Informatik 1/152 50 Years of Social Simulation: Why We Need Agent-Based Social Simulation (and Why...

Informatik

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50 Years of Social Simulation:

Why We Need Agent-Based Social Simulation (and Why Other Approaches Fail),

Klaus G. TroitzschUniversität Koblenz-LandauESSA Summer School 2010

10/04/23 50 Years of Social Simulation

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Outline

• Simulation from the 1960s to 2010– historical background– main features of some of the approaches

• system dynamics, microsimulation, discrete event analysis, sociophysics, cellular automata

• early extensions

– some first conclusions

• Why complex social systems are even more complex than other complex systems– peculiarities of human social systems– requirements for computational social science– and how they can be met: recent extensions


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From World Models to Multi-Agent Models


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Early Simulations

• 1963 Simulmatics

• Simulation as Science Fiction: Simulacron 3 (1964)– movies after this novel (“Welt am

Draht” [“World on Wire”], Reiner Werner Fassbinder; “13th Floor”; “MATRIX”)


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• Microanalytical simulation of effects of tax and transfer regulations (since 1957)

• Club of Rome simulations by Forrester and the Meadows group (early 1970s)

• Thomas Schelling’s segregation model (1971)• Abelson’s and Bernstein’s referendum campaign

simulation (1963)• Kirk’s and Coleman’s simulation of human behaviour in

small groups (1968)• The Global 2000 Report to the President [Jimmy Carter],

ed. Council on Environmental Quality and U.S. Department of State (1980)

More Early Simulations


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Outline







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System Dynamics

• obviously has its roots in systems of differential equations from which it seems to differ mostly in two technical aspects:– discrete time is used as a coarse approximation for

continuous time to achieve numerical solutions, and– functions of all kinds, including table functions, can be

used with the help of the available tools like DYNAMO or STELLA.

• is restricted to the macro level in that it models a part of reality (the ‘target system’) as an undifferentiated whole, whose properties are then described with a multitude of attributes which typically come as ‘level’ and ‘rate’ variables representing the state of the whole target system and its changes, respectively.


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A PowerSim example


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World Models

Systems Dynamics and DYNAMO have received public interest mainly because they were used to build large world models:•WORLD2 (World Dynamics, Forrester 1970)•WORLD3 (The Dynamics of Growth in a Finite World, Meadows et al. 1974)•WORLD3 revisited (Beyond the Limits, Meadows et al. 1992)•WORLD3 (The 30-Year Update, Meadows et al. 2004)


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Main Features of Forrester’s World Model (1)


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Main Features of Forrester’s World Model (2)


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WORLD2 completeAll these feedback loops are, of course, tied together by auxiliaries and controlled by constants not shown in the previous diagrams.


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WORLD2: Results

Prediction results of Forrester’s WORLD2 model for births, deaths and population size


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Retrodiction

Retrodiction results of Forrester’s WORLD2 model for births, deaths and population size are obviously wrong.


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Types of Validity

• With Zeigler we should distinguish between three types of validity and three different stages of model validation (and development):– replicative validity: the model

matches data already acquired from the real system (retrodiction),

– predictive validity: the model matches data before data are acquired from the real system,

– structural validity: the model “not only reproduces the observed real system behaviour, but truly reflects the way in which the real system operates to produce this behaviour.”

– [Zeigler 1976:5]


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Outline







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The microsimulation approach

• Microanalytic simulation models were first developed to predict demographic processes and their consequences for tax and transfer systems (Orcutt 1986). They consist of two levels at least:– the level of individuals or households (or in the rare

case of simulating enterprises, the level of enterprises)– the aggregate level (e.g. national economy level)

• More sophisticated MSMs distinguish between the individual and the household levels, thus facilitating models in which persons move between households and can found and dissolve new households (e.g. by marriage and divorce).

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… what the founding fathers said …

• “. . . in microanalytical modelling, operating characteristics can be used at their appropriate level of aggregation with needed aggregate values of variables being obtained by aggregating microentity variables generated by microentity operating characteristics” [Orcutt 1986, p. 14].

The main advantage of this kind of procedure is that• “available understanding about the behaviour of

entities met in everyday experience can be used ... to generate univariate and multivariate distributions” [ibid.].

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Types of micro simulation• The classical micro simulation comes in three

different types, the first of which is most common, but does not actually describe a (stochastic) process:– “static micro simulation”: change of the demographic

structure of the model population is performed by reweighting the age class according to external information;

– “dynamic micro simulation”: change of the demographic structure of the model population is performed by ageing the model persons individually (and by having them give birth to new persons, and by having them die) according to life tables;

– “longitudinal micro simulation”: simulation is done on an age cohort and over the whole life of this cohort, thus omitting a population’s age structure (but children of the cohort members may still be simulated).

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How it proceeds

• All types of micro simulation, in contrast to many other simulation approaches, are data driven instead of concept driven: – Starting from data of a population or rather a

sample from some population, normally on the nation state level,

– this approach models the individual behaviour in terms of reproduction, education and employment,

– simulates this individual behaviour and – aggregates it to the population level in order to

generate predictions about the future age or employment structure.

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How it proceeds

current population with all properties of all individuals

future population with all properties of all individuals

representative sample with

selected properties

predicted sample with

selected attributes

updated for all individuals

simulation

real process

projectionsampling

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Subprocesses

• To realise the simulation, several subprocesses have to be modelled:– demographic processes: ageing, birth,

death, marriage, divorce, regional mobility, household formation and dissolution

– participation in education and employment, employment income

– social transfers– taxes and social security– consumption– wealth

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Subprocesses

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Structure of a typical micro simulation model• Initialise the individuals from an empirical data base• Link them together according to their current household structure

and to other information on networks (kinship or friendship networks, where the latter information will usually not be available)

• Then, for every simulated period– organise the marriage market,– and for every simulated individual

• increase its age,• decide whether it dies,• decide whether, if it represents a woman, it gives birth to one or more

children,• decide whether, if it represents a person currently married, it is divorced,• decide whether and whom it will marry,• decide whether it will move from one household to another or form a new

household,• decide on transitions in education and employment, respectively

– and execute all these transitions and changes.– Store all the data needed for the analysis and interpretation of the

simulated history and perhaps output some intermediate results.• Analyse and interpret the collected data, aggregate them, calculate

distributions etc.

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An alternative: event orientation instead of period orientation (1)• Usually microsimulation proceeds in a period-oriented

manner.• Every agent has to check in every period whether anything

happens with respect to it.• Alternatively, the simulation could proceed from event to

event, and every event generats one or more new events:– At the time of birth, the events “child enters school” and

“mother gives birth to another child” are scheduled for some time in the future (the waiting time being distributed according to some frequency distribution):

• enter school– P(tschool = tbirth+5 = 0.2), – P(tschool = tbirth+6 = 0.5), – P(tschool = tbirth+7 = 0.3)

• next birth– P(tnextbirth < tthisbrth+1 = 0.0), – P(tthisbrth+1 < tnextbirth < tthisbrth+25 = f(tnextbirth < tthisbrth)), – P(tnextbirth > tthisbrth+25 = 0.0)

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An alternative: event orientation instead of period orientation (2)• Event-oriented agent-based microsimulation makes it

necessary to look for other types of parameters than in period-oriented microsimulation:– instead of an age-dependent probability of giving birth to a child

during the next period (year) we need an estimate of (e.g.) the frequency distribution of the time between the birth of the first and the second child,

– instead of the age-dependent probability of marrying next year we need an estimate of the frequency distribution of the time between (e.g.) the time a person finishes school and the time when (s)he tries to find a partner: at the time of this event (s)he will look around for partners whose respective events are scheduled for the next very short period of time and select the best match from them,

– instead of an age-dependent probability to die within the next period, we need the distribution of lifetimes;

• some of these distributions are easily estimated, others are not.

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Are microsimulation microentities agents?• Agents are

√ autonomous: they apply rules to beliefs and make decisions, perhaps also plans

√ reactive: they perceive stimuli from their environment and respond to them

√ proactive: they have goals which they try to achieve

¿ socially capable: they have models of their environment and of other agents, and they can communicate with other agents

[at least in Aparicio Diaz/Fent 2005]

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UMDBS as one tool for micro simulation• micro data base• model• parameters / coefficients (life tables …)

Universal Micro DataBase System UMDBS (Windows) [Sauerbier 2000,http://www.fh-friedberg.de/sauerbier/umdbs]

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Output

• tables• graphs• distributions (one- and

two-dimensional)• queries

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A pessimistic view• What such a micro analytical simulation model yields is in a

way prediction, but not in the strict sense. • It is the outcome of one realisation of a stochastic process

whose parameters are not exactly known but estimated on the base of more or less reliable empirical data.

• The distribution of the outcome of this stochastic process can only be estimated (as it were, on a higher level of estimation) if a large number of parallel runs of the same model was run; then confidence intervals can be estimated on a Monte Carlo base.

• After this time-consuming procedure we arrive at an estimate of the distribution of, e.g., the age distribution among women ten years from now, or of the distribution of the proportion of people over 65 with living daughters (to nurse them in case of sickness) — but only for the one set of parameters with which we initialised our simulation model earlier on, and not much is then known about the sensitivity, namely the dependence of the distribution of the outcomes of the stochastic process on slight changes on one or several of the large number of input parameters.

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… and the optimistic view• Results of micro analytical simulation models

have their value as they show possible paths into the future,

• and Monte Carlo simulations of this type even show the reliability of the predictions, while multiple runs of similarly parameterised models give a first glance at the validity of the model:

• if there is no sensitive dependence on initial conditions then the problem of estimating parameters is not a hard one.

• And if we happen to have a long panel or a series of cross-sections then we can validate our model in comparing results of simulations of past periods with the empirical data of the same period.

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Outline







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Models from Econophysics and Sociophysics• Opinion formation or product choice• Simple case: two alternative opinions

(“yes”/ “no”) or two alternative products (“MS-DOS” / “MacOS” or “VHS” / “Betamax”)

• Probability of choice depends on global majorities

• Typical approach:


nn

nnx

x

x

)(exp

)exp(

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Opinion formation in one population

• NetLogo model


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Opinion formation in several disjoint populations• NetLogo model


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Outline







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10/04/23

Cellular Automata

• Defining features• Standard examples• Social science examples

50 Years of Social Simulation

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A grid of cells


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Defining features

• A grid or lattice of a large number of identical cells in a regular array– e.g. a square

• Each cell can be in one of a (small) set of states – e.g. ‘dead’ or ‘alive’

• Changes in a cell’s state are controlled by rules


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Defining features (ii)

• The cell’s rules depend only on the state of the cell and its local neighbours– e.g. the immediately surrounding cells

– Consequently cells can only influence their immediate neighbours


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Defining features (iii)

• Simulated time proceeds in discrete steps – often called steps, cycles or generations

• At each step, the state of every cell (at time t+1) is calculated using the states of neighbouring cells at time t.


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Famous examples

• The Game of Life– rules:

• a ‘living’ cell remains alive if it has 2 or 3 living neighbours, otherwise it dies

• a ‘dead’ cell stays dead unless it has exactly 3 living neighbours, when it bursts into life.


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A Life sequence


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The Game-Of-Life Glider


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Neighbourhoods

• von Neumann neighbourhood

• Moore neighbourhood

NorthEast

SouthWest

NorthNorth-east

EastSouth-east

SouthSouth-west

WestNorth-west


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The universeRight neighbour is left edge cell

Bottom neighbour istop edge cell


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Spreading gossip


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Majority ruleStarting configuration:

50% random ‘on’

Rule: ‘on’ if 5 or more Moore neighbours

and self are ‘on’,‘off’ if 5 or more Moore

neighbours and self are ‘off’

Result: stable blocks of ‘on’ and ‘off’ form


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The effect of individual differences

At start Later

Rule: majority rule with uniform random threshold variation(if 4 neighbours on and 4 off, new state is either on or off at random)


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Outline







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Extensions to basic cellular automata• Migration models

– Actors can move around the grid

• Larger neighbourhoods– Transitions depend on more than the

immediate neighbours

• More complex rules– e.g. rules involving memory


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Migration models

• Agents can move around the grid• Rules determine when and where they move

to• Agents must be distinguished from cells

(locations)• Agents can only move to a vacant space on

the grid


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An example:segregation

• Suppose that (e.g. in the US) there was a threshold of ‘tolerance’, so that white people are content so long as at least 3/8 of their neighbours are also white (i.e. less than a majority), the rest being black

• If less than 3/8th are white, they move to a neighbourhood where they are content with the ratio

• And the same applies to black people in reverse


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An example

• Thomas Schelling proposed a theory† to explain the persistence of racial segregation in an environment of growing tolerance

• He proposed: If individuals will tolerate racial diversity, but will not tolerate being in a minority in their locality, segregation will still be the equilibrium situation

†Schelling, Thomas C. (1971) Dynamic Models of Segregation. Journal of Mathematical Sociology 1:143-186.


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A segregation model

• Grid 50 by 50• 1500 agents, 1050 green, 450 red

– so: 1000 vacant patches

• Each agent has a tolerance– A green agent is ‘happy’ when the ratio of greens to

reds in its Moore neighbourhood is more than its tolerance

– and vice versa for reds


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Aggregation

• Randomly allocateRandomly allocate redsreds andand greensgreens to to patchespatches

• With a tolerance of 40%:With a tolerance of 40%:– An agent is happy when more than 3/8 ( = An agent is happy when more than 3/8 ( =

37.5%) of its neighbours are of the same 37.5%) of its neighbours are of the same colourcolour

• Then the average number of neighbours Then the average number of neighbours of the same colour isof the same colour is 58%58% (about 5)(about 5)

• And aboutAnd about 18%18% of the agents are of the agents are unhappyunhappy


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At the start


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Tipping

• Unhappy agents move along a random walk to a patch where they are happy

• Emergence is a result of ‘tipping’– If one red enters a neighbourhood with 2 reds

already there, a previously happy green will become unhappy and move elsewhere, either contributing to a green cluster or possibly upsetting previously happy reds and so on…


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Emergence• Values of tolerance above

30% give clear display of clustering: ‘ghettos’

• Even though agents tolerate 30% of their neighbours being of the other colour in their neighbourhood, the average percentage of same-colour neighbours is typically 75 - 80% after everyone has moved to a satisfactory location (risen from 58% before relocations)


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Outline







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Views on simulation can be quite different

• Sugarscape:– the question “can you explain it?” is interpreted as “can you

grow it?”, and– “a given macrostructure [is] ‘explained’ by a given

microspecification when the latter’s generative sufficiency has been established.”

• [Epstein and Axtell 1996:177]• Microanalytical simulation:

– starts from a large collection of observational data on persons and households and the population as a whole,

– is initialised with empirical estimates of transition probabilities, age-specific birth and death rates and so on,

– tens of thousands of software agents are created with data from real world people.

– And all this aims at predicting something like the age structure or kinship networks of this empirical population in the far future


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Simulation as a thought experiment• Simulation may be seen as a thought experiment which is

carried out with the help of a machine, but without any direct interface to the target system: We try to answer a question like the following.

• Given our theory about our target system holds (and given our theory is adequately translated into a computer model), how would the target system behave?

• The latter has three different meanings:– Which kinds of behaviour can be expected under arbitrarily

given parameter combinations and initial conditions?– Which kind of behaviour will a given target system (whose

parameters and previous states may or may not have been precisely measured) display in the near future?

– Which state will the target system reach in the near future, again given parameters and previous states which may or may not have been precisely measured?


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Qualitative prediction

• This is either the prediction – which modes of behaviour are possible for a given type of

systems or – which of several possible modes of behaviour a particular

target system will have in the near future, • provided the theory we have in mind holds for this kind of

target systems or for this particular target system.– Will this system stabilize or lock in (and in which of several

stable states will it do so), will it go into more or less complicated cycles, will it develop chaotic behaviour (such that long-time quantitative predictions are impossible)?

– Will this system display some emergent structures like stratification, polarization, or clustering?

• Note: Most quantitative social simulation aims only at qualitative prediction. And: Most qualitative prediction is done by quantitative simulation.


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Qualitative predictions


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Quantitative prediction• This is the prediction

– which state the system will reach after some time, given we know its actual state precisely enough.

– which state the system will acquire if we change parameters in a certain manner, i.e. if we control parameters to reach a given goal.

• Here it is only possible to calculate trajectories starting from the measured initial state of the target system and using the parameters of the target system (which, too, must have been measured or adequately estimated beforehand).

• Quantitative prediction is the field of microanalytic simulation models which are very often used for prediction in demography and policy making.


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A quantitative prediction


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Another quantitative prediction


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Quantitative prediction: problems• Two additional problems have to be kept in

mind here:– If sensitivity analysis has yielded the result that

the trajectory of the system depends sensitively on initial conditions and parameters, then quantitative prediction may not be possible at all (which is a very valuable result!).

– And if the model is stochastic, then only a prediction in probability is possible, i.e. confidence intervals can be estimated from a large number of stochastical simulation runs with constant parameters and initial conditions.


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A first conclusion …

• It should have become clear by now that social science simulation has at least two very different types of purposes.– One of them might be called explanatory — this

includes also teaching —, while– the other comprises different types of prediction

and prescription, including parameter estimation, retrodiction, and decision making.

• In most cases, the explanatory type of simulation — exploring would-be worlds [Casti 1996] — has to be done before the prediction and prescription type of simulation can be accessed.


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Outline







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The whole is greater than the sum of its parts• … the history of an aggregate is the

union of the histories of its members …• … the history of the whole [system]

differs from the union of the histories of its parts …

• … an accurate version of the fuzzy slogan of holistic metaphysics, namely The whole is greater than the sum of its parts.


[Bunge 1979:4]

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Emergence and emergent properties

• emergent properties (of a system) are properties that cannot belong to the parts (elements of the composition) of the same system

• they come into being through emergent processes: things unconnected initially (forming “aggregates”) begin to interact with the effect of self-assembly: the aggregate becomes a system with properties which none of its parts has


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Social systems: what is special about them?• social systems, unlike most others,

consist of – elements that can interact symbolically (not

by pheromones, but by words, for instance)– elements that can take over different roles

in different contexts– elements that can belong to different

systems (including: systems of different kinds) at the same time


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Human social systems: objects of economics and social science• are among the most complex systems

in our world• consist of human actors which

– are autonomous– interact in numerous different modes– take on different roles even at the same

time– are conscious of their interactions and roles– communicate in symbolic languages even

about the counterfactual


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Complex systemsPhysical systems consist of

Living systems consist of

Human social systems consist of

particles which• obey natural laws• interact only in a

few different modes

• have no roles

• are not conscious of their interactions

• do not communicate

living things which• are partly

autonomous• interact in several

different modes• can play different

roles

• are only partly conscious of their roles and interactions (but not all are at all)

• communicate only in a very restricted manner (and never about counterfactuals)

human actors which• are autonomous• interact in

numerous different modes

• take on different roles even at the same time

• are conscious of their interactions and roles

• communicate in symbolic languages even about the counterfactual


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Fields and forcesPhysical particles interact with the help of

Living things additionally interact with the help of

Human actors additionally interact with the help of

• (a small number of different) forces

• fields which can change due to the movements of particles

• chemical substances and their concentration gradients

• by sounds (halfway symbolic, very restricted lexicon)

• by observing each other and predicting next moves

• by sounds and graphical symbols (symbolic, unrestricted lexicon, also referring to absent or non-existing things, e.g. unicorns and angels)

• by observing each other, predicting next moves and deriving regularities from what they observed (but they can also learn about regularities from others)


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Adaptation

• many systems can adapt to their environment

• finding a local minimum of some potential or a concentration maximum, following a concentration gradient

• adaptation of a population of systems via evolution (“blind watchmaker” metaphor)

• adaptation via norm learning• mutual adaptation via norm emergence

and norm innovation10/04/23 50 Years of Social Simulation

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Decision making

• in physical particles: according to natural laws or probabilistic (no decision making in any reasonable sense of the word)

• in animals: instinct (mechanisms not well understood)

• in humans: after deliberation of different possible outcomes of different action alternatives, boundedly rational, often after discussion among groups of actors


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Emergence

• definable as the supervenience of characteristics of a system that cannot be owned by the parts of this system– atoms and molecules have a velocity, but

no temperature, the gas or fluid or solid body has a temperature

– families have places where they live, but they do not have a degree of segregation (but the city has)

– voters have attitudes, but no attitude distribution (the electorate has)


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Emergence, immergence and second-order emergence• emergence of order in slime moulds

works via the concentration gradient of some chemical substance

• emergence of an attitude distribution (e.g. polarisation of voter attitude during an election campaign) works via communication, persuasion and publication of opinion poll results (as humans have no “objective” measuring instrument for attitude “gradients”)


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Outline






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Micro and macro level

• “sociological phenomena penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durkheim 1895]


macro cause

micro cause micro effect

macro effect

“downwardcausation”

“upwardcausation”

[Coleman 1990]

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Micro and macro level• “sociological phenomena penetrate into us by

force or at the very least by bearing down more or less heavily upon us” [Durkheim 1895]


macro cause


macro effect



[Coleman 1990]

• both interpretations can be applied to

physical and to social systems• both interpretations can be applied

to physical systemso macro cause = field, “downward causation” = force, micro effect

= movement, “upward causation” = field change

to social systemso macro cause = “social field”, social norms, “downward causation”

= immergence, micro effect = norm adoption, “upward causation” = norm innovation

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macro cause


macro effect



[Coleman 1990]• but the difference

is: in physical systemso the effect of the “downward causation” is transitory, as is

the effect of the “upward causation” as there is usually no memory on either level

in social systemso the effect of the “downward causation” lasts for a long

time, it changes the state of the micro entity forever, as it is stored symbolically in his or her memory, and the effect of the “upward causation” also lasts for a long time, as there is a long-term memory in society (folklore, libraries codes of law …)

o the “downward causation” takes only effect after being interpreted by the individual, and this interpretation is dependent of his or her past

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Markets

Many agents trading with each other Each trying to maximise its own welfare Neo-classical economics assumes that markets are at

equilibrium, where the price is such that supply equals demand

But with agents, we can model markets in which the price varies between localities according to local supply and demand

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Example: Sugarscape

• Agents located on a grid• Trade with neighbours• Two commodities: sugar and spice. All agents

consume both these, but at different rates• Each agent has its own welfare function,

relating its relative preference for sugar or spice to the amount it has ‘in stock’ and the amount it needs

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Agent strategies

• An agent moves to the cell it prefers that is within its range of vision to replenish sugar and spice stocks• But can also trade (barter) with other neighbouring

agents

• Agents trade at a price negotiated between them when both would gain in welfare

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Example: Sugarscape

QuickTime Movie

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Results The expected market clearing price emerges

from the many bilateral trades (but with some remaining variations)

The quantity of trade is less than that predicted by neoclassical theory- since agents are unable to trade with others than

their neighbours The effect of trade is to make the distribution

of wealth (measured in sugar) more unequal

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Outline







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Lake Anderson revisited


Original model,SystemDynamics style

Variant 1 with strat-egies applied within the model

Variant 2 withfeedbacks on sev-eral levels

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Anderson‘s model: variables• The behaviour of the lake is described in a number of equations

for the following main “level” variables:– nutrient: the amount of fertiliser and other nutrients in the lake,

increased by fertiliser discharge, by respiration and decay of the biomass, and decreased by the growth of the biomass,

– biomass: the amount of algae in the lake, increased by their growth, and decreased by their death rate, by respiration and, possibly, by harvesting algae,

– detritus: the amount of sediment at the bottom of the lake, increased by dying algae, and decreased by detritus decay and, possibly, by the dredging the lake ground,

– oxygen: the concentration of oxygen available to the algae for their metabolism; this level variable is composed of two parts, the epilimnion oxygen concentration (which is considered to be constant because oxygen is always replenished from the air above the lake surface) and the hypolimnion oxygen concentration which is increased by the diffusion of oxygen from the epilimnion into the hypolimnion, and decreased by the oxygen consumption (due both to the respiration of the algae and to the detritus decay process) and, possibly, by artificial aeration.


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Anderson‘s model: policies• Anderson describes five policies to avoid eutrophication of the

lake:– applying algicides: the application of algicides can increase the

natural death rate of the algae,– dredging the detritus: the detritus can be dredged from the ground

of the lake, which results in a decrease of nutrient (which otherwise would have been produced from the detritus naturally) and in an increase in the hypolimnial oxygen concentration (because less oxygen is consumed in the detritus process),

– aeration of the lake: oxygen can be bubbled into the water of the lake, thus increasing the hypolimnial oxygen concentration,

– harvesting algae: biomass can be harvested, thus decreasing the biomass (and, in consequence, its oxygen comsumption and its conversion into detritus); the harvested biomass can be used for agricultural purposes,

– reducing nutrient (fertilizer) discharge into the lake: Anderson suggests an artificial discharge of fertiliser into the lake which is ten times the natural discharge of nutrient from outside the lake at the beginning of most of his simulation runs; moreover he suggests a yearly increase of the artificial fertiliser discharge of two per cent if no specific measures are taken.


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Extensions

• In the original model, these policies are applied by the experimenter;

• in extended models, – one or more simulated “governments” or – other agents/agencies under the control

(tax reduction, fines, …) of local authorities

• perform the task to apply strategies to avoid or fight eutrophication.


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Environmental protection


FarmFarm

FarmFarm

governmentagency

tourists

tourist board

industry

information

levying taxes

voting

booking /cancellation

lobbying

smelling

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Outline







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Another example: Co-ordination and sustainability

• Agents who move in a world much like Sugarscape [Epstein/Axtell 1996], feed there, reproduce and perhaps communicate.

• Some agents act as co-ordinators for others: co-ordinators and subordinates co-operate, informing each other about resources.


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Example continued ...

• Co-ordinators gather information about available resources from subordinates, forward it as hints to other subordinates and receive a contribution from successful subordinates.

• Resourcs grow on fields, spread to neighbouring fields, and are consumed.


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Example continued …

• If a field is exhausted by harvesting, new crops can grow if seed is spread on it.

• An agent can harvest all or part of the crop in the field (the latter acts in a sustainability mode).

• The simulation programme allows for numerous parameter changes.


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Result

• One of the simulation results is that an agent society with co-ordination is more likely to achieve sustainability than a society with isolated agents.


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The model


Circles and triangles: agents

: co-ordinators

: subordinates

: independent agents

colour shade of agents:

degree of saturation

black: dead

colour shade of fields:

amount of resources

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Agents can ...


... feed on their individual supply,

... die (either from hunger or from old age),

... recognise the state of neighbouring cells (resources, agents) and store it in

their memories,

... estimate the results of possible

actions,

... decide which to apply,

and finally

... act.

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Needs and actions

actions needs

survival wealth breeding influence curiosity

gather X X D D D

move X X D D X

breed X X X D D

start co-ordinating X X D X X

end co-ordinating X X D X X

subordinate X X D D D

unsubordinate X X D D D

rest D D D D D

10/04/23 Klaus G. Troitzsch: Complex Systems Simulation in Sociology10/04/23 50 Years of Social Simulation

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Decision making weight actions sum

needs j i = 1 i = 2 i = 3

D E F D..F

1 j = 1 0.7 0.4 0.6 0.8

2 j = 2 0.3 0.9 0.6 0.3

3 j j satij 0.55 0.60 0.65

4 j j satij - mini i j satij 0.00 0.05 0.10 0.15

5 P(i) 0.00 0.333 0.666 1.00

Actions are taken with a certain probability which depends on the degree to which an action satisfies a need and the weights of theneeds for a particular agent.

10/04/23 Klaus G. Troitzsch: Complex Systems Simulation in Sociology10/04/23 50 Years of Social Simulation

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Simulation results

• Populations of isolated agents die out soon,• those with co-ordinators and subordinates

survive for a long time,• and we find Lotka-Volterra cycles.


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A society with co-ordinators survives for a longer time,


0

10000

20000

30000

40000

50000

60000

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sumFood flat

sumFood co-ordinated

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50

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numAll flat

numAll co-ordinated

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... the population with isolated agents dies out.


0

10

20

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avAllFood flat

avAllFood co-ordinated

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sumFood flat

sumFood co-ordinated

excessive exploitation of resources

extinctionreluctant exploitation

survival

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Become self-employed, when times are getting better!


0

10000

20000

30000

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50000

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80000

5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000

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350sumFood

numAll

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5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000

avNormsFood

avCoordsFood

avSubsFood

good times

bad times

good times

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Conclusions drawn from complex antecedents

• Conclusion from a complex set of simple assumptions:

• Co-ordination and subordination in this artificial agent society facilitate sustainability of resources.


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replicative validity: the model matches data already acquired from the real system (retrodiction),predictive validity: the model matches data before data are acquired from the real system,

• Our conclusion is unlikely to ever be validated empirically:

• real-world human societies have an overwhelmingly more complex structure of co-ordination and subordination than the simple artificial society of our model.


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replicative validity: the model matches data already acquired from the real system (retrodiction),predictive validity: the model matches data before data are acquired from the real system,

• Indigenous societies, however, show some aspects of the behaviour of our simulation model:

• In a society of herdsmen and farmers in Western Africa, decisions which rest on friendship networks (“friend-priority” decisions) proved to be much more effective then decisions which were made on pure cost deliberations (“cost priority” decisions).

• [Rouchier et al. 2000, 2001:189].


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structural validity: the model “not only reproduces the observed real system behaviour, but truly reflects the way in which the real system operates to produce this behaviour.”

• In this respect, the multi-agent model is superior to simpler mathematical models such as– a Lotka-Volterra process,

• either deterministically on the macro level– dx/dt = a x – b x y– dy/dt = c x y – d y

• or stochastically on the micro level– pb1(n1, n2) = α n1 pb2(n1, n2) = β n1 n2

– pd1(n1, n2) = γ n1 n2 pd2(n1, n2) = δ n2


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Outline







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Urban Development: MASUS


Flávia F. Feitosa et al.: MASUS: A Multi-Agent Simulator for Urban Segregation, ESSA 2009, paper

30Flávia da Fonseca Feitosa: Urban Segregation as a

Complex System. An Agent-Based Simulation Approach, Diss. Geogr. Bonn 2010

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São José dos Campos, São Paulo, Brasilien 1991-2000

is the percentage of similar households in the neighbourhood

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Outline





• Why complex social systems are even more complex than other complex systems– peculiarities of human social systems– requirements for computational social science– and how they can be met: outlook on a new approach


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What this simulation is about

• This simulation is about the emergence and immergence of norms.

• Our example is taken from everyday life: a scenario with children crossing a street between two playgrounds and with car drivers using this street, both of whom learn to avoid collisions to invent traffic rules and to respect them.


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Emergence in the Loop: EMIL• Istituto di Scienze e Tecnologia

della Cognizione − Consiglio Nazionale delle Ricerche, Rome, Italy

• Universität Bayreuth, Germany• The University of Surrey, United

Kingdom• Universität Koblenz−Landau,

Germany• Manchester Metropolitan

University, United Kingdom• AITIA International Informatics Inc.,

Budapest, Hungary


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macro cause


macro effect



[Coleman 1990]

• both interpretations can be applied to physical and to social systems• both interpretations can be applied to physical systems

o macro cause = field, “downward causation” = force, micro effect = movement, “upward causation” = field change

to social systemso macro cause = “social field”, social norms, “downward causation”

= immergence, micro effect = norm adoption, “upward causation” = norm innovation


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macro cause


macro effect



[Coleman 1990]• but the difference

is: in physical systemso the effect of the “downward causation” is transitory, as is

the effect of the “upward causation” as there is usually no memory on either level

in social systemso the effect of the “downward causation” lasts for a long

time, it changes the state of the micro entity forever, as it is stored symbolically in his or her memory, and the effect of the “upward causation” also lasts for a long time, as there is a long-term memory in society (folklore, libraries, codes of law …)

o the “downward causation” takes only effect after being interpreted by the individual, and this interpretation is dependent of his or her past


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A: You must not cross the streetwhen I am approaching in my car, B!

(B abstains from crossingthe street when A is approaching

with her car.)

(not only B, but others, too, abstain from smoking, not only in the presence of A, but also on other occasions.)(not only B, but others, too, abstain from crossing streets,

not only in the presence of A’s car, but in most other cases.)

Immergence and second-order emergence• norm-invocation messages • motivate individual agents to change the

rules controlling their actions• if this happens often enough, “sociological phenomena

penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durkheim 1895]

• and as a consequence, these norm invocations – and the resulting behaviour – occur more and more often and become a “sociological phenomenon”


A: “I don’t like your smoking here, B!”

(B abstains from smoking in the presence of A.)

… and we have programmed something much like this

in an agent-based simulation system!

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Agent activities

• “child” agents can– observe– move– admonish

• “car driver” agents can– observe – stop– slow down– speed up– admonish– honk the horn


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Theoretical Framework• Inter-agent communication uses a

message concept, triggering the processing of events and corresponding actions

• Agents can learn (form normative beliefs into their minds): own experience (reinforcement

strategies) observation of other agents’ experience

(imitation) listening to other agents’ reports of their

experiences (normative learning)

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Theoretical Framework• Learning capabilities of a normative agent:

• own experience (reinforcement strategies)• “Pedestrian experiences a near-collision with a car

because of not using the striped area for crossing a street.”

• observation of other agents’ experience (imitation)

• “Pedestrian or car driver observe a near-collision between another pedestrian and another car because this pedestrian did not use the striped area for crossing a street.”

• listening to other agents’ reports of their experiences (normative learning)

• “One pedestrian tells another pedestrian: ‘You should use the striped area for crossing a street!’”

• Norm-invocation (messages)• Necessity of observer agents

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Architecture of a Normative AgentBasics• Agents perceive events within the environment in which they are situated and

influence the environment by corresponding actions “Car Driver: Collision with a pedestrian”

Environmental events• Introduction of events which allow the

assessment of (environmental) events by positive/negative valuations or sanctions ( normative learning) “You should use the striped area for crossing a street!”

Norm-invocation events

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Basic Structures: Messages

SenderRecipientModalContentTime Stamp

Modals- messages orginated from an agent‘s perception (assertion, behavior, …) Environmental messages- messages received by notifications from other agents (valuation, sanction, …) Norm-invocation messages

(Norm-oriented) agent behavior

Norm formation

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Basic Structures: Initial Rules

„accelerate“ „slow down“

„stop“

Environmental actions

„admonish“ „honk the horn“

Norm-Invocation actions

• Initial Rule Base: Describing the basic behavioural elements, constituting the seeds for more complex rules

emerging from the simulation process.

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EMIL-S • the first (simulated) minute (20 children, random cars

– children and cars run into each other, near-collision is interpreted as norm invocation (“You have to stop when I am stepping on the street!”, “You must not step on the street when I am around with my car!”)

• several (simulated) minutes later (again 20 children, random cars)– children have learnt that they have to use the striped area

for street crossing, car drivers have learnt that they are expected (obliged) to slow down or stop in front of the striped area (which has emerged into an institution after the first successful norm learning happened there) when there are children visible in the neighbourhood

• the same, some children have not learnt that the striped area is something special– some children still do not use the striped area but stop for an

approaching car

• the same with perception sectors (only four children)– approaching the street, children enlarge their perception

area; approaching the striped area, cars enlarge their perception area


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How EMIL-S works:an overview ofour agent architecture

Step

Structure

6. Negative valuation for A4 arrives

7. Rule for NF is created from EB entry

8. Valuation is added to NF entry and probability of valuated action is adapted

Environment (ENV)

Event Board Entry (EB)

Normative Frame Entry (NF)

E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33 A4

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

NegV

al(A4)

NegV

al(A4)

RuleE2S00000

E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

E2

RuleE2S00000

E2 G2

A3

A5

1.00.42

0.42

A40.16E2 G2

A3

A5

1.00.42

0.42

A40.16E2 G2

A3

A5

1.0 A4E2 G2

A3

A5

1.0 A4A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

E2

Neg

Val

(A4)

Step

Structure

1. E2 occurs in ENV and is added to EB

2. Rule for E2 is retrieved from IRB

3. Current ES is saved in EB

4. Action is selected and sent to ENV

5. ES is adapted

Environment (ENV)

Event Board Entry (EB)

Environmental State (ES)

Initial Rule Base (IRB)

E2

E2

E1 G1A1

A2

1.00.5

0.5E1 G1

A1

A2

1.00.5

0.5E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33

E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33 E2 G2

A3

A5

1.00.33

0.33

A40.33E2 G2

A3

A5

1.00.33

0.33

A40.33 A4

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 0

A1A2A3A4A50 0 0 0 01

A4


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Other applications• Agent-based modelling can also be applied

to politically relevant scenarios:– emergence of loyalties within criminal

organisations and collusion between criminals and their victims: the example of extortion rackets

– emergence of trust (and of mechanisms justifying trust) in online transactions between sellers, intermediaries and buyers

– ethnic conflicts: the emergence of consciousness of belonging to a certain group

– emergence of practices in microfinance


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Dynamics of Legality and Illegality: Agents• DyLeg agents will be

– members of criminal or terrorist organisations,

– members of organisations which fight such organisations,

– victims of such organisations – supporters of such organisations and – others who are something like a reservoir

for the other four breeds.


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Dynamics of Legality and Illegality: Behaviours• DyLeg agents will have to be able

– to influence one another, learn, etc.;– to discriminate between social norms and

coercive requests;– to distinguish revenge from normative sanction;– to perceive not only those messages which

were sent to them individually, i.e. to listen to communication between others, and

– to cope with conflicting goals • (e.g. surviving and not paying extortion money,

surviving and whistle blowing.)


surviving is always allowed and never commanded, paying extortion money is forbidden in civil society

but commanded in gangland, whistle-blowing is allowed or even commanded by civil society and

forbidden in gangland.

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EMIL-S features in DyLeg

• Agents can influence each other – by direct (reporting) communication, – by norm invocation and – even physically,

• they make a difference between learning – by explicit norm invocation (“forbidden”) and – by direct experience or report from others

(“dangerous”)• (i.e. it is not necessary for them to experience or consider

direct negative or positive consequences of their behaviour once they have been told that something is forbidden or commanded).


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Systems of systems in DyLeg

• DyLeg also necessitates a multi-level (not just two-level) model: – Besides the agents of different types, different

kinds and different levels of organisations have to be modelled, where agents may be members of different organisations at the same time

• (e.g. a member of a criminal organisation and an undercover agent)

– and organisations might work differently (have different norms) in different cultural contexts

– a requirement that is also easily fulfilled in EMIL-S.


cosa nostra, ‘ndrangheta, police, political party

cosa nostramandamento

famiglia

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Learning strategies

• Reinforcement learning: increase propensities of successful strategies, decrease propensities of unsuccessful strategies

• Recombination of event-action trees (similar to crossover in genetic algorithms, but without survival and selection over generations of agents): learning to react to new events by changing the structure of one or more event-action trees


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Changing the structure of event-actions trees• copy, prune and graft …


E1: fellow hasbetrayed other fellow

G1 G2

A11: blame him A:12 injure him

E2: publican refusesto pay extortion

G3 G4

A:31 destroy hisapartment

A:32 shoot him

A:21 .. A:21 .. A41:.. A:42 ..

E1: fellow hasbetrayed other fellow

G1G2

A11: blame him A:12 injure him

E2: publican refusesto pay extortion

G3 G4

A:31 destroy hisapartment

A:32 shoot him

A:21 .. A:21 .. A41:.. A:42 ..

• In the end, this means that actions of groups G1, G3, G4 and G2 can be considered in both situations

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Messages and their interpretations• In the current examples, messages are still simple, but

they are already interpreted:• the message “Don’t deliver me into the hands of police even when you get caught!” is

interpreted as “this is a situation where both of us are in danger of being caught, and if one has a chance to escape the other should do whatever possible that this escape is successful, as it is important that at least one of us can escape and tell the others …”

• Unlike agents in gradient and pheromone metaphor models, both sender and receiver of messages are “free” to make their choices.

• Choices will depend on a long individual history.– Whether a person gets infected by a virus and how severe the infection will be also

depends on a long individual history, but the outcome in this case is one-dimensional!

– One can successfully vaccinate a person against her will to protect her from smallpox, but a “vaccination” to protect a person from being infected with terrorism against his will is in vain

– Choices made with a variety of decision trees are much more polymorphic.


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Can computational social science contribute to a better understanding of complex social systems?• Computational social science aims at

understanding the adaptive behaviour of humans and systems of humans.

• Simulation is one way to improve [the communication of] our understanding (to make Adam Smith’s invisible hand visible) as a simulation is analytically narrative and – in contrast to verbal theory – produces data of the same kind as the real world.

• And we can look into the minds of software agents.


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References: General• Anderson, Jay M. The Eutrophication of Lakes, in: Dennis and Donnella Meadows:

Toward Global Equilibrium,Cambridge MA (Wright Allen) 1973, pp. 171–140• Bunge, Mario (1979). Treatise on Basic Philosophy. Volume 4: Ontology II: A

World of Systems. Dordrecht/Boston: Reidel• Carley, Kathleen M., Michael Prietula, eds. (1994): Computational Organization

Theory. Hillsdale/Hove: Lawrence Erlbaum• Carpenter, Stephen, and William Brock and Paul Hanson: Ecological and Social

Dynamics in Simple Models of Ecosystem Management. In Conservation Ecology 3 (2):4 1999, URL: http://www.consecol.org/vol3/iss2/art4

• de Sola Pool, Ithiel, and Robert Abelson. The Simulmatics Project. Public Opinion Quarterly 25, 1961, 167-183

• Epstein, Joshua M., and Robert Axtell. Growing Artificial Societies. Social Science from the Bottom Up. Cambridge, Mass., London: MIT Press, 1996

• Forrester, Jay W. World Dynamics. Cambridge, Mass., London: MIT Press 1971 • König, Andreas, Michael Möhring and Klaus G. Troitzsch.

Agents, Hierarchies and Sustainability, in: Billari, Francesco, and Alexia Prskawetz. Agent-Based Computational Demography. Berlin: Physica 2003


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References• Meadows, Dennis L., William W.,Behrens III, Donnella H. Meadows, Roger F. Naill, Jørgen

Randers, and Erich K.O. Zahn, (1974). Dynamics of Growth in a Finite World. Cambridge: MIT Press.

• Meadows, Donnella H., Dennis L. Meadows, and Jørgen Randers (1992). Beyond the Limits. Post Mills: Chelsea Green.

• Meadows, Donnella H., Dennis L. Meadows, and Jørgen Randers(2004). The Limits to Growth: The 30-Year Update . Post Mills: Chelsea Green.

• Möhring, M. & Troitzsch, K.G. (2001). Lake anderson revisited. Journal of Artficial Societies and Social Simulation, 4/3/1, http://jasss.soc.surrey.ac.uk/4/3/1.html.

• Rouchier, Juliette, François Bousquet, Mélanie Requier-Desjardins, Martine Antona: A multi-agent model for describing transhumance in North Cameroon: comparison of different rationality to develop a routine. Journal of Economic Dynamics and Control, 2001, 25: 527-559.


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References: General (continued)• Rouchier, Juliette, François Bousqet, Olivier Barreteau, Christophe LePage, Jean-

Luc Bonnefoy: Multi-Agent Modelling and Renewable Resources Issues: The Relevance of Shared Representations for Interacting Agents, in: Moss, Scott, and Paul Davidsson: Multi-Agent-Based Simulation, Springer, Berlin 2000 (LNAI 1979), pp. 181–197

• Schelling, Thomas. Dynamic Models of Segregation. Journal of Mathematical Sociology 1971 (1), 143—186

• Troitzsch, Klaus G. Multi-agent systems and simulation: a survey from an application perspective. In Adelinde Uhrmacher and Danny Weyns, editors, Agents, Simulation and Applications, pages 2.1–2.23. Taylor and Francis, London, 2008. to appear.

• Zeigler, Bernard P. Theory of modelling and simulation. Malabar: Krieger 1985 (Reprint, originally published: New York: Wiley 1976)


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Further reading: System dynamics• Mario Bunge. Ontology II: A world of systems. Treatise on basic philosophy, vol. 4. Reidel, Dordrecht, Boston,

London, 1979.• Diether Craemer. Mathematisches Modellieren dynamischer Vorgänge. Eine Einf ührung in die

Programmiersprache DYNAMO. Teubner, Stuttgart, 1985.• Manfred Eigen and Peter Schuster. The Hypercycle. A Principle of Natural Self-Organization. Springer, Berlin,

Heidelberg, New York, 1979.• Jay W. Forrester. World Dynamics. MIT Press, Cambridge, Mass., London, 1971.• Jay W. Forrester. Principles of Systems. MIT Press, Cambridge, Mass., London, 1968, 2nd preliminary edition

1980.• Robert A. Hanneman. Computer-Assisted Theory Building. Modeling Dynamic Social Systems. Sage, Newbury

Park, 1988.• Juan Carlos Martinez Coll. A bioeconomic model of Hobbes’ “state of nature”. Social Science Information,

25(2):493–505, 1986.• John Maynard Smith. Evolution and the Theory of Games. Cambridge University Press, Cambridge, 1982.• Dennis L. Meadows et al. Dynamics of Growth in a Finite World. MIT Press, Cambridge, Mass., London, 1974.• Dennis Meadows, Donella Meadows, Erich Jahn, and Peter Milling. Die Grenzen des Wachstums. Bericht des Club

of Rome zur Lage der Menschheit. Deutsche Verlagsanstalt, Stuttgart, 1972.• Meadows, Donnella H., Dennis L. Meadows, and Jørgen Randers(2004). The Limits to Growth: The 30-Year Update

. Post Mills: Chelsea Green.• Donella H. Meadows, Dennis L. Meadows, and Jørgen Randers. Beyond the Limits. Chelsea Green, Post Mills,

Vermont, 1992.• Donella H. Meadows, Dennis L. Meadows, and Jørgen Randers. Die neuen Grenzen des Wachstums. Die Lage der

Menschheit: Bericht und Zukunftschancen. Deutsche Verlagsanstalt, Stuttgart, 1992.• Alexander L. Pugh III. DYNAMO User’s Manual. MIT Press, Cambridge, Mass., 1976.


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Further reading : Microsimulation• Hauser, Richard, Uwe Hochmuth, and Johannes Schwarze. Mikroanalytische Grundlagen der Gesellschaftspolitik.

Ausgewählte Probleme und Lösungsansätze. Ergebnisse aus dem gleichnamigen Sonderforschungsbereich an den Universitäten Frankfurt und Mannheim, Band 1. Akademie-Verlag, Berlin, 1994.

• Hauser, Richard, Notburga Ott, and Gert Wagner. Mikroanalytische Grundlagen der Gesellschaftspolitik. Erhebungsverfahren, Analysemethoden und Mikrosimulation. Ergebnisse aus dem gleichnamigen Sonderforschungsbereich an den Universitäten Frankfurt und Mannheim, Band 2. Akademie-Verlag, Berlin, 1994.

• Habib, Jack. Microanalytic simulation models for the evaluation of integrated changes in tax and transfer reform in Israel. In Guy H. Orcutt, Joachim Merz, and Hermann Quinke, editors, Microanalytic simulation models to support social and financial policy, Information Research and Resource Reports, vol. 7, pages 117–134. North Holland, Amsterdam, New York, Oxford, 1986.

• Harding, Ann. Microsimulation and Public Policy. Contributions to Economic Analysis. North Holland, Amsterdam, Lausanne etc. 1996

• Heike, Hans-Dieter, Kai Beckmann, Achim Kaufmann, Harald Ritz, and Thomas Sauerbier. A comparison of a 4GL and an object-oriented approach in micro macro simulation. In Klaus G. Troitzsch, Ulrich Mueller, Nigel Gilbert, and Jim E. Doran, editors, Social Science Microsimulation, chapter 1, pages 3–32. Springer, Berlin. Heidelberg, New York, 1996.

• Henize, John. Critical issues in evaluating socio-economic models. In Tuncer I. O¨ ren, Bernard P. Zeigler, and Maurice S. Elzas, editors, Simulation and Model-Based Methodologies: An Integrative View, NATO Advanced Science Institutes Series, Series F: Computer and Systems Science, vol. 10, pages 557–590. Springer, Berlin, Heidelberg, New York, Tokyo, 1984.

• Lietmeyer, Volker . Microanalytic tax simulation models in Europe: Developmentand experience in the German Federal Ministry of Finance. In Guy H. Orcutt, Joachim Merz, and Hermann Quinke, editors, Microanalytic simulation models to support social and financial policy, Information Research and Resource Reports, vol. 7, pages 139–152. North Holland, Amsterdam, New York, Oxford, 1986.

• Merz, Joachim . MICSIM: Concept, developments, and applications of a PC microsimulation model for research and teaching. In Klaus G. Troitzsch, Ulrich Mueller, Nigel Gilbert, and Jim E. Doran, editors, Social Science Microsimulation, chapter 2, pages 33–65. Springer, Berlin. Heidelberg, New York, 1996.

• Lavinia Mitton, Holly Sutherland and Melvyn Weeks (eds) (2000): Microsimulation Modelling for Policy Analysis: Challenges and Innovations. Cambridge University Press, Cambridge.

• Orcutt, Guy H., Joachim Merz, and Hermann Quinke, editors, Microanalytic simulation models to support social and financial policy, Information Research and Resource Reports, vol. 7. North Holland, Amsterdam, New York, Oxford, 1986.

• Sauerbier, Thomas. UMDBS — a new tool for dynamic microsimulation. Journal of Artificial Societies and Social Simulation, 5/2/5. http://jasss.soc.surrey.ac.uk/5/2/5.html.

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Further reading: Cellular automata• Hegselmann, R. (1996) ‘Cellular automata in the social sciences:

Perspectives, Restrictions and Artefacts’, in R. Hegselmann, U. Mueller and K. Troitzsch (eds.) Modelling and simulation in the social sciences from the philosophy of science point of view. Dordrecht: Kluwer.

• Wolfram, S. (1986) Theory and applications of cellular automata. Singapore: World Scientific.

• Wolfram, S. (2002) A new kind of science. Wolfram Media. • Toffoli, T and Margolus, N. (1987) Cellular Automata Machines

Cambridge, Mass: MIT Press.• Nowak, A. and Latané, B. (1994) ‘Simulating the emergence of

social order from individual behaviour’, in N. Gilbert and J. Doran Simulating Societies, London: UCL.

• Lomborg, B (1996) ‘Nucleus and Shield: the evolution of social structure in the iterated prisoner’s dilemma’, American Sociological Review Vol 61, 278-307.


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Further reading: Cellular automata• Thomas C. Schelling (1971) ‘Dynamic models of segregation’ J.

Mathematical Sociology, Vol.1, 143–186.• Thomas C. Schelling (1978) Micromotives and

macrobehaviour. New York: Norton• K. M. Kontopoulos (1993) The logics of social structure.

Cambridge University Press.• Stephen Wolfram (2002) A new kind of science. Wolfram

Media.


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Further reading: Agent-Based Models

• Ahrweiler, P., Pyka, A., & Gilbert, N. (2004). Simulating knowledge dynamics in innovation networks (skin). In R. Leombruni & M. Richiardi (Eds.), Industry and labor dynamics: The agent-based computational economics approach. Singapore: World Scientific Press.

• Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann & P. Terna (Eds.), Simulating social phenomena (pp. 21-40). Berlin: Springer.

• Batten, D., & Grozev, G. (2006). Nemsim: Finding ways to reduce greenhouse gas emissions using multi-agent electricity modelling. In P. Perez & D. Batten (Eds.), Complex science for a complex world (pp. 227-252). Canberra: Australian National University.

• Deffuant, G., Amblard, F., & Weisbuch, G. (2002). How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation, 5(4).

• Dray, A., Perez, P., Jones, N., Le Page, C., D'Aquino, P., White, I., et al. (2006). The atollgame experience: From knowledge engineering to a computer-assisted role playing game. Journal of Artificial Societies and Social Simulation, 9(1).

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Further reading:Agent-Based Models• Epstein, J. M. (1999). Agent-based computational models and generative

social science. Complexity, 4(5), 41-60.• Epstein, J. M., Axtell, R., & Project. (1996). Growing artificial societies : Social

science from the bottom up. Washington, D.C. ; Cambridge, Mass. ; London: Brookings Institution Press : MIT Press.

• Friedman-Hill, E. (2003). Jess in action : Rule-based systems in java. Greenwich, Conn.: Manning.

• Gilbert, N. (2006). A generic model of collectivities, ABModSim 2006, International Symposium on Agent Based Modeling and Simulation University of Vienna: European Meeting on Cybernetic Science and Systems Research.

• Gilbert, N., & Abbott, A. (Eds.). (2005). Special issue: Social science computation (Vol. 110 (4)). Chicago: The University of Chicago Press.

• Gilbert, N., & Terna, P. (2000). How to build and use agent-based models in social science. Mind and Society, 1(1), 57 - 72.

• Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., & Balan, G. (2005). Mason: A java multi-agent simulation environment, . Simulation: Transactions of the Society for Modeling and Simulation International, 81(7), 517–527.

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Further reading: Agent-Based Models

• Macy, M., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143-166.

• Miles, R. (2006). Learning UML 2.0. Sebastopol, CA: O'Reilly.• Ramanath, A. M., & Gilbert, N. (2004). The design of participatory agent-

based social simulations. Journal of Artificial Societies and Social Simulation, 7(4).

• Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143-186.

• Strader, T. J., Lin, F.-r., & Shaw, M. J. (1998). Simulation of order fulfillment in divergent assembly supply chains. Journal of Artificial Societies and Social Simulation, 1(2).

• Tesfatsion, l., & Judd, K. (2006). Handbook of computational economics (Vol. 2): North-Holland.

• Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University.

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• Andrighetto, Giulia, Marco Campennì, Rosaria Conte, and Marco Paolucci. On the immergence of norms: a normative agent architecture. In Proceedings of AAAI Symposium, Social and Organizational Aspects of Intelligence, Washington DC, 2007.

• Andrighetto, Giulia, Rosaria Conte, and Paolo Turrini. Emergence in the loop: Simulating the two way dynamics of norm innovation. In Guido Boella, Leendert van der Torre, and Harko Verhagen, editors, Dagstuhl Seminar Proceedings 07122, Normative Multi-agent Systems, Vol. I, 2007.

• Andrighetto, Giulia, Marco Campennì, Federico Cecconi, and Rosaria Conte. Conformity in Multiple Contexts: Imitation vs. Norm Recognition. Paper submitted to WCSS 08.

• Campennì, Marco. The norm recogniser at work. Presentation at AAAI'2007, Washington.

• Lotzmann, Ulf, and Michael Möhring. A TRASS-based agent model for traffic simulation. Paper presented at the 22nd European Conference on Modelling and Simulation ECMS 2008.

• Lotzmann, Ulf , Michael Möhring, Klaus G. Troitzsch. Simulating Norm Formation in a Traffic Scenario. Paper accepted for ESSA 2008.

• Troitzsch, Klaus G. Collaborative Writing: Software Agents Produce a Wikipedia. Paper accepted for ESSA 2008.


Further reading: EMIL and EMIL-S

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