1

UK Political Studies Association (PSA) Annual Conference

Glasgow, 10-12 April 2017

The Cogs of Policy Learning: Mechanisms, Triggers and Hindrances

Claire A. Dunlop, University of Exeter, UK

Claudio M. Radaelli, University of Exeter, UK

c.a.dunlop@exeter.ac.uk; c.radaelli@exeter.ac.uk

This is the first full draft, please do not cite

Abstract

Policy learning has fascinated three generations of political scientists, but the sceptics still

wonder: what are the mechanisms of learning in public policy? In this paper, we start with

four modes of learning to pin down exactly the core mechanism for each type, what is learned,

what is it good for, as well as triggers, hindrances and pathologies. We find that we can

identify a mechanistic framework for the analysis of learning as dependent variable, whilst

future research should connect this framework to the mechanisms that produce policy

change.

Keywords

Causality, mechanisms, policy learning, theory of the policy process

2

Introduction

Policy learning and mechanisms have one thing in common: they fascinate political scientists,

but for most of us they are still too elusive to deserve a thorough investigation. With this

paper, we confirm the fascination and make steps to reduce the elusiveness of both.

When searching for the mechanisms associated with policy learning we must be clear about

our explanans and explanadum. Here, our interest is in mechanisms for learning. Treating

policy learning as our dependent variable, our task is to identify social mechanisms that

generate and explain why different types of learning occur in the policy process. To be clear,

we do not follow the causal chain further to address the mechanisms (triggered by policy

learning) that result in policy change of varying degrees (that is for another day and another

paper).

Our choice to treat policy learning as the dependent variable addresses a gap in our

knowledge. Though learning itself is often treated as a causal mechanism in itself (Hedström

and Swedberg, 1998: 3; Falleti and Lynch 2009), we lack accounts that address the

intermediate level of analysis of what mechanistically explains learning in the first place. The

time for such theorising is ripe. In recent years, authors have pushed the learning agenda

toward more systematic accounts that provide us with theoretical frameworks in to which we

can insert mechanistic reasoning (Dunlop and Radaelli, 2013; Heikkila and Gerlak, 2013).

We proceed as follows. Section one rehearses our model of policy learning which contains

four ideal types. In section two, we delineate our realist approach to mechanisms. Following

on, we model our mechanisms in two steps. In section three, we outline the causal processes

by which our learning outcomes are facilitated by particular mechanisms. As analytical

constructs, mechanisms are not visible or self-evident in themselves (Hedström and

Swedberg, 1998: 13) and so we draw on empirical evidence from the policy learning literature

to illustrate how these work to generate learning. In doing this, we ask what is this mechanism

for and what is learned. In our final substantive section (four), we take our second step in

modelling mechanisms by identifying the triggers and constraints on learning outcomes. We

explore what conditions mechanisms for policy learning drawing on realist thinking to suggest

enabling and hindering factors. In our conclusion, we trace a plan for further research and

offer comments on the design implications of mechanisms for learning.

3

Section 1 Varieties of Policy Learning

To identify mechanisms that generate policy learning, we must first be clear about how we

understand policy learning. Despite using contrasting ontologies and epistemologies, policy

learning studies are founded upon a general definition of learning as ‘the updating of beliefs

based on lived or witnessed experiences, analysis or social interaction’ (Dunlop and Radaelli,

2013: 599). Thus, in identifying mechanisms, we are capturing the ways in which the

knowledge that comes from these experiences, analysis and interaction becomes considered

by policy actors. This centrality of the process of knowledge acquisition and belief updates

reveals why policy learning is amenable to a mechanistic approach. Critical to our interest in

mechanisms is that we have something to explain. Learning may be unintentional, but it does

not occur randomly – not all policy processes have the same chance of producing learning

outcomes. Thus, any answer to the question ‘why does learning happen?’ cannot be a

statistical one. Rather, it requires analysis that specifies the processes by which learning

outcomes are facilitated.

With our definition in hand, the next step is to delineate our dependent variable: the possible

types of policy learning. While policy learning is dominated by empirical studies, over the last

three decades there have been various attempts at systematising our knowledge using

typologies (Bennett and Howlett, 1991; Dunlop and Radaelli, 2013; Heikkila and Gerlak, 2013;

May, 1992). Mechanisms offer an explanatory bridge between theories and evidence, and to

identify them we require an explanatory model of learning. The varieties of learning approach

(Dunlop and Radaelli, 2013) has strong attachment both to empirics and theory and so offers

a promising analytical framework from which we can extrapolate mechanisms.

The building blocks of the varieties model is the policy learning literature (for details on the

literature underlying the model see Dunlop and Radaelli, 2013: 601, endnote 2). This

literature reveals that four learning modes dominate empirical studies – epistemic, reflexive,

bargaining and hierarchical. These types are explained by high or low values on two conditions

of the policy-making environment: the tractability (Hisschemöller and Hoppe, 2001; Jenkins-

Smith, 1990) and certification of actors (McAdam, Tarrow and Tilly, 2001) associated with a

policy issue.

4

Let us zoom in on these dimensions for a moment. Tractability concerns the degree of

uncertainty linked to the policy issue. In highly tractable cases, preference formation is

relatively straightforward – this is the arena of interest groups and political elites – or policy-

making operates on auto-pilot where institutional rules and bureaucratic rules take over. At

the polar case, tractability is low. This radical uncertainty results in either reliance on

epistemic experts or being opened up to widespread social debate. Learning type is also

conditioned by variation in the existence of a certified actor enjoys a privileged position in

policy-making. So, we can think of expert groups (epistemic learning) and institutional

hierarchies – e.g. courts and standard setting bodies – as possessing such certification

(learning by hierarchy). Where an issue lacks an agreed set of go-to actors, policy participants

are plural. Just how plural depends on the level of issue tractability. Where this is high we

have interest-driven actors (learning through bargaining); where both tractability and

certification are low we have the most plural and social of policy arenas (reflexive learning).

Taken together, these two dimensions provide the axes along which the four types vary (see

figure 1).

5

Figure 1 Conceptualising modes of policy learning

2. Reflexive Learning

3. Learning through Bargaining

1. Epistemic Learning

4. Learning in the Shadow of

Hierarchy

Source: adapted from Dunlop and Radaelli, 2013, Figure 1: 603.

Section 2 Developing a Realistic Approach to Learning Mechanisms

So, analytically, mechanisms are tools with which we can model the hypothetical links

between events (Hernes, 1998). Keith Dowding helpfully introduces the idea of mechanisms

as conceptual ‘narrations’ that allow scholars to fill the black box of explanation and takes us

beyond the particularism descriptive accounts (2016: 64). This requires mechanisms of

sufficient generality (Hedström and Swedberg, 1998: 10) that go beyond reference to specific

events or tactics.

Before identifying our mechanisms, we must also think about the analytical level our

mechanisms operate (Stinchcombe, 1991: 367; see also Coleman, 1990). In short, what or

who are these mechanisms acting on? The pre-eminent way of thinking about this is to treat

HIGH LOW

LOW

CERTIFICATION OF

ACTORS

HIGH

HIGH

PROBLEM TRACTABILITY

6

‘the action being analysed [as] always action by individuals that is oriented to the behaviour

of others’ (Hedström and Swedberg, 1998: 13). The varieties of learning approach follows the

‘weak methodological individualism’ of mechanistic analysis (Hedström and Swedberg, 1998:

11-13) placing homo discentis – learning, studying and practicing people – at the centre of

policy-making (Dunlop and Radaelli, under review). Yet, this does not mean that policy action

is located only at the micro level. While we agree that agency is ultimately embodied in

individual action, we understand that policy learning processes are social phenomena

generated by individual action at a variety of levels – between powerful elites (micro), in

groups (meso) and societal (macro) – which may or may not work in sequence with each other

(see Dunlop and Radaelli, 2017). Rather than artificially restrict our focus to the micro level

alone, the key to analytical clarity is that our mechanisms levels are distinct from the level of

the entity being theorised (Stinchcombe, 1991: 367).

There are, of course, several approaches to mechanisms in the social sciences and, closer to

home, analytical sociology and political science (Gerring, 2007 lists nine distinct meanings;

see also 2010; Hedström and Swedberg, 1998). Following on our discussion, we first stick to

a definition of social mechanism as causal relationship between causes and effects in a given

context.

Second, we understand mechanisms as part of a context, following a realistic ontology of the

social sciences (Pawson and Tilley, 1997; Pawson, 2006): a mechanism generates an outcome

in a given time or space context, but not in other contexts. For example, a mechanism of

prime ministerial leadership may produce effective decisions in a Westminster system with

single-party government but not in a parliamentary system with coalition governments.

Third, given a certain historical, political, administrative context, we accept the possibility that

more than one mechanism may be at work. It follows that a certain mechanism may be

counter-acted by another mechanism. Think of the well-known case, explored by Charles

Sabel (1994), when learning in a system is muted by the presence of monitoring in that same

system. In fact, monitoring may suppress innovation and serendipity, and limit the learning

options of policy actors. Thus, in our analysis of learning modes, a mode may work

inefficiently because the underlying mechanisms are incoherent.

Fourth, mechanisms do not just happen all the time given a certain cause and a certain

7

outcome variable affected by the cause. They have their own triggers and hindering factors.

Thus, if we say that accountability produces trust in government, we have to specify what is

it exactly that makes accountability ‘productive’ in terms of trust. It can be something about

the structure of the policy context (in which case we are back to the analysis of context and

its effects on mechanisms) or something about agency – in particular, the style of interaction

within a constellation of actors. The two are related: interaction is affected by decision-rules,

and these are often given by the structural properties of a policy system.

In both cases, unless we fully theorize causality and say how mechanisms affect the outcome,

we only have a very partial causal story about mechanisms. There are different options

available, and indeed we find notions such as the ‘power’ (of mechanisms), ‘disposition’ or

‘capacity’ in the literature (Cartwright, 1999, Salmon, 1990). To simplify matters, we direct

our theorization towards triggers and hindrances. As mentioned, the search for triggers and

hindrances covers both structure and agency. This is our take on the much more complex

discussion of whether mechanisms belong to the structural or to the agency properties of a

system – a debate that we cannot rehearse here (see Wight, 2009).

A social mechanism, then, ‘is a precise, abstract, and action-based explanation which shows

how the occurrence of a triggering event regularly generates the type of outcome to be

explained’ (Hedström and Swedberg, 1998; Hedström, 2005: 25). Mechanisms define

tendencies and probabilities of certain outcomes. Consequently, they belong to a type of

social science that has the ambition to generalize (Gerring, 2010). As Mill put it in 1844,

mechanisms describe a tendency towards a result, or ‘a power acting with a certain intensity

in that direction’ (as cited by Hedström, 2005: 31).

A concise way to summarize the points about context, triggers and hindrances is to think of

‘mediating’ or ‘moderating’ effects to unfold the mechanism. Such unfolding can be modelled

in two ways. The first is to grasp the ‘generative process’ as a chain of observable antecedents

and consequents (Morgan and Winship, 2014; Pearl, 2000). The second considers the

generative process as an unobserved hypothesis and focuses instead on the conditions which

together enable, trigger or hinder it – were the causal claims true. This second route to

explanation is the one we choose.

We acknowledge that there is a big debate out there concerning whether mechanisms are

8

compatible with any notion of empirical observation of reality or, as Bunge (1997: 421) once

put it, ‘no self-respecting empiricist (or positivist) can condone the very idea of a mechanism’

(cited by Gerring, 2007). By outlining the dimensions below in table 2, we certainly do not

answer the big question, but we are clear about the concepts about mechanisms that guide

empirical observations. We say this to be explicit about our aims and motivation as

researchers: for us it does not make sense to provide a conceptual apparatus if it does not

allow us to go out in the field and make observations. In short, we believe that although the

mechanism itself is theorized and cannot be falsified, we can systematically make empirical

observations about:

• the definition of a mechanism. This is the ‘what mechanism is this?’ question.

Especially in policy analysis, we cannot simply say that there is a mechanism

determining learning. We want to know whether this is a mechanism of, say,

conflict or dialogue;

• the key resource involved in the mechanism, be it information, experience,

knowledge and so on;

• the triggers;

• the hindrances;

• the key resource that we should find if a given mechanism is active;

• the content of learning (what is learned), and;

• what is learning good for. Within policy processes, different modes of learning are

productive of different ‘qualities’ such as exploiting the gains of cooperation or

problem-solving. We will go in the detail of each dimension in our discussion

below.

Note that we do not say that to go mechanistic means to deny the value of other aspects of

causation – such as equifinality or correlation. All we need for our analysis is to accept that

mechanisms are a sufficiently interesting aspect of causation to deserve our attention.

Section 3 Mechanisms for Policy Learning

With these definitions and dimensions, we are ready to begin our discussion of mechanisms

in the four modes or types of learning, starting with epistemic learning (see table 1).

9

Epistemic learning is found in situations where issue intractability is high, decision-makers

need to learn. When this is coupled with the existence of an authoritative body of knowledge

and experts who are willing and able to teach, teaching mechanisms have fruitful ground.

Teaching involves the translation and transmission of new ideas, principles and evidence by

socially certified, authoritative actors. The search for cognitive authority and evidence may

be driven by either side – for decision-makers this could be a technological problem or

complex disaster and, for experts, the push may be a scientific breakthrough and diffusion of

innovation.

Three elements ensure the potency of teaching. First, and most obviously, experts must have

authoritative knowledge which is policy-relevant. Consider Peter M. Haas’s (1990) landmark

case study, UN decision-makers learned that the Mediterranean needed to be and could be

‘saved’ because the epistemic community had both exclusive access to specialist knowledge

and had a credible policy plan that made the knowledge real to decision-makers. This latter

point is often omitted in epistemic community analyses, but experts’ ability to read the

political environment and use their knowledge to speak to decision-makers’ needs is crucial

for the prospects of learning. Where experts are politically ignorant, the rejection of scientific

knowledge often follows (see the famous case of the EU and hormone growth promoters,

Dunlop, 2010, 2017a).

We can add to this. The most skilled teachers have more than just cognitive authority, they

have the soft skills – notably, communication and leadership – to hold the attention of

powerful elites (Davis-Cross, 2013; Dunlop, 2014: 216-217). Boehmer-Christiansen’s (1994)

work on the early years of the Intergovernmental Panel on Climate Change (IPCC) highlights

the role of the panel chair – Bert Bolin – and his charismatic authority which was, in part,

responsible for the step-change in epistemic learning on carbon emissions by governments in

the 1990s.

For our third ingredient in teaching, we turn the spotlight on the learner. To absorb new

knowledge, decision-makers must be in a ready to learn state. In the conventional classroom,

part of the teacher’s role is to baseline their students to get a sense of their readiness

emotionally, physically, experientially and in terms of their knowledge (Lichtenthal, 1990). In

the policy arena, we think more in terms of how ready the system is to taken on board new

10

information. Here, the teaching mechanism is at its most potent where experts are willing

and able to accommodate the often erratic and unpredictable timelines of policy-making.

What is being learned and what is it good for? In an ideal typical manifestation, teaching

mechanisms help delineate complex cause-and-effect relationships for decision-makers and

how this knowledge can be linked to desired policy outcomes. Of course, this is the world of

evidence-based policy-making (EBPM) (Nutley et al, 2007) marked by the ‘intended use by

intended users’ of science (Patton, 1997). Though rightly criticised as overly-functional

(Cartwright and Hardie, 2012) and even at times mythological (Cairney, 2016), at the very

least EBPM does afford a reduction in uncertainty. Moreover, the teaching it entails forces a

forensic examination (if not always justification) of the logic and content of policies and how

these link to outputs (as opposed to focusing only on outcomes).

Reflexive learning is generated by mechanisms of dialogue and debate. This most social form

of learning takes place against the backdrop of radical uncertainty about how to move an

issue forward. We scrutinise and reform the logic of appropriateness in policy-making through

debate: this is how we confront the ideas held by ourselves and others (Majone, 1989). Such

exposure, and the scrutiny it entails, makes reason and social consensus possible (Habermas,

1984). Here, the ‘how’ of learning is more pertinent than the ‘what’ (Freeman, 2006).

Thus, learning outcomes are reliant on force-free deliberations involving a wide range of

social actors of myriad backgrounds who bring a range of codified and uncodified knowledge

types to bear on debate. Yet, for dialogue to deliver learning results, these debates must be

convened in some way. Most commonly, this is achieved using public engagement

technologies. These vary in terms of their openness. So, deliberative tools are the most open

allowing iterative processes of communication where what is learned and the possible ends

to which those lessons are put are entirely open. The idea of ‘upstream’ policy engagement

exercises fits this bill. Here, citizens are invited to critique technological and policy prototypes

in substantive and normative ways before any social roll-out is agreed (Stirling, 2005). Such

early and often interactions are rare however. More commonly, public engagement is

synonymous with tools such as consultation or notice and comment that fall some way short

of a genuinely plural dialogue (for a review see Blanc and Ottimofiore, 2016).

11

One alternative is to structure social questions into institutional architectures. Take, for

example, the experimental governance arrangements in the EU. Policy success has been the

result of recursive framework of rule-making, revision and adjustment by EU and national

actors across policy sectors as wide ranging as genetically modified organisms (GMOs),

financial markets, data privacy and anti-discrimination. This experimentalist architecture

holds the measures and policy goals against which success can be gauged, but critically, lower-

level policy actors have autonomy in how they achieve these policy ends and who in society

they work with to get results. In exchange for this freedom, they participate in peer review

exercises where they compare their approaches with European colleagues. The lessons

generated by these groups are then fed back into the policy framework through group

deliberation (Sabel and Zeitlin, 2010).

Through open dialogue we learn about social norms (Checkel, 2001). This is a deep form of

learning; by exploring social norms and the identities bound up within them, policy actors

generate new consensus and new definitions of what is appropriate. This is why the inclusive,

energetic debates about fundamental values that fuels reflexive learning is most closely

associated with paradigmatic policy change down the line (Hall, 1993). As well as the proto-

lessons generated around values, dialogue also holds the promise of deutero or triple-loop

learning (Argyris and Schön, 1978; Bateson, 1972). Simply stated, by arguing and debating

policy actors may get a clear picture of how we can build consensus and adjust our norms –

i.e. we learn about how to learn and develop (Argyris, 1999).

Most of this part of the learning literature goes beyond policy analysis to offer prescriptive

comment on what dialogue is good for and how to mainstream it into policy-making. Opening

up a wide social frontier for debate and value-driven argumentation is commonly connected

with ideals of Dewey’s practical-moral deliberation (Sanderson, 2004), achieving the

legitimacy of law through communicative rationality (Habermas, 1984) and the design of

‘good’ deliberative governance (Dryzek, 2010).

Learning through bargaining is generated in arenas where issues are eminently tractable and

authority is plural. These are dominated by interest actors who must accept there is no settled

monopolistic position on an issue. Rather, policy and politics is what they make of it. As such

actions and interactions are underpinned by mechanisms of exchange. Though intuitively, we

tend to link negotiation to material outcomes, we think of the way information handled and

12

changed during exchanges as an intrinsic part of the generation of learning. After all, how

actors select, acquire and trade information to inform their negotiating positions ultimately

influences what they are willing to ‘give’ to competitors.

The precise nature of exchange and the learning that is generated is, of course, dependent on

the situation and specifically, the levels of risk and transparency it entails. In policy arenas

where stable policy communities dominate, interaction will be routinized and repeated. Here,

decision-making risk is calculable and exchange mechanisms underpinned by actors’

probability judgements derived from long-standing experiences (for more on decision-making

under risk see Elster, 1989: 26). While these calculations will be adjusted and re-calibrated

over time, the lessons generated may be thought of as little more as the realisation of

expectations as opposed to any new discovery. In such circumstances, though it is never

complete, transparency will be sufficient for actors to be able to make an accurate prediction

of other parties’ stances. On the other hand, where interactions are novel or one-off, or a

new actor enters the arena, risk increases and transparency reduces. In this context of

incomplete information, interaction will be marked more by negotiation and bet-hedging.

Here, exchanges do not simply create lessons about the most efficient means to secure

mutually beneficial outcomes they may create new understandings about the issue entirely.

Recent governance of the Eurozone provides an instructive example (Dunlop and Radaelli,

2016: 117-119). Key episodes in the implementation of the European Semester fiscal

surveillance system have been characterised by exchanges between influential member

states – notably Italy and France – at risk of falling foul of the rules on excessive deficits. In

one instance, Italy offers to exchange going beyond official deficit tolerances for further

progress on reforms of public sector finance (notably pensions). France too played a similar

game of offering offsets to the Commission; delaying the reforms needed to exit the excessive

deficit procedure. Here, we see a selective interpretation of policy and exchange mechanisms

as the engine of learning about the boundaries of the negotiable.

What is learned? Mechanisms of exchange generate lessons about preferences and the costs

of cooperation. Taking preferences first, through bargaining and negotiation we learn about

the composition of preferences on an issue, the salient outcomes around which parties can

coalesce and about breaking points – the red lines held by ourselves and others beyond which

an agreement cannot be forged. We also learn about the cost of reaching agreements (for a

13

deeper discussion of these Elster, 1989: chapter 4). Where policy problems are time-sensitive,

actors stand to lose if negotiation appears to be extending indefinitely is not reached by a

designated point and may radically adjust their stances to secure a quicker closure.

What is it good for? Exchange generates important lessons both functionally and normatively.

In terms of policy outcomes, ongoing negotiation uncovers the set of resource allocations

required to ensure that no one gains at the expense of another. With this Pareto frontier

revealed, decision-makers can work through the possible trade-offs implied by the resulting

narrow set of choices. There are also normative gains. The processes of partisan mutual

adjustment generated by repeated exchange encapsulates Lindblom’s Intelligence of

Democracy (1965) whereby policy stability is generated by increased appreciation and

understanding of rivals’ positions. Indeed, a famous way of looking at bargaining is partisan

mutual adjustment (PMA) (Lindblom and Woodhouse, 1963). PMA has normative properties

(as well as empirical leverage): it argues that a society based on preferences is better than a

society based on knowledge or ‘intellectual cogitation’. The contrast with epistemic learning

is then also normative.

Our exploration of mechanisms in the shadow of hierarchy starts with the acknowledgement

that rules-based systems can be formal or informal, contained in institutions or simply

believed and trusted by a society. Indeed, what matters for a hierarchical rule to have power

is that someone is obeyed. In some societies, family norms and standards set by communities

like the village, the tribe or the neighbourhood are much more important than laws and

regulations (Banfield, 1958: Putnam, 1993). This leads us to argue that the mechanism we are

interested in is compliance. The concept of hierarchy also reminds us of the vertical nature of

this mode of learning. In that, there is similarity with epistemic knowledge. In the latter, we

have a teacher and a pupil, whilst in hierarchical learning we have those who set the rules and

those who follow the rules.

Often we think of hierarchy as instructions, command, and everything else that seems the

antithesis of learning. What’s hierarchy to do with our analysis of learning then? It is here

because in compliance there is an important dimension of learning. Consider this: in

compliance mechanisms, over time actors learn about the scope of rules, their flexibility, and

what happens when they are not followed. In a sense, this is the shadow of hierarchy: the

range of social phenomena projected by the existence of a system of rules. It may be a set of

14

court decisions or more generally the nature of the legal system (Kelemen, 2010) to illuminate

the scope of rules, or the attitude of inspectors (Blanc, 2016). The rule per se is an incomplete

contract that is defined over time via implementation.

Three powerful explanations capture the essence of the shadow of hierarchy. One is Fritz

Scharpf’s analysis of rules set at different levels of governance, like in federalism systems or

in the European Union (1988). Another is Elinor’s Ostrom’s institutional grammar tool

(Ostrom, 2005). The third is the veto-players approach by George Tsebelis (2002), which does

not deal explicitly with hierarchy but offers a template to analyse decision-making systems.

Scharpf’s approach sheds light on hindrances and pathologies, as we explain below. Ostrom’s

approach has the advantage of specifying the conditions that lead a rule to generate social

behaviour, and is explicit on what happens if that type of behavior does not appear. Tsebelis

provides yet another way to look at hindrances – we will talk about this in the next section.

Turning now to what this learning is good for, hierarchical rules are indispensable to organized

societies. They define roles and stabilize expectations: an inspector has a role that is generally

understood by companies and it is on the basis of expectations about this role that regulatory

conversations between inspectors and firms take place (Blanc, 2016). Hierarchy also delivers

on ‘monitorability’: some learning processes can be measured, compared, appraised because

there is someone on top who sets the standards for monitoring compliance. Finally, this type

of learning allows societies to sanction non-compliant behaviour. Sanctions and clear

expectations about ‘what happens if the rule is not followed’ allow hierarchies to guide

communities and societies. In international organizations, hierarchical learning is a common

way to steer the behaviour of states and to allow the international society to affirm its norms.

15

Table 1 Unpacking policy learning modes

Learning as … Epistemic Reflexive Bargaining Hierarchical

Predominant

actors …

experts citizens interests courts and

standard setters

Motivating logic

guiding action …

cognition appropriateness consequence habit

Knowledge use as

…

instrumental conceptual political / symbolic imposed

Interactions as … cooperative

asymmetric

cooperative

symmetric

competitive

symmetric

competitive

asymmetric

Decision-makers

attention as …

directed diffuse/divided selective routinized

Governance as ... school agora arena pyramid

Mechanism teaching dialogue exchange compliance

What is learned? • cause and

effect

relationships

• policy-

relevance of

science

• exposing norms

• learning how to

learn (deutero)

• composition of

preferences

• costs of

cooperation

• scope of rules

• significance and

rigidity of rules

What is it good for? • reduction of

uncertainty

• thinking

through the

links between

policy means

and ends

• upholding and

renewing

legitimacy

• conflict

resolution

• exposing the

Pareto frontier

• intelligence of

democracy

• monitoring

• sanctioning

16

Section 4 Triggers, Hindrances and Pathologies – What Regulates Mechanisms for

Policy Learning?

Mechanisms do not play regardless of the state of institutions but only make sense in a given

context. As real features of the world, mechanisms connect and are mediated by wider

features of society. So, we must explore the conditions under which they come into being and

constraints on their operation (Merton, 1968: 43-44). For learning cogs to turn they need to

be connected to a wider set of nuts, bolts and wheels. We can identify triggers and hindrances

of mechanisms as either the result of psychological filters and/or features of the wider

institutional context (Elster, 1989: 13) (summarised in table 2).

In epistemic learning, an important trigger or facilitating condition is the fact that an

epistemic community has some right to be consulted by policy-makers. This is often a

necessary but not sufficient condition for epistemic learning. In addition, the mechanisms of

teaching we described above is also facilitated by a pluralistic approach to the use of

expertise. Some years ago the European Commission launched an exercise on ‘democratising

expertise’ which culminated in the Liberatore report (2001). It is useful to recall the message

of this report. For the mechanism of teaching to be effective, the expertise used in the policy

process should be socially robust. This often implies a delicate balancing act between

scientific quality and social, economic, and political preferences. Note that this trigger – the

Liberatore report argues – is not about ‘majority voting in science’, but rather about

guaranteeing ‘due process’ in the way expertise is developed, used and communicated’

(Liberatore 2001: 7). The focus is therefore on criteria such as access and transparency of the

process of selection of expertise; accountability to citizens; effectiveness; early warning and

foresight to assist the identification of new issues and threats; independence and integrity;

plurality of types and sources of expertise consulted by EU policy-makers; and quality of

expertise (Liberatore 2001, 15).

For other triggers, we can search the literature on Europeanisation (Börzel and Risse, 2003).

Here, we find that norms entrepreneurs and cooperative informal institutions facilitate the

mission of epistemic communities. New lessons are not simply taught ex cathedra. They

require alignment between epistemic norms, on the one hand, and shared understandings

and structures of social meaning, on the other. Norm entrepreneurs actively seek this

alignment via persuasion and advocacy.

17

Turning to hindrances, one problem in public policy controversies is the fragmentation of

epistemic communities. We mentioned the case of the Mediterranean, but other policy

domains are fraught with epistemic controversies. A good example is economic policy in the

European Union since the crisis of 2008 – with equally strong epistemic arguments for

‘austerity’ and ‘flexibility’, even within the same profession of economics. Obviously, without

consensus on the lesson to be taught, there cannot be a clear message coming from the

teacher. To carry on with the same example of the European Union, controversies among

economists are compounded by political attacks on the experts and expertise in general. To

denigrate expertise in a given domain pays off politically, but gradually demolishes the basis

of EBPM and social trust in science. Often expertise, economic appraisal of public choice, risk

science are contrasted with the principles of democracy. Following the argument that ‘once

we elect representatives, there should not be any constraint on their action except the law’,

expertise can only provide know how and answers to specific questions rather than

enlightenment and social learning. Dunlop and Radaelli (2013) refer to this as one the possible

sub-types of epistemic learning when they talk about the ‘facilitator’: in this sub-type the

teacher does not contribute to the definition of the preferences of the learner, she simply

helps the learned with know-how and technical responses to the questions. And to conclude

with hindrances, no matter how accurate and well-understood the lesson is, low policy

capacity is always a powerful barrier (Dunlop, 2015). All too often we have heard in our

interviews that policy-makers were persuaded by this or that lesson, they knew how to act,

but were constrained by the limitations on the side of organizational, administrative, and

managerial capacity.

Let us know consider pathologies. It is perfectly possible that epistemic communities are not

constrained, but they are teaching the wrong lesson – as shown by policy fiascos (Dunlop,

2017b). It is difficult for public opinion to understand that science is not about the ultimate

truth, but a process of conjectures and confutations. Science is about discovery, not dogma.

In this sense, the lessons provided by epistemic teachers are neither contingent arguments

ready to be demolished nor ever-present truths. Absent this understanding of science, the

epistemic mechanism we described above degenerates into the pathology of the dialogue of

the deaf, in this case the lack of understanding between the world of science and the world

of public opinion and public policy. In their expansion of the epistemic type into dysfunctional

18

types, Dunlop and Radaelli (2013) talk about policy-makers mobilizing counter-epistemic

communities to politicize controversies and diluting the influence of experts and scientists.

There are two important triggers for reflexivity. One is institutional. It has to do with the

governance architecture of deliberative spaces. We know that, for example, the design of

committees, the role assigned to the chair, the style of interaction have causal effects on the

presence or absence of the mechanism of dialogue crucial to reflexivity (Joerges and Neyer

1997). More generally, Rise and Klein (2010; Rise 2013) identified a full range of institutional

scope conditions that trigger reflexivity in negotiations, and precisely institutional settings

that support overlapping role identities, the transparency of negotiation settings with actors

uncertain about the preferences of their audience (or, at the opposite: low transparency with

certainty about the preference of the audience whose consent is required), norms and

institutional procedures that privilege authority based on moral competence rather than

formal power roles and hierarchy. Always at the aggregate level (organizations and political

systems), institutions that promote and support socialization are a strong pathway to

reflexive learning – as shown by the vast literature on socialization and policy change.

The other trigger operates at the individual level. Here what matters is the predisposition of

the actors. We must assume that, at least at some point in time during the course of a policy

process, actors have the predisposition to listen and to ‘move’ and therefore change their

preferences. A good set of case studies is included in Frame Reflection, where reflexive

learning is triggered only when actors go beyond the dialogue of the deaf (Rein and Schön,

1994). In turn, we can hypothesize that repeated failure and a deterioration of the status quo

push actors into this predisposition. In her Currency of Ideas, McNamara (1999) illustrates

how repeated failure with a certain paradigm of monetary policy has historically opened up

the minds of policy-makers to new options and ultimately reflexivity.

As for hindrances, they operate in organizational and political cultures where there is not a

deliberative tradition and compromise is considered almost like losing one’s honour and

reputation. Another important hindrance is the presence of genuinely incommensurable

arguments. In his exposition of the myth of the best argument, Pellizzoni (2001) shows that,

in these circumstances, deliberation is either fake or covers the aggressive attempt to silence

an argument (hence it is domination). This also shows how deliberation can degenerate in a

pathology or dysfunctional learning. Karolewski (2011) has argued that the conventional

19

deliberative methods used in the EU have their own dark side, particularly the ‘false will

formation’ and ‘rational hijacking of deliberation’. Thus, not all learning via deliberation is

normatively desirable.

Can reflexivity present pathologies? One recurring problem is scale: the mechanisms of

dialogue can be triggered in small scale deliberative fora. However, we do not know exactly

how to couple deliberation in, say, mini-publics with decision-making, e.g. legislative

committees (for this discussion see Hendriks, 2016). The research of appropriate institutional

mechanisms to link disconnected sites of deliberation and real-world public choice is still

going on. In the meantime, de-coupling remains a pathology. Papadopoulos asks the question

whether attempts to empower citizens and promote reflexivity ‘do matter’ for decision-

making and institutional choice: until they do, they may distract us from fundamental issues

of democratic governance, especially if they suffer from bureaucratization and

disproportionately favour specialized, professional non-governmental organizations over

ordinary citizens (Papadopoulos 2013: 143).

Provocatively, Lynn Sanders (1997) in Against Deliberation lists a number of reasons, broadly

speaking concerned with domination, why deliberation violates normative standards of

democracy. Lack of inclusiveness, expertocracy and domination may also feature in one of

the sub-types of reflexivity described by Dunlop and Radaelli (2013); that is experimentalist

governance.

Bargaining brings us back to the world of negotiation and to the mechanisms of exchange.

For exchange to take place, barriers to contract must be low. This trigger is not just about the

absence of legal or technological barriers. It is also about the overall transparency of the

negotiation settings and the circulation of information, so that actors can ‘mutually adjust’ on

the basis of robust assumptions about what they exchange, with whom, and with what

consequences. PMA is facilitated by Popper’s (1945) open society with enforceable contracts

and rule of law. Cultural, religious, technocratic dogmas limit exchange.

If we move from the individual level to the organizational and social level, bargaining requires

low barriers to aggregation of preferences. Eberlein and Radaelli (2010) distinguish between

aggregation techniques and transformation techniques. Aggregation is particularly important

for bargaining. It comes in two forms: one is issue-aggregation, the other is arena-

20

aggregation. On the one hand, actors in organizations and political systems must be able to

recombine issues to reach consensus and learn how to exploit cooperation. They should also

be able to re-order issues temporarily, by exploiting delayed compensation and other classic

ways of composing conflict. On the other hand, actors must be free to shift arenas and even

to create a new arena, or break a complex conflict by allocating portions of the conflict to

different arenas (Eberlein and Radaelli, 2010; Radaelli and Kraemer, 2008 on the break-up of

multi-dimensional conflict in different arenas).

The separation of procedures from substance reduces the friction in bargaining. If actors

cannot successfully bargain on the outcome, they may find it easier to reach agreement on

the procedure through which the outcome will be reached (Eberlein and Radaelli, 2010). It is

the separation between procedure and substantive policy issues that is the trigger – not the

procedure itself. There is another possible trigger based on de-coupling, that is, the

separation between higher level framework agreements and lower level policy issues. In this

case the trigger is the possibility to agree on the framework agreement ‘under the veil of

vagueness’ (Gibson and Goodwin, 1999, cited by Eberlein and Radaelli, 2010: 788).

Game theory suggests an important trigger – absent which we have the hindrance: the

mechanism of exchange must be repeated, not one-shot, so that trust can be developed. The

fuel of PMA and more generally bargaining is that the winners and losers are reshuffled in

different iterations. No-one wants to play the same game if the result is always to be on the

side of the losers. Dunlop and Radaelli (2016) illustrate this point with the case of the

negotiations on the excessive deficit procedure in the European Semester of the EU. The

European Semester is an essentially iterative mechanism of coordination of economic policy

and promotion of structural reform. Dunlop and Radaelli (2016) find that bargaining

deteriorates if a group of countries is systematically losing and the other group tends to win

in all iterations. So, this ossification of winners and losers is a major hindrance. Another is the

low cost of defection, which makes PMA and bargaining in general very unstable.

Bargaining may end up in dysfunctional learning. This happens when actors have widely

different endowment of resources. The scale is tilted from the very beginning. Further,

incremental changes and PMA do not allow constellations of actors to produce radical policy

change. And, learning without transformation of preferences is not desirable when sticky

preferences are exactly ‘the’ problem from a normative point of view. If a system needs new

21

beliefs, re-conciliation of trade-offs, and radical policy innovation to come out of a crisis, it

may not benefit from the type of learning generated by bargaining and its mechanism,

exchange.

It is not easy to pin down exactly the triggers for hierarchy. Studies of political culture point

to legitimacy, trust in authority, historical memories, and even deference as pre-conditions

for political hierarchies to work (Dahl, 1971; on memories see Rothstein 2000). Yet this is only

part of the story about the facilitating conditions. First, consider Tyler (2003): why do people

pay taxes, obey and so on? Trust in authority is not a given. A condition for compliance to kick

in as mechanism of hierarchical learning is that ‘the decision-making is viewed as being

neutral, consistent, rule-based, and without bias; that people are treated with dignity and

respect and their rights are acknowledged; and that they have an opportunity to participate

in the situation by explaining their perspective and indicating their views about how problems

should be resolved’ (Tyler, 2003: 300-301). Translation: institutions must earn trust, and

compliance must be socially deserved.

Second, consider Chayes and Chayes (1993): deterrence, sanctions, enforcement and, in

short, interests represent only one side of compliance. The other is (yet again) trust, identity,

beliefs and, in short, norms. Thus, the trigger to compliance can be found in both the logic of

interests and in the logic of norms. It depends on how interest constellations are composed

once a rule is enforced, how norms have developed in a given society or policy setting.

As for hindrances, we can revert to what we said about Scharpf (1988) and Tsebelis (2002).

Starting with the latter, a high number of veto players hinders compliance. Actors may learn

dysfunctionally how not to comply if veto players proliferate in the implementation arenas.

The presence of a high number of veto players makes the chain of compliance murky, and

reduces the probability that actors will in the end really learn something about rules and rules-

following. Scharpf (1988) instead draws our attention to the joint-decision trap in systems of

multi-level governance. Under joint-decision trap conditions, the very possibility of agreeing

on rules to be enforced hierarchically is stymied. These are institutional hindrances. However,

the hindrance may occur at the policy level: not always is the solution known in advance, or

at least agreed on the top level of the hierarchy. Learning from the top (Radaelli, 2008) is

possible only if there is a relatively clear, unambiguous, agreed-upon solution that is then

pushed down the chain of hierarchy.

22

The pathologies of hierarchy are clear. A perfect shadow of hierarchy system with full

compliance has no breathing spaces. It does not adapt to the environment. It requires

accurate coupling with democratic institutions and standards, otherwise it violates

democratic norms. It stifles innovation and deep learning. Once actors have learned how to

comply, there is no room to explore learning in other ways. Finally, hierarchies are biased on

the libertarian-authoritarian dimension that, together with the left-right dimension, matters

so much to ordinary citizens in contemporary democracy.

Table 2 Learning modes and their mechanisms

Learning as … Epistemic Reflexive Bargaining Hierarchical

Mechanism teaching dialogue exchange compliance

Mechanism is

triggered by …

• open,

Galilean

attitude to

science

• cooperative

institutional

structures

• willingness to

move position

• convened

deliberative

spaces

• low barriers to

contract

• low barriers to

preference

aggregation

• repeated

interaction

• suitable political

culture

• trust in

institutions

Mechanisms are

hindered by …

• scientific

scepticism

• low policy

capacity

• incommensurable

beliefs

• absence of

deliberative

tradition

• winners and

losers are always

the same

• options for

defection are

cheap

• joint-decision

trap

• veto-players

• availability of

solutions at the

top level

Pathologies as … • teaching the

wrong lesson

• mobilization

of counter-

epistemic

communities

• de-coupling

between

deliberative fora

and public choice

• domination

• different

endowment of

resources

produces unfair

outcomes

• options for

radical

innovation are

limited

• blocked learning

• limited

adaptation to

environment

23

Conclusions

This paper contributes to the theory of policy learning by identifying the mechanisms leading

to different types of learning, their triggers and hindrances, and the pathologies of learning.

It adds to our understanding of mechanisms by connecting institutional and individual

dimensions of mechanistic action. Further research should allow us to differentiate the micro-

foundations and the organizational-institutional properties of mechanisms, and how they are

aggregated in political systems. Another extension of our research is to take learning as

independent variable and explore its causal effects on policy as dependent variable –

including feedback effects between mechanisms of learning and mechanisms of policy

change.

We should also direct our attention to design and governance architectures. Knowing about

the triggers, hindrances and pathologies should inform our policy recommendations and the

design of architectures to promote specific learning mechanisms and outcomes at the level

of policy sectors and, why not, political systems. This is a challenging but fascinating research

agenda.

This is only the beginning of mechanistic thinking for policy learning. Empirical exploration of

our propositions is required. Our mechanisms can only be considered plausible where we find

them in multiple cases. Our propositions must be strengthened further by examining negative

as well as positive cases; the measure of a good mechanism is that we can use it to explain

variations in empirical phenomenon. If our mechanisms that generate learning are sound, we

must be able to find counter-examples where the mechanism did not apply (see Dowding,

2016: 63).

Moreover, we know little of the temporal dimension of mechanisms for policy learning

whereby their influence is amplified or diluted by another mechanisms that comes later. Such

interaction effects and matters of sequencing are complex but with solid analytical

foundations can be unpicked.

24

References

Argyris, C. (1992) On organizational learning Oxford: Blackwell.

Argyris, C. and Schön, D. (1978) Organizational learning: A theory of action perspective

Reading, MA: Addison Wesley.

Banfield, E. (1958) The moral basis of a backward society New York, NY: Free Press.

Bateson, G. (1972) Steps to an ecology of mind Chicago, IL: University of Chicago Press.

Bennett, C.J. and Howlett, M. (1992) ‘The lessons of learning: reconciling theories of policy

learning and policy change’, Policy Sciences 25(3): 275-294.

Bernstein, S. (2001) The compromise of liberal environmentalism New York, NY: Columbia

University Press.

Blanc, F. (2016) From chasing violations to managing risks PhD Thesis, University of Leiden.

Blanc, F. and Ottimofiore, G. (2016) ‘Consultation’, in Dunlop, C.A. and Radaelli, C.M. (eds)

Handbook of Regulatory Impact Assessment. Cheltenham: Edward Elgar.

Boehmer-Christiansen, S. (1994) ‘Global climate protection policy: the limits of scientific

advice: parts I and II’, Global Environmental Change 14(2&3)

Börzel, T. (2010) ‘European governance: negotiation and competition in the shadow of

hierarchy’, Journal of Common Market Studies, 48(2): 191-219.

Börzel, T., and Rise, T. (2003) ‘Conceptualising the domestic impact of Europe’, in

Featherstone, K. and C. Radaelli (eds) The Politics of Europeanization, Oxford: Oxford

University Press.

Cairney, P. (2016) The politics of evidence-based policy-making London: Palgrave.

Cartwright N. and Hardie J. (2012) Evidence-based Policy Oxford: Oxford University Press.

Cartwright, N. (1999) The dappled world: a study of the boundaries of science, Cambridge:

Cambridge University Press.

Chayes, A. and Chayes, A.H. (1993) ‘On compliance’, International Organization 47(2): 175-

205.

Checkel, J.T. (2005) ‘International institutions and socialization in Europe: introduction and

framework’, International Organization 59(4): 801-826.

Coleman, J.S. (1990) Foundations of social theory Cambridge, MA: Belknap Press of Harvard

University Press.

Dahl, R.A. (1971) Polyarchy: participation and opposition New Haven, CT: Yale University

Press.

25

Deutsch, K.W. (1966) The nerves of government New York, NY: The Free Press.

Dowding, K. (2016) The philosophy and methods of political science London: Palgrave.

Dunlop C.A. (2010) ‘Epistemic communities and two goals of delegation: hormone growth

promoters in the European Union’, Science and Public Policy 37(3): 205–217.

Dunlop C.A. (2015) ‘Organizational political capacity as learning’, Politics and Society 34(3):

259-270.

Dunlop, C.A. (2017a) ‘Epistemic communities and the irony of policy learning’, Politics and

Society

Dunlop, C.A. (2017b) ‘Pathologies of policy learning: what are they and how do they

contribute to policy failure?’, Policy and Politics 45, 1.

Dunlop, C.A. and C.M. Radaelli (under review) ‘Does policy learning meet the standards of a

theory on the policy process’ Policy Studies Journal

Dunlop, C.A. and Radaelli, C.M. (2013) ‘Systematizing policy learning: from monolith to

dimensions’, Political Studies, 61(3): 599-619.

Dunlop, C.A. and Radaelli, C.M. (2016) ‘Policy learning in the Eurozone crisis: modes, power

and functionality’, Policy Sciences 49(2): 107-124.

Eberlein B. and Radaelli, C.M. (2010) ‘Mechanisms of conflict management in EU regulatory

policy’, Public Administration 88(3): 782-799.

Elster, J. (1989) Nuts and bolts for the social sciences Cambridge, MA: Cambridge University

Press.

Falleti, T.G. and Lynch, J.F. (2009) ‘Context and causal mechanisms in political analysis’,

Comparative Political Studies 42(9): 1143-1166.

Gerring, J. (2007) ‘The mechanismic worldview: thinking inside the box’, British Journal of

Political Science 38(1): 161-79.

Gerring, J. (2010) ‘Causal mechanisms: yes, but…’, Comparative Political Studies 43(11): 1499-

1526

Haas P.M. (1990) Saving the Mediterranean – The politics of international environmental co-

operation New York, NY: Columbia University Press.

Haas, P.M. (1992) ‘Introduction: epistemic communities and international policy

coordination’, International Organization 46(1): 1-36.

Habermas, J. (1984) The theory of communicative action volume 1 Boston, MA: Beacon Press.

Hall, P.A. (1993) ‘Policy paradigms, social learning and the state: the case of economic policy-

making in Britain’, Comparative Politics 25(3): 275-296.

26

Heclo, H. (1974) Modern social politics in Britain and Sweden New Haven, CT: Yale University

Press.

Hedström, P. (2005) Dissecting the Social: On the Principles of Analytical Sociology.

Cambridge: Cambridge University Press.

Hedström, P. and Swedberg, R. (eds) (1998) Social mechanisms: an analytical approach to

social theory Cambridge, MA: Cambridge University Press.

Heikkila, T. and Gerlak, A.K. (2013) ‘Building a conceptual approach to collective learning:

lessons for public policy scholars’ Policy Studies Journal 41(3): 484-511.

Hendriks, C.M. (2016) ‘Coupling citizens and elites in deliberative systems: the role of

institutional design’, European Journal of Political Research 55(1): 43-60.

Hernes, G. (1998) ‘Real virtuality’, in Hedström, P. and Swedberg, R. (eds) (1998) Social

mechanisms: an analytical approach to social theory Cambridge, MA: Cambridge

University Press.

Hisschemöller, M. and Hoppe, R. (2001) ‘Coping with intractable controversies: The case for

problem structuring in policy design and analysis’, in Hisschemöller, M., Dunn, W.N.,

Hoppe, R. and Ravetz, J. (eds) Knowledge, Power and Participation in Environmental

Policy Analysis. Policy Studies Review Annual Volume 12 New Brunswick: Transaction

Publishers.

Hopf, T. (2010) ‘The logic of habit in international relations’, European Journal of International

Relations 16(4): 539-561.

Jenkins-Smith, H.C. (1990) Democratic politics and policy analysis Pacific Grove, CA:

Brooks/Cole.

Joerges, C. and Neyer, J. (1997) ‘From intergovernmental bargaining to deliberative political

processes’, European Law Journal 3(3): 273-299.

Karolewski, I.P. (2011) ‘Pathologies of deliberation in the EU’, European Law Journal 17(1):

66-79.

Kelemen, R.D. (2010) ‘Globalising European Union environmental policy’, Journal of European

Public Policy 17(3): 335-349.

Liberatore, A. (2001) Report of the working group on democratising expertise and establishing

scientific reference systems, European Commission, Brussels, May.

Lichtenthal, C. (1990) A self-study model on readiness to learn Unpublished manuscript.

Lindblom, C.E. (1965) The intelligence of democracy, New York, NY: The Free Press.

Lindblom, C.E. and Woodhouse, E.J. (1963) The policy-making process Engelwood Cliffs, NJ:

Prentice Hall.

27

Majone, G. (1989) Evidence, argument, and persuasion in the policy process, New Haven, CT:

Yale University Press.

May, P.J. (1992) ‘Policy learning and failure’, Journal of Public Policy 12(4): 331-54.

McAdam, D., Tarrow, S. and Tilly, C. (2001) Dynamics of contention Cambridge, MA:

Cambridge University Press.

McNamara, K.R. (1999) The currency of ideas: monetary politics in the European Union Ithaca,

NY: Cornell University Press.

Morgan, S.L. and Winship, C. (2014) Counterfactuals and causal inference Cambridge, MA:

Cambridge University Press.

Nutley S.M., Walter, I. and Davies H.T.O. (2007) Using evidence Bristol: Polity Press.

Ostrom, E. (1990) Governing the commons Cambridge, MA: Cambridge University Press.

Papadopoulos Y. (2013) Democracy in crisis? Politics, governance and policy, Palgrave

Macmillan.

Patton M.Q. (1997) Utilization-focussed evaluation Thousand Oaks, CA: Sage.

Pawson, R. (2006) Evidence-based policy: a realist perspective London: Sage.

Pawson, R. and Tilley, N. (1997) Realistic evaluation London: Sage.

Pearl, J. (2000) Causality: models, reasoning and inference, Cambridge, MA: Cambridge

University Press.

Pellizoni, L. (2001) ‘The myth of the best argument’, The British Journal of Sociology 52(1): 59-

86.

Popper, K.R. (1945) The open society and its enemies vol. 2 Routledge.

Putnam, R. (1993) Making democracy work Princeton, NJ: Princeton University Press.

Radaelli, C.M. (2008) ‘Europeanization, policy learning, and new modes of governance’,

Journal of Comparative Policy Analysis 10(3): 239-254.

Radaelli, C.M. and Kraemer U. (2008) ‘Governance arenas in EU direct tax policy’, Journal of

Common Market Studies 46(2): 315-336.

Rein, M. and Schön, D.A. (1994) Frame reflection: toward the resolution of intractable policy

controversies New York, NY: Basic Books.

Rise, T. (2013) ‘Arguing about arguing: a comment’, Critical Policy Studies 7(3): 339-349.

Risse, T. and Klein, M. (2010) ‘Deliberation in negotiations’, Journal of European Public Policy

17(5): 708-726.

28

Rothstein, B. (2000) ‘Trust, social dilemmas and collective memories’, Journal of Theoretical

Politics 12(4): 477-501.

Sabel, C. (1994) ‘Learning by monitoring: the institutions of economic development’, Smelser,

N. and Swedberg, R. (eds) Handbook of Economic Sociology Princeton, NJ: Princeton

University Press and Russell Sage Foundation, pp. 137-165.

Sabel, C. and Zeitlin, J. (2008) ‘Learning from difference: the new architecture of

experimentalist governance in the European Union’, European Law Journal, 14(3):

271-327.

Sabel, C. and Zeitlin, J. (2010) Experimentalist governance in the European Union Oxford:

Oxford University Press.

Salmon, W.C (1990) Four decades of scientific explanation Minneapolis, MN: University of

Minnesota Press.

Sanders, L. (1997) ‘Against Deliberation’, Political Theory 25(3): 347-76.

Sanderson, I. (2004) ‘Getting evidence into practice’, Evaluation 10(3): 366-379.

Scharpf, F. (1988) ‘The joint decision trap: lessons from German federalism and European

integration’, Public Administration 66(3): 239-278.

Stincombe, A.L. (1998) ‘Monopolistic competitition as a mechanism: Corporations,

universities and nation-states in competitive fields’, in Hedström, P. and Swedberg, R.

(eds) (1998) Social mechanisms: an analytical approach to social theory Cambridge,

MA: Cambridge University Press.

Tsbelis, G. (2002) Veto players: how political institutions work Princeton, NJ: Princeton

University Press.

Tyler, T.R. (2003) ‘Procedural justice, legitimacy, and the effective rule of law’, Crime and

Justice 30: 283-357.

Wight, C. (2009) ‘Theorising terrorism: the state, structure and history’, International

Relations, 23(1): 99-106. Merton, R.K. (1968) Social theory and social structure New

York, NY: Free Press.