (1980), p. 151. 117 In order of mentioning: Epstein (1994), Kahneman (2002) and Sloman (2002). 118 Cp. Slovic et al. (2002), p. 398, and Epstein (1994), p. 709.

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Table 3 gives a survey of the characteristics of both systems.119 System one processes

information automatically, rapidly, effortlessly and efficiently.120 Hereby it is not very

accessible to conscious awareness, it may be even possible to identify the source of an

inference by content of awareness.121 With the experiential system (1) usually only the

result of an evaluation is conscious. When confronted with an emotionally significant

event, the experiential system searches the memory banks for related events and recalls

them including their emotional accompaniments.122 Decision making is guided by the

retrieved images, which are marked by pleasant and unpleasant feelings.123

In contrast system two operates in an analytical, deliberate, slow, effortful manner that

is governed by rules and normative thought.124 Advantages of system two are its relative

flexibility and controlled functioning. It is capable of high levels of abstraction and does

not strive for immediate gratification.125

How do these very different mental processes work together? Some argue that the

presence of two systems makes life difficult and that conflict is ubiquitous in decision

making.126 However, one can formulate the interaction of the systems more positively:

“All behavior is assumed […] to be product of the joint operation of two systems. Their relative dominance is determined by various parameters, including individual differences in style of thinking and situational variables, such as the degree to which a situation is identified as one that requires formal analysis. Emotional arousal and relevant experience are considered to shift the balance of influence in the direction of the experiential system.”127

Hence the two systems usually operate interactive and do not command exclusive

problem domains, although not every problem confronted is solved by both systems.128

Thinking is transformed by emotional arousal, becoming more categorical, personal,

concretive, unreflective and action oriented.129 Reversely thinking supports the emotio-

nal interpretation of events. System two further has the function to endorse, correct or

override the results of system one.130 This intervention takes on most frequently the

form of adjusting a previous result, alternatively it is rejected and replaced altogether.131

119 The table is taken from Kahneman/Frederick (2002), p. 51. 120 Cp. Epstein (1994), pp. 709/710/715, similar in Slovic et al. (2002), Kahneman (2002) and Kahneman/ Frederick (2002). 121 Cp. Sloman (2002), p. 383, Slovic et al. (2002), p. 311. 122 Cp. Finucane et al. (2000), p. 2, Epstein (1994), p. 716. 123 Cp. ibid. Also note the similarity to the somatic marker hypotheses (see section 5). 124 Cp. Kahneman/Frederick (2002), p. 50, Slovic et al. (2002), p. 311, Epstein (1994), p. 710. 125 Cp. Epstein (1994), p. 715. This point is interesting with regard to discounting. Empirical findings favor hyperbolic discounting in the short-term. Probably then system 2 is overruled by system 1 as the emotional significance of a payoff increases. 126 Cp. Sloman (2002), p. 380. 127 Epstein (1994), p. 715. 128 Cp. Sloman (2002), p. 382. 129 Cp. Epstein (1994), p. 710. 130 Cp. Kahneman/Frederick, p. 50. 131 Cp. Kahneman (2002), p. 473. In adjustment the intuitive impression serves as an anchor.

18

The monitoring exercised by system two normally is quite lax and depends on the

circumstances under which a decision is made.132 High incentives, intelligence and

exposure to statistical thinking tend to enhance the ability of system two to detect errors

of system one.133 Time pressure, multi-tasking and surprisingly a positive affective state

work against.134 Next to these general properties, context in which a problem is presen-

ted is also influential. Successful corrections occur more frequently in games of chance

than in psychological judgments.135 For finance, resembling more the gambling task,

one can expect an active role of system two.

Noteworthily not only reasoning can override intuition, in some cases system one can

prevail despite better knowledge.136 The decision process is sometimes described as an

internal dialogue or as conflict resolution.137 Mediation between the systems mostly

works impalpably and successfully, but naturally research concentrated on those

decisions and judgments, which are subject to biases.

4.3. Heuristics and biases reorganized

When starting their research project on judgment and decision making, Kahneman and

Tversky assumed that people rely on a limited number of heuristic principles to facilita-

te complex tasks.138 In their initial analysis they presented various biases and explained

them by three heuristics, representativeness, availability and anchoring. But in the years

to come the fertile field discovered numerous biases and ever new heuristics.

Economists remained skeptical of the seemingly arbitrary heuristics offered by psycho-

logy.139 Their rationale of individual behavior canceling out in the market place was

illustrated in connection with efficient market hypotheses (see section 2.3.). Recall the

counter-argument that biases are not incidental but predictable, arising from common

roots shared by most people. An attempt to organize the existing heuristics may shed

some light on their origin and the role of emotion in this context.

Following the two system view one can infer that heuristics are applied by system one,

avoiding a straining employment of system two. Hence a bias involves the failure of

132 Cp. Kahneman (2002), p. 451. 133 Cp. Kahneman (2002), p. 473. 134 For the effect of an induced positive feeling state consider Isen et al. (1982). 135 Cp. ibid., p. 472. 136 Cp. Epstein (1994), p. 718. In a famous experiment, a bean is drawn from a sample of white and red beans (red beans winning). Participants often prefer a 7 out of 100 chance to 1 out of 10 for the sheer number of winning beans. They report despite their knowledge (generated by system two) of the lower probability it “just felt right” (system one) to choose the higher number of winning beans. 137 Cp. McFadden (1999), p. 82. 138 Cp. Kahneman (2002), p. 465. See also Tversky/Kahneman (1982). 139 Cp. Hirshleifer (2001), p. 1539.

19

both systems, of system one in actually generating it and of system two in missing to

detect and correct it.140 Given the affective nature of system one and its responsibility

for the exertion of heuristics it suggests itself to conclude that all biases and heuristics

are more or less caused by emotion. This assertion confirms the close interplay of emo-

tion and cognition but unfortunately does not serve the purpose of an organization.

Hirshleifer offers a categorization of biases identifying four sources: cognitive resource

constrains, self-deception, emotion and social interaction.141 Cognitive constrains

include limited attention, time, memory and processing capacities. Self-deception

describes the tendency to adhere to a positive image of the own abilities in judgment

and decision-making. Social interaction comprises biases related to interpersonal

communication and positioning. Emotion as a separate category addresses biases

directly evoked by feelings and affects, but is certainly also present in self-deception

and social interaction and even in cognitive constrains since limitations demand a

substitution by something else.142

Cognitive constrains

Self-deception Emotional biases Social interaction

representativeness overconfidence loss/regret aversion herd behavior

anchoring confirmatory bias endowment effect attribution error

base-rate neglect biased self-attribution status quo bias false consensus

gambler’s fallacy hindsight bias ambiguity aversion curse of knowledge

availability cognitive dissonance availability Table 4

143

Table 4 attempts a classification of some well-known biases and anomalies, but it is by

no means mandatory or unambiguous. For example availability is enlisted both under

cognitive constrains and emotional biases, since the availability heuristic can be inter-

preted in different ways. It may be understood cognitively as the ease to remember and

recall instances, or alternatively as linked to mental images or emotional represen-

tations.144 To discriminate one has to find out, why in individual cases some factors are

better available than others. This imposes the necessity of a lower level of abstraction

and generality, perhaps contrary to the wish of a general framework.145

In the realm of finance loss and regret aversion play a major role, here considered

emotionally induced. They appeal directly to the feelings of regret or disappointment,

140 Cp. Kahneman (2002), p. 471. 141 Cp. Hirshleifer (2001), pp. 1540ff. He also admits the impact of emotion in most or all of the effects (p. 1550), but the relative dominance may differ. 142 A similar argument is employed in the theory of attribute substitution, Kahneman (2002), pp. 466ff. 143 The table is partly based on Hirshleifer (2001). 144 Cp. Loewenstein (1996), p. 280 and Slovic et al. (2004), p. 317. 145 Cp. Kahneman (2002), p. 483.

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for loss aversion accompanied by reference-point dependence, which is - analogously to

endowment effect and status quo bias - interpreted as emotional attachment to one’s

own belongings. Further financial effects like disposition effect or mental accounting

are more ambiguous. The former seems to be partly self-deceptive (not admitting wrong

decisions) and partly cognitive (similar expectations as in gambler’s fallacity). The lat-

ter has cognitive (limits in calculation of an overall account) and emotional aspects.

In this way the presented framework may help to illuminate the roots of biases and

anomalies in human judgment and decision-making. It certainly has its limitations,

particularly with regard to emotions – they are just omnipresent.

An Analysis of biases of course is only the first step, but how to eliminate or at least

reduce them? The decisive factor to exercise control in decision situations is awareness

of one’s mental processes.146 If system one operates unconsciously as assumed, this pre-

condition is not met and a deliberate activation of system two is needed.147

4.4. The affect heuristic

Paul Slovic and collaborators recently developed a concept called “the affect heuris-

tic”.148 It is based on the aforementioned idea of two systems of mental processing,

addressing the experiential system. Affect is understood as “faint whisper of emotion”,

which is experienced as a feeling state and demarcates the valence of a stimulus.149 The

affect heuristic then works as follows:

“Representations of objects and events in people’s minds are tagged to varying degrees with affect. People consult or refer to an ‘affective pool’ […] in the process of making judge-ments.”150

When comparing the affect heuristic to other heuristics, e.g. the representativeness heu-

ristic, it becomes apparent that its conception is broader and more general. Representa-

tiveness depicts a specific, relatively well-defined phenomenon, whereas the affect

heuristic has a wide array of applications. It may be criticized, that it is not a heuristic in

the classic sense at all, but rather describes the operation of system one.

The affect heuristic works by retrieving images that contain associated feelings and are

therefore evaluable in decision tasks.151 It depends on context, whether probabilities,

proportions, frequencies or monetary quantities convey a precise and strong affective

146 Cp. LeDoux (1999), p. 63. 147 Kahneman (2002), pp. 471ff. describes his hypotheses of attribute substitution as a “silent process”. Statistical rules are generally lowly accessible, therefore even statistically trained people tend to be sub-ject to the same biases (they do not notice that their knowledge is to be applied in the specific situation). 148 Cp. Slovic et al. (2002), Slovic et al. (2004) and Finucane et al. (2000). 149 Cp. Slovic et al. (2004), p. 312. 150 Finucane et al. (2000), p. 3. 151 Cp. Slovic et al. (2002), p. 409.

21

meaning.152 On the one hand the rapid availability of affective impressions supports

easy and effective decision making, but on the other hand it is manipulable and limited

facing decision problems of modern times.153

In practice the affect heuristic serves to explain a variety of phenomena, beginning with

the exposure effect (mere exposure induces preference/liking).154

Preference reversals in gambles are also probably caused emotionally. Usually people

prefer high probability / low profit gambles in bilateral choice situations, while they

assign higher monetary values to low probability / high profit gambles. Different aspects

of a gamble are affectively evaluated: probabilities in a choice situation and monetary

outcomes in valuation.155

Evidence for the affect heuristic concentrates on such cases of relative weighting of

certain features, finding for example dominance of probability in some decision situa-

tions and insensitivity to probability in other.156 Slovic et al. note:

“Even important attributes may not be used by a decision maker unless they can be translated precisely into an affective frame or reference.”157

A central finding of research regarding the affect heuristic is its role in judgment of

risks and benefits. In environment (and in finance) risks and benefits are normally posi-

tively correlated. When judging these features however, people assign low risks to

highly beneficial options and vice versa.158 The assumption is that initially the global

advantageousness of an option is evaluated by means of emotional cues, and as a second

step risk and benefit judgments are derived from the result.159 The affect is prior to

cognitive assessment and prevails, if not a correction by system two sets in. Conse-

quently negative correlation should be more pronounced, when cognitive resources are

reduced. This was confirmed in an experiment under time-pressure.160

In appraisal of unfamiliar financial assets experimental results are similar, expected

return and perceived risk are negatively correlated.161 In contrast familiar assets are seen

in context of financial markets and financial models (e.g. CAPM), hence their risk and

return are judged to be positively correlated.162

152 Cp. Slovic et al. (2002), p. 410. 153 Cp. Slovic et al. (2004), pp. 314/319, Slovic et al. (2002), p. 416. 154 Cp. Slovic et al. (2004), p. 400, and already Zajonc (1980). 155 Cp. Slovic et al. (2002), pp. 401ff. 156 For an extensive survey consider Slovic et al. (2002) and Slovic et al. (2004). 157 Slovic et al. (2002), p. 406. 158 Cp. Finucane et al. (2000), pp. 3/4. Objects of judgment are technological, environmental and health hazards. 159 Cp. Finucane et al. (2000), p. 4, Slovic et al. (2002), p. 315, and Slovic et al. (2004), p. 411. 160 Cp. Finucane et al. (2000), pp. 5ff. 161 Cp. Ganzach (2000), p. 355. 162 Cp. Ganzach (2000), p. 356.

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Time will prove, whether the affect heuristic is a useful tool in explaining emotional

influence on information processing and decision making. It has its merits in drawing

attention to feelings, which are viewed as neural and psychological substrate of

utility.163 It provides insights into the when and how situational factors or aspects of a

problem are affectively evaluated. One can consider it as an implementation of somatic

marker hypothesis (see section 5.2.), given the pictorial mapping of information on

emotional impressions.164 Yet the concept is a bit vague and does not go far beyond the

general description of the experiential system within two-system view.

4.5. Risk

It was already argued that risk assessment might be a product of an overall emotional

evaluation. Risk and return then need not to obey common economic theories. Due to

the prominence of risk especially in finance it is worthwhile to take a closer look at the

psychological findings in this area.

Risk is, scientifically understood, characterized by two components, uncertainty and

exposure.165 Alongside exist the psychological risk dimensions of “unknown risk” (felt

extent of uncertainty, ignorance, unobservability), and “dread risk” (felt extent of indivi-

dual lack of control).166 Risk perception is subjective, since its inputs – probabilities,

estimations of outcomes, level of information, individual attitudes to risk – are highly

subjective. Objective metrics of risk (e.g. variance) can only capture some aspects of

risk.167

Given the previous sections, it comes to no surprise that reaction to the prospect of risk

takes place on the level of emotion and cognition.168 They diverge for two reasons: the

emotional response to probabilities and outcomes can differ from the cognitive evalua-

tion and secondly additional factors may be consulted by emotion.169 Based on eviden-

ce from reality and experiments concerning both divergences, Loewenstein et al. deve-

loped their hypotheses of “risks as feelings”.170 They particularly investigate factors that

are not part of a cognitive analysis of risk, but influence its emotional evaluation.171

163 Cp. Slovic et al. (2004), p. 321, Slovic et al. (2002), p. 420. 164 Indicated by references to Damasio in Finucane et al. (2000), p. 2, Slovic et al. (2002), p. 399 and Slovic et al. (2004), p. 314. 165 Cp. Holton (2004), pp. 21f. 166 Cp. Lerner/Keltner (2000), p. 480, Loewenstein et al. (2001), p. 269. 167 Cp. Holton (2004), p. 24. Predictions of future variance are of course themselves subject to estimations which include subjectivity. 168 Cp. Loewenstein et al. (2001), p. 280, Slovic et al. (2004), p. 311. 169 Cp. Loewenstein et al. (2001), p. 274. 170 Loewenstein et al. (2001). 171 Cp. Loewenstein et al. (2001), p. 271.

23

These factors include vividness of imagination, personal experience, past history of

conditioning, etc. Part of the risk-as-feelings hypotheses also is the mutual interaction

and interdependence of emotion and cognition.172 It therefore bears resemblance to the

two system view and the affect heuristic. The so resulting structure of the decision

process is displayed by figure 2.

Figure 2173

Whereas risk-as-feelings hypotheses tries to provide a general framework for emotional

reactions to risky situations, some research was conducted to examine the impact of

separate emotions on risk-taking behavior. These studies face the difficulty how to

induce emotions in controlled experiments. Reliably this can be done, adopting a more

general notion of positive, neutral and negative emotional states, instead of specific

emotions like envy or anger.

Positive affect typically has consequences for all stages of decision making depicted in

figure 2. It acts as a retrieval cue for positive material in memory, thus increases opti-

mism and alters subjective probabilities accordingly.174 It also influences the decision

making process by avoiding cognitive strain relying more upon system one.175 Thirdly it

modifies the rating of outcomes increasing the disutility of loss, what can be interpreted

as a strategy to preserve the current positive emotion.176

Thus with regard to risk the prediction is not straightforward, two effects work in

contrary direction.177 A more favorable estimation of probabilities confronts a greater

loss aversion. In experiments a positive affective state results in a higher participation in

low risk or low stake gambles, which are unlikely to change the prevailing feeling, but a

172 Cp. ibid. 173 Taken from Loewenstein et al. (2001), p. 270. 174 Cp. Isen et al. (1982), pp. 243f., Isen/Geva (1987), p. 146. 175 Cp. Isen et al. (1982), p. 246. 176 Cp. Isen/Geva (1987), p. 146, 177 Cp. Isen et al. (1982), p. 254.

24

lower participation is observed for risky bets.178 An interpretation would be that people

after previous gains (e.g. at financial markets) behave more conservatively.179 House

money effect however suggests the opposite.

Negative emotions are not producing symmetrical effects that could be inferred from the

results above, they tend to be more complicated.180 Yet in analogy to positive emotion,

negative feelings lead to more pessimistic estimates in judgment of frequencies or

probabilities of negative events.181 Risk taking however depends on specific emotions,

rather then on uniform influences of negative affective states. Risk seeking behavior is

found in unpleasant emotions accompanied by high arousal (e.g. shame, anger), while

other negative emotions (e.g. sadness) exhibit no distinctive impact on risk taking.182

Another approach supposes risk assessment to be related to the realization of risk along

the mentioned risk dimensions (dread and unknown risk). A feeling of low control and

high uncertainty, as in fear, leads to a pessimistic risk perception, whereas high control

and high certainty, as in anger, results in a more positive risk assessment.183

Although evidence is mixed and literature differs in its assumptions about risk and

feeling, it should have become clear, that assessment of and attitude to risk varies with

the current emotion.

4.6. Psychological modeling

4.6.1. Introduction

Until now the impact of emotion on decision behavior was analyzed relatively

abstractly. To utilize the gained insights for economic purposes it is necessary to trans-

form them into a formal model. Given the complexity of the issue modeling can

incorporate only some aspects of emotion.

It is useful for this purpose to introduce the distinction of experienced and anticipated

emotions, which was implicitly present in the discussion before. Experienced emotions

are the actual feelings that shape decision process, information processing and memory

recall.184 The box “feelings” in figure 2 can be understood in this sense. Anticipated

emotions are the feelings expected to be experienced in the future.185 They are a

component of the expected consequences of a decision and have to be considered within

178 Cp. Isen/Geva (1987), p. 151. 179 Cp. Isen/Geva (1987), p. 152. 180 Cp. Isen et al. (1982), p. 259, Leith/Baumeister (1996), pp. 1250f. 181 Cp. Johnson/Tversky (1983), pp. 20ff. 182 Cp. Leith/Baumeister (1996), p. 1250 183 Cp. Lerner/Keltner (2000), p. 481. 184 Cp. Mellers/Schwartz/Ritov (1999), p. 332, Loewenstein et al. (2001), pp. 267/68. 185 Cp. ibid.

25

the outcome.186 This is also indicated in figure 2. The models to be presented here

differ, whether they focus on anticipated emotions, what amounts to concentrating on

future valence, or whether they include other aspects of emotion, their individual

differences or their procedural effects.

4.6.2. The valence-based approach

The interaction of emotion and preferences is most commonly modeled by viewing the

former as psychic costs or benefits that enter into the utility function.187 Costs and

benefits reflect the valence dimension of emotion, which is often assumed to dominate

the dimensional structure of emotion.188

Representatively “decision affect theory” of Mellers, Schwartz and Ritov, which is

based on the influential and well-known regret theory and disappointment theory, shall

be presented here in greater detail.189 By its focus it bears relevance to and has possible

applications in finance.

Regret and disappointment are feelings with negative valence released by counterfactual

comparisons across alternative choices or alternative states of the world, respectively.

They occur not until outcomes are determined, but may be foreseen in the decision

situation. Thus they are anticipated emotions within the previous definition, which alter

by their valence the desirability of an outcome. Decision affect theory combines the

approaches of regret and disappointment theory and models - in a choice between two

gambles (with the outcomes A, B for gamble one and C, D for gamble two) - the

emotional response R to be:

RA(C) = JR [uA + d (uA – uB)(1 – sA) + r (uA – uC)(1 – sAsC)]

In the function it is assumed that the decision maker has chosen gamble one, which has

yielded outcome A, while the other non-chosen gamble resulted in outcome C. Then the

decision maker obtains the utility of A (uA) and confronts two further effects:

disappointment that the outcome of his gamble was not B, and regret that he has not

chosen the other gamble with the outcome C. Disappointment function (d) depends on

the difference between uA and uB, whereas regret function (r) depends on the difference

of uA and uC. Both values not necessarily need to be negative, instead of disappointment

and regret one could in this case speak of elation or relief. The utility comparisons are

weighted by the surprisingness of the event, indicated by one minus the subjective

186 Cp. Loewenstein et al. (2001), p. 268. 187 Cp. Elster (1998), p. 64, and Elster (1999), p. 301, where some further examples of valence-based approaches are cited, see also Lerner/Keltner (2000). 188 Cp. Zajonc (1998), p. 608. 189 For the following description cp. Mellers/Schwarz/Ritov (1999).

26

probability (s) of A or the joint event A and C. The reasoning behind is that

disappointment of getting A instead of B is greater, when the subjective probability of A

was small, so this outcome is surprising. All factors finally enter into a linear response

function (JR) to calculate emotional response. Up to this point the formula comprises

nothing more than an emotional valuation given the actual outcomes.

To guide decision making the authors developed the construct of subjective expected

pleasure (SEP). SEP of a gamble is the sum of the predicted emotions for all possible

combination of outcomes weighted by their respective subjective probabilities. A

decision maker should chose the gamble with the higher SEP. Formally he has to

compare sAsCRA(C) + sAsDRA(D) + sBsCRB(C) + sBsDRB(D) (SEP of gamble 1)

to sCsARC(A) + sCsBRC(B) + sDsARD(A) + sDsBRD(B) (SEP of gamble 2).

A smaller monetary win will be preferred within this framework, if it is expected to feel

better including beliefs and counterfactual comparisons.

The advantage of decision affect theory clearly lies in its parallel formulation to

subjective expected utility theory that makes it easy accessible to economic thought. It

has plausible assumptions and implements them plainly and logically.

Presented evidence finds significant effects of regret and disappointment in emotional

evaluation of outcomes. It further attempts to show that these emotions are anticipated

and included in decision behavior. In this context the role of the functional parameters J,

d and r remains somewhat unclear, particularly whether they allow an ex post

adjustment to the experimental data. The explanatory power of decision affect theory

thus has to be interpreted with caution.

Experimental design is altogether not very convincing as for example the outcomes of

gambles are not determined by chance but by experimenters needs, and payments to

participants are not related to their performance.190

4.6.3. Models of experienced emotions

Anticipated emotions can be integrated in the cognitive evaluation of a decision

problem and it is probably perfectly rational to do so.191 They therefore pose the minor

challenge to modeling compared to experienced emotions. Furthermore the concentra-

tion on valence confronts the limitation that different feelings of same valence may have

totally different behavioral consequences.192

190 Such criticism is often applied to psychological experiments, but here it seems particularly appropriate. 191 Purists would of course disagree, as e.g. the before discussed counterfactual comparisons are nonsense from (normative) rational perspective. 192 Cp. Lerner/Keltner (2000), p. 473.

27

In their “appraisal-tendency approach” Lerner and Keltner try to address these short-

comings and analyze the effects of specific emotions experienced during the decision

process.193 An appraisal tendency is understood as a goal-directed process through

which an emotion exerts an effect on judgment and choice.194 It is equivalent to the here

used definition of action tendencies or action readiness (see section 3.1.). The target is

to systematically link specific emotions to specific decision behavior by identifying

such appraisal-tendencies.195

To differentiate emotions the authors employ six cognitive dimensions (certainty,

pleasantness, attentional activity, control, anticipated effort and responsibility).196 By

means of rating each emotion along these dimensions, they generate predictions for

behavior.197 An example was given in section 4.5., where the influence of anger and fear

on risk perception was described.

The “appraisal-tendency approach” helps to structure the assessment of different

feelings. It is presented in a verbal form that does not allow to directly generate testable

hypotheses. Instead hypotheses are the product of psychological reasoning rather than

of mathematic rigor. Nevertheless the approach may deliver some insights about

emotion-specific behavior.

Closer to an economic formulation is Loewenstein in his analysis of “visceral

factors”.198 The notion of visceral factors is broader than the term emotion, additionally

containing drives, moods and pains. Visceral factors exert a direct hedonic impact and

modify the relative desirability of other goods and actions. The impact of visceral fac-

tors on behavior is represented by the intertemporal utility function

U = ∑t u(xt1,…, xtn, αt1,…, αtm, t)

with consumption vector (xt1,…, xtn) and the vector of visceral factors (αt1,…, αtm) in t.

This general formulation is of course not very meaningful, and therefore the further

assumption is introduced that is possible to separate visceral factors into subsets influ-

encing only a single consumption variable. In the simplest case this results to

U = ∑t u(v1(xt1, αt1, t),…, vn(xtn , αtn, t))

The v-functions denote the value of a consumption of good or activity x given the level

of the corresponding visceral factor α. The separated triples are themselves independent

of other variables, utility increases in x, decreases in t and in- or decreases in α.

193 Cp. Lerner/Keltner (2000). 194 Cp. Lerner/Keltner (2000), p. 477. 195 Cp. Lerner/Keltner (2000), pp. 479/80. 196 Cp. Lerner/Keltner (2000), pp. 478ff. 197 Cp. ibid. 198 For the following description cp. Loewenstein (1996).

28

The fact that x can alternatively be interpreted as activity reflects the relatedness of

emotions to action tendencies. Whereas drives such as thirst or hunger may be satisfied

by the consumption of tangible goods, this is not necessarily true for emotions.

Loewenstein then establishes seven propositions that shape the characteristics of the

utility function.199 The propositions are based on observations and examples from the

literature, which for instance indicate a high decision value of actual visceral factors

compared to an underappreciation of future ones, which is explained by an incomplete

recall of absent visceral factors. Indirectly this challenges modeling of anticipated emo-

tions, because it questions the ability to correctly predict future emotions.

How the propositions are translated into formal language should be exemplarily shown

with proposition 3. It says: “Increasing the level of an immediate and delayed visceral

factor simultaneously enhances the actual valuation of immediate relative to delayed

consumption of the associated good”200 This means that beyond discounting a rising

intensity of a visceral factor shifts attention to the presence.

If α’ > α and v (x, α, 0) = v(x’, α, t) => v (x, α’, 0) > v(x’, α’, t)

Because of discounting x’ had to be already greater than x so that the valuations

equal.201 With α rising to α’ the equation no longer holds, the immediate presence is

rated higher. In a similar fashion the other propositions are implemented.

The visceral factor model is an overall approach to incorporate all forms of non-

cognitive phenomena into the utility framework. It captures many aspects of actual,

desired, predicted and recollected influences of visceral factors on behavior and

emphasizes the intertemporal perspective.

On the other hand generality prevents the model from being applicable to very concrete

problems, thus it is unlikely to attract the direct adoption in economics.

Feelings are underrepresented within the observations and examples, although the

propositions are assumed to hold for them as well. For confirmation some further

research might be necessary.

There is certainly not the one standard model to codify the numerous and diversified

results from psychology and it is doubtful whether to construct such a model would be

possible or useful.202 The sample presented here included three models of different

scope and different methodology. It depends on the context which approach suits the

needs of the user.

199 For an overview cp. table 1 in Loewenstein (1996), p. 278. 200 Loewenstein (1996), p. 278. 201 This is especially true for hyperbolic discounting, which is behaviorally more correct. 202 Cp. McFadden (1999), p. 81.

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5. Brain Science and Neuroeconomics

5.1. The neuroscience of emotion

“She led me away toward her apartment, where we burst through the

door, disrobed our way through the living room, and collapsed into her

queen-sized bed. And what happened there all felt very good, in the

medial forebrain bundle, where I experienced pleasure, but not quite so

much in the conscience.” Benjamin Kunkel 203

The psychological view on decision making involves some kind of circular reasoning,

when behavior is explained by theoretical constructs that are themselves built on

behavior.204 Neuroscience could provide an alternative by exploring the very origin of

behavior within the neural structures of the brain. It could further identify the impact of

emotions during decision process. The perspective on emotion is albeit a bit different:

“I view emotions as biological functions of the nervous system. I believe that figuring out how emotions are represented in the brain can help us to understand them. This approach contrasts sharply with the more typical one in which emotions are studied as psychological states, independent of the underlying brain mechanisms.”205

The methods to investigate the operation of the brain are manifold, they include brain

imaging techniques like functional magnetic resonance imaging (fMRI), positron

emission tomography (PET), and others, next to studies of brain damage in humans, and

physiological measurements like skin conductance response (SCR) or blood pressure.206

Other researchers employ animal experiments, which allow to conduct brain lesions,

single neuron recording and electrical stimulation of brain areas.207

5.1.1 Brain systems and circuitry involved in emotion

Typically a neuroscientific survey begins with the localization of brain areas involved in

certain functions such as language, memory or emotion. This is though only the first

step to gain understanding how the brain works.208 Therefore the sometimes applied

criticism that neuroscience provides nothing more than a functional map of the brain is

misplaced.209 Beyond mere localization it is necessary to interpret the results and to

203 Kunkel (2006), p. 81. 204 Cp. Kenning/Plassmann (2005), p. 343. 205 LeDoux (1999), p. 12. 206 Camerer/Loewenstein/Prelec (2005), pp. 11ff. give a brief overview of neuroscientific methods. For a detailed description of brain imaging techniques consider e.g. Jäncke (2005). 207 Experiments are mostly done with rats or monkeys. 208 Cp. LeDoux (1999), p. 73. 209 Cp. Stein (1996), p. 109, for this kind of criticism. Even more generally Lazarus (1991), p. 356., claims that “we must not try to explain the psychological on the basis of the physiological”.

30

develop hypotheses about the underlying processes.210 The truth of these conjectures

can only be addressed indirectly by accumulated evidence.

Figure 3211

A good starting point for the investigation of brain structures concerned with emotion is

the thalamus, which acts as a relay station for stimuli perceived by the sense organs.212

From here information proceeds to sensory cortices (e.g. visual cortex, somatosensory

cortex, see figure 3), where it can be cognitively evaluated.213 But there obviously exists

a short-cut from thalamus to subcortical structures, which also receive the incoming

stimuli.214 This pathway is of particular importance for emotion, as the processing takes

place beyond awareness. Emotional action in the brain is to a great extent uncon-

scious.215 The conscious experience of an emotional feeling is just one part of a more

fundamental mechanism.216 Emotion so to speak integrates conscious and unconscious,

cortical and subcortical processes.217

It was long time argued that the so-called “limbic system” is responsible for emotions.

The limbic system consists of a conglomeration of brain structures including the hypo-

210 Cp. Rolls (1999), p.3, and Camerer/Loewenstein/Prelec (2005), p.14. 211 Midline view of the right hemisphere taken from http://brainmind.com/images/BrainMind1Split.jpg. Own inscription that shows the approximate position of the indicated brain structures. 212 Cp. LeDoux (1999), p. 82. 213 Cp. ibid. 214 Cp. Zajonc (1998), p. 607. 215 Cp. LeDoux (1999), pp. 17, 64. 216 Cp. LeDoux (1999), p. 18. 217 Cp. Damasio (1995), p. 180.

31

thalamus, the hippocampus, the amygdala, cingulate gyrus and nucleus accumbens.

However, only some of these brain areas actually play a role in emotion and the limbic

system is not a system in a functional sense at all.218 Today the term is still in frequent

use, sometimes as useful generalization, sometimes for convenience.219 One part of the

limbic system, the amygdala, has attracted the special attention of emotion theorists.

The amygdala is an almond-shaped structure located in the medial temporal lobe (see

figure 3). The evidence for amygdalaic involvement in emotion is overwhelming220, yet

the nature of that involvement is discussed.221 Some authors argue that different emo-

tions demand different neural systems222, consequently amygdala may serve only for

some negative emotions, most prominently fear.223 Others reply that such a specialized

role for amygdala is “most unlikely”.224

In simple terms the amygdala receives an emotional stimulus via the thalamus and

triggers a response reaction.225 This reaction is then carried out by the diverse output

pathways of the brain, notably basal ganglia (especially ventral striatum), hypothalamus

and brain stem.226 They activate the autonomous nervous system (ANS), the motor

system, hormonal system and neurotransmitter release.227 The stimulus-response chain

is thus completed without conscious awareness or volitional control.

This subcortical route is a rather crude mechanism that can account for a variety of

simple emotional responses. Encountering complex situations and uncertainty the

activity of the cortex is needed, not only in thinking and planning, but also in emo-

tion.228 The latter seems to fall into the faculty of the prefrontal cortex (particularly

orbitofrontal cortex).229 Brain damage in this area changes emotional experience and

results in emotional flatness.230 Despite cognitive functions remain almost completely

intact, decision making is also impaired, supporting the view that emotions contribute to

sound decision behavior.231 The prefrontal cortex evaluates (consciously or uncon-

218 Cp. LeDoux (1999), pp. 74, 99. 219 Cp. Panksepp (1998), p. 71, and Damasio (1995), p. 57, respectively. 220 Cp. Rolls (1999), pp. 94ff., Aggleton/Young (2000), pp. 106,117, Kolb/Taylor (2000), p. 81, Zajonc (1998), p. 598, Damasio (1995), p. 107, Damasio (2000), pp. 81ff., LeDoux (1999), pp. 157ff. 221 Cp. Aggleton/Young (2000), p. 106. 222 Cp. Damasio (2000), p. 79, LeDoux (1999), pp. 103, 106. 223 Cp. Aggleton/Young (2000), p. 121. 224 Cp. Rolls (1999), p. 110. 225 Cp. Damasio (1995), p. 184, also fig. 7.1, and LeDoux (1999), p. 158. 226 Cp. Rolls (1999), pp. 138ff. 227 Cp. ibid. and Damasio (1995), pp. 189ff. 228 Cp. Damasio (1995), p. 178, Heilmann (2000), p. 332. 229 Cp. Damasio (1995), pp. 189ff. and Rolls (1999), p. 131, who assumes that orbitofrontal cortex could be the seat of “emotional intelligence”. 230 Damasio (1995) provides several case studies. Cp. also Rolls (1999), pp. 128ff. 231 Cp. Damasio (1995), p. 12.

32

sciously) the emotional significance of incoming stimuli, establishes a representation of

the emotional state and has also access to emotion-laden memories (via temporal

pole).232 Thus it provides a more integrated view of emotion compared to the subcorti-

cal stimulus response circuitry.

Nonetheless prefrontal cortex maintains strong reciprocal connections to amygdala and

thalamus.233 Its effect on behavior is often seen in overriding or correcting automatic

amygdalaic processes.234 In any case the prefrontal cortex projects to the same output

pathways; hereby basal ganglia seem to secure a coherent stream of emotional responses

within the body (ANS, etc.).235

5.1.2 The hypothesis of somatic markers

These bodily responses are crucial for the hypothesis of somatic markers. So far it was

not explained how feelings, in the sense of conscious emotional experiences, come

about. Somatic marker hypothesis sees feelings as mental perception of the bodily land-

scape.236 This thought is related to the idea of feedback theory (see section 4).

A dedicated brain system (prefrontal cortex, amygdala) triggers a collection of changes

in body and brain and this way establishes a somatic state.237 This ensemble of respon-

ses is what constitutes an emotion.238 Somatosensible brain structures then sense the

induced somatic state, relate it to prefrontal cortex, where it is compared to available

representations of somatic states, the somatic markers.239 If a bodily response matches a

somatic marker, a feeling is generated.240 Somatic markers are mostly formed during

education and socialization, some very basic markers might be innate.241

Following the theory, there are two mechanisms of somatic markers, the so-called

“body loop” and “as if body loop”. The body loop involves the actual activation of the

described response mechanisms (humoral and neuronal signals).242 The as-if mecha-

nism bypasses the body, the somatosensory cortex is directly instructed to set up a

somatic representation that corresponds to the equivalent bodily state.243

232 Cp. Lane (2000), pp. 356/358. 233 Cp. Rolls (1999), p. 113. 234 Cp. Lane (2000), p. 362, LeDoux (1999), p. 164, Rolls (1999), pp. 116/128. 235 Cp. Rolls (1999), p. 181. 236 Cp. Damasio (1995), p. 15. 237 Cp. Bechara/Damasio (2005), p. 339. 238 Cp. ibid. 239 Cp. Damasio (1995), pp. 247ff. 240 Cp. Bechara/Damasio (2005), p. 341. 241 Cp. Damasio (1995), p. 243. 242 Cp. Damasio (2000), p. 102, Bechara/Damasio (2005), pp. 342f. 243 Cp. Damasio (1995), p. 251, Damasio (2000), p. 102. The inner-brain circuitry is organized via the release of neurotransmitters by the hypothalamus [cp. Bechara/Damasio (2005), p. 344].

33

Somatic markers are presumed to be engaged in decision making. As Bechara and Da-

masio put it, they provide “an emotional mechanism that rapidly signals the prospective

consequences of an action, and accordingly assists in the selection of an advantageous

response action.”244 In a decision situation unpleasant results produce a negative gut

feeling that by means of the somatic marker mechanism reduces the cognitively

considered alternatives.245 The emotional signals are not supposed to substitute the

deliberate thought process, but they increase the accuracy and usefulness of the decision

process.246 An impairment to the perception of somatic markers consequently should

degrade the speed and quality of decision.247

The evidence for the somatic marker hypothesis is albeit mixed. In their own gamble

experiment Bechara and Damasio find an inferior performance of patients with prefron-

tal cortex damage.248 Patients exhibit significant differences in SCR, interpreted as an

inability to experience anticipatory somatic reactions to guide behavior.249

Other experimental tests appear to contradict the predictions of somatic marker hypo-

thesis.250 Diverse studies show that only in some experimental settings the decisions of

patients and controls differ significantly.251 But still the findings suggest an impairment

in the immediate experience of emotions in decision situations.252

On a more theoretical basis the hypothesis of somatic markers raises some questions. Is

the feedback mechanism really an efficient way to induce feelings? Somatic responses

may indeed have the requisite specificity and may be also fast enough to contribute to

emotional experience.253 But even then the execution of feelings through a peripheral

response mechanism appears to be rather inefficient and unnecessary.254 This does not

exclude a role for body feedback in emotion, yet it seems not to be critical.255

As an alternative one can safely assume that brain systems triggering emotional respon-

ses directly relate to brain areas concerned with conscious feelings.256 But what was said

in this section about the emotional influence on decision making can be largely main-

tained, even if somatic marker hypothesis proves wrong.

244 Cp. Bechara/Damasio (2005), p. 339. 245 Cp. Damasio (1995), pp. 237/238. 246 Cp. ibid. 247 Cp. Bechara/Damasio (2005), p. 339. 248 For a description of the experiment consider Bechara/Damasio (2005), pp. 344ff., or Damasio (1995), pp. 285ff. 249 Cp. Bechara/Damasio (2005), p. 346. 250 Cp. Leland/Grafman (2005), p. 404. 251 Cp. Leland/Grafman (2005), pp. 405ff., for a survey. 252 Cp. Leland/Grafman (2005), p. 408. 253 Cp. LeDoux (1999), pp. 293/294, for a different opinion cp. Rolls (1999), p.72. 254 Cp. Rolls (1999), p. 73. 255 Cp. Heilman (2000), p. 339. 256 So proposed e.g. by Rolls (1999), p.73.

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5.1.3 Emotion, cognition, consciousness and decision making

The previous sections deal with the mostly unconscious processes that bring about our

emotions. This is reasonable as it is widely believed that one cannot choose or control

volitionally what emotion to have.257 The more or less automatic emotional mecha-

nisms may account for a good portion of behavior, more than one is generally ready to

admit.258 But as already the introductory statement from Kunkel’s novel suggests, there

are other brain operations paralleling, influencing and maybe contradicting emotions.

From evolutionary perspective this is fairly easy to interpret:

“While many animals get through life mostly on emotional automatic pilot, those animals that can readily switch from automatic pilot to willful control have a tremendous extra advantage.”259

Willful control, the capacity to plan and to think ahead, the ability to reason analytically

is often associated with the language system.260 Language hereby is not necessarily ver-

bal language, but syntactic manipulation of symbols.261 This allows to plan actions and

evaluate their consequences through many stages.262 Brain areas responsible for langua-

ge are mostly situated in the left-hemispheric temporal lobe.263

Secondly planning ahead requires drawing on past experiences, thus a memory.264 One

can then base a current decision on one’s individual history of interaction with the

environment.265 The hippocampal formation seems to constitute the seat of (long-term)

memory.266 Hippocampus communicates by mental representations of stimuli and expe-

riences with temporal lobe and prefrontal cortex.267

A prerequisite for both to operate within cognitive decision making is consciousness.

Consciousness itself is sometimes thought to depend on language and/or memory, but

this needs not to be the case.268 Instead consciousness is meant here as awareness of the

self and its relation to the environment. This includes the capacity of higher-order

thoughts: the ability to reflect on one’s own and other people’s thoughts.269 Conscious-

ness allows to perceive mental states, feelings, memories, beliefs, intentions, and

expectations.270

257 For an opposite view cp. Solomon (2003). 258 Cp. Camerer/Loewenstein/Prelec (2005), p. 31. 259 LeDoux (1999), p. 175. Similar in Baars (1996), p. 157, Lane (2000), p. 346. 260 Cp. Rolls (1999), p. 245. 261 Cp. Rolls (1999), p. 248, Damasio (2000), p. 224. 262 Cp. Rolls (1999), p. 247. 263 Cp. Panksepp (1998), pp. 332ff., e.g. Broca’s area and Wernicke’s area. 264 Cp. Lane (2000), p. 346. 265 Cp. ibid. 266 Cp. LeDoux (1999), pp. 186ff. 267 Cp. ibid. 268 Cp. Damasio (2000), pp. 133ff. and 139ff. 269 Cp. Rolls (1999), p. 248. 270 Cp. Baars (1996), p. 3.

35

Consequently consciousness is vital for decision making, problem solving, planning,

information processing, error detection, and action selection.271 It makes sense then that

emotions become conscious by feelings. Otherwise behavior resulting from conscious

consideration could not take emotions into account. Conscious experiences of emotion

deliver information about the success in achieving personal goals in relation with the

external world.272 They enter the cognitive linguistic symbol-manipulation system and

this way support the above mentioned functions.273

It is sometimes argued, that consciousness is related to working-memory.274 Working

memory has a limited capacity and receives information from other parts of the brain

(so-called buffers). One can interpret consciousness as momentary content of working-

memory.275 While there are several theories of working- or short-term memory, it seems

consensus that prefrontal cortex is centrally involved.276 It is further hypothesized that a

goal state represented in working-memory is implemented in dorsolateral prefrontal

cortex, whereas other parts of the prefrontal cortex supply working memory with

representations of behavioral incentives.277 Finally feelings become conscious, when the

activity of emotion systems (e.g. amygdala) is relayed to working-memory.278

Bringing the parts together one can form an impression, how volitional decision making

might be achieved by the human brain. One can now renew the question of the relation-

ship between cognition or reasoning and emotion.

An interpretation of the presented neuroscientific evidence would be, that there are two

routes to action: one implicit through subcortical structures with perhaps some involve-

ment of the orbitofrontal cortex, another explicit through conscious representations of

goals, preferences, alternatives, and feelings under participation of the language-/logic-

system, memory and working-memory.279

Quite in line with psychological two-system view Camerer, Loewenstein, and Prelec

argue for such a concept on neural level.280 They extend it to four quadrants, what

allows a differentiation of affective and cognitive next to automatic and controlled

processes.281 This perhaps shows a certain uneasiness with the original proposition,

271 Cp. Baars (1996), pp. 157ff., Damasio (2000), pp. 362/363. 272 Cp. Lane (2000), p. 346. 273 Cp. Rolls (1999), p. 251. 274 Cp. LeDoux (1999), pp. 269ff. 275 Cp. LeDoux (1999), p. 279. 276 Cp. Davidson (2000), p. 374, Rolls (1999), p. 258. 277 Cp. Davidson (2000), p. 374. See also section 5.2.3. 278 Cp. LeDoux (1999), p. 282. 279 This notion is put forward e.g. by Rolls (1999), pp. 255ff. 280 Cp. Camerer/Loewenstein/Prelec (2005), pp. 15ff. 281 Cp. ibid.

36

which mixes up this two distinctions (see section 4.2). The main parting line may in-

deed lie between automatic and willful behavior. By contrast emotion and cognition are

(practically and physiologically) deeply entangled within decision process.282 Ratio-

nality as a non-emotional strategy to obtain the best outcome will not work for human

beings.283 Redefined, rationality - in a non-technical sense - is a matter of maintaining a

proper collaboration of the different contributing systems to behavior.284 Decision ma-

king then is a multi-component but rather holistic process.285

The definite interactions of cognition and affect in the control of behavior are by now

not well understood.286 Neuroimaging for example can provide insights of simultaneous

brain area activations, but especially in complex mental operation this is not sufficient

to understand the underlying processes. Nevertheless two examples (of possible interac-

tions) shall be mentioned here to illustrate probable mechanisms:

Within the somatic marker theory emotion was introduced as a device in the selection of

alternatives. The emotional appraisal of a stimulus reduces the available response

options.287 One aspect hereby is the temporal primacy of emotion that permits an

internal emotional reaction before cognition sets in.288 Once an emotional system (e.g.

amygdala) is activated, it projects to almost all cortical areas, directing attention to

emotionally significant stimuli.289 The connections in opposite direction are weaker,

explaining why emotional information can easily invade conscious thought, but

controlling emotion is difficult.290 An important further ingredient is the arousal mecha-

nism. On neural level arousal intensifies the firing activity of cells that are currently

processing information.291 Arousal directs attention, and cognitive decision making

becomes actively focused on the arousing situation, other inputs are blocked out of

working-memory.292 In the ideal case this process guides the individual to the point,

where the instruments of the language-/logic-system can be applied optimally.293

282 Damasio (1995), pp. 239, speaks of a “close partnership” of emotion and cognition. [This argument is only valid in context of conscious decision making. Basic, subcortical emotions are independent of cog-nition, see e.g. the cases of split brain surgery, decorticated animals (LeDoux 1999) or amnesia (Damasio 1995, LeDoux 1999)]. 283 Cp. Damasio (1995), pp. 235/236. 284 Cp. Camerer/Loewenstein/Prelec (2005), p. 28. 285 Cp. Glimcher/Dorris/Bayer (2005), p. 216. See also the analogy of the brain as a company in Camerer/ Loewenstein/Prelec (2004), p. 561. 286 Cp. Camerer/Loewenstein/Prelec (2005), p. 30. 287 Cp. LeDoux (1999), p. 69 and recall section 5.1.2. 288 Cp. Zajonc (1998), p. 592, Camerer/Loewenstein/Prelec (2005), p. 26. 289 Cp. LeDoux (1999), pp. 284/285. 290 Cp. LeDoux (1999), p. 265. 291 Cp. LeDoux (1999), p. 287. 292 Cp. LeDoux (1999), p. 291. 293 Cp. Damasio (1995), p. 13.

37

Second emotions also shape memories in a way that will explain some of the psycho-

logical results presented in section 4.5. Associative neural networks in the hippo-

campus might store co-occurrences of inputs, that is forming contextual memories.294 If

emotional arousal leads to a release of neurotransmitter noradrenalin, memory can be

enhanced, one normally remembers situations better that were accompanied by strong

feelings.295 The emotional meaning of situations and events is part of the context stored

with the memory. The recall of such a memory works best, when the retrieval cue is

closest to the original input pattern.296 This may account for the finding that in a posi-

tive emotional state more positive memories are accessible.

5.2. Neuroeconomics

“The brain may devise laws for the blood, but a hot temper leaps o'er a

cold decree” William Shakespeare297

5.2.1. What is and does neuroeconomics?

Within the scientific community there is some disagreement, what neuroeconomics

actually is. The majority of authors proposes the field to be concerned with the explora-

tion of the neural substrates underlying economic decisions by means of neuroscientific

measurement techniques.298 Others inversely want to employ economic tools to examine

the functional abilities of the nervous system.299

The difference between the two lines of research is quite fundamental. Whereas the lat-

ter accepts standard economic theories as a benchmark and assumes neural processing

to be close to efficiency300, economists are searching for alternatives beyond rational

choice theory to explain anomalies and deviations.301 Nevertheless both approaches are

useful, because when one group of researchers is eager to replace standard theory, it will

do no harm, if another group tries to neurally substantiate these very theories.

Zak describes the interaction of economics and neuroscience as follows:

“There is a natural affinity between neuroscience and economics as one has produced and tested many behavioral models without asking what produces the behavior, whereas the other is able to open the black box that generates behaviors but is searching for interesting behaviors to study.”302

294 Cp. Rolls (1999), p. 144. 295 Cp. LeDoux (1999), pp. 206-208. 296 Cp. Rolls (1999), pp. 144/145. 297 Shakespeare (1998), The Merchant of Venice, act I, scene II. 298 Cp. Zak (2004), p. 1737. Similarly in Camerer/Loewenstein/Prelec (2005), p. 10, Kenning/Plassmann (2005), pp. 343/344. 299 Cp. Glimcher (2004), p. 200. Rustichini (2005) seems to take in an intermediate position. 300 Cp. Glimcher (2004), p. 166. 301 Cp. Camerer/Loewenstein/Prelec (2005), p. 54. 302 Zak (2005), p. 1738.

38

In principle all stages of economic decision – obtaining information, evaluating alterna-

tives, and choosing actions – are accessible by neuroscientific measurement.303 In such a

concept emotions have a prominent place, as they are no longer treated as “useless

intervening constructs”.304 Emotions are (again in principle) measurable and may

become suitable predictors for behavior.

Eventually neuroeconomics aims to approach a general theory of economic behavior.305

A unified neural model would be more true in a descriptive sense than established “as-

if”-theories and could improve or even replace them.306

But as this is an ambitious goal, for the meantime it would be enough, if neuroscience

could explain some of the existing anomalies. There is good hope that especially finan-

ce will profit from this development.307

5.2.2. Implications from (affective) neuroscience for economics

The results presented here are only preliminary as neuroeconomics is still in its begin-

nings. Starting point is the assumption derived from the previous sections that emotions

constitute an integral part of sound decision making. More precisely emotions can be

beneficial if they are related to the task at hand, but also disruptive, if they are unrelated

to it.308 Bechara and Damasio prove this fact in a series of instructive experiments.309

Emotions appear to be particularly important in the realm of intertemporal choice,

preferences, and decision making under uncertainty.

Intertemporal choice is traditionally modeled as a trade-off between present and future

utility, represented by a discount factor. In the market place the equilibrium discount

factor is captured by the risk-free real interest rate.310 Rationality allows a variety of

interest rate regimes (yield curve shapes) as long as they are free of arbitrage. On

individual level though, preference reversals over time are a quite common observa-

tion.311 This can be explained by a conflict of the implicit, automatic reaction, which

insists on an immediate reward, with the cognitive evaluation of the situation.312 The

303 Cp. Zak (2004), p.1737, Glimcher (2004), p. xviii. 304 Cp. Camerer/Loewenstein/Prelec (2005), p. 10. 305 Cp. Rustichini (2005), 306 Cp. Camerer/Loewenstein/Prelec (2005), p.10. In their critique of neuroeconomics Gul/Pesendorfer (2005) argue that an accurate portray of the formation of human decision is not the function of economic models (like EUT). Instead theories are judged by their predictive power. They ignore however the possi-bility that a model could accomplish both. 307 Cp. Camerer/Loewenstein/Prelec (2005), pp. 53/54, for such a claim. 308 Cp. Bechara/Damasio (2005), p. 337. 309 Cp. Bechara/Damasio (2005). 310 At least from macroeconomic perspective [δ = (1+r)-T]. On real markets the relation is more complex. 311 Cp. Rabin (1998), pp. 38-40, Camerer (1998), p. 173. See also footnote 125. 312 Cp. Rolls (1999), pp. 259f., Zak (2004), p. 1743, Camerer/Loewenstein/Prelec (2005), p. 39.

39

language-/logic-system enables humans to defer rewards in favor of future benefits.

Neurally this probably works by imagining the future and in doing so creating an

immediate quasi-reward.313 Preference reversals occur, if the relative strength of the

involved mechanisms changes.314 The mere passage of time (future rewards becoming

present) can be sufficient for such a change, or it is caused by other factors, for example

the workload imposed on the cognitive system.315

This view on intertemporal choice is supported by brain imaging research seeing an

activation of limbic areas with immediate monetary rewards and cortical areas with

delayed rewards.316

The notion of the brain as a “black box“ is especially true for preferences. Economists

were seldom concerned with the formation of preferences, their causes and influencing

factors. Some believe that this is a matter of no interest to economics and can be left for

psychology.317 However, working with ex post models based on revealed preferences

that explain the very data they were developed for is not satisfactory either.318 It is thus

perfectly sensible to ask for the origin of preferences – and this might well be the (ven-

tromedial) prefrontal cortex.319 Recall that this area is also central for emotion.320 Prefe-

rences may therefore often reflect a liking and not a reasoned appraisal of all relevant

aspects.321 It was also found, that the favorite alternative elicits distinct brain

activation.322 With this result a prediction of choice is conceivable.

Simple preferences are often explained by homeostasis, the necessity of the organism to

maintain a stable body condition.323 This implies that a stimulus is evaluated relative to

a reference point.324 Sensitivity to change rather than to absolute levels is reflected by

activity of neurons in the ventral striatum (in monkeys).325 It would be premature to see

prospect theory326 confirmed by this evidence, but it presents a physiological explana-

tion why changes and reference points are important in human judgment.

313 Likewise in Camerer/Loewenstein/Prelec (2005), p. 41. 314 Cp. Camerer/Loewenstein/Prelec (2005), pp. 40/41. 315 Cp. ibid. 316 Cp. McClure et al. (2004) cited with Camerer/Loewenstein/Prelec (2005), pp. 39/40. Also Zak (2004), p. 1743 317 Cp. Gul/Pesendorfer (2005), pp. 2-4. 318 Cp. Chorvat/McCabe (2005), p. 109. 319 Cp. Kenning (2005), p. 347. 320 In the previous sections prefrontal cortex was mostly left unspecified. Damasio (1995) rests his so-matic marker theory on ventromedial prefrontal cortex, whereas e.g. Rolls (1999) emphasizes the role of orbitofrontal cortex. 321 This point was already made by Zajonc (1980), p. 155. See also footnote 98. 322 Cp. Kenning (2005), p. 347. 323 Cp. Camerer/Loewenstein/Prelec (2005), p. 27, Camerer/Loewenstein/Prelec (2004), pp. 562/563. 324 Cp. ibid. E.g. warmth is felt pleasurable in a cold environment, but no longer after some time in a sauna. 325 Cp. Camerer/Loewenstein/Prelec (2005), p. 28. 326 Kahneman/Tversky (1979).

40

There are more issues about preferences, but many are only in the phase of speculation

right now. Having located where preferences are formed, it would be interesting to

know, what actually causes them. And even though homeostasis may well be one of this

causes, it cannot be sufficient for preferences in complex decisions.

5.2.3. Risk

Section 4.5. was devoted to risk and it makes sense to review this topic again in the light

of neuroeconomic insight. Gul and Pesendorfer contrast two statements on risk aversion

and polemically ask which one is more true:

“Much aversion to risks is driven by immediate fear responses, which are largely traceable to a small area of the brain called the amygdala.” and “A decision maker is (globally) risk averse, […] if and only if his von Neumann-Morgenstern utility is concave at the relevant (all) wealth levels.”327

At first glance it seems difficult to reconcile both propositions. But again the utility

function is only an abstraction, behind it brain processes are responsible for decision

makers being risk averse.

Risk aversion is one of the key assumptions of modern portfolio theory. Markowitz

believes that it is a sound hypothesis of investment behavior to consider risk an

undesirable thing.328 As risk aversion is indeed observed in the market, the case could

be closed: investor behavior confirms the hypothesis. However, there are some pro-

blems like the so-called “equity premium puzzle” or the simultaneous exhibition of risk

aver-sion and risk seeking that still remain unsolved.329

Evolutionary risk aversion makes good sense for example in foraging of animals, risky

alternatives can be life-threatening in last consequence. Damasio reports a behavior of

bees that is in line with µ-σ-optimization.330

Confronting risk leads to a greater emotional activation compared to a decision under

certainty.331 Investment traders are here no exception, they show higher physiological

responses in volatile markets.332 An emotional reaction occurs, because risk is emotio-

nally evaluated alongside cognitive processing.333 Risk can be associated with fear, the

fear of loss or the fear to be exposed to negative emotions like regret or disappointment

(see 4.6.2.). The neural fear circuitry is well understood and involves the amygdala,

327 Cp. Gul/Pesendorfer (2005), p. 3. The original quotations are taken from Camerer/Loewenstein/Prelec (2004) and from Ingersoll. 328 Cp. Markowitz (1952), p. 77. He actually declares variance of return to be undesirable, but later (p. 89) admits, that the terms variance of return and risk are interchangeable. 329 Classical example for the latter is both to gamble and to contract an insurance. Cp. Camerer/Loewen-stein/Prelec (2004), p. 570. 330 Cp. Damasio (1995), p. 255. 331 Cp. Bechara/Damasio (2005), p. 350, Zak (2004), p. 1742. 332 Cp. Lo/Repin (2002), p. 329. 333 Cp. Loewenstein et al. (2001), p. 270. See also section 4.5.

41

which triggers a fear response, as the quotation above already expressed.334 A strong

emotional fear response could perhaps explain the implausible high risk premia that

constitute the “equity premium puzzle”.335

Still debated is the question, whether the emotional risk reaction is beneficial for the

performance in a decision situation. The evidence from the experiments of Bechara and

Damasio suggests that it is, in contrast Lo, Repin and Steenbarger found a negative

influence of strong feelings.336 The truth may lie in the middle: emotions contribute to

good decisions as long as they are in a due proportion to other decision mechanisms.337

The distinction of risk and uncertainty (ambiguity) is also reflected in the brain by

disparate areas becoming active.338 Ambiguity is experienced especially discomforting

and the orbitofrontal cortex receives a corresponding signal from insula and amyg-

dala.339 Consequently ambiguity aversion is anchored in behavior, even though ratio-

nality recommends no different treatment of risk and ambiguity.

5.2.4. A reward system in the brain

It was just stated that the “orbitofrontal cortex receives a signal”, “the amygdala triggers

a response” and other obscure processes are at work. What is the essence of these

processes and how do emotions produce behavior?

One assumption is linked to the valence-dimension of emotion, it asserts that pain and

lust are the driving forces of the organism.340 Or to put it more technically that emotions

have underlying reward and punishment mechanisms in order to bring about appropriate

behavior.341 Rewards and punishers provide a goal for behavior and so present a

solution of the brain to action selection.342 In a complex environment with different

emotional and non-emotional stimuli rewards allow the brain to compare all inputs in a

common currency.343 They not only motivate to take in food, to drink, or to avoid pain,

the language-/logic-system also strives to obtain rewards.344 It can, as was argued be-

fore, engage in intertemporal choice, trade off immediate against future rewards, but

still rewards and punishers remain its objectives.

334 For a detailed description of fear consider LeDoux (1999). 335 Cp. Caplin/Leahy (2001), who propose a similar explanation based on anxiety. Earlier Benartzi/Thaler (1995) developed the concept of myopic loss aversion (also based on emotion, even though less explicit). 336 Cp. Bechara/Damasio (2005), pp. 346ff., and Lo/Repin/Steenbarger (2005), p. 18. 337 Cp. Damasio (1995), p. 165. 338 Cp. Smith et al. (2002), p. 717, Camerer/Loewenstein/Prelec (2005), p. 45. 339 Cp. Camerer/Loewenstein/Prelec (2005), p. 45. 340 Cp. Damasio (1995), p. 345, Damasio (2000), p. 99. 341 Cp. Rolls (1999), pp. v/1/5. 342 Cp. Rolls (1999), p. 5. 343 Cp. Rolls (1999), pp. 267ff. 344 Cp. ibid. Otherwise its genesis would not have been evolutionary adaptive.

42

The neurotransmitter dopamine released by dopaminergic neurons is considered to be

fundamental for reward processing.345 Dopaminergic neurons are activated following

rewards or reward-predicting stimuli.346 Dopamine is experienced pleasurable and thus

represents a reward on neural level. Activity of dopaminergic neurons is also associated

with reward anticipation, their firing being correlated with prediction errors.347

Figure 4348

The orbitofrontal cortex determines the reward value of available behavioral alterna-

tives.349 It forms representations about previous rewards and is thus engaged in a simple

form of reinforcement learning.350 Orbitofrontal cortex may provide a link between

amygdala and working-memory.351 The computations of orbitofrontal cortex need not to

be conscious, but the cognitive system relies on their results.352

Other brain structures assigned to the reward system were already described, for example

the dorsolateral prefrontal cortex, earlier (section 5.1.3.) associated with working-me-

mory and cognitive processing, is again responsible for goal maintenance and executive

control.353 Figure 4 depicts the functions and connections within the reward system.

345 Cp. Schultz (2002), Preuschoff/Quartz/Bossaerts (2006), p. 6, Glimcher (2004), p. 333, Glimcher et al. (2005), p. 244, Rolls (1999), pp. 2168ff. 346 Cp. Schultz (2002), p. 241. 347 Cp. Schultz (2002), pp. 243-245, Preuschoff/Quartz/Bossaerts (2006), p. 10, Glimcher (2004), p. 333, Zak (2004), p. 1742. 348 Taken from Camerer/Loewenstein/Prelec (2005), p. 36, slightly adapted. 349 Cp. Camerer/Loewenstein/Prelec (2005), p. 20. 350 Cp. Rolls (1999), p. 137, LeDoux (1999), pp. 277/278. 351 Cp. LeDoux (1999), p. 278. 352 Cp. Rolls (1999), pp. 138/258. 353 Cp. Kenning/Plassmann (2005), p. 348.

43

Apparently all mentioned structures relate to the striatum (basal ganglia), the system for

behavioral output. The various inputs compete to induce a behavior and basal ganglia

“select” a path of action depending on relative strength of the inputs.354 This is some-

times called a “winner takes all”-nature of neural processing.355

Rewards and punishers are only one element of a cost-benefit analysis, which must also

consider the costs to acquire a reward or to avoid a punisher.356 It is argued, whether

such a cost-benefit analysis is necessarily cognitive, but one can safely assume that in

social or economic situations it often is.357 Particularly in dealing with abstract rewards,

the participation of ventromedial or dorsolateral cortex is inevitable.358 The following

section discusses an attempt to describe brain processes even closer to an economic

approach.

5.2.5. A second chance for expected utility?

Among biologists it is widely agreed that the global function of the nervous system is to

serve evolutionary fitness.359 Consequently the reward system should only reward beha-

vior that contributes to evolutionary fitness.360 With fitness as a single target variable a

utility function can in principle embody this optimization problem.361 And if so, the

operations of the brain should reflect a computation of expected utility. Some research

is indeed conducted to find the neural underpinnings of EUT.362 Having followed the

debate of biases and anomalies, humans behaving irrationally or applying heuristics,

this may come as a surprise, but Glimcher et al. offer another interpretation:

“The neural decision-making process is always rational with regard to [the] internal represen-tations of desirability. When choosers deviate from [technical] rationality it is this physiological encoding of desirability […] that departs.”363

Due to evolutionary constraints actual neural systems never reach perfect computational

efficiency.364 It can be assumed that inefficiencies especially arise when one encounters

a problem that the neural architecture was not evolved to solve – then physiological

expected utility and economic expected utility do not match.365 Unfortunately many

modern decision situations belong into this category.

354 Cp. Rolls (1999), p. 138. 355 Cp. Glimcher/Dorris/Bayer (2005), p. 220, Camerer/Loewenstein/Prelec (2005), p. 25. 356 Cp. Rolls (1999), p. 274. 357 Cp. LeDoux (1999), pp. 176f., for a different opinion see Rolls (1999), p. 275. 358 Cp. Bechara/Damasio (2005), p. 355. 359 Cp. e.g. Glimcher (2004), p. 174, Rolls (1999), p. 276. 360 Cp. Rolls (1999), p. 276. 361 Cp. Glimcher (2004), p. 200, Zak (2004), p. 1738. 362 Notably Glimcher/Dorris/Bayer (2005), Glimcher (2004) and Knutson/Peterson (2005). 363 Glimcher/Dorris/Bayer (2005), p. 220. Italics added. 364 Cp. Glimcher (2004), p. 155. 365 Cp. Glimcher/Dorris/Bayer (2005), p. 220

44

Experimental settings in the here reviewed experiments are however simpler and provi-

de promising results. Knutson and Peterson test the response to monetary rewards in an

fMRI-study and find an activation in the ventral striatum that scales with the magnitude

of the anticipated monetary gain.366 The actual outcome then activates the orbitofrontal

cortex, both areas show no similar activation for anticipated or actual losses.367 The

anticipated reaction is preceding a behavioral response (button press), thus the reward

system theory would predict a striatum activation, the outcomes are – also in line with

reward system theory – evaluated by orbitofrontal cortex. The extension towards EUT

now lies into a correct representation of the magnitude and probability of rewards. At

least for the former Knutson and Peterson present some evidence.368 The authors further

argue that the dopaminergic intervention in striatum and orbitofrontal cortex confirms

an emotional basis of utility.369

Glimcher and collaborators claim that both, probabilities and values, and as a conse-

quence expected utility, are represented in the nervous system.370 They have identified

area LIP in monkeys to contain neurons that fire in correspondence with expected uti-

lity.371 Area LIP may be equivalent in humans to a region in parietal cortex that has a

linking function between somatosensory and motor cortex.372 It is this an area that was

not considered in the previous sections and well reflects the fact that probably several

brain structures independently or within a system code expected utility.373 The findings

are quite striking in terms of accuracy and consistency, but the authors admit that the

results cannot yet be generalized to complex human decisions.374 Section 6.3.2. moves

further in this direction and presents some studies of financial decision making. For the

time being one can conclude with Camerer et al., that rational choice models might be

most useful to explain comparably simple decisions and are less suitable facing abstract,

complicated problems.375

Interestingly the same might be true for Bayes’ theorem. As was already stated, humans

often fail to apply Bayes’ rule, when they cognitively evaluate a problem. Other species

also confront situations, where it would be helpful to calculate probabilities to choose

366 Cp. Knutson/Peterson (2005), p. 310. Precisely nucleus accumbens. 367 Cp. ibid. The authors use the term mesial or medial wall of prefrontal cortex. Inferring from the location of this area (see their fig. 2), this might well be identical with orbitofrontal cortex. 368 Cp. Knutson/Peterson (2005), p. 310. 369 Cp. Knutson/Peterson (2005), pp. 305f., see also Zak (2004), p. 1742. 370 Cp. Glimcher (2004), Glimcher/Dorris/Bayer (2005). 371 Cp. Glimcher (2004), p. 263, 315, Glimcher/Dorris/Bayer (2005), p. 230. Single neuron measurements were conducted, for that reason the results are not so easily replicated in humans. 372 Cp. Glimcher (2004), pp. 245ff., Glimcher/Dorris/Bayer (2005), pp. 249f. 373 Cp. Knutson/Peterson (2005), p. 312. 374 Cp. Glimcher/Dorris/Bayer (2005), p. 251. 375 Cp. Camerer/Loewenstein/Prelec (2005), p. 55.

45

the optimal course of action. They seem to learn about probabilities implicitly in an

efficient way, but of course without deliberately using statistical calculus.376 Humans

additionally can consciously form expectations of probabilities and frequencies under

participation of ventromedial prefrontal cortex.377 Is this ability actually a step back-

wards, if it obviously produces wrong estimates? Not necessarily, when it incorporates

the capability of “mentalizing”, which allows to anticipate what others will do or how

they feel.378 In social encounters, mentalizing is likely to be a superior alternative to

Bayesian updating. Again evolution could not foresee that on anonymous financial

markets perhaps a powerful Bayesian device would do better.

6. What Can and Does Finance Make of it?

“We would love to have three laws that explain 99 percent of economic

behavior; instead, we have about 99 laws that explain maybe 3 percent of

economic behavior!” Andrew W. Lo379

6.1. Financial markets and emotion

6.1.1. Behavioral finance and the marketplace

The previous account of psychological and neuroscientific research contained only few

references to finance. Instead a more universal picture of emotion and decision behavior

was presented. But of course these insights have applications to finance that are exami-

ned by behavioral finance and neurofinance.380 The emergence of behavioral finance is

partly a response to the deficiencies of the traditional finance paradigm, which is built

on the rationality assumption.381 It entails preferences that obey expected utility theory,

beliefs updated by Bayes’ law, and the theory of efficient markets (see section 2). Over

the years growing evidence challenged this notion, and behavioral finance tries to

explain phenomena that are inconsistent with the traditional paradigm.382 In line with

other fields (see section 3.3.) research in behavioral finance, focusing on cognitive bia-

ses, has only paid scant attention to the role of emotion.383 Though there are some

notable exceptions that will be reviewed here.

376 Cp. Glimcher (2004), p. 330, also pp. 217ff., Damasio (1995), pp. 254f. 377 Cp. Bechara/Damasio (2005), p. 356. 378 Cp. Camerer/Loewenstein/Prelec (2005), p. 34. 379 Cp. Lo (2002), p. 80, he is referring to “physics envy” of economists. 380 The latter might be considered as a subarea of the former (cp. Olsen [2001], p. 157). Nonetheless they will be treated separately here as this reflects the organization of the previous sections. 381 Cp. Barberis/Thaler (2003), p. 1053. 382 Cp. Frankfurter/McGoun (2000), p. 201. 383 Cp. Ackert /Church/Deaves (2003), p. 28.

46

Behavioral finance is concerned with both individual investment behavior and aggrega-

ted price formation on financial markets.384 The impact of feelings is of course more

direct and more pronounced in the case of individual investors (see section 6.2.). But

this would be only of limited interest, if market prices remained totally unaffected.

Within an efficient market environment rational investors will exploit emotional

mispricing and arbitrage it away. However, in real markets there exist severe limits to

arbitrage.385 As a consequence asset prices can be influenced, if a sufficiently large

subset of investors experiences similar emotions.386

The critical assumption then is that fluctuations of moods or emotions are undergone

widely and uniformly.387 Given the subjective and personal nature of emotion this is

usually not the case. Yet some researchers argue for a role of mass emotional change in

financial markets.388 For such mass emotions only stimuli come into question, that are

perceived by a considerable number of investors. One can distinguish, whether these

stimuli are market-related or not.

A market-related stimulus par excellence are prices themselves. When prices rise or fall,

they directly generate positive or negative emotions. These emotions are experienced

more or less alike by all investors in a particular asset. If then expectations are generated

by a hedonic process based on these emotions, this could lead to lasting trends in the

market.389 This idea is supported theoretically, as positive (negative) emotions produce

more optimistic (pessimistic) expectations (see 4.5.), and empirically, as overreactions

are frequently observed in the short term.390 As Peterson notes:

“Several market price anomalies appear related to collective shifts in investor’s moods, from risk taking and reward seeking to risk-averse and loss avoidant.” 391

Brain activity reflects these shifts by different regions responsible for anticipation of

reward and loss, their reaction amplified by past experiences.392

When people react in a similar manner, another interpretation is conceivable: herding.

Herding behavior is a social phenomenon related to communication among people that

results in uniform thinking.393 It exists in financial markets, especially when there are

384 Cp. Barberis/Thaler (2003), p. 1053, who call these the “two building blocks of behavioral finance”. 385 Cp. Barberis/Thaler (2003), pp. 1056ff. Especially arbitrage is mostly neither riskless nor costless. 386 Cp. Lucey/Dowling (2005), p.218. 387 Cp. ibid. 388 Cp. Prechter (2001), p. 120. 389 Cp. Peterson (2005a), p. 396. 390 Cp. Barberis/Thaler (2003), pp. 1085ff. 391 Cp. Peterson (2005a), p. 394. 392 Cp. Peterson (2005a), pp. 393ff. The mentioned regions are nucleus accumbens and anterior insula. 393 Cp. Shiller (2005), p. 157.

47

many people following the advise or example of others.394 In the course of epidemic

herding emotions like hope, euphoria, caution or panic are in control.395 Acting within a

large group proves counterproductive to a deliberate reflection of the situation. Reliance

in the judgment of a group or experts goes along with the pressure to act in confor-

mity.396

Next to these market-related events inducing emotions, people allow other unrelated

feelings to influence their financial decisions.397 Again relevant stimuli must be percei-

ved by many people and carry emotional significance. Political events or fatalities, e.g.

terrorist attacks, must be discarded as they may well alter the fundamental value of

assets. Therefore most research has concentrated on – in this respect unsuspicious –

phenomena like the weather.398 The weather certainly affects people’s moods, but the

effect on investment behavior is less unambiguous.399 Others search for the influence of

biorhythm, moon phases or social events like sports on equity return.400 Many of the

results seem dubious and have to be replicated before they can be regarded as emotional

factors in financial decision making. Among the more serious approaches is the

investigation of seasonal effects associated with mood and depression.401 The impact of

reduced daylight on the mood or even health of many people is well documented.402

Altogether the field of “mood misattribution”403 is worth further investigation, yet sus-

ceptible to data mining.

Some authors even in the behavioral finance arena doubt that emotions are prominent

on aggregated market level. For instance Shiller writes:

“In fact, during the most significant financial events, most people are preoccupied with other personal matters, not with the financial markets at all. So it is hard to imagine that the market as a whole reflects the emotions described by these psychological theories.” 404

The quote suggests that financial events might be no emotional stimuli for ordinary peo-

ple. But particularly in market phases associated with emotions, like bubbles or crashes,

these stimuli become very salient. If one further assumes that professional traders are

not free from such emotions, their role can be significant. The argument of people being

preoccupied by other matters seems to allow for misattributions as previously discussed.

394 Cp. Prechter (2001), p. 121, who asserts that “most people get virtually all of their ideas about financial markets from other people”. 395 Cp. Prechter (2001), pp. 121/124. 396 Cp. Shiller (2005), pp. 158/159, p. xii, for an example. 397 Cp. Lucey/Dowling (2005), p. 212. 398 E.g. Saunders (1993), Hirshleifer/Shumway (2003). 399 Cp. e.g. Goetzmann/Zhu (2005), who see no weather effect for individuals, but for market-makers. 400 Cp. Lucey/Dowling (2005), pp. 223ff. for an overview. 401 Cp. Kamstra/Kramer/Levi (2003). 402 Cp. Kamstra/Kramer/Levi (2003), pp. 324-325. 403 Lucey/Dowling (2005). 404 Shiller (2005), pp. 147f.

48

6.1.2. A market model of emotional and rational investors

Regardless whether emotions are induced by market or non-market events, the hypo-

theses about their impact remain vague. There were only few attempts to formalize the

relationship of emotions and financial decision making. One is the model of Salzman

and Trifan, which will be presented here in greater detail.405

The authors follow the tradition of the noise trader approach406 and enhance it by a third

type of investor: emotional investors (next to rational investors and noise traders).407

This choice is a bit unfortunate, as there are no such distinct groups in reality: everyone

experiences and acts on emotions. The rationale behind their categories of investors is

that “professional traders […] dispose of sufficient resources and motivation in order to

make decisions in a way approaching the rational type”.408 However, professional

traders also show emotional activation during market events409 and are even more

vulnerable to some biases, such as overconfidence.410 The dominance of one system

within two-system view the authors refer to411 is only temporarily and cannot account

for a general division of the investor population.

The primary result that emotional traders survive in the market412 is therefore not very

enlightening. This was obvious as human beings survive in the market.

The model is set up in two steps. First the processing of information and generation of

beliefs is formulated. This results in the following distribution functions of expected

market returns for rational and emotional investors respectively413:

rrt│Ft-1 ~ N (ct-1 + cr rr t-1 + ce re t-1 + cn rn t-1, σ² + (cr σ r)² + (ce σ e)² + (cnσ n)²)

ret│Ft-1 ~ N (kt-1 + ke re t-1, σ² / b + (ke σ e)² / a)

The indices refer to the different investor groups, c and k denote constant parameters or

group specific weights (when with index). Without going to much into detail the func-

tions show that rational investors incorporate the opinions of the other groups in their

expectations, while emotional investors focus on their own intuition. Arguing within

two-system view the emotional investors use a simplifying heuristic.

Secondly market pricing is modeled using a market maker mechanism. To derive the

demand functions the authors consider “investors to be concerned not only with

405 Cp. Salzman/Trifan (2005). 406 See e.g. Black (1986) or Shleifer/Summers (1990). 407 Cp. Salzman/Trifan (2005), p. 3. 408 Salzman/Trifan, p. 3. 409 Cp. Lo/Repin (2002), Lo/Repin/Steenbarger (2005), consider also neuroscientific evidence. 410 Cp. Montier (2005), pp. 12ff. 411 Cp. Salzman/Trifan (2005), p. 9. 412 Cp. Salzman/Trifan (2005), p. 18. 413 Cp. (also for the following) Salzman/Trifan (2005), pp. 6ff.

49

individual survival, but also with the survival of their own kind”.414 It seems opaque

that investors, whether emotional or rational, should be interested in something like

group survival. The given analogy to evolution is not well-grounded as the categories of

investors form no species, flock or suchlike. Since the market outcome is derived from

this assumption it will not be further reviewed here.

The model of Salzman and Trifan presents some interesting starting points for a

formalization of emotions within a financial market environment. It highlights the

fundamental question, how to capture the parallel existence of rational and emotional

investment behavior. It further draws attention to the process of belief formation and

perception of information. Here the authors adopt a powerful approach that can simul-

taneously account for undervaluation (conservatism) and overvaluation (representative-

ness) of current information.415

6.2. The individual investor and emotion

6.2.1. Investor behavior emotionally understood

On individual level, the Salzman/Trifan model yields an alternative to Bayesian upda-

ting. It hereby speculates how an emotional perception of information differs from the

rational paradigm. Indeed the increasing interest in the actual behavior of investors – in

contrast to a normative prescription – demands for new explanations. The first step in

this development was the accumulation of biases and formulation of heuristics.416

Among these findings, several are of particular importance to finance. For example, the

disposition effect accounts for the tendency to sell winning stocks too early and hold on

to losers too long.417 The home bias describes the phenomenon of insufficient diversifi-

cation within portfolios.418 Overconfidence provides an explanation for excessive

trading.419 And the mentioned conservatism and representativeness are active in the

generation of beliefs about investment prospects.

The endeavor of identifying human biases in financial decision-making is not fully

satisfactory as it lacks an underlying theory or a common framework. In an investment

setting it is left to the researcher to decide which bias is relevant, creating an extra

degree of freedom for modeling.420

414 Salzman/Trifan, p. 9. 415 Cp. Hirshleifer (2001), pp. 1546/47. Cp. also the model of Barberis/Shleifer/Vishny (1998), pp. 308/9. 416 See Tversky/Kahneman (1982). 417 Cp. Barber/Odean (2005), pp. 543ff. 418 Cp. Barberis/Thaler (2003), pp. 1099ff. 419 Cp. Barber/Odean (2005), pp. 554ff., Shiller (2005), p. 154. 420 Cp. Hirshleifer (2001), p. 1564.

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In the meantime the (behavioral) finance community mostly agrees that emotions play a

considerable role in investment decisions.421 The psychological and neuroscientific

literature presented in sections 4 and 5 has found a wide reception among economists.

Some establish a connection between the effect of emotions and the concept of satis-

ficing.422 Satisficing denotes a decision-making process that searches for a solution,

which is good enough given the particular circumstances, instead for an optimum.423 It

can be argued that emotions constitute an efficient instrument for satisficing.424

The advice to reach a satisficing solution would simply be to follow one’s intuition,

which can be understood here as the initial judgment delivered by system one. Lo and

Repin observe corresponding behavior in their trading experiment:

“Most successful traders seem to trade based on their intuition about price swings and market dynamics, often without the ability (or the need) to articulate a precise quantitative algorithm for making these complex decisions. […] [E]motional mechanisms are at least partly responsible for the ability to form intuitive judgments.” 425

Despite this rather positive record, there is yet no agreement, if emotions are likely to

improve or disrupt financial decision making.426 As behavioral finance pursues a mainly

descriptive approach, this issue is not crucial for the moment.

What are the feelings experienced in financial markets? Traditionally, this question is

answered by greed and fear, the classic antagonists.427 In addition, hope, regret, irrita-

tion, disappointment, euphoria, pride, confidence, panic and anxiety are mentioned.428

These emotions can be classified as experienced emotions confronting a financial

decision (hope, fear, anxiety, etc.) or anticipated emotions confronting the outcome of a

financial decision (regret, pride, disappointment, etc.).429 Furthermore, they differ in

their valence, which is positive for hope, confidence, euphoria and pride and negative

for fear, regret, anxiety, panic, irritation and disappointment.

421 Cp. Shiller (2005), pp. 155/56, Lo (2002), pp. 82/83, Ackert/Church/Deaves (2003), p. 30, DeBondt (1998), p. 842. 422 Cp. Lucey/Dowling (2005), p. 217, Lo (2002), p. 76, Gigerenzer/Selten (2001). 423 Cp. Simon (1959), pp. 262f. 424 Cp. Lucey/Dowling (2005), p. 217. 425 Cp. Lo/Repin (2002), p. 332. 426 See Ackert/Church/Deaves (2003), pp. 27-30 for a positive opinion. In contrast Shiller (2005), p. 155, sees decision “clouded by emotions”. Review also section 5.2.3., where the topic was discussed in greater detail, including the results of the additional study of Lo/Repin/Steenbarger (2005). 427 Cp. Haugen (2004), p. 18, Lo (2002), p. 76. Typically enough a book by Hersh Shefrin on behavioral finance is titled “Beyond Greed and Fear”. 428 Fear, hope, regret and disappointment are all found in an essay of Lopez (1987), p. 290, regret, pride, hope and fear are mentioned by Fisher/Statman (1999), pp. 92/94. Bailey/Kinerson (2005) discuss regret and to a lesser degree disappointment [recall the model of Mellers/ Schwartz/Ritov (1999), section 4.6.]. Anxiety and euphoria are stated by Ackert/Church/Deaves (2003), p. 31, anxiety and irritation are repor-ted by subjects in an investment experiment of Bosman/van Winden (2005), p.17. Slovic (1972), p. 783, notes the feeling of confidence, when investors are supplied with information. Finally Prechter (2001), p. 124, sees hope, euphoria and panic at work in financial markets. 429 See also section 4.6.1. for the distinction of anticipated and experienced emotions.

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Emotions of negative valence seem to dominate positive feelings in monetary decisions,

as the existence of loss aversion suggests. Loss aversion can be regarded as a fear

reaction that deters people from incurring losses.430 The fear of loss it greater than the

attractiveness of equal gains (about two-times).431 Consequently if one associates the

prospect of gains with hope and of losses with fear432, the valence of the latter emotion

must be felt stronger. One can ask, whether such an imbalance is justified or if it is a

maladjustment in financial markets, where losses are less life-threatening than in evolu-

tionary past.433 However, if these experienced emotions in decision-making correspond

to an emotional valuation of the outcomes434, acting on loss-aversion can be perfectly

sensible. If losses feel worse, then one should fear them.435 One can of course object

that this feeling is as unreasonable as the preceding fear response. But whereas one may

recommend to disregard emotions in financial decision making (if possible at all), this

recommendation goes astray for emotions experienced with outcomes, as they are part

of the utility rendered by that outcome.

In line with this argument, prospect theory incorporates loss aversion treating it as a

constituent of human preferences.436 The popularity of prospect theory in behavioral

finance is due to its superior descriptive power in many decision situations.437 Recall

that in section 5.2.2. prospect theory was related to the concept of homeostasis. The

affective evaluation of gains and losses relative to a reference point appears to be well-

grounded.438

Closely related to the issue of loss aversion and perception of outcomes is risk taking

behavior. In the previous accounts on risk (4.5. and 5.2.3) it was stated that mathematic

moments of a distribution, especially variance, might have a psychological or even

physiological reality.439 Behavioral finance has however also realized that risk always

contains an emotional dimension.440 Variance can not fully capture riskiness, other

aspects like probability or amount of loss are determinants, or more precisely the

430 Cp. Camerer (2005), p. 9. 431 Cp. Benartzi/Thaler (1995), p. 74. 432 In accordance with Lopez (1987). 433 Cp. Camerer (2005), p. 9, who makes this claim. 434 Cp. Kahneman/Tversky (1979), p. 279. They derive loss aversion not from the feelings of hope and fear, but from pleasure or aggravation experienced with outcomes. 435 See Benartzi/Thaler (1995), p. 74, for an anecdotal example (originally from Samuelson) in which a 50/50-bet (win 200$ vs. loose 100$ ) is turned down by the rationale: “I would feel the 100 $ loss more than the 200 $ gain.” 436 Cp. Kahneman/Tversky (1979). 437 Cp. Barberis/Thaler (2003), p. 1067: “Of all the non-EU theories, prospect theory may be the most promising for financial decisions.” 438 Cp. Barberis/Thaler (2003), p. 1068. 439 Cp. also Lopez (1987), p. 287. 440 So e.g. Olsen (2001), p. 158.

52

perception of these figures.441 The revised version of prospect theory models probabili-

ties within a cumulative weighting function.442 Such a function allows to consider

probabilities not at their face value, but to reassess them by certain criteria. For example

Tversky and Kahneman assume diminishing sensibility for the weighting function, that

is, a change in probability has greater impact on decision weight when it is close to the

extremes of certainty or impossibility.443

Does such a reweighting lend itself to an emotional interpretation? Shefrin and Statman

try for an answer within their own model (using decumulative probabilities):

“Formally, fear affects attitude toward a reweighting of the decumulative probabilities. […] the emotions of fear and hope reside within all individuals and [..] each emotion serves to modify the decumulative weighting function.”444

A theory of risk taking should consider these motivational and emotional factors that

give risky choice its experiential texture.445 A proposal in this context would be to

associate certain emotions with the shape of distributions in risky decisions, for example

positive skewness with hope and negative skewness with fear.446 Structured finance is

currently engaged in designing investment products that have attractive shapes with

regard to their outcome distribution. This is a way to exploit the emotional added-value

those shapes generate for individuals.447

6.2.2. Behavioral portfolio theory

To illustrate how emotionality can be integrated into a formal approach of portfolio

selection, the behavioral portfolio theory of Shefrin and Statman shall be briefly revie-

wed.448 The authors define a utility function that depends on the probability that the

payoff will exceed an aspiration level (A) and on expected wealth calculated by means

of reweighted probabilities (Eh(W)). The aspiration level reflects the fear of people to

fall short a minimum that guarantees survival or in financial terms avoids ruin.449

The expected wealth is calculated by decumulative probabilities (D) that are altered by

the emotions of hope and fear.450 Decumulative probabilities result from a ranking of

441 Cp. Slovic (1972), p 794. 442 Cp. Tversky/Kahneman (1992). 443 Cp. Tversky/Kahneman (1992), p. 303. 444 Shefrin/Statman (2000), p. 132. Note that Tversky and Kahneman would perhaps disagree for their theory being strongly cognitive. 445 Cp. Lopez (1987), p. 263. 446 Cp. Lopez (1987), p. 267. 447 What is meant here is that institutional investors are usually hedged when offering such products (with small residual risk due to imperfect hedge). If buyers are prepared to pay a higher price than justified, because the offers are emotionally attractive, a revenue potential for institutions arises. 448 Cp. Shefrin/Statman (2000). 449 Cp. Roy (1952), p. 432, who calls it “the principle of Safety First”. 450 Cp. Shefrin/Statman (2000), p. 131.

53

wealth levels: the lowest possible amount is certain, a decreasing probability is assigned

to any further increment.451 Fear should attach disproportionate weight to the most

undesirable outcomes, that is the higher values of D.452 Thus the probabilities are

transformed by a function hs(D) = Ds1+q.453 In contrast the hope for upside potential

takes people to overweight small values of D: hp (D) = 1 – (1 – D)1+Q. Both features are

combined in the final transformation function h (D) = δ hs(D) + (1 – δ) hp(D).

The parameter δ reflects the relative strength of both emotions. With the transformed

decumulative probabilities Eh(W) can be calculated, it will usually differ from expected

value E(W). The equivalent to µ-σ-space in traditional portfolio theory is the Eh(W)-

D(A)-space in behavioral portfolio theory.454

Shefrin and Statman then compute optimal portfolios under the given premises. These

portfolios contain bonds and lottery tickets.455 A result that mirrors the simultaneous

desire for potential gains and fear of losses, particularly such losses that jeopardize

aspiration levels.

Behavioral portfolio theory is a fine achievement in drawing on psychological insights

to develop an alternative to classic portfolio theory. It can account for an attitude to risk

that is broader than its representation merely by variance. The authors further introduce

two schemes of mental accounting to include framing effects. It remains inconclusive

whether this complication is justified by the additional insights. Nevertheless, as a

prototype for a formal integration of emotions into the investment decision process it

may inspire a wider discussion of the issue.

6.2.3. The affect heuristic – an implementation

Behavioral finance as a science is mostly experimental and empirical, using different

research methods than traditional finance.456 The just presented behavioral portfolio

theory for example, albeit theoretic in nature, rests on a series of experiments by Lopez

investigating the role of hope and fear in choice of lotteries.457 Behavioral biases and

simplifying heuristics are also derived from experiments or empiric data.458 In section

4.4. the affect heuristic was introduced as a way people use emotional representations

and images attached to an object to form judgments and decisions.

451 Up to this point this is simply a reformulation. 452 Cp. Shefrin/Statman (2000), p. 132, also in the following. 453 With q > 0. The index s stands for security, fear being associated with the desire of security. 454 Cp. Shefrin/Statman (2000), p. 133. 455 Cp. Shefrin/Statman (2000), pp. 149f. 456 Cp. Frankfurter/McGoun (2000), p. 201. 457 Cp. Lopez (1987). 458 See Barber/Odean (2005) for the empirical approach. E.g. Tversky/Kahneman (1982) for experiments.

54

When the general ideas of the affect heuristic concept are applied to financial markets,

this yields two assumptions:

1. Investment decisions (in company stocks) are influenced by the affective image

of a company, by the liking or disliking experienced for it.459

2. A global evaluation of image and liking leads to predictions of high future re-

turns for companies associated with low risks (contrary to theoretic finance).

Evidence for the latter found by Ganzach was already mentioned (see 4.4.). He supposes

that people are unable to judge risk and return of financial assets meaningfully when

information is meager, and instead resort to an affective judgment of global prefe-

rence.460 Several experiments confirm this view, the participants all trained in finance

and thus acquainted with the proper relationship between risk and return.461 But a

correction of an initial judgment by an intervention of system two does not set in

automatically.462 Rather other factors, for example the order in which risk and return

predictions are made, determine whether subjects access relevant financial know-

ledge.463

The first and more general proposition is tested in two recent studies. Anginer, Fisher

and Statman use the Fortune quality rating of companies to ask if good companies make

good investments.464 They find that the Fortune score of companies is highly correlated

both with an affect score they introduce and the perception of investment prospects.465

That is, the affect score delivered rapidly and intuitively, unspoiled by the knowledge of

fundamentals, still closely resembles the judgments of firm quality and investment

value.466 Interestingly, the real performance of the companies is not matched by either

of these scores.467 The authors suppose that positive affect towards a company is rewar-

ding for investors and therefore they are prepared to accept lower returns.468

Similar results renders the work of MacGregor et al., they find a strong impact of affect

and imagery on judgments of the quality of financial stimuli.469 Participants in an expe-

riment are asked to provide images that quickly come into their mind, when thinking of

459 Cp. for both Lucey/Dowling (2005), pp. 228f. 460 Cp. Ganzach (2000), p. 368. 461 Cp. Ganzach (2000), p. 358. The possibility that participants do not accept the positive relationship of risk and return (like Haugen [2004], pp. 83-87) is ruled out by further experiments. 462 See also section 4.2. Recall that the affect heuristic was stimulated by two-system view. 463 Cp. Ganzach (2000), p. 361. 464 Cp. Anginer/Fisher/Statman (2007). 465 Cp. Anginer/Fisher/Statman (2007), pp. 6/11. 466 Cp. Anginer/Fisher/Statman (2007), pp. 11f. 467 Evidence is mixed whether admired or despised firms achieve higher returns. Cp. Anginer/Fisher/ Statman (2007), pp. 2-5. 468 Cp. Anginer/Fisher/Statman (2007), p. 11. 469 Cp. MacGregor et al. (2000), p. 110.

55

specific industry groups.470 They then rate these images on a scale from highly negative

to highly positive and additionally give affective ratings along several dimensions (e.g.

boring/exciting).471 Again the obtained ratings are positively correlated with the return

estimates of these industry groups, but have poor predictive power for actual perfor-

mance.472

All three studies support the idea that investors employ an affect heuristic in financial

judgment and decision making. Possibly even professional analysts forecast risk and

return mainly on affective grounds.473

6.3. Neurofinance

6.3.1. A new science and what it reveals

A behavioral finance that relies exclusively on psychology is limited. Particularly when

it comes to emotion, theories often remain speculative or rather general. Inferring deci-

sion behavior merely from readily observable features can be superficial.474 And experi-

menters have to draw on problematic self-reports instead of reliable measurements.475

Neurofinance in contrast applies the techniques of neuroscience (see section 5) to the

analysis of financial decision-making. It tries to relate brain processes directly to

investment processes. There was a first hypothesis in section 5.2.3. that can serve as a

starting point for the discussion of neurofinancial insights. It was stated that emotions

are present in financial decisions, that they might be beneficial in their mild form, but

are disruptive when they become overwhelming.476

An investment task conducted with brain damaged patients and control subject reveals

that patients, who are impaired in experiencing emotions, make more advantageous

decisions.477 Controls switch to a more conservative strategy both after wins and losses

and so abstain from profitable investment opportunities.478 They exhibit loss or risk

aversion, which were previously associated with emotion. Concordantly Peterson notes:

“Investors’ financial decisions are most likely to suffer if the individual investor is emotionally reactive or has poor impulse control.” 479

470 Cp. MacGregor et al. (2000), p. 106. 471 Cp. ibid. 472 Cp. MacGregor et al. (2000), pp. 107ff. 473 Cp. Ganzach (2000), p. 368. On analyst forecasting see also Montier (2005). 474 Cp. Preuschoff/Quartz/Bossaerts (2006), p. 2. 475 The earlier cited Bosman/von Winden (2005) and Bailey/Kinerson (2005) make use of self-reported emotions in their financial decision tasks. 476 This was the result of the physiological studies of Lo/Repin (2002) and Lo/Repin/Steenbarger (2005). They are counted among neurofinance, as the measured body reactions are triggered by brain processes. 477 Cp. Shiv et al. (2005), p. 435. 478 Cp. ibid. 479 Peterson (2005b), pp. 10f.

56

But what, if the role of emotion is more in focusing and prioritizing, in quickly allowing

to overlook and deal with a situation?480 Then the perspective of the experiment might

be too narrow, as it precisely defines the framework of the decision and asks the

participants to make one. It is telling that reportedly three of the brain damaged subjects

experienced private bankruptcy.481

Besides the question, whether emotions contribute to performance, it is of interest to

review some of the neuroscientific theories to understand financial decision-making:

Market news and thoughts about the own behavior in response to them can be interpre-

ted as inducers of a somatic state.482 The prevalent somatic state then influences the

appraisal of subsequent economic stimuli. The tendency of financial markets to follow

trends, most pronounced in bubbles and crashes, is then explained by the impact of

streaks of gains and losses on neurotransmitter release and hereby the threshold of

neuronal firing in different brain areas. In a bubble for example the pre-existing positive

somatic state reinforces subsequent positive somatic states, while impeding negative

ones. Background somatic states not related to the market may also interfere with finan-

cial decision, as it was predicted by the literature about mood misattribution.483

Other ideas rest on the reward system (see section 5.2.4.) as it comprises the fundamen-

tal neural processes of goal evaluation and preference formation that precede financial

behavior.484 The reward system may urge investors to realize paper gains that are

rewarding, while hold on to losing stocks, avoiding a punisher.485 Indeed money was

found to represent a direct reward and so do investment gains.486

The house money effect can probably be attributed to a reaction of the brain when it

confronts unexpected rewards.487 An additional dopamine release might be responsible

for risk-seeking behavior observed after such unanticipated gains.488 In line with this

argument, the general readiness to take risks, which is an evolutionary necessity, may

lie in the dopamine signals associated with unexpected rewards.489

Similarly to the theory of somatic markers, the reward system can also account for

ongoing trends. A feeling of increased impulsivity and excitement experienced by

480 Cp. Ackert/Church/Deaves (2003), p. 27. Cp. also Damasio (1995). 481 So a weblog by Zack Lynch at Corante. 482 Cp. Bechara/Damasio (2005), p. 363, also for the following. 483 Cp. Bechara/Damasio (2005), pp. 364ff., cp. also section 6.1.1. 484 Cp. Peterson (2005a), p. 391. 485 Cp. Kenning/Plassmann (2005), p. 348. 486 Cp. Camerer/Loewenstein/Prelec (2005), p. 35. 487 Cp. Kenning/Plassmann (2005), p. 305. 488 Cp. ibid. See also section 5.2.4. for the relationship of anticipation and dopamine release. 489 Cp. Bechara/Damasio (2005), p. 365.

57

investors when they identify an investment opportunity, a potential reward, is reinforced

when the reward really materializes and forms eventually a positive feedback cycle.490

Overconfidence is the result on individual level, and as in a moving market (for exam-

ple in reaction to new fundamental information) many investors receive similar stimuli,

the market trend will be enforced beyond the informationally justified.491

6.3.2. Two examples of research in neurofinance

To provide for a better impression of the way neurofinancial research is conducted, two

of the still very few studies in neurofinance shall be portrayed below.

Under the promising title “Markowitz in the brain?” Preuschoff, Quartz and Bossaerts

investigate how expected return and variance, as the key factors of modern portfolio

theory, are perceived by the brain.492 Two alternative hypotheses are imaginable, the

encoding of risk and reward (represented by expected return and variance) might be

one-dimensional resembling expected utility or decomposed as in portfolio theory.493 Ex

ante a neural implementation of the finance approach is considered to be more effective

as it simplifies the analysis of simultaneous investment opportunities and facilitates

learning and re-evaluation.

The experiment to test this conjecture comprises a card game in which two cards

numbered 1 to 10 are drawn from a deck.494 Participants have to place a bet, which card

will be the highest. Afterwards the first card is displayed and thus the brain receives a

cue to evaluate risk and expected reward. Indeed an almost immediate reaction is found

in the nucleus accumbens with a signal increasing in the magnitude of expected reward.

With a short delay again the nucleus accumbens displays a response to risk that is

consistent with variance. The timely distinct activation favors a decomposed perception

of risk and return by the human brain.

Within seven seconds, the uncertainty is resolved by showing the second card. As the

brain reactions were observed even earlier, it is unlikely that the values of the parame-

ters were explicitly computed.495 The authors even doubt that some of their subjects

were able to do so. Instead, expected return and variance are encoded subcortically and

their computation remains unconscious, evoking a certain impression about the

dimension of both variables that becomes conscious.

490 Cp. Peterson (2005a), pp. 395/396. 491 Cp. ibid. Note that limited arbitrage is implied. 492 Cp. Preuschoff/Quartz/Bossaerts (2006). 493 Cp. Preuschoff/Quartz/Bossaerts (2006), p. 3, also for the following. 494 Cp. Preuschoff/Quartz/Bossaerts (2006), pp. 16ff. for the description of the experiment and results. 495 Cp. Preuschoff/Quartz/Bossaerts (2006), p. 21.

58

The authors leave the reader with two question: One is, if and how emotions are

involved in the process of risk and reward perception.496 And second, by what means

the respective signals are recombined in a decision that demands a trade-off of risk and

return, as it is usually the case in investing.

Probably an experiment accomplished by Kuhnen and Knutson can shed some light on

these issues.497 In the previous study bets on cards were made before information about

risk and return was revealed. There was no need to use this information for subsequent

decisions. The set-up of Kuhnen and Knutson is different, the subjects have to act on

information they obtain in the course of the experiment.

In an investment task subjects have to choose among a bond and two stocks, the bond

guarantees a sure payoff (1$), while there exists a good stock (expected return 2.50$)

and a bad stock (expected return –2.50$). The quality of the stocks remains constant for

ten trials, after each trial the outcome for the stocks and the bond is displayed.

Obviously, an optimal strategy is to choose the bond until the probability computed by

Bayesian updating is high enough suggesting to switch to the stock regarded as the good

one.498 The authors define two types of mistakes, a risk-seeking mistake if participants

choose a stock, when the bond is optimal and inversely a risk-aversion mistake.499

Riskless choices (including risk-aversion mistakes) are preceded by an activation of the

anterior insula, shown by fMRI.500 Activity of the nucleus accumbens precedes risky

choices (including risk-seeking mistakes). Recall that these regions were also associated

with the anticipation of losses and gains respectively.501 Both reactions are more pro-

nounced, when a decision represents a transition from one type of asset to another.

Departures from optimal behavior can be interpreted as an excessive activation of the

involved brain structures.502

Distinct regions active in risk-seeking and risk-avoidant behavior indicate different

responsible mechanisms. Candidates for such mechanisms might be different emotions

like fear and hope or anxiety and excitement.503 Particularly because insula and nucleus

accumbens are commonly associated with emotion and project to other emotionally

496 Cp. Preuschoff/Quartz/Bossaerts (2006), pp. 23/24. 497 Cp. Kuhnen/Knutson (2005), also for the description of the experiment. 498 At the beginning expected return for both stocks is 0$, as there is a fifty/fifty-chance for each stock to be the dominant one. If one stock reaches a probability of at least 0.7 being good, it is time to switch. Note that the authors assume risk neutrality, as the payoffs for each trial are all in the range [-1$;1$]. 499 Cp. Kuhnen/Knutson (2005), p. 763. 500 Cp. Kuhnen/Knutson (2005), p. 765, also for the following. 501 Cp. section 6.1.1., especially footnote 392. 502 Cp. Kuhnen/Knutson (2005), p. 767. 503 Cp. Kuhnen/Knutson (2005), p. 763, cp. also section 6.2.1.

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relevant structures within the limbic system.504 Again an explicit calculation of proba-

bilities in the experiment seems virtually impossible.

7. Conclusion

Not least the experiments closing the section on finance support the impression that

human beings in most situations rely on internal processes that are different from

deliberate, cognitive and conscious computations.

Slovic noted already in 1972 that the vast quantity of information in financial markets

imposes serious problems on their skillful integration in decision-making and judg-

ment.505 In the era of the internet, computer-based trading and the invention of ever

more sophisticated financial products this information overload has significantly increa-

sed.506 People need to navigate within this environment and they are very likely to do so

with the aid of their emotions.

In a recent article Anderson points to an aspect of emotion that was missed out in the

discussion so far and is difficult to capture within an experimental setting. He asks,

when individuals actually perceive that they face a decision.507 Decisions are not some-

thing the environment provides for, but rather emotions act as a primary shaper of

decisions – without them not many decisions would be made at all.508 The resources to

make decisions are limited and have to be assigned carefully to the matters of real

importance.509

Only after that, the dual role of emotions described before becomes effective: Mood and

emotions felt during a decision affect the perception of decision variables (like risk) and

the attitude towards them.510 And emotions that can be anticipated and associated with

certain outcomes are included into the evaluation of these outcomes.

Behavioral portfolio theory models the former by integrating the feelings of hope and

fear in the choice of an optimal portfolio.511 Decision affect theory combines the fee-

lings of regret and disappointment in their model of anticipated emotions, which is more

general, but also vital for finance.512

504 Cp. Davidson/Irwin (1999), p. 19. 505 Cp. Slovic (1972), pp. 779f. 506 Cp. Barber/Odean (2005), p. 562, according to the authors every online investor has access to over three billion pieces of financial data, against payment to over 280 billion pieces. 507 Cp. Anderson (2006), p. 4. 508 Cp. Anderson (2006), pp. 5f. 509 Cp. ibid. 510 Cp. Hirshleifer (2001), p. 1550, cp. also sections 2.2., 4.5. and 4.6.1. 511 Cp. section 6.2.2. 512 Cp. section 4.6.2.

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A famous metaphor for the functioning of financial markets is Keynes’ beauty

contest.513 In the light of the presented ideas, it comprises a truth that is different from

the insight it is usually praised for. Beauty is a matter of subjective perception, perhaps

there are some features commonly accepted as beautiful, but at last the individual has to

decide what or whom to consider beautiful. However, even without an exact definition

of beauty people are willing to judge it and feel confident of their judgments.514 In

financial markets a different opinion prevailed for a long time: it was the idea that the

fundamental value, a figure that could be unambiguously determined, defines the price

of an asset.515 Even though market prices cannot fully or infinitely depart from their

fundamental value, the way people approach factors such as risk may resemble more the

concept of beauty.516

This is virtually the quintessence of the affect heuristic and its application to finance.517

The appreciation of investment opportunities works at least to some extent affectively.

Estimates of risk and return might well be as subjective as estimates of beauty.

If this is so, pessimists may conclude, there is no hope to reach a sound explanation of

decision behavior and judgment in financial markets. But at this very point neuroscience

and its offspring neuroeconomics and neurofinance set in. If human behavior is more

complex and intransparent than assumed, it is certainly helpful to study the very origin

of behavior that resides within the brain.

Neuroscience has identified structures that are active in the appraisal of stimuli, in the

experience of emotions and in conscious thinking.518 It has offered theories how

emotions might influence perception and thought, most prominently the theory of

somatic markers. Neuroeconomics is eager to know what people ultimately strive for,

how their preferences and goals are formed. In this context the reward system and the

apprehension of risk are central concepts.519

A start has been made in finance to borrow from these ideas. Time will show whether

this marks the emergence of a new body of knowledge. Therein, that is for sure, emo-

tions deserve a prominent place.

513 Keynes (1936), ch. 12, sec. V. 514 Cp. Ganzach (2000), p. 354. 515 Cp. section 2.3. 516 Cp. Ganzach (2000), p. 354. 517 Cp. section 4.4. and 6.2.3. 518 Cp. section 5.1.1. and 5.1.3. 519 Cp. section 5.2.3. and 5.2.4.

IV

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