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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
[email protected]; [email protected]
This is the first full draft, please do not cite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
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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.
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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.
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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).
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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
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‘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
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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
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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).
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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
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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).
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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
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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
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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
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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.
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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
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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.
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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.