WP2012/02
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Abstract
A research puzzle for EU scholars interested in decision making is the repeated pattern
of co-operation in the Council of Ministers. There is much evidence to suggest that there
exists a strong norm of cooperation in the EU, which seems to guide the collective EU
decision process and in particular restricts the EU member states from engaging in
bilateral deals with each other to the detriment of other EU member states and the Union
as a whole. Why do individual member states actually comply with this EU norm and
moreover how does this norm operate as a mechanism of cooperation across the
member states in the EU decision process? We argue the norm of cooperation between
member states is sustainable, because the configuration of members’ positions and
interests gives rise to a decision situation that resembles a repeated Prisoner’s Dilemma
(PD). It is rational for individual member states to comply since not doing so would mean
large forgone gains in the future. We test this by making use of two current models of
collective decision making: the Position Exchange Model (PEM) and the Externalities
Exchange Model (EEM). The results suggest that the predictive power of PEM should
vary inversely with the average proportion of ‘winners’ in the data set. More winners
implies that for actors the shadow of the future becomes smaller, since there will be
fewer occasions on which they will actually be in a PD payoff structure.
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1. Introduction
There are now a variety of different EU studies which suggest that decision
making in the Council of Ministers is characterised by a strong norm of cooperation.
Some studies suggest that the member states will work extensively together to achieve
unanimous agreement with the final collective decisions taken, even where the formal
decision rule procedure allows for a qualified majority vote (Heritier, 1999 ; Richardson,
2001 ; Jönsson C.et al, 1998 ; Lewis, 2003; Schneider et al, 2006). Examples are
provided by different case studies which give in-depth, detailed descriptions of the EU
decision process and map the extended bargaining activity at work in the EU committees
surrounding the Council of Ministers, as well as the European Commission (Wessels,
1998; Westlake, Galloway et al., 2006; Lewis, 2000). Other EU studies suggest there are
a variety of accepted practices, informal rules or “culture of compromise” which
permeate the EU decision process and the participants involved therein (Hayes-
Renshaw et al, 2006; Lewis, 1998; 2005; Heisenberg, 2005). One can imagine that
given the range of different member states of different sizes, resources and sectoral
interests, there will be variation in the level of compliance with the EU norm of
cooperation. In this research we ask: why do member states choose to conform to the
EU norm of cooperation, how does this norm operate as a mechanism of cooperation
across the member states in the EU decision process, and are there conditions when we
can expect to find that member states do not comply with this EU norm of co-operation?
In order to be able to address these research questions, we need to examine the
different decision processes and conditions for cooperative EU decision making.
Traditionally EU scholars have focused their attention on larger scale phenomena such
as understanding the varying trends over time towards European regional integration or
key institutional developments within the EU, such as the Treaty negotiations for
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example. Research within the neo-functionalist approach has been concentrated on
determining the main drivers of ever closer integration, pointing to the role of
supranational institutions and the logic of spillover effects from one EU policy area to
another. Another major school of EU research, intergovernmentalism, highlights that it is
the member states that control the pace of integration and development of EU policy
competences but that the member states will pursue their domestic interests first above
international or supranational institutions. Given their research focus, it is not surprising
that these two core EU research approaches and others are not well adapted for the
study of processes and conditions of decision making for specific day-to-day issues.
However, the neo-institutionalist perspective is useful in this regard as it
examines the way in which institutions pervade human behavior through norms, rules
and cultural practices (Lewis, 2010; Tallberg, 2010).
Within neo-institutionalism, there co-exist different strands such as sociological,
cognitive, rational choice and historical institutionalism. Rational choice institutionalism is
most relevant for the research presented in this paper. Unlike traditional rational choice,
this variant of the approach begins with the assumption that an actor’s choices are
constrained (i.e ‘bounded rationality’). In this approach the core assumption is that while
actors behave strategically to maximize their own benefits, they are also assumed to
realize that their own goals are best achieved through institutions, which are understood
as systems of rules and inducements to behavior. Generally the rational choice,
institutionalist perspective is combined with a formal deductive modeling approach.
Combining this deductive modeling approach with real decision data allows contrasting
model assumptions about the collective decision process to be set out explicitly, as well
as carefully tested as to their veracity in predicting real decision outcomes (Thomson
and Hosli, 2006). Moreover, within the rational choice field, it is cooperationve game
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theory, and in particular the focus on bargaining and vote-trading, which provide the key
theoretical basis for the assumptions of collective decision models described below
(Achen, 2006).
This paper begins with the recent empirical research conducted by Arregui,
Stokman and Thomson (2006), who applying the rational choice institutionalist
perspective, compare two types of formal decision models based on contrasting
assumptions about the EU bargaining process. The input data for both kinds of models
is the same: actor policy positions, salience attached to decision issues and actor
resources. The cooperative models, including the Van den Bos Compromise model
(1991) and the Stokman and Van Oosten Position Exchange model (1994), assume that
actors reach collective decisions by making binding decisions, which cannot be reneged
on. In the Van den Bos Compromise model (1991), EU decision making is assumed a
simple, cooperative process involving mutual persuasion or influence amongst the
member states, including a “special role for the Presidency of the Council, probably in
collaboration with the Commission” where strong pressure is exerted to reach a decision
outcome that is acceptable to all (Arregui et al. 2006: pages 131-132). In this model, the
assumptions about the EU decision process are kept very simple: disagreement
amongst the member states is the least favorite option compared with any of the other
alternatives available and as no further assumptions are made explicit in this decision
model about an intervening process prior to the collective decision being taken, the
collective decision outcome is predicted as a simple compromise, which is the weighted
average of member states policy positions. Hence, the model predicts that the collective
decision is reached when all member states’ positions are taken account of, and where
each member state position is weighted by the resources that the member state can
bring to bear on the negotiations and the salience they attach to the issues (Van den
Bos, 1991)
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The second cooperative model is the Stokman and Van Oosten Position
Exchange model (PEM) (1994). This model builds some more complexity and realism
into the simple compromise model by making a further assumption about the EU
decision process. In the PEM, the EU decision process is assumed to have two stages,
wherein the member states try to increase support for their most salient issues by
bargaining with other member states. In the first stage, a very simple ‘logrolling’ process
is elaborated where a member state is allowed, once-off, to exchange issue support (i.e.
voting positions) with another member state across two issues and this exchange
bargain is assumed to be binding on both member states. In the model, it is also
assumed that this exchange may occur only when the two member states attach
different levels of salience to each of the two issues, as well as holding different policy
positions on each issue. In PEM, the second stage is arrived at when no further
exchanges are possible and compromise is assumed to take place on the basis of the
new voting positions, using the Compromise model solution1.
The non-cooperative model, referred to as the Challenge model (Bueno de
Mesquita, 1994; 2002), assumes that member states try to build a coalition in support of
their own policy positions, by challenging other members’ opposing positions and if
possible, compelling them to shift their positions closer to that of the challenger. In this
model, EU bargaining is viewed as a conflict-based process where member states are
assumed not to be bound by the commitments they make to others during the
negotiations. Unlike the cooperative models, a member state is not concerned with what
benefit others may gain in this process and “power dominance matters more than
convincing arguments according to this mode of conception of political bargaining”
(Arregui et al., page 127).
1 In this respect, according to the Compromise model, the predicted decision outcome on each issue is the
average of all member states voting positions on that issue, where each member state’s position is weighted
by the member state’s resources and the level of salience the member state attach to that issue.
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Using a large-scale, representative data set of routine EU decisions, Arregui et
al. (2006) tested the cooperative and the non-cooperative models and showed that their
cooperative decision models generated the most accurate model predictions of the EU
decision outcomes. Given the numerous case studies describing the cooperative nature
of the EU decision process, these findings are not too surprising. However it is surprising
that in the Arregui et al. (2006) study, it is the Compromise Model that is overall more
successful, although not statistically better than the PEM, as a predictor of the EU
decision outcomes and this is despite its’ very simple and highly unrealistic model
assumptions.
We argue in this paper that the limited success of the PEM reflects the model
assumptions about the exchange process in the EU negotiations. The PEM does not
require any pair of actors, involved in an exchange of voting positions, to take into
account the potentially negative impact (i.e. negative externalities) of their agreement for
all other actors. But in fact, as Arregui et al. (2006) point out in their study, there are a
high number of instances where there are negative externalities after exchange in their
EU data set (2006). In these instances, the PEM does not seem applicable as a decision
model for the EU negotiation process. However there are some other instances in the
Arregui et al. (2006) study where the PEM is the most successful model at predicting the
decision outcomes, namely where there are highly polarized issues or where the
negative externalities from exchange are lower (Arregui et al, 2006).
In 2008, Dijkstra et al. elaborated a new cooperative model, called the
Externalities Exchange Model (EEM). This model assumes that the actors take account
of the potential for negative externalities for other actors. Moreover the actors can
choose different strategies to restrict the deals they make with other actors, so as to
minimize the potential for negative externalities for others accordingly. The EEM model
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was applied and tested using the same EU data set, as used in the original Arregui et al.
(2006) study, and was significantly better than the PEM at generating more accurate
predictions of the EU decision outcomes (Dijkstra et al., 2008).
Comparing the results of these cooperative models and in particular the PEM
and EEM (Arregui et al, 2006; Dijkstra et al, 2008) suggests that under alternate
conditions, the bargaining strategy of EU member states will vary and moreover, under
these alternate conditions, member states may choose to comply with or to defect from
the EU norm of cooperation. As mentioned earlier, cooperative game theory provides the
theoretical basis for the elaboration of the various decision model assumptions
discussed in the paper, Extending the application of this cooperative game theory
approach, this paper aims to provide a rigorous study of the conditions in EU collective
decision making under which actors will adhere to the norm of cooperation. In this
regard, we argue that Axelrod’s seminal work on the ‘durable, iterated Prisoner’s
dilemma’ wherein he elaborates the conditions for emergence and durability of
cooperative behavior, is of particular relevance for addressing our research problem and
the development of our research hypotheses (Axelrod, 1984).
In the present paper we argue that the norm of cooperation between member
states exists and is sustainable, because the configuration of members’ positions and
interests gives rise to a decision situation that resembles an n-player infinitely repeated
Prisoner’s Dilemma Game. In fact, it is exactly the negative externalities already
mentioned by Arregui et al. (2006) that give rise to this Prisoner’s Dilemma. We thus
argue that in a repeated setting that contains large negative externalities it is actually
rational for individual member states to comply with the norm of cooperation, since not
doing so would mean suffering from those negative externalities large forgone gains in
the future. We make the argument precise and test its implications by making use of two
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current models of collective decision making, the Position Exchange Model (PEM) of
Stokman and van Oosten (1994) and the Externalities Exchange Model of Dijkstra et al.
(2008).
The remainder of the paper is organized as follows. In the next section we
expound our theoretical argument and derive our hypotheses. The subsequent section
describes the data used to test the hypotheses, the results of which are discussed in the
section after that. The paper is concluded with a discussion of the results and their
implications.
2. Theory and Hypotheses
The ‘norm of cooperation’ described in the introduction means that (groups of) EU
member states pass up the realization of profitable deals, if these deals harm other EU
members not involved in them. In doing so, member states thus forgo opportunities of
increasing their utility. The question now arises why member states of the EU, would
adhere to such a norm that apparently runs counter to their self-interests. To answer this
question we take a rational choice stance, assuming that actors in the long run do seek
to maximize their self-interests. From this perspective actors, adhering to the norm of
cooperation, abstain from the realization of short-term profits only in order to gain
something more valuable (or to prevent losses) in the future.
2.1. EU decision making and Cooperation Theory
According to Axelrod’s Cooperation Theory (CT; Axelrod 1984) adherence to a norm
of cooperation such as described above can be sustained if at least two criteria are met,
namely: (i) the interaction is characterized by a prisoner’s dilemma-like (PD) payoff
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structure, and (ii) the interaction horizon is sufficiently long. Thus we will argue in this
section that EU decision making resembles an n-person infinitely repeated PD game.
Requirement (ii) of CT means that there must be a sufficiently high probability that
actors will interact repeatedly in the future. It is self-evident that this is characteristic of
EU decision making processes, where every member country expects to remain a
member in the foreseeable future. The way in which requirement (i) is applicable to EU
decision making is less self-evident and is the topic of this section.
A social situation has a PD-like payoff structure when individually rational behavior
leads to collectively undesirable outcomes (Dawes 1980). Thus, the basic property of
such situations is that if all actors pursue their self-interests (called ‘defection’ in this
context) all will be worse off compared to the situation where actors abstain from the
pursuit of their self-interests (called ‘cooperation’ in this context). The problem however
is that, given what others do, any individual actor is always better off pursuing his self-
interests. Hence a social dilemma.
The principal argument of Cooperation Theory (Axelrod 1984) is that cooperative
behavior (i.e., actors refraining from realizing their immediate self-interests) can
nonetheless be sustained, if the shadow of the future (i.e., the likelihood that the same
actors will repeatedly interact again) is long enough. The idea is that all actors can
threaten to retaliate an ‘act of selfishness’ of any individual actor (i.e., this actor pursuing
his immediate self-interest to the detriment of the other actors) by withholding their
cooperation with that particular actor (or with every actor for that matter) in future
interactions. Moreover, since pursuing their short-term self-interests is itself an
equilibrium strategy in the repeated game (i.e., it is the best thing any actor can do when
everybody else does it too), this threat to withhold future cooperation is credible (e.g.,
Fudenberg and Tirole 1991). The credibility of this threat is exactly what makes it
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effective: no actor will want to break the cooperative pattern since doing so will destroy
cooperation forever. And with no cooperation everybody, including the actor
contemplating breaking the cooperative pattern, is worse off.
In order to apply CT to EU decision making we need to define exactly what it means
for member states to defect or to cooperate. This is no easy task, since EU member
states typically have a multitude of strategies at their disposal and it is not
straightforward to label any of these ‘defective’ or ‘cooperative’. Thus, we need to make
assumptions concerning the strategy spaces of the EU member states, to make the
problem tractable.
In order to do so, we first make the assumption that EU decision making is
fundamentally an exchange process. Thus, we assume that eventual agreement among
EU member states is reached through reciprocal shifts of positions held by these
member states on different issues. Other decision strategies such as persuasion or
seeking confrontation are excluded from consideration. Second, we assume that these
exchanges take place only on pairs of issues, i.e., we assume member states
contemplate only exchanges on pairs of issues. These assumptions lead us to the
consideration of two models of collective decision making called the Position Exchange
Model (PEM; Stokman and van Oosten 1994; Thomson et al. 2006)2 and the
Externalities Exchange Model (EEM; Dijkstra et al. 2008). We will argue that we can use
the PEM as a model for the situation where every member state defects, while the EEM
models the situation where all member states cooperate.
The PEM and EEM model collective decision making as decision making about
controversial issues with single peaked preference functions, as most well-known
models do (Black 1958; Bueno de Mesquita, Newman & Ravushka 1985; Bueno de
2 For a short overview of the application of various bargaining models, including PEM, to EU
decision making, see Sullivan and Selck (2007).
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Mesquita & Stokman 1994; Steunenberg 1994; Tsebelis & Garrett 1996, and many
others). Decision making may well require simultaneous decisions on several issues. In
these cases, different issues should represent independent controversial elements of the
decision making situation and as a set should cover the full range of possible outcomes.
The dynamics in the decision making process result from actors, with different intensity
and potential, trying to realize their preferred outcome on an issue (their initial position),
whereas per issue only one outcome that is binding for all actors can be chosen. In a
complex situation, possibly involving many actors and issues, actors will try to build a
coalition as large as possible behind their initial positions or behind a position that is as
close as possible to this. This informal bargaining process can be envisaged as
proceeding formal decision making and affecting the final positions of the actors in the
decision making, aiming at a collective outcome that reflects their interests as much as
possible.
The PEM and the EEM are both models of exchange or logrolling of this informal
bargaining process. The PEM models decision makers as only concerned with their own
immediate welfare, implying they will realize any immediate opportunity for gain,
regardless of the consequences for others or for themselves in the future. The EEM
models decision makers as concerned with the welfare of all: actors will only realize a
personal gain if this does not hurt any other actor.
The PEM and EEM assume the same structure of collective decision making. It is
assumed that there exists a finite set M of controversial issues, which can each be
represented by a one-dimensional interval scale. These issues are mutually exclusive
and exhaustive, i.e., an actor can take a position on one issue, irrespective of his
position on other issues (mutual exclusiveness), and the issues together cover the entire
collective decision problem (exhaustiveness). It is further assumed that each actor n
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from the finite set of actors N, takes a position, nmx , on the scale of each issue m,
representing n’s most preferred outcome of m. Furthermore, each actor n is assumed to
have a salience, nms , for each issue m, expressing the relative importance of issue m to
the actor n. Finally, each actor n has a capability, nc , reflecting n’s potential to affect the
final outcome of each of the issues in M. The actors’ positions, saliences and capabilities
are assumed to be common knowledge. Based on this common knowledge, all actors
are supposed to have a common expected outcome, mO , of each issue m. In both the
PEM and EEM, mO is assumed to be the weighted average of the actors’ positions, with
weights equal to the actors’ capabilities times their saliences, as in equation (1) below:
n
nmn
n
nmnmn
msc
xsc
O (1).
2.2. The PEM and Defection
The basic idea of the PEM is that pairs of actors can mutually increase their utilities
compared to their utilities of the expected outcome in (1) by exchanging their positions
on pairs of issues. The PEM assumes that actors have single-peaked preferences: an
actor’s initial position on an issue represents his preferred outcome, and any deviation of
the final outcome from it, is evaluated as strictly worse. In the PEM the utility of actor n (
nU ) over the outcomes of all the issues in M is assumed to be:
Mm
mnmnmn OxsU || (2).
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Equation (2) shows that an actor’s utility is assumed to be (i) additive over all issues, and
(ii) decreasing linearly in the absolute distance of the outcome from the actor’s position,
with the salience of the issue determining the rate of decrease.
***
Figure 1
***
In the PEM, two actors are assumed to be able to exchange on a pair of issues only
if they have positions on opposing sides of the expected outcomes on both issues. With
two issues, and their expected outcomes, we can partition the set of actors into four
groups, A, B, C, and D, as is shown in Figure 1. Members of group A are on the left
hand side of the expected outcomes on the interval scales of both issues, those of group
D are on the right side of both issues. Members of group B are on the left hand side of
the expected outcome on issue 1, and on the right hand side on issue 2, with members
of C having opposite positions. From this it immediately follows that according to the
PEM members of A can only exchange with members of D, and members of B can only
exchange with members of C.
Exchange between two actors is mutually profitable only if the actors have different
relative saliences for the two relevant issues. Without loss of generality, consider two
actors, i and j, and two issues, 1 and 2. Assume i and j are on opposite sides of the
expected outcomes of issues 1 and 2. Denote the changes in the expected outcomes on
issues 1 and 2, caused by position shifts of actors i and j, as 1 and 2 , respectively.
Then i and j can only exchange profitably if either (3) or (4) is true.
2
1
1
2
2
1
j
j
i
i
s
s
s
s
(3)
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2
1
1
2
2
1
j
j
i
i
s
s
s
s
(4)
Equations (3) and (4) show that exchange is only mutually profitable if the exchange
ratio (1
2
) is in between the relative saliences (see Dijkstra et al. 2008 for a proof).
If (3) holds, i shifts his position on issue 1 in the direction of j, whereas j shifts his
position on issue 2 in the direction of i. Issue 1 is then called the supply issue of i and the
demand issue of j, whereas issue 2 is the demand issue of i and the supply issue of j. If
(4) holds, issue 2 is the supply issue of i and issue 1 is the supply issue of j. The latter
situation is depicted in Figure 1.
In the PEM all possible bilateral exchanges are determined for each pair of issues
from M. For each of these exchanges, position shifts are determined such that the utility
gains of the exchange partners are equal and at a maximum. The exchanges are then
listed in the order of the size of the utility gains. The exchange with the highest utility
gains is then executed, and all other possible exchanges involving one or both of the
partners of this first exchange, and in which these partners use the same supply issues
as in this first exchange, are deleted from the list. This process is then repeated with the
remaining exchanges on the list, until the list is empty. Then, (1) is applied to all issues
with the new actor positions, and these are the predictions of the PEM. See Stokman
and Van Oosten (1994) for details about this algorithm.
Thus, the PEM is based on the assumption that pairs of actors engage in bilateral
position exchanges on pairs of issues when such an exchange is mutually profitable for
the two actors involved. However, equations (1) and (2) immediately show that an
exchange between any pair of actors will affect the utility of all actors, not just of the
actors involved in the exchange. To see this, note that position shifts of the two
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exchanging actors will affect the expected outcome, according to (1). According to (2)
this will affect the utility of all actors, whether or not they are a partner to the exchange.
Such utility effects for actors not directly (i.e., as ‘exchange partners’) involved in the
bilateral exchange are called externalities, and can be either positive or negative. In the
case of positive externalities, bilateral deals cause decision outcomes to become better
for other actors not involved in the deal, whereas in the case of negative externalities,
decision outcomes become worse for actors not involved in the deal. Moreover, one
particular bilateral deal can have positive externalities for some actors and negative
externalities for others.
Once more consider Figure 1. It shows how the position exchange of actors i and j
causes the expected outcome (according to equation (1)) to shift away from the
positions of actors in group C on both issues. Thus, actors in this group experience
negative externalities from the exchange between i and j. Actors in group B, however,
experience positive externalities from this exchange, since the expected outcomes on
both issues shift toward their positions. For actors in groups A and D externalities can be
either positive or negative, depending on how their relative saliences compare to the
exchange rate that i and j agree on (see equations (3) and (4)). See Van Assen et al.
(2003) and Dijkstra et al. (2008) for more elaborate discussions of externalities in
collective decision making.
What is central to the current paper is that the PEM models decision making as a
process of bilateral exchanges on pairs of issues, without taking into account the positive
or negative externalities for other actors. Thus, when actors in the PEM contemplate the
desirability of a potential exchange, they are assumed to only consider their own
immediate utility gains from this exchange, and to disregard any effects for others.
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Now consider a decision situation in which the negative externalities caused by
bilateral exchanges are so large that, when all bilateral exchanges are consumated
according to the PEM, they outweigh the benefits of exchange for any single actor. In
this case, all actors in the decision situation would have been better off if no bilateral
exchanges had taken place. Thus, the situation resulting from the bilateral exchange
procedure of the PEM is Pareto inferior to the situation in which no bilateral exchanges
take place. However, from the perspective of any single actor in the PEM, completing a
profitable exchange is always better than not completing it, regardless of what others do.
Hence, in a situation with large negative externalities for all actors, we argue that actor
behaviour according to the PEM resembles the strategy profile where all players defect
in the PD.
2.3. The EEM and Cooperation
In the EEM externalities are taken into account. Dijkstra et al. (2008) introduce two
variants of the EEM. In the first (labeled EEMb) actors block exchanges that cause
negative externalities for fellow-members of their ‘group’. The groups considered by the
EEMb are the groups labeled A through D in Figure 1. In the second version (labeled
EEMb&w) actors block all exchanges that entail any negative externality for any actor, no
matter that actor’s group membership.
In the current paper we only consider the latter version of the EEM and simply refer
to it as the EEM. We make this choice because it best reflects the generality of the norm
of cooperation we try to understand. As indicated in the introduction, this norm is
assumed to imply that actors generally try to prevent negative externalities for any other
actor, not just for actors with whom they happen to agree on the two issues under
consideration (i.e., their fellow members of group A, B, C or D).
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Since the EEM assumes all actors try to prevent negative externalities for others, it
does not explicitly model position shifts of individual actors, but directly models the shifts
in the expected outcomes of the pair of issues. In essence, the EEM assumes that on
each pair of issues actors collectively look for changes in the expected outcomes such
that the utility of no actor is decreased and the utility of at least one actor is enhanced. In
other words, the EEM looks for Pareto improvements with respect to the initially
expected outcome. If more than one such improvement exists, actors choose the
outcome specified by the Generalized Nash Bargaining Solution (GNBS; see Chae and
Heidhues 2004).
In more detail, the EEM model works as follows. In the EEM, equation (1) is
computed for each issue and is taken as this issue’s initially expected outcome (just as
the PEM does). Then, for all possible pairs of issues, alternative outcomes are sought
that constitute a Pareto efficient outcome for all actors. That is, in the EEM only outcome
shifts on pairs of issues are considered that for all actors yield at least as much utility
(according to equation (2)) as the initially expected outcome. In addition, the alternative
outcome cannot be improved for any actor by further shifting on one or both of the
issues, without decreasing the utility of another actor. Thus, in the EEM negative
externalities (compared to the initially expected outcome) for any actor is prohibited, but
positive externalities are allowed.
If there is more than one Pareto efficient alternative to the initially expected outcome
the GNBS is used to find a single prediction. Letting denote the pair ),( 21 of shifts
on issues 1 and 2,respectively, the GNBS is the value of that maximizes the weighted
product of utility gains. More formally,
Nn
r
nP
n)](U[Max (5).
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The EEM takes as weights the capability of the actors relative to the summed capability
of all the actors. Formally, the weight assigned to actor n’s utility in (5), nr , is:
Ni
i
nn
c
cr (6),
If there exist no Pareto improvements with respect to the initially expected outcomes,
the EEM simply identifies the initially expected outcomes as it’s solution.
With more than 2 issues involved, the EEM implements the following voting
procedure.
(i) Compute (1) for all issues
(ii) Compute the prediction of the EEM for all M(M-1) exchange possibilities
(iii) Actors vote for their most preferred exchange opportunity
(iv) Select from the list of (remaining) issue pairs the one with the highest weighted
votes
(v) Eliminate all issue pairs from the list containing one of the two issues on which
the exchange in (iv) took place
(vi) If the list is not empty after (v), go back to step (iv)
In step (iii) the EEM assumes that each actor votes for that exchange opportunity in the
list that yields him the largest positive utility change. Hence the EEM excludes strategic
voting. In EEM’s voting procedure, an actor’s vote is weighted by the capability of the
actor, relative to the sum of capabilities of all actors in N. The exchange with the highest
sum of weighted votes is executed first. Actors vote only once, at the beginning of the
process. If there is a tie, one issue pair is selected at random. In the data analyzed in
20
this paper, such ties did not occur. See Dijkstra et al. (2008) for the details of this
algorithm.
Now consider again the decision situation in which the negative externalities caused
by bilateral exchanges are so large that, when all bilateral exchanges are consumated
according to the PEM, they outweigh the benefits of exchange for any single actor. We
previously saw how actor behaviour according to the PEM can then be conceived as the
PD strategy profile ‘all defect’, with the associated Pareto inefficient outcome. When
applying the EEM to this situation, no negative externalities exist, and outcomes are
Pareto efficient. Thus, when the actors choose to make their decisions according to the
EEM process, rather than according to the PEM process, this can be interpreted as
actors playing the PD strategy profile ‘all cooperate’. Note that, even if all other actors
choose to ‘comply with the EEM’ (i.e., cooperate), bilateral exchanges according to the
PEM will in general still be possible and will moreover by profitable for the two actors
involved. In other words, when all others cooperate, any pair of potential exchange
partners experiences the temptation to defect.
2.4. Hypotheses
In the previous sections we have theoretically constrained the strategy spaces of the
EU member states to only include exchange processes (either bilateral or group-wise)
concerning pairs of issues. Subsequently we have argued that, in a situation where
negative externalities of bilateral exchanges are very large, the PEM and the EEM
predictions can be regarded as the outcomes of strategy profiles where all actors defect
or cooperate, respectively. With these models in hand we can make requirement (i) of
Cooperation Theory, that the interaction situations have a PD-like payoff structure, more
precise.
21
First, observe that all the individual bilateral exchanges modeled by the PEM are
mutually profitable for the two exchanging actors. Thus, as required by a PD payoff
structure, completing the available exchanges is always the best thing an actor can do in
the short run, regardless of what the other actors do (i.e. whether or not they comply with
the norm of cooperation). However, when all actors follow their immediate self-interests
(i.e., exchange according to the PEM) this might entail large negative externalities for all
actors. The decision situation would resemble a PD payoff structure if these negative
externalities would override the utility gains the actors reap from their own exchanges
and possibly from positive externalities, compared to the situation where actors
abstained from pursuing their short-term private interests. The latter situation (adherence
to the norm of cooperation) is modeled through the EEM. Thus, a PD-like payoff
structure would exist if all actors would be better off (i.e., receive higher utilities
according to equation (2)) when the decision making process followed the EEM
procedure than when it followed the PEM procedure. Indeed, using the same data set
that we use in the current paper and comparing the outcomes predicted by the PEM to
the initially expected outcome of equation (1), Arregui et al. (2006) find that for all
proposals of the European Commission, and summed over all member states negative
externalities of exchange outweigh the sum of gains from exchange and positive
externalities.
In various studies discussed in earlier sections of this paper, the research presented
there signaled the existence of a norm of cooperation in EU decision making. From the
framework of Cooperation Theory outlined above, we formulate the following
expectations and hypotheses:
22
First of all, one would expect the EEM to better predict the actual outcomes than the
PEM. Indeed, Dijkstra et al. (2008; Table 1) found that the EEM predictions have higher
correlations with the actual outcomes than the PEM predictions.
Second, we expect the number of actors preferring the PEM predictions to the EEM
predictions to be low, i.e., we expect the decision situation to have a PD payoff structure
for almost all actors. Moreover, as the number of issues in the set M increases, the
number of exchange possibilities naturally increases as well. Thus, the potential gains
from exchange will generally increase in the number of issues considered. However, if
negative externalities are as important as we argue they are, they should grow at a
faster rate than the gains from exchange. Therefore, we hypothesize that the number of
actors preferring the PEM predictions to the EEM predictions decreases as the number
of issues considered increases.
Third, if the decision situation is characterized by a PD payoff structure, there should
be no actors that consistently prefer the PEM predictions to the EEM predictions. Note
how according to Cooperation Theory, the sustainability of cooperation hinges on the
fact that actors who pursue their short-run self-interest can be punished in the future.
However, if an actor consistently prefers the PEM over the EEM, punishment in this
sense is not possible. To make precise predictions of how strong this threat of
punishment would have to be to induce an actor to cooperate requires information about
actors’ discount rates (i.e., the degree to which they care about future outcomes).
However, apart from the obvious difficulties of estimating discount rates for countries, we
simply do not have this information available in the data set. Therefore, we formulate the
weaker hypothesis that on average no actor prefers the PEM over the EEM more
frequently than the other actors do. Thus, no actor is significantly less vulnerable than
others to the threat of refused cooperation
23
Fourth, exchanging according to PEM frequently yields negative externalities for
other actors, when compared to the EEM outcome that does not allow any negative
externalities. However, individual exchanges that are part of PEM are still mutually
profitable for the exchange partners. Therefore, this situation resembles a repeated
Prisoner’s Dilemma (PD), where bilateral exchanges represent defection. Cooperation
can emerge in such a situation when the ‘shadow of the future’ is sufficiently dark
(Axelrod 1984). This being the case in EU decision making can explain the relatively
poor performance of PEM in this dataset (Dijkstra et al, 2008). However, there might be
instances even within this dataset (i.e., commission proposals) where some actors gain
under PEM, i.e., the negative externalities do not override the gains from private
exchanges. For these actors these proposals are not a PD. In fact, of the 49 proposals in
our dataset, only 1 is a PD in the sense that all actors lose under PEM. Thus, we expect
that in general PEM will predict better when there are more ‘winners’.
Fifth, powerful actors are interesting exchange partners for others, since they have a
lot to offer (i.e., they can affect the expected outcome more strongly than weaker actors).
Hence, the assumption is that they are sought as exchange partners more than weaker
actors are. Since the same reasoning holds for themselves, they will mainly seek each
other as partners. In the EU data set examined here, France, Germany, Italy and the
UK are the four most powerful actors: they all have the same maximum power index.
Thus, these four powerful actors as a group can strongly affect the extent to which
exchanges take place. Therefore we expect that whether or not they gain or lose under
PEM (compared to the EEM) has a significant impact on the mean absolute prediction
error (MAE) of PEM.
The first hypothesis was already tested in Dijkstra et al. (2008). The remaining
four hypotheses will be investigated in the following sections of the paper. Note how
24
these expectations derived from Cooperation Theory amount to specifying conditions
under which we expect one model (the EEM) to do better than another (the PEM).
3 Research Design
This research uses a large-scale EU data set which was originally collected by Thomson
et al. (2006). This EU data is uniquely suited to this research as it covers a wide range of
EU policy sectors and is an excellent representative sample of EU Commission
proposals. For the selection of these Commission proposals and the collection of model-
based data, 125 expert-based interviews were conducted and these experts were
selected on the basis of their own in-depth knowledge of the proposal negotiations
(Thomson et al. 2006). The Thomson et al. (2006) data set is comprised of 162
controversial issues, which originate from 66 European Commission proposals, and
which were discussed by the Council in the period January 1999-December 2000. The
experts selected these Commission proposals on the basis of three criteria: legislative
procedure or decision rule to which they were subject, the time frame within which they
were introduced and debated, and their political importance. Furthermore, with regard to
each of these Commission proposals, experts were asked to identify those issues to
which attached a considerable level of controversy (Thomson et al., 2006). For a more
detailed discussion of this data collection process, the reader is referred to the original
research by Thomson et al. (2006). Here follows a discussion of the data used for this
present research.
3.1 Data
25
For this research we identified, from the original Thomson et al. (2006) EU data set,
a total of 49 suitable Commission proposals, which contained 137 issues, We required
that the Commission proposals selected should contain at least 2 issues, since the EEM
and PEM models conceive of decision making as a process of exchanges or outcome
shifts on pairs of issues. In this research the maximum number of issues identified per
proposal was six issues. Moreover each of these issues was defined on a uni-
dimensional, interval scale where the preferred policy outcome of each actor for that
issue could be placed. Overall, Commission proposals, which we selected for this
research, are representative of the different EU legislative procedures relevant for the
time period 1999-2000. In this respect, these proposals differ in terms of whether they
were subject to either the Consultation or Co-decision rule and whether Unanimity or
Qualified majority was the relevant formal decision rule for the Council of Ministers. As in
earlier research using this data (Thomson et al., 2006; Dijkstra et al., 2008), the decision
models are applied to each of the Commission proposals separately and it is assumed
that the collective decision negotiations are restricted to issues within each proposal, so
that no exchanges are permitted to take place across issues from different proposal
sets. This reflects the actual EU decision process where each of the Commission
proposals was dealt with at different points in time (Dijkstra et al. 2008).
The actors involved in the decision process include the members of the Council,
which at the time included fifteen Member States, as well as the European Commission
and the European Parliament (Thomson and Stokman, 2006). All these actors were
treated as unitary actors for the purposes of the research.
Each actor’s preferred policy outcome is defined as the actor’s position on that issue.
The actors’ issue positions were standardized so that positions at 0 and 100 defined as
the most extreme positions favoured by any of the actors. In terms of missing data, on
26
average 15.61 of the 17 actors took positions on each of the 162 issues, where 33 of
these issues were dichotomous (i.e. only two possible positions) (Dijkstra et al, 2008).
The data set for this research includes a measure of each actor’s salience for each
issue. The measure of salience of an actor for an issue is the level of importance that the
actor attaches to the issue. An actor’s salience is ranked as a score between 0 and 100.
If an actor’s salience for an issue is rated at 0, this means that the issue is of no
importance at all, whereas a score of a 100 implies that the issue is of the highest
importance to that actor. The capability of an actor is a measure of potential resources
an actor can exert during the negotiations of the issues in a proposal. In this research,
the actors’ capabilities were estimated using the Shapley Shubik Index (Shapley and
Shubik, 1954).
Furthermore for the purposes of later examining the impact of the decision context
on the EU decision making, we describe here the data in terms of (i) the number of
proposals that have 2, 3, …, 6 issues; (ii) the number of proposals in different policy
sectors; (iii) the number of proposals under different decision procedures. In Table 1 we
see that almost half (49%) of the proposals had just two issues and another
approximately 32% of the proposals had no more than 3 issues. This suggests that the
complexity of the EU decision proposals in this data set, as measured by the number of
issues in the proposal set, is modest with just under 19% of the proposals having 4 or
more issues.
Table 1: Number of proposals having 2 -6 issues:
27
No of
Issues
No. of Proposals Percentage
2 24 49,0
3 16 32,7
4 5 10,2
5 3 6,1
6 1 2,0
Total 49 100,0
In Table 2, we examine the proportion of proposals, which belong to policy
sectors and which may be described as more or less “integrated”. Following
Lane and Mattila’s earlier research (1998), in our research we defined Agriculture
and the Internal Market as “more integrated” and all other policy sectors as “less
integrated”. Mattila and Lane’s ‘roll-call of votes research’ proposes that the
deeper the integration of the EU policy sector, the more difficult decision making
by unanimity (2001).
Table 2: Number of proposals in policy sectors
Level of
Integration
Frequency of
proposals
Percent
More (,00) 23 46,9
28
Less (1,00) 26 53,1
Total 49 100,0
In Table 3, while the spread of proposals is reasonably distributed across the
different procedures, Consultation (CNS) and Co-decision (COD), the majority of the
proposals under both these procedures use the Qualified Majority decision rule (QMV)
compared with the Unanimity decision rule (Unam).
Table 3: Decision Rule and Procedure
Decision Rule and
Procedure
Frequency of
Proposals
Percent
QMV CNS 20 40,8
Unam CNS 9 18,4
QMV COD 16 32,7
Unam COD 4 8,2
Total
49
100,0
29
4. Results
Now let us turn to the model based research results. In this research, we argue that the
norm of cooperation between member states in the European Union is sustainable,
because the configuration of members’ positions and interests gives rise to a decision
situation that resembles a repeated Prisoner’s Dilemna (PD). It is rational for individual
member states to comply since not doing so would mean large forgone gains in the
future. We have elaborated this argument through five specific hypotheses and we test
these by comparing the results of two contrasting collective decision model scenarios.
First, the PEM which captures an ‘unfettered’ exchange decision mechanism and
second, the EEM which presents a more ‘restricted’ collective decision mechanism
taking account of the potential for negative externalities.
To evaluate our expectations, we first determined the predictions of the PEM and the
EEM for each issue. Subsequently, we computed for each member state the utility of the
predictions of both of these models, according to equation (2) presented earlier 3. We
determined, for each proposal separately, which of the member states preferred the
PEM prediction to the EEM prediction, and dubbed them ‘winners’. Thus, winners are
member states that have higher utility under the PEM predictions than under the EEM
predictions. Two observations can be made at this point. First, note how for member
states that are not winners (i.e., those that prefer the EEM to the PEM), the proposal has
a PD-like payoff structure: although individual exchanges according to the PEM are
profitable for them, they would be worse off if all actors exchanged according to the
3 We computed for each member state the utility of the predictions of both of these models, according to equation (2).
We did so for each proposal separately, so the symbol M in equation (2) refers to the set of issues belonging to a
particular proposal.
30
PEM. Second, observe how the set of winners may be different for different proposals
(indeed, this is one of our hypotheses).
In this research we found that the overall mean proportion of winners is 0.41. If one
assumes no missing data, this is calculated as an average of 6.56 out of 16 actors being
a winner, (i.e., preferring the PEM prediction to the EEM prediction). This seems to be
quite a large number and runs counter to our expectation that the number of winners
should generally be low. However as we saw earlier in Table 1, there are a reasonably
high proportion of the proposals under analysis (49%) which have only 2 issues, which
suggests that the tendency for higher than expected number of winners is enhanced in
these specific circumstances.
Next, to evaluate our expectation, that the overall number of winners is generally low
and that it is decreasing as the number of issues per proposal increases, we apply a
standard linear regression modeling approach. In our first regression model, the
dependent variable is identified as the proportion of member states per proposal that are
classified as winners. Member states having missing data on at least one of the issues in
a proposal were excluded from the analysis. We define the independent variable as the
number of issues per proposal minus 2. Since only proposals that contain at least 2
issues are included in the analysis, this variable has values from 0 to 4.
In the regression model, we find a constant coefficient of 0.472 (t47 = 11.45, p < 001)
and an expected negative coefficient for the number of issues per proposal of -0.073 (t47
= -2.25, p = 0.029). The high and significant constant indicates that there are sets of
issues (proposals) in which not every actor is strictly worse off in the situation where
everybody exchanges according to PEM. However, the significantly negative value of
the parameter for the independent variable (i.e. which counts the number of issues per
31
proposal) confirms our hypothesis that individually rational exchanges (modelled by
PEM) very quickly become collectively irrational, due to negative externalities.
These regression results indicate that, in a proposal containing only 2 issues,
the proportion of winners is about 0.47. Assuming no missing data, this would
mean that, on average, 7.55 out of 16 actors are classified as winners in
proposals containing 2 issues. However, for each additional issue, the proportion
of winners decreases by approximately 0.07. Again assuming no missing data,
this would mean that for each additional issue the number of winners decreases
by 1.17.
Extending this argument, in a second regression model, we found that, in addition,
the predictive performance of the PEM (as measured by the model’s mean absolute
error per proposal) gets worse as the number of issues increases. For instance, these
findings show that, for a proposal containing 2 issues, the mean absolute error of the
PEM is 19.77. However, this number already increases by 3.98 (20% increase) with the
first additional issue. Moreover, in this second regression model, we sought to examine
whether some aspects of the EU decision context, such as the decision procedure or the
type of policy sector, might facilitate higher or lower levels of cooperation across actors
in the negotiations for different proposals. In this regression model, we controlled for the
decision procedure (see Table 3) and type of policy sector (see Table 2) but neither of
these control variables had a significant effect.
To test our hypothesis that no actor prefers PEM over the EEM more frequently than
other actors do, we examine whether, on average, no actor is a winner more frequently
than the other actors. In order to do this, we determined the ‘winner distribution’ (i.e., the
count of how many times each actor is a winner across all proposals). The results of our
analysis of the “winner distribution” are presented in Table 4 below. The results
32
presented in Table 4 show that statistically all actors are a winner in 16.25 of the 49
proposals and that the winner distribution does not significantly depart from uniformity
(Chi-square = 15.82, df = 15, p = 0.3944). This concurs with the earlier research findings
of Thomson et al. (2006) which points to the instability of actor alignments in the Council
of Ministers.
Table 4: Winner Distribution across all actors
Actor Aus Belg Den
Com/EP Fin Fra Ger Gre Ire Ita Lux NL Por Sp Swe
UK Total
Obs. 11 12 12 16 14 24 17 16 16 15 12 21 14 18 17 25
260
Exp. 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25
260
34
This result taken together with the Pareto inefficiency of the PEM helps explain the
existence of a cooperative norm in such a decision setting, where (i) unfettered bilateral
compromises (in the form of exchanges) have very large social costs, and (ii) there is no
consistent group of actors that would benefit from insisting on bilateral compromise as the
dominant mode of decision making. In other words, the costs that would result from bilateral
agreements would be borne by all actors alike (or ‘in turn’). Moreover these results imply that
the shadow of the future is a real threat to these actors. The losses (from negative
externalities) are generally (when everybody exchanges) larger than gains (from exchange
and positive externalities), and no actor can evade future punishment, since no actor is
consistently a ‘winner’. In this regard, Arregui et al. (2006) have shown that, using the same
EU data set as in this present research, the losses due to negative externalities are indeed
larger than gains, summed over all actors and all proposals. Therefore, Cooperation Theory
offers a plausible explanation of the observed EU norm of cooperation.
There are now remaining two hypotheses be tested. If actors exchange according to
PEM, then this yields negative externalities for other actors and the more actors that
experience negative externalities, the less attractive the PEM for the actors overall.
However, in some circumstances and for some actors, the negative externalities do not
outweigh the gains from private exchanges. Hence, we turn to our fourth hypothesis which
proposes that in general PEM will predict better when there are more winners. Related to
this, is the fifth hypothesis which proposes that the four most powerful actors (i.e. France,
Germany, Italy and the UK) can strongly affect the extent to which exchanges with potential
negative externalities can occur in the collective decision process. In other words, we expect
that whether or not they gain or lose under PEM (compared to the EEM) has a significant
impact on the mean absolute prediction error (MAE) of PEM.
Moving to test these final two hypotheses, the first step was to examine which actors stand
to gain or lose from implementing PEM versus EEM. In order to do this, we compute, for
each Commission proposal, the difference of payoffs under PEM and under EEM and then
35
correlate the absolute error of PEM and the Root Mean Square Error (RMSE) of PEM with
the utility gains per country. This yields the result that for every country (i.e. actor) except
Greece, there is the expected negative correlation: the higher a country's gain (or the lower
its loss) the lower PEM's prediction error. Moreover, the results show that the utility gains
under PEM of France and Germany had the highest correlation with the prediction error of
PEM. However, our analysis showed that none of these correlations were significant and
which may reflect the low number of cases (i.e. 49 Commission proposals).
To test the fifth hypothesis, we examined the structure of the externalities and
identified which countries bear 'similar consequences' from PEM. Applying a factor analysis
approach to the spread of utility gains per proposal of the countries, the analysis revealed 4
components4. The results presented in Table 5 show that the first component is by far the
largest and contains 11 actors: Austria, Belgium, the European Commission, Finland,
France, Greece, Ireland, Italy, Luxembourg, Portugal and Spain. The second and third
components comprise Germany and the Netherlands and then Denmark and Sweden
respectively. The fourth component has just one actor, the UK. Notably, three of the four
most powerful actors (i.e France, Germany and the UK) occupy different components which
suggests that there is very limited overlap in the proposals wherein even pairs of the most
powerful four actors can be ‘winners’ under PEM (i.e. PEM is profitable for them compared
to EEM).
Table 5: Factor Analyses on Actor Utility Gains per Proposal
Rotated Component Matrixa
Component
1 2 3 4
Austria_mean ,858 -,123 ,390 -,180
4 Each of the four components had a eigenvalue greater than 1 and after rotation, each country was assigned to
the component where it had the highest loading.
36
Belgium_mean ,964 -,067 ,006 ,041
Commision_mean ,719 -,111 -,403 -,312
Denmark_mean -,311 ,520 ,644 -,105
Finland_mean ,927 ,084 ,167 -,062
France_mean ,831 -,386 ,069 -,020
Germany_mean ,082 ,876 -,125 -,205
Greece_mean ,876 -,233 -,152 ,180
Ireland_mean ,912 ,234 -,143 -,147
Italy_mean ,905 ,221 -,169 -,135
Luxembourg_mean ,969 ,033 -,099 ,139
NL_mean ,111 ,814 ,350 ,184
Portugal_mean ,907 ,149 -,331 ,140
Spain_mean ,886 ,267 -,119 -,249
Sweden_mean ,004 ,011 ,993 -,005
UK_mean -,053 -,065 -,045 ,989
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
For example, when we examined the proposals wherein France and Germany are both
winners under PEM (compared to EEM), the analysis showed that there are only 9 proposals
in which both these countries gain from PEM. Furthermore, when we compared the
prediction error on these 9 issues with the remaining available issues5, the results, presented
5 36 issues are used for this comparison and 4 issues were excluded from this analysis since either
France or Germany had missing values on these issues.
37
in Table 7, showed that indeed the average prediction error of PEM is lower in these 9
cases, and is marginally significant.
Table 7: T-Test with France and Germany both winning
Fr_Gr_both
_win N Mean
Std.
Deviation Std. Error Mean
PEM_RMSE 1,00 9 23,4678 17,40127 5,80042
,00 36 29,2401 12,82581 2,13764
PEM_Absolute_Error_mean 1,00 9 19,1364 13,54816 4,51605
,00 36 25,9505 11,26914 1,87819
Extending this to an analysis to the mean gain of all (non-missing) actors for each proposal,
we found a negative, but not significant (one-sided p-value 0.1055), correlation with the
prediction error of PEM. Moreover, for each individual country, with the exception of Greece,
the mean gain under PEM is also negatively related to MAE under PEM. However no single
country alone significantly affects the MAE of PEM.
Using regression modelling, and controlling for the decision procedure as well as the
EU policy area, we systematically explored the effect of different independent variables on
the MAE of PEM. The results showed that the overall number of winners and the overall
mean gain are each negatively related to the MAE of PEM but neither has a significant
impact. However, when we focused our regression analysis on the impact of different
combinations of the four most powerful actors (i.e France, Germany, Italy and the UK), our
38
results showed that all together as a single group, the four most powerful actors do
significantly (i.e. p=0.033) and inversely affect the MAE of PEM, as expected. We also split
up the group of these four powerful actors in pairs or in groups of three to check whether the
mean gain under PEM of these subgroups negatively and significantly affects the MAE of
PEM. The results showed that this was true for the following pairs and triplets: (France,
Germany p= 0.039), (France, UK p=0.026), (Germany, UK p=0.023), (France, Germany, UK
p= 0.011), (France, Italy, UK p=0.051), (Germany, Italy, UK p=0.043). These results suggest
that there is sufficient, albeit indirect, evidence that whenever PEM results in large negative
externalities for the four powerful countries, countries abstain from bilateral exchanges.
5 Discussion and Conclusion
This paper addresses the research problem of why Member States’ bargaining strategies
seem guided by a strong norm of co-operation when engaged in collective decision making
in the EU Council of Ministers. Given their very diverse sectoral and political interests, as
well as contrasting resources, why do member states behave in this co-operative manner?
The present research makes use of a recent and very comprehensive data set of EU policy
decisions in the Council of Ministers. In this research we draw on Co-operation theory and
apply the insights from the Prisoner’s Dilemna Game to identify how the configuration of
members’ positions and interests makes it rational for individual member states to comply
with the norm of cooperation, since not doing so would mean large forgone gains in the
future. We apply two alternative collective decision models, PEM and EEM, to identify the
conditions for ‘unfettered’ or ‘restricted’ exchange bargaining in collective decision making in
the EU Council of Ministers.
The theory and research results presented in this paper suggest that the predictive
power of PEM should vary inversely with the average proportion of ‘winners’ in the data set.
More winners implies that for actors the shadow of the future becomes smaller, since there
will be fewer occasions on which they will actually be in a PD payoff structure. The results
39
presented in this paper support our hypotheses and contribute significant research insights
to the analysis of the core mechanism by which co-operative bargaining strategy dominates
EU decision processes in the Council of Ministers. Moreover the research presented here
yields a more general hypothesis concerning the conditions determining the predictive power
of the PEM and the EEM. Future forthcoming research in this field will apply this analysis to
a much larger and more varied decision data set to test these general hypotheses.
40
Figures and Tables
Figure 1. Exchange between actors Ai and Dj on issues 1 and 2. 1O and 2O indicate
the expected outcomes on issues 1 and 2, respectively, before the exchange. A, B, C, and D
indicate groups of actors
Issue 1
A
B
C
D
O1
i j
Issue 2
A
C
B
D
O2
i j
41
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