Deliberation and Disagreement: Problem-Solving, Prediction, and Positive Dissensus
Hélène Landemore
Yale University
[email protected]
Scott E. Page
University of Michigan and Santa Fe Institute
[email protected]
Paper prepared for presentation at the Conference “Epistemic Democracy in Practice,”
Yale University, New Haven, CT, October 20-22, 2011
THIS IS A DRAFT. PLEASE DO NOT QUOTE WITHOUT PERMISSION
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Deliberation and Disagreement: Problem-Solving, Prediction, and Positive Dissensus
Hélène Landemore and Scott Page
Abstract: Consensus plays an ambiguous role in deliberative democracy. While it formed
the horizon of early versions of deliberative democracy, it is currently under attack as an
empirically unachievable goal and an undesirable normative ideal. We argue that this
move away from consensus has so far generated more confusion than clarity and
threatens to dissolve the normative appeal of deliberative democracy compared to the
aggregative democracy model it was supposed to improve on. The paper further argues
that, from an epistemic point of view, the normative appeal of consensus versus
disagreement will vary depending on the deliberative context. We distinguish between
two such contexts: problem solving and prediction. When it comes to problem solving,
the Habermasian ideal of rational consensus retains a strong normative appeal as a
stopping rule for deliberation. When it comes to predictive tasks, however, the search for
a consensus can be epistemically counter-productive. Instead deliberators should seek to
achieve or preserve a form of deliberative disagreement we call “positive dissensus,”
which paves the way for more accurate aggregated predictions.
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Deliberation and Disagreement: Problem-Solving, Prediction, and Positive Dissensus
Hélène Landemore and Scott Page
Deliberative democracy has long promoted the quest for a consensus on, if not truth per
se, at least the “better argument,” to which the intrinsic force of the latter is supposed to
guide reasonable participants in a deliberative process. Habermasians have thus
famously promoted the search of a rational consensus as the goal of an ideal deliberative
procedure, characterized as “the ideal speech situation.”
Consensus, however, is now the object of some disillusionment and suspicion,
including among deliberative democrats. The main criticism is that consensus is too
utopian a goal to be worth pursuing. Others argue that building the goal of a consensus
into the definition of deliberative democracy is counterproductive, empirically creating a
pressure to conform conducive to group think, polarization, and generally detrimental to
both the epistemic and procedural properties of deliberation. Critics of deliberative
democracy finally—from liberal pluralists to agonistic pluralists and difference
democrats—more radically argue against the artificial and even violent pacification of
politics that a search for a rational consensus inevitably entails in their view.
In this paper, we want to raise some skepticism towards the general goal of a
rational consensus favored by deliberative democrats, although not on the grounds
advanced by existing critics. In particular, while we think that there is something to the
celebration of pluralism and disagreement advanced by critics of rational consensus and,
specifically, agonistic pluralists and difference democrats, we will partially side with
their conclusions as to the value of disagreement from within the framework of
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deliberative democracy and on the basis of epistemic arguments that should, in fact,
appeal to more traditional consensus-seekers.
It is well known that the consensus following some deliberations need not indicate
an epistemic improvement (e.g., polarization effect). This paper argues that conversely,
the fact that a group ends up disagreeing more after deliberation than prior to it may, in
some cases, mark a collective epistemic improvement and pave the way for a better
collective decision.
Our contribution in this paper is four-fold. First we argue that something has been
lost in the deliberative democrats’ current move away from consensus and towards
disagreement, absent any clear idea of what stopping rule should replace rational
unanimity as the normative horizon of democratic deliberation. Second, we introduce
what we think is a necessary and rarely made distinction between two main tasks that
deliberators usually face: problem solving and predictive (also evaluative) tasks. Third,
we show that while the Habermasian ideal of a rational consensus on an outcome retains
normative appeal as a stopping rule to deliberation in the context of problem solving, it is
generally less attractive, and in fact often counterproductive, for predictive/evaluative
tasks. When it comes to making predictions, deliberative groups are better off fostering
more of a certain type of disagreement rather than more consensus or convergence on an
outcome. Our fourth contribution consists in distinguishing, within the category of
“deliberative disagreement” (Gutmann and Thompson 1996) between the epistemically
fruitful kind—we shall call it “positive dissensus”—and the epistemically useless or even
harmful kind—“negative dissensus.”
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Although we direct our claims to an audience of political scientists and
democratic theorists, the distinctions made in this paper will hopefully prove useful
outside of political sciences. In particular, although we do not enter here the question of
the nature of political questions and their possible differences with moral, scientific or
other questions, our belief is that the differences are not such that they would preclude a
generalization of our claims from one domain to the others. We see for example obvious
parallels between the pluralism of the scientific community, for example in its effort to
make predictions about climate change (e.g., Parker 2006), and the pluralism of the
political community of a given country in its assessment of what is most conducive to the
common good for that country, for example on the question of budget deficit. Both
communities attempt to make predictions in a situation characterized by vast uncertainty,
both tasks involve factual and normative judgments, and in both cases diverse and
sometimes even incommensurable models are used to support conflicting conclusions.
The first section of this paper assesses the current standing of the deliberative
democracy literature on the question of consensus, diagnosing a general move away from
consensus, specifically rational consensus, under the pressure of empirical results that
challenge both the feasibility and desirability of actual consensus and normative theories
that challenge the value of rational consensus, or any kind of consensus, as a viable ideal
in the first place. The second section introduces the difference between problem solving
and predictive tasks that allows for this more nuanced account of the value of consensus.
and argues that rational consensus retains normative appeal for the task of problem
solving. The third section turns to the predictive context. Here we distinguish between
four degrees of deliberation, for which the search for a consensus turns out to be
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generally less attractive. We conclude by distinguishing two types of deliberative
disagreement: negative dissensus and positive dissensus.
1. Deliberative Democracy and Disagreement: Moving Away from Consensus
The ideal of consensus as unanimous agreement on a decision was initially at the heart of
the ideal of deliberative democracy. In a much quoted passage, Joshua Cohen, one of the
first theorists of deliberative democracy, writes: “Outcomes are democratically legitimate
if and only if they could be the object of free and reasoned agreement among equals”
(Cohen 1989: 22). The meaning of “free and reasoned agreement among equals” in this
passage is generally interpreted as a requirement of unanimity on a particular outcome
(e.g., Bächtiger et al. 2010: 37, Dryzeck and Niemeyer 2006: 635), by contrast in
particular with aggregative approaches to democracy in which a mere majority can
impose their choice, without further reasoning, on a dissenting minority. In another
famous statement considered as a founding principle of deliberative democracy,
Habermas defined the “discourse principle” as the principle according to which:
Only those norms can claim to be valid that meet (or could meet) with the
approval of all affected in their capacity as participants in a practical discourse
(Habermas 1990a, 66, our emphasis).
Here the requirement of explicit approval by all is made the stopping rule for a legitimate
decision or choice. While the discourse principle was initially developed in the context of
Habermas’ general theory of communicative action, which was not specifically designed
for a political context, Habermas explicitly retains consensus as the regulative ideal of
deliberation in the realm of the law and democratic decision-making as well (1996: 179).
Specifically, deliberators are expected, in the ideal speech situation where there are no
6
time and information constraints, to reach an uncoerced agreement on the “better
argument.”1
John Rawls is another author who made consensus a defining stapple of
deliberative democracy, even though the nature of consensus in his own theories is
somewhat ambiguous and the “democratic” nature of deliberation arguably a late
development of his ideas. In A Theory of Justice, the unanimous agreement supposed to
be achieved in the stylized situation of the Original Position—a hypothetical situation in
which deliberators are placed under a veil of ignorance to deliberate and reach a decision
about the best principles of justice—is essentially an artifact of the assumption that all
deliberators are rational clones bound to think alike and converge on the same
conclusions. Their consensual agreement, in other words, is a foregone conclusion. Rawls
later substituted for what was deemed an overly monological definition of unanimous
agreement the more realistic and dialogical ideal of an “overlapping consensus” between
actual (rather than hypothetical) reasonable individuals (1993).2 In its latest formulations,
Rawls’ overlapping consensus arguably came to share even more traits of the
Habermasian ideal of rational consensus (Rawls 1995, see also the analysis by McGann
2006: 161-166).
The centrality of consensus in these early and influential versions of deliberative
democracy explains that both proponents and critics of the theory would converge in
1
Even though Habermas also acknowledges, particularly in later writings, the legitimacy of majority rule
and bargaining (a strategic rather than communicative form of action), he firmly maintains the priority of
rational discourse and consensus on both.
2
The concept of an overlapping consensus is distinct in that it recognizes the irreducible plurality of
incommensurable worldviews characteristic of pluralistic liberal societies and yet assumes that even from
within such comprehensive worldviews, individuals can share enough core values to achieve a stable
common ground of principles, which is less than a rational consensus yet more than a mere modus vivendi
(a temporary compromise between positions). For example, Lockean protestants, Kantians, and Millians
can all endorse the principle of religious toleration on moral grounds, albeit from within irreducible moral
perspectives (Waldron 2004: 95).
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assessing that “The core of the theory [of deliberative democracy]… is that there would
not be any need for an aggregation mechanism, since a rational discussion would tend to
produce unanimous preferences” (Elster 1986: 112). Or, more succinctly: “The goal of
deliberation is to arrive at consensus” (Young 1996: 122). The legacy of this influential
version of deliberative democracy can be seen in the title of real-life experiments in
deliberative democracy, such as the famous Danish “consensus conferences.”
Many criticisms have been raised, however, towards the ideal of consensus and
specifically the Habermasian ideal of “rational consensus.” The first kind of criticism is
that there is too much of a gap between the normative ideal and reality, to the point that
the normative ideal loses relevance. In practice, as evidenced in actual deliberative
experiments (e.g., List, Luskin, Fishkin, McLean 2000/2005), post-deliberative unanimity
is rarely, if ever achieved. Full unanimity seems in any case utterly utopian in the case of
deliberation on a mass-scale (although, there, a preemptive question is whether we can
speak of “deliberation” in the first place). Critics suggest that it would be more useful to
think up other ways to bring deliberation to a close than consensus, let alone rational
consensus.3
From a more theoretical perspective, some people are concerned that the ideal of
consensus is not so much utopian as setting the wrong kind of goal for participants in a
deliberation, in effect creating pressure on participants to reach an agreement. Social
pressures and peoples’ “non-rational conformism” (Mackie 2006: 285) may induce
people to converge on a non-rational, epistemically and even morally inferior consensus
3
This
common criticism is, however, rather weak if one subscribes to the value of regulative ideals even
when those can only be asymptotically reached. However descriptively unrealistic, the goal of getting
closer to consensus might still retain some normative value.
8
(see Asch’s 1951 for dispiriting evidence).4 Other theoretical objections are based on
studies that show how group polarization (a post-deliberative reinforcement of previously
held beliefs in like-minded groups) tends to plague certain groups of deliberators
(Sunstein 2003). These objections are not addressed to rational consensus as a goal per
se, however, but more generally to the practice of democratic deliberation as potentially
inferior, epistemically speaking, to mere judgments aggregation (see also Surowiecki
2004).5
At a deeper normative level yet, many critics object to the very assumption behind
the ideal of consensus, whether rational, overlapping, or otherwise, by denying that
democracy has much to do, if anything at all, with communicative action. In the view of
these critics, pluralism, specifically value pluralism, is a fundamental fact of politics and
cannot be tamed through deliberation. Ian Shapiro thus provocatively argues that politics
is only about “power and interests” so that calling for deliberation and consensus is at
best a rhetorical device masking self-serving strategies, at worst an entirely useless and
utopian endeavor (Shapiro 1999). In a distinct vein, agonistic pluralists or difference
democrats (more radical versions of early liberal pluralists) see in deliberative democracy
itself an instrument of domination imposing the communicative norms of powerful white
men—dispassionate, rational arguments—on what should be an open forum to all forms
of expression, including story-telling, greetings, and non-rational narratives (Mouffe
4
Again,
it is questionable whether the fallibility of human beings is enough to refute the normative validity
of rational consensus per se. The fact that people will tend to achieve an a-rational, conformist consensus
instead of the normatively desirable rational consensus does not make the ideal less desirable. One may
argue that there are institutional ways to remedy for the human tendency to deviate from the goal “rational
consensus” so that the ideal need not necessarily be abandoned.
5
Such claims arguably refute only a certain type of communication, namely discussion among like-minded
people (see Manin 2005 and Landemore and Mercier 2010 for the argument that this kind of exchanges
should not count as deliberation per se). Again, like the objections above, it is in any case unclear what they
prove as to the normative desirability of consensus, specifically rational consensus.
9
1999, Sanders 1997, Young 2001 and 2002). From this point of view, the ideal of rational
consensus is seen as hegemonic and dangerous. Instead, agonistic pluralists and
difference democrats propose to celebrate differences and disagreement.
As a result of these battle-lines between being drawn and redrawn between
advocates of consensus and advocates of disagreement, within and outside deliberative
democracy, the current position of deliberative democracy as a field on the ideal of
consensus is somewhat confusing. For recent commentators, we ought to distinguish two
“camps”: on the one hand, the camp of Type I deliberation theorists, who stick to the
Habermasian rational consensus as a valuable ideal, although they do so at some
normative and empirical costs; and on the other hand, Type II deliberation theorists who
have engaged into a more empirical program rid of rational consensus—a program that
has, however, several blindspots of its own (Bätchiger et al. 2010). What is for sure is
that many deliberative theorists have taken their distance from the ideal of consensus,
particularly rational consensus, and come to re-assess the value of disagreement. From a
necessary evil, disagreement is now touted as an often desirable good (e.g., Gutmann and
Thompson 1996; Thompson 2008: 508). Theorists have come to endorse the full
legitimacy of stopping rules for deliberation that used to be considered as “second-best”
of consensus, such as majority rule or other forms of judgment aggregation, or even the
kind of non communicative and thus non-“rational” (in a Habermasian sense) agreement
reached through bargaining. Type II deliberative democrats led by Jane Mansbridge for
example argue that
when interests and values conflict irreconcilably, deliberation ideally ends not in
consensus but in a clarification of conflict and structuring of disagreement, which
sets the stage for a decision by non-deliberative methods, such as aggregation or
negotiation among cooperative organisms (Mansbridge et al. 2010: 68, our
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emphasis).6
In this approach, the ideal termination of deliberation is not agreement but disagreement,
followed by a non-deliberative decision rule.
The problem with these new developments is that they tend to blur the line
between deliberative democracy and the original aggregative model of democracy on
which deliberative democracy was supposed to be an improvement. If majority rule
rather than consensus is the only realistic way to reach collective decisions, if
disagreement, self-interests, and bargaining are re-introduced in the ideal of deliberative
democracy or, conversely, the principle of deliberation introduced into the practices of
bargaining (as in Mansbridge’s (2009) concept of “deliberative negotiation”), what is,
then, the remaining specificity and advantage of this paradigm?
The recent literature in deliberative democracy has, however, also produced some
constructive distinctions, which do seem to preserve some specificity for the deliberative
ideal and can help us move forward. It will be useful to briefly introduce them here.
Guttmann and Thompson have thus proposed a distinction between “deliberative
disagreement” and “non-deliberative disagreement” (Gutmann and Thompson 1996).
Deliberative disagreement is, roughly, a disagreement that subsists even as the
deliberation satisfies some valuable procedural standards: people listened to each other,
expressed norms of reciprocity, equality, and respect and recognized the legitimacy of
their opponents’ positions. By contrast, non-deliberative disagreements of the kind
present in aggregative models of democracy simply express the brute confrontation of
different judgments or interests without any efforts being made at convincing,
6
Mansbridge et al. (2010) not only give up on rational consensus as the normative ideal of deliberation but
reintroduce in deliberation interests, power, and forms of strategic action such as bargaining.
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understanding, or otherwise rationally engaging the opponent.
Cass Sunstein has also usefully introduced the notion of “incompletely theorized
agreements” (Sunstein 1995), in which the agreement is on a general principle but not
necessarily on any supporting rationale or theory, or on each and every of its particular
implications. Originally introduced in the field of legal theory, this concept nicely extend
to deliberative democracy as well, where it offers a more realistic and generalizable goal
than rational consensus or even overlapping consensus. Unlike Habermas’ rational
consensus, an incompletely theorized agreement does not demand consensus all the way
up and down, so to speak, on all the reasons supporting a particular decision and all the
implications of this decision. Unlike Rawls’ overlapping consensus, which refers to the
common core of values and principles concerning the basic structure of a just society
shared by otherwise diverse and sometimes conflicting comprehensive worldviews, an
incompletely theorized agreement can apply to any subject.
The notions of “meta-agreement” or “meta-consensus” have also come to enrich
the vocabulary of deliberative democracy. Christian List thus contrasts the ideal of
“substantive agreement”—a post-deliberative consensus on a particular outcome—with
what he analyses as the more useful category of “meta-agreement.” Meta-agreement is a
post-deliberative consensus on how a problem should be conceptualized (List 2003).7 For
List, deliberation should aim at reaching meta-agreement rather than substantive
agreement, both because meta-agreement is a more realistic goal and because it is also
likely to facilitate further non-deliberative democratic decision-making through majority
rule. List indeed suggests that in the two distinct contexts in which meta-agreement
7
Incidentally, for List, a meta-agreement is a more general case of what Sunstein calls imcompletely
theorized agreement.
12
applies—preference aggregation and judgment aggregation—, meta-agreement may offer
attractive escape-routes from the paradoxes and impossibility theorems of social choice
theory and thus render possible the emergence of a meaningful collective decision.8
John Dryzeck and Simon Niemeyer, in a similar vein, have developed the ideal of
“meta-consensus” on values, beliefs, and preferences or a combination of them and
proposed to use this ideal as a new foundational principle of deliberative democracy
meant to replace Joshua Cohen’s and other early deliberative democrats’ focus on strict
unanimity on all three dimensions (Dryzeck and Niemeyer 2006: REF). Dryzeck and
Niemeyer convincingly argue that it makes no sense to be “for pluralism, against
pluralism, for consensus, or against consensus” (Dryzeck and Niemeyer 2006: 647) and
that the ideal of meta-consensus can reconcile both camps. In other words, Dryzeck and
Niemeyer embrace pluralism and consensus but at different levels: pluralism at the level
of first-order values, beliefs, and preferences and consensus on either or all of these
dimensions only at a second-order or meta-level.
Finally, at a more empirical and descriptive level, researchers have recently
identified a new category of consensus, which differs from strict unanimity and
somewhat blurs the line with voting (Urfalino 2011, Steiner REF). Urfalino labels it
“apparent consensus” (Urfalino 2012) or also, and more accurately, “decision by nonopposition” (Urfalino 2011). While unanimity rule is a well-known stopping rule for
deliberation, generally contrasted with voting, decision by non-opposition is a rarely
noted, yet empirically frequent decision-procedure (representing a third of observed
8
In the context of preference aggregation, meta-agreement may thus imply preference single-peakedness
and the existence of a Condorcet winner. In the context of judgment aggregation, meta-agreement may
similarly imply “unidimensional alignment” (List 2003) and the emergence of the median voter’s judgment
as a coherent collective decision in a proposition-by-proposition majority voting (List 2003: 12 and List
2010).
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decision procedures in Steiner REF). It does not have the stringent requirement of
unanimity and often takes place “in the shadow” of voting (Urfalino 2011, Novak 2010).
Whereas the unanimity rule requires that all involved explicitly approve of a decision,
which gives a veto power to those who refuse to, decision by non-opposition is a decision
procedure by which deliberation ends not when all approve but when few enough people
voice an objection and the rest remains silent, expressing consent, although not
necessarily approval. The reasons why they express consent may be varied, including that
they think they would be outnumbered if a vote were to be taken
The main problem with the current state of the theory is that, because of the
growing disaffection for consensus, combined with the “concept stretching” (Steiner
2008) that this occasionally leads to, there is, well, no consensus on what should take the
place of consensus as the natural stopping rule for deliberation. This might not be a bad
thing if this disagreement paved the way for a nuanced typology of when and where
consensus or disagreement should be privileged, but this is missing as well. All the while,
majority rule remains seen with some suspicion by many deliberative democrats who fear
conceding too much to aggregative democrats. Indeed, if majority rule is all we can
aspire to, why not just skip the deliberative phase, aggregate pre-deliberative judgments
and call it a day? If the point of deliberation is not to bring us closer to consensus, why
engage in it at all?9
9
In their advocacy of alternative “outcomes” for deliberation, Dryzeck and Niemeyer, who present metaconsensus and intersubjective rationality (rather than truth) as substitute ideals to the original ideal of
rational consensus (Niemeyer and Dryzeck 2007: 499) recognize that that these alternative outcomes do not
qualify as “collective decisions” per se (Dryzeck and Niemyer 2006: REF), i.e., ways to bring deliberation
to an end. Thus they acknowledge that “Our emphasis on meta-consensus might seem to leave open the
question of how collective decisions get made. But whatever the mechanism used (short of dictatorship), be
it majority rule, approval voting, unanimity, bargained resolution, or agreements that majorities support and
minorities can live with, meta-consensus makes collective choice more tractable. Sometimes tractability
will enable agreement on what is to be done, but other kinds of outcome are possible” (REF). List, for his
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Another gap in the current state of the theory is that even those who celebrate
disagreement and offer useful distinctions between different types of disagreement have
little to say about the epistemic value of these different types of disagreements. In
particular, assuming that disagreements can have epistemic value, in the sense of
increasing the odds that the group reaches a more enlightened collective decision, is it
always the case that deliberative disagreements, i.e., the kind that satisfies certain
procedural criteria deemed valuable by deliberative democrats, have such epistemic value
while non-deliberative disagreements don’t? Similarly, is it necessary for an
epistemically superior judgment to be supported by a meta-agreement or meta-consensus
on some higher order set of preferences or judgments?
We suspect that there is no necessary alignment of procedural and epistemic
properties. It is more likely, on the contrary, that sometimes (although not necessarily
often) a disagreement that violates norms of equality, respect, reciprocity is epistemically
more fruitful than a deliberative disagreement that satisfies all these procedural criteria. A
heated exchange between political adversaries might ultimately do more to change their
views than a polite conversation that fails to prod deep enough into the sources of
disagreement. Conversely, it is possible that a procedurally satisfying deliberative
agreement could pave the way for terrible epistemic results.
part, helpfully proposes that deliberation can be fruitful even if it falls short from yielding a consensus if
the remaining post-deliberative disagreement corresponds to a meta-consensus on preferences or judgments
that has the properties of preparing the way for meaningful collective decisions through majority rule. He
warns, however, that this possibility depends on highly contingent empirical circumstances. While it has
been empirically demonstrated that deliberation, at least in the context of preference aggregation, tends to
bring preferences closer to single-peakedness (List 2010 and Farrar et al. 2010), this existing empirical
research does not positively prove that deliberation actually reduces the likelihood of Condorcet cycles, that
is can turn non-single peaked preferences into single-peaked preferences. Thank you to Sean Ingham
(Harvard University) for the latter, important point.
15
The relation between epistemic properties of a given disagreement and the presence
or absence of meta-agreement or consensus might be trickier to figure out and it would
seem plausible that there be some correlation between the existence of a meta-consensus
and the epistemic properties of the corresponding disagreement. But even if List (2010) is
right to hypothesize that meta-agreements on preferences or judgments allow us to escape
the paradoxes and impossibility theorems of social choice theory (respectively Arrow’s
impossibility theorem for preferences and the general version of the so-called Discursive
Dilemma for judgments), the rationality of a collective choice according to the criteria of
social choice theory is itself a distinct problem from the question of the epistemic value
of that collective choice, which is our main focus in this paper. In any case, we think that
the answer to these questions cannot be taken for granted.
This paper does not ambition to answer all the questions at once. Our effort here will
simply be to address the following: Is consensus, understood as rational, unanimous
agreement on an outcome, ever epistemically desirable? If so, when and where?
Conversely, when and where is disagreement, as the lack of rational and unanimous
consensus on an outcome, epistemically preferable? We limit our inquiry by focusing on
the epistemic properties of disagreement in different deliberative contexts.10
2. Why Rational Consensus Retains Normative Appeal in Pure Problem Solving Contexts
Let us introduce briefly the distinction between the two types of tasks that we argue
deliberators can apply themselves to: problem solving and predictions. A prediction is
10
In the terms of Christian List, we place ourselves in the context of judgment aggregation and assume a
general background consensus on preferences where necessary (which may be reached through deliberation
as well).
16
some statement made about the future. It is an estimate of some future state of the world,
for example “Will the Egyptian demonstrations lead to a regime change?” or “How high
will the unemployment rate be in May 2011?” Predictions can thus be qualitative or
quantitative. Quantifiable predictions are often referred to as forecasts. These include
economic forecasts of unemployment and growth and meteorological forecasts of
tomorrow’s high temperature.
Problem solving, by contrast, consists of finding or constructing a solution to a
problem. It is a less clean-cut task than prediction. In some instances of problem solving
– such as how to send a person to the moon or Hilbert’s fifth problem – no answer exists
at the time the problem is posed. In other instances, many putative solutions exist and the
challenge is to select from among them. In this latter case, if the values of these solutions
(i.e., how they rank compared to each other and in absolute terms) are not known,
problem solving appears to be similar to prediction. To draw a bright line that separates
the processes, we introduce the following formal definitions:
A prediction consists of an object or event of unknown future value (for example
an unemployment rate or the size of an uprising). Each predictor has a model that she
uses to make an estimate of that future value.
Problem solving involves finding the best from among a set of possible solutions.
Individual problem solvers possess perspectives and heuristics that enable them to search
part, but not all of the set of solutions (Page 2007). People will therefore differ in the
solutions they propose. The will not differ, though, in how they evaluate proposed
17
solutions. In a problem solving context, any proposed solution can be evaluated
accurately and at a minimal cost in time and effort.11
In problem solving contexts, the process of deliberation can follow two scenarii: it
can either allow a group to leverage their diverse representations and search heuristics to
find the solution of a given problem or it can generate one from scratch by production of
new ideas and recombination of old ones.
Imagine first that the French government must choose a city to experiment with a
new program. Three députés deliberate this choice, one from Calvados, one from Pas de
Calais, one from Corrèze. They are aware of different possible solutions, which have
each a different objective value for the experiment. The highest possible value is ten,
which is achieved only by Caen (a solution initially not considered by the deliberators).
Initially, each député applies her perspective and heuristics to the problem and arrives at
a proposed solution. In the language of mathematics, these would be called local optima.
The goal is for the group to find the global optimum, that is, the city with the highest
objective value (Caen).
Prior to the meeting, we can assume that each député has thought about the
problem and formulated a solution (a city). Assume that these solutions, which are local
optima are as follows (we have written their values in parenthesis):
Calvados: Marseille (7)
Corrèze: Paris (8)
Pas de Calais: Grenoble (9)
11
A
canonical “problem” given this construction would be the famous traveling salesperson problem, in
which one seeks a minimal route between a set of cities. Finding the best solution to a traveling
salesperson problems with even one hundred cities is not easy, but determining the length of any particular
route takes little time or effort.
18
Initially, the député from Calvados proposes Marseille because her way of representing
the problem led her to think of Southern coastal cities. However, the député from
Corrèze pushes Paris, which all three immediately agree to be the better choice. The
député from Pas de Calais then suggests Grenoble , which all three again accept as an
improvement.
If the deliberative process were to stop here, deliberation would create no
synergies. It would be merely a process of picking the best from a collection of
proposals. Our point is that the deliberative process need not stop here, and typically
won’t. The reason is that the other two députés, those that did not propose Grenoble, may
now have new ideas. They may not, for example, have considered medium sized cities.
So, if in proposing Grenoble, the député from Pas de Calais mentions the expense of
running the project in a big city, he might well spur the député from Calvados to think of
other medium sized cities, including Caen (value 10) that are even better choices than
Grenoble.
The logic that underlies these iterative improvements rests on the diversity of
perspectives. Neither Caen nor Grenoble initially entered the mind of the député from
Calvados because she was comparing only large cities. It is not that she could not
imagine a medium size city, cleary she could, only that her way of solving the problem
led her to be searching larger cities. Once a moderate sized city, in this case Grenoble,
was proposed, she was then able to think of other moderate sized cities, including Caen.
Steven Johnson (2010), in writing about innovation, refers to the opening of these new
19
potentials as the adjacent possible. In our formulation, deliberation enlarges each
deliberator’s adjacent possible and leads to more and often better solutions.
Deliberation among those three people has epistemic properties that deliberation
among less cognitively diverse people would lack. The pool of information was enlarged,
as the député from Calvados, who only knew about one local peak (Marseille), ends up
knowing about the qualities of Paris and Grenoble as well. The députés from Corrèze and
the député from Pas de Calais similarly learn about two other local peaks than the one
they could think of (respectively Marseille and Grenoble, and Marseille and Paris).
Notice that even if the information gained is sometimes of lesser objective quality than
that which the person already held, nonetheless, only by acquiring it can the members of
the group reach the highest local optimum with certainty. The député of Calvados might
never have considered an option she actually knew about, her own city of Caen (10), if
she had not been spurred away from her initial choice (of value 7) by the other two
députés who offered better but still suboptimal solutions (of respective values 8 and 9).
Deliberation also allowed the group to weed out the good arguments from the
bad. While it seemed at first a good argument to look for a big city (Marseille, Paris), it
turns out that it was better to look into moderate sized cities (Grenoble, Caen).
Finally, deliberation led to a consensus on the “best” solution, namely the solution
that allowed the group to reach the optimum of 10, when the pre-deliberative beliefs
about the best solution could have been respectively 7, 8, and 9.
By contrast, if all three députés were thinking exactly alike—say like the député of
Calvados who thinks of her lower local optimum first—no matter how long they
deliberated, their group would stay stuck on the local optimum of Marseille (7) and
20
would never be able to reach the higher local optimum of Caen (10) because nothing
would have spurred them away from that low local optimum. Thus, the extent of
deliberation as well as the potential benefits may well increase with the diversity of
problem solving approaches.
Let us now turn to a second example of more creative problem solving. Here
deliberation does not simply reveal a pre-existing solution but builds it from scratch on
the basis of shared information, arguments, and ideas. A neighborhood faces a recurrent
safety issue on a dark bridge that separates it from the city’s downtown. Muggings occur
with sufficient regularity that people have become scared to walk home after dark. All
members of a committee formed to solve this problem share the same objective: to stop
the muggings but to do so a minimal costs. Deliberation here can play three roles: it can
produce ideas, it can refine ideas, and it can combine ideas. Prior to the meeting it could
be that no one has any concrete proposals. Through dialogue three solutions to the
problem might emerge. A: Have the neighbors walk each other home and organize
watches; B: Station a police car near the bridge after dark; C: Install public lighting on
the bridge.
Once these ideas are on the table, deliberation can refine and improve the
solutions. When is the police car stationed? How do the hours change depending on the
time of year? Can someone call to ask for the police car? Keep in mind that we assume
that proposals can be evaluated accurately, so we have no disagreement about whether or
not an idea is good. Finally, ideas can be combined. Suppose that proposal C has the
lowest cost but, unlike the other two, it won’t reduce muggings to zero. The existence of
proposal B, the costly police car option, might lead someone to propose option D to
21
complement the lighting with an emergency phone. Proposal D might then be the option
selected.
The role of deliberation in pure problem solving contexts then is to get ideas on
the table, improve them, and sometime combine them to create ever more ideas.
Disagreement would mean to ignore another’s proposed solution, even when its value is
known to be positive. Given that everyone shares the same values and that all solutions
can be evaluated, disagreement would be irrational and epistemically disastrous. There is
simply no reason not to reach a consensus.
Of course, one might object that our characterization of deliberation in problemsolving contexts assumes an “oracle,” that is a machine, person, or internal intuition that
can tell us the value of proposed solutions. One might think that only for rare problems,
and these would tend to be more engineering based problems, would such an oracle exist.
We disagree. Often the values of competing solutions are relatively self-evident, at least
in relative if not absolute terms. For example, in the example developed here, the solution
“Improve public lightning on the bridge and put in an emergency phone” is
straightforwardly better—by intuitive and obvious standards such as simplicity, cost, and
long-term efficiency—than alternatives such as having neighbors take care of the
problem or have a police car permanently stationed by the bridge after dark. We assume
that the self-evident nature of some solutions correspond to what Habermas called “the
unforced force of the better argument.” The unforced force of the better argument is,
indeed, a form of oracle.
Another objection would remark that in many situations the problem solving
context is impure. Supposethat we have an oracle but that outcomes are
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multidimensional. Specifically, consider a status quo policy A that we want to improve
on. Assume that each proposed policy produces some level of equality and efficiency. To
keep this simple suppose that equality and efficiency each have scores from 1 to 10
where 10 is better. A can be characterized as:
A: (4,8)
After a thoughtful deliberation in which we have used our various perspectives
and heuristics, we come up with an alternative policy B, characterized as:
B: (6,5)
Obviously, B is better than A on the equality dimension but worse than A on the
efficiency dimension. If we have preference diversity among the deliberators, we won’t
reach a consensus. If there is third option on the menu, say C (5, 7), we could even cycle
as a group: we could prefer A to B to C and then C to A. This scenario won’t allow us to
find a peak. We will just roam around.
The objection is right to remark that in real life problem solving will tend to be
mixed rather than pure. In fact, the objection invites us to distinguish between two types
of consensus.
PREMISE CONSENSUS: do we agree on the mapping F?
PREFERENCE CONSENSUS: do we agree on our rankings of outcomes in Y?
It is certainly useful to distinguish analytically between the epistemic dimension
of problem solving—figuring out a peak or agreeing on the mapping F—and the
alignment of background preferences problem solving also requires—agreeing on our
ranking of outcomes in Y, that is reaching a meta-consensus on the dimensions to be
23
privileged when ranking outcomes. Our point in this paper, however, is limited to
showing what deliberation can do for the latter case.
Even if we assume a meta-consensus on preferences (a preference consensus) and
the existence of an oracle, there are also situations in which time constraints simply do
not allow for the emergence of a solution. In such cases, the solutions have to be
evaluated in some way and the task facing deliberators also involves figuring out which
of the available options is the most likely to succeed. This means that problem solving
often bleeds into prediction. Consensus on the better solution or the unforced force of the
better argument cannot always be expected to end the process.
If so, some other way to end the deliberative process than consensus as
unanimous agreement must be agreed on. Alternative stopping rules for deliberation—
that is, stopping rules that produce a decision are then either what Urfalino calls “decision
by non-opposition”—i.e., an apparent consensus in which those who disagree with the
majority refrain from expressing disapproval, or a vote, in which everyone can voice their
judgments but settles the issue by a majoritarian decision. It is generally the case that
decision by non-opposition takes place “in the shadow of the vote.” In other words, the
reason by dissenters refrain from voicing their disagreement is because they know they
would be outnumbered if a vote were to be taken and they may have reasons not to want
to be exposed as dissenters if nothing is going to come out of it (see Novak 2009).
If there is uncertainty as which group would win or the dissenters refuse to
surrender without an actual count of their voices, the group can go right away for the
aggregation mechanism. What happens then? For example, assume that the group
couldn’t reach a unanimous, rational agreement or even an apparent consensus on one
24
solution but is equally split between two rival ones, say A and C. In this case,
straightforward majority rule can be, under some assumptions, a fairly reliable way to
generate an accurate collective prediction (see CJT but also, e.g., Page 2007, Landemore
2007, and Lupia and McCubbins 1998). There are several problems, however, with such
a move.
A first problem is that, in many cases, there are more than two options to choose
between, which raises social choice theoretical problems such as the possibility of
cycling majorities. For 3 options such as A, B, and C, it could be the case that in pairwise
comparisons A beats B, but C beats A, and B beats C, so that no clear outcome can be
determined. What is needed to avoid such problems might be more deliberation: ideally
the group would deliberate until they reduce their options to two or at least considerably
increase the single-peakedness of their preferences, if it can indeed be assumed (as
suggested by Miller 1992, Knight and Johnson 1994, Dryzek and List 2003, List 2010)
that an increase in single-peakedness correspondly decreases the probability of cycling
majorities in situations where there does not exist a Condorcet winner.
Second, even if deliberators manage to reduce the options to two or to somehow
minimize the risks of cycles in the case of three options or more, it could be that the raw
individual predictive models are not terribly accurate, despite all the problem-solving
deliberation that went on before, and could be improved on by more deliberation of a
different nature. In other words, instead of ending deliberation after the problem-solving
phase, the group might be better off starting a new phase, which would consists in
exchanging fresh reasons as to why such or such solution is likely, in each person’s view,
to perform better.
25
In our example, participants in the deliberation no longer confront the question
“What could help us solve the mugging problem on Court Bridge?” but they are asked to
answer a different one: “Given that the group is divided between options A and C, which
do you think is most likely to get the job done?” The exchange of arguments is likely of
course to overlap somewhat with the deliberative content of the problem-solving phase
(after all, the deliberation here still consists in trying to give arguments for A or C) but it
might also bring about completely new arguments.
Should deliberators in the phase concerned with predictions seek to reach some
kind of rational consensus on the better prediction, in the same way that deliberators in
the problem-solving phase aim to converge on a common solution?
Before we turn to the analysis of deliberation in predictive contexts, let us
emphasize the following point. In many, perhaps most cases of interest, deliberation
involves both problem solving and prediction. In our examples, as in any political
example involving the formulation of a policy, the task involves both problem solving –
the crafting of new potential solutions – and predicting or forecasting the effects of these
solutions. Policy-making in general, or so we argue, involves both problem solving and
prediction. We now turn to prediction.
3. Why Disagreement Can Pay Off in Predictive Contexts
Recall that the predictive context involves for deliberators the task of making an estimate
of the future value of an object based. In our construction, we presume that individuals
bring to this task some amount of information as well as a cognitive model of the
situation at hand. Before we turn to the question of what deliberation can do to improve
26
individual and collective accuracy, let us posit that the accuracy of aggregated individual
predictions depends on two equally important factors: individual accuracy and collective
diversity (Hong and Page 2009, Page 2007).
Specifically, the accuracy of an individual equals the distance between her
prediction and the actual value. A priori the accuracy of individuals won’t be known exante. We can nonetheless assume that an individual has an expected accuracy, which we
can assess based on historical data documenting her past predictive accuracy or on an exante analysis of the quantitative or qualitative model that she uses. For example, we can
ask: “What is the past predictive record of a Fox News pundit?” Or, “What kind of
regression equation does this analyst use to predict unemployment rate and does his
choice of variables seem plausible?”
A collection of predictors will exhibit some degree of heterogeneity or diversity.
We can measure this at the level of their models – do they use the same variables and
categories and similar functional forms? – or through the statistical properties of their
predictions. If we have data on past predictions, correlation serves as a useful proxy for
diversity. More diverse predictors make less correlated predictions.
On any given instance the accuracy of the equally weighted collective prediction
depends on the accuracy of the individual predictions and on their diversity according to
the following formula:
Average Collective Error = Average Individual Error – Diversity of Predictions
27
Page (2007) calls this the Diversity prediction theorem. If we consider the predictions to
be random variables then we obtain the following formula known as the bias variance
decomposition:
Ave. Collective Error = Ave. Bias + Ave. Individual Error + Ave. Correlation
These two formulae reveal that smaller individual errors make for a more accurate
collective prediction as do more diverse predictions – holding accuracy constant.
If individuals use distinct models, then their predictions will likely differ. In the first
formula those differences are captured in the “diversity of predictions term.” In the
second formulae, where the predictions are thought of as random variables, diversity
arises from negative correlation. If two individuals rely on distinct attributes to make
sense of the world, then they will, under some rather mild conditions, have negatively
correlated predictions (Hong and Page 2009).
Note that these results all take the group composition as fixed. One can also
consider the question of whom, or what types of predictors to include in the group of
forecasters. Optimal group composition depends critically on the size of groups. Small
groups should consist of the most accurate types, while large groups should include more
diverse types (Lamberson and Page 2011)
Most formal models of prediction – economists and political scientists call this
information aggregation and business professors call this forecasting – abstract from the
reality that people have models and assume that people get signals. The signals either
28
take numerical values or correlate with the truth or falsehood or some event. We
consider the former type of signals as they provide a richer evaluative context.
What can deliberation among predictors prior to the aggregation of predictions do
to help improve the resulting outcome? Should predictors converge towards similar
models and/or predictions or should they, on the contrary, preserve the disagreement
between them? In what follows, we restrict attention to the case where people have
identical preferences. If we relax that assumption, communication can play other roles
including enabling people to learn the state of the world (Feddersen and Austen-Smith
2006). We begin by questioning what deliberation can do in the starkest possible
deliberative environment, what we will call the land of signals.
We begin with a distinction between three kind of signaling environments: Pure
signaling environments, in which no deep “reasons” of the kind deliberative democrats
insist on exist. People can only spout values. “I say 7.4%” or “I say 8.6%.” To borrow
the vocabulary of people who write signaling models, deliberators simply “report” their
signals. We will call the kind of communication at stake when people merely report their
signals Deliberation of degree 0.
Second, we consider environments where people exchange not just signals but
also the accuracy of their signals. Accuracy could perhaps be signal by reputation, status,
or past success. We will call this second type of exchange among deliberators
Deliberation of degree 1.
Third, we consider environments where people exchange signals, the accuracy of
the signals, and also the value of the correlations between their respective predictions.
We will call this third category of exchange of information Deliberation of degree 2.
29
For deliberative democrats, only deliberation of degree 1 and 2 would count as
deliberation per se, since they involve at least some superficial or procedural reasons for
individuals to take these signals into account in their own reasoning. In these situations,
however, deliberation has limited value.
This won’t be the case in what we call, finally, Deliberation of degree three.
Individuals share their information as well as the predictive models that they use to
transform that information into a prediction, that is they exchange the deep reasons
behind their respective predictions. This is the deepest kind of deliberation and the closest
to the ideal of deliberative democrats.
Recall that in the problem solving context, we distinguished between premise
consensus and preference consensus. In the predictive context, the ideal of consensus has
to be defined relative to the information that people have, that is the information structure
that is assumed. In the deliberation of degree 0 case, there are no “premises” of a
reasoning process, there are only the signals. A naïve notion of consensus would then be
to agree on a prediction, on the conclusion. In deliberation of degree 1, the premises
extend to include strength/accuracy and in deliberation of degree 2 to take into account
correlation. In deliberation of degree 3, we can have full agreement on premises
(variables and their weights) and therefore on conclusions.
Let us now go over each type of deliberation, as this should make things clearer.
We first present a simple case with three predictors and walk through several scenarios.
Suppose that we have three predictions of the expected social benefit (in millions) of
some government project, such as the cash for clunkers program. Suppose that the
benefit of the project (at the time unknown) will be 100 million. Our three predictors
30
Ali, Baruk, and Chamile each make predictions, and those predictions have the following
values:
Ali: 120 million
Baruk: 115 million
Chamile: 80 million
The simple average of these predictions equals 105 million.
What happens, or could happen, if our three predictors were given a chance to
deliberate prior to judgment aggregation? Let us first consider the case where
“deliberation” simply consists of reporting to others the value of one’s signals.
(deliberation of degree 0). It’s as though each person is either precluded or incapable of
giving any rationale for his or her value.
We consider two possibilities of what might occur through deliberation. First,
people might move their predictions in the direction of others. Second, people might
abandon their models for those of others. Does either of these outcomes—which mark a
step towards greater consensus on a single decision—improve on the initial collective
prediction?
Moving Predictions and Weighting
Hypothesis 1: Each person moves some percentage of the way toward the mean
Suppose in our example that each person moves one-fifth of the way to the mean. The
post deliberation predictions will remain 105, so deliberation in this case—in the sense
of being exposed to other people’s predictions and updating as a result one’s predictions
towards theirs—does not help. The following claim makes this point in the general case
31
Claim: if each predictor adjusts their prediction towards the mean by the same
percentage, the mean prediction does not change.
This claim can be extended to the following.
Claim: There do not exist a set of adjustment rules based only on the value of signals
one for each predictor, such that if each predictor adjusts their prediction according
to her rule, the mean prediction becomes more accurate. 12
In other words, deliberation of degree 0 may lead to greater consensus on an
outcome but it cannot improve the group’s collective judgment. To put this more
formally, for communication of signals to improve accuracy, predictors must learn
something other than the values of the signals. Aggregation must leverage some
additional knowledge as to why individuals produce these predictions in the first place.
This should seem intuitive to deliberative democrats, for whom the point of deliberation
is not just to reveal information, but to give each other “reasons” to support their claims
(predictive or otherwise).
Another way to say this is that mere pooling of predictions in a conversation does
not amount to genuine deliberation per se. In our example, in order for their exchange of
views to count as deliberation and for the exchange of views to have any epistemic
properties, individuals would thus need to learn something, for example, either about the
12
For
example, suppose that agents follow the rule ‘Move by a fixed amount toward the mean.’ In our
example, let that unit be three million dollars. The new collective prediction will be 104 million, so the
collection becomes more accurate. However, that’s an artifact of having two predictions below the true
value. If two had been above the true value the collective prediction would have gotten worse.
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substantive reasons supporting the choice of a predictive model so that they can assess its
accuracy or directly about the predictive accuracy of the models that produce the signals.
If someone had a particularly good predictive record or used a particularly reliable model,
that would be in itself, perhaps, a good reason to move somewhat towards their judgment
than toward the judgment of someone with a lesser record.
To show how communication of signal strength can improve predictive
performance, suppose that in our example Baruk were to admit that he had no real basis
for his prediction. We thus move from Deliberation of degree 0 to Deliberation of degree
1. If, based on this new information, the members of the group would drop Baruk’s
prediction, and the new average prediction will be exactly correct.
Using statistical arguments, one can show that weighting models according to
accuracy, where accuracy equals the inverse of the expected error, should on average,
result in better predictions. In other words, some degree of convergence towards if not a
full consensus, at least an agreement on the weights to be attributed to various predictions
should help the group get closer to the right answer. This is also the method
recommended by the De Groot-Lehrer-Wagner model of belief updating (De Groot 1974
and Lehrer-Wagner 1981). In practice, the evidence is more mixed and tends in fact to
support simply averaging (this is the so-called forecast combination puzzle, see for
example Smith 2009). Many advocate equal weighting unless strong evidence supports
otherwise (Armstrong 2001). In our example, Baruk’s admission of making up his
prediction would satisfy that criterion.
As has also been shown, collective predictions can be improved even further by
taking into account the diversity of the models that produce the signals. Theoretical
33
results show that optimal combinations of predictive models should take into account
both their accuracy and their correlation (see Armstrong 2001 or Lamberson and Page
2011).
One way to take into account correlation is to differentiate signals by their type,
i.e., the methodology or information used to construct the prediction. Attempts to take
into account the type of predictive models and to assign weights to classes of models as
opposed to individual models characterize what we call Deliberation of degree 2. Here
individuals reveal their value, their past accuracy, and the type of model that they used. In
this type of deliberation, the more models of a particular type, the less weight that they
get owing to the correlation of predictions of the same type.
In our example, suppose that Ali and Baruk both stated that they were using
models based on price sensitivity (what economists call elasticity) but that Chamile’s
model was based on the current new car inventory. One could then presume that Ali and
Baruk’s predictions would be more positively correlated than the other pairs of
predictions and might assign them less weight. Doing so, in this case, would in fact lead
to a better collective prediction. Though, as mentioned above, differential weighting
need not improve accuracy. For example, if it were the case that their model type was
more accurate that Chamile’s, reducing their weight would harm accuracy.
Deliberation of degree 0 to deliberation of degree 2 comprise what we call
signaling land. Individuals can learn the value, accuracy, and type but nothing more. To
capture the full value of deliberation we need to a richer characterization than signals.
We need to conceptualize the signals as models that transform information into
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predictions. When people deliberate over their models – their information and their
reasoning, they’re engaging in what we call Deliberation of degree 3
Now, the individuals provide one another with exhaustive accounts of what kind
of models they used, why they resorted to such and such variables, what data, and even
conceptualized the problem a certain way. This kind of deliberative exchange makes
available to all participants a full toolbox of concepts, perspectives, data, and variables to
complicate and refine their own predictive models.
What is striking about Deliberation of degree 3, where people trade data,
variables, and so forth, and make publicly available a whole buffet of predictive tools is
that although here too consensus on the more accurate model might be the better
epistemic strategy for the group, it will often be advantageous to encourage a greater
diversity of post-deliberative predictions, even as the individual models share more
variables and data.
Let’s return to our three policy analysts predicting the number of citizens that will
take advantage of a program, like Cash for Clunkers. Now, rather that getting passive
signals, they each present models based on data and logic. When Chamile describes her
model based on inventories, the others will be able to assess its accuracy as well as its
correlation with their own models. Thus, deliberation of degree 3, subsumes the lower
levels. It is of course possible that upon hearing her model, the other two could reach a
consensus that hers is the best, or all three might converge to some compromise between
the three models. In either case, the dynamics involve a process of averaging among the
three and building consensus. That could happen.
35
However, there is an alternative possibility in which both Ali and Baruk, who
used similar elasticity based models, do not converge toward Chamile’s prediction. Such
as result might occur if Ali had a much larger and better data set than Baruk. This might
lead Baruk to abandon his model in favor of Ali’s. Neither of the two may feel any
compulsion to move toward, much less accept, Chamile’s prediction. Thus, after
deliberation, we are left with two predictions and those are the most extreme.
Now, we might ask whether we should expect a consensus to be reached across
these two models. However, these two models rely on different data and different logic,
and it may be difficult or even impossible to combine them into a grand model.
Deliberation might therefore end with an agreement to disagree, or a consensus that there
is dissensus. The decision may be to average the two predictions, which in our contrived
example gives a collective prediction of 100 million, the true amount.
The potential for dissensus or what we might also call a sustainable plurality of
models depends on levels of uncertainty and complexity (see Page 2008 for a distinction
between the two concepts). Contexts that have high levels of uncertainty may produce
multiple equally viable explanations. A reading of nearly any day’s discussion of the
various weather forecasting on NOAA’s website reveal this to be the case. Contexts of
high complexity present problems for different reasons. Macro level outcomes can vary
substantially based on minor changes in micro level assumptions. Such systems may be
path dependent or produce large unexpected changes. Global climate change models
must cope with both uncertainty and complexity. As a result, many argue for pluralism
in models (Parker 2006).
36
To illustrate the potential of an even greater dissensus following deliberation than
prior to it, let us consider the following scenario. Consider a group of people trying to
determine which political candidates within a given party is most competent to run for
president (we are focusing on intra-party decisions to bracket some of the problems
raised by fundamental value differences between Republicans and Democrats). The two
contenders are Barack Obama and Hilary Clinton. In order to make a prediction, person
A only considers economic issues, whereas person B only considers foreign policy. In
technical terms, you might say that A partitions the world into two categories: candidates
that are good on economic issues and candidates that are not good on economic issues.
Candidate B partitions the world of candidates into another, orthogonal set of categories,
having to do with how competent they are on foreign policy.
Prior to deliberation, A and B agree that Obama is the best candidate, although
they agree for different reasons (a case of “incompletely theorized agreement” (Sunstein
1995)). Note however that A thinks that Obama is only marginally better than Clinton on
economic issues, while B thinks that Obama is much better than Clinton on foreign
policy issues. Now allow them to deliberate with others who disagree with them. In the
process of this exchange of information, arguments, data, variables, etc., each reveals the
variable they used to make their respective prediction. Let us say, for the sake of
simplicity, that A and B, although they are exposed to a wide array of variables
(unemployment figures, oil prices etc.), are only convinced by each other’s contributions
so they ignore the other models but decide to borrow each others’ variables to refine their
own predictive models. Person A now decides he needs to factor in foreign policy along
with economic issues. As it turns out, on foreign policy issues, A thinks Clinton is
37
infinitely better than Obama so that (assuming economic issues and foreign policy are
equally weighted) his prediction as to the better candidate now switches from Obama to
Clinton. Meanwhile, B now factors in his model economic issues but, because like A he
thinks that Obama is marginally better than Clinton on these issues, he still concludes that
Obama is the best candidate.
Notice that B’s post-deliberative prediction, though identical to his predeliberative prediction, is arguably more reliable, because it is based on a more complex
and presumably more accurate picture of the world due to the combination of at least two
ways to partition it. Despite the fact that A and B’s models converged in terms of the
variables they used (a form of meta-agreement a la List or meta-consensus a la Dryzeck
and Niemeyer), their first order conclusions are now less in agreement than they were
initially!
Now, is the increased disagreement induced by deliberation a good thing or a bad
thing? In the examples we gave, it is clearly a good thing. We propose to call “positive
dissensus” the kind of deliberative disagreement that results from a deliberation in which
people exchange reasons that lead them to update their predictions or predictive models
so as to increase the accuracy of these predictions or models while preserving their
diversity (i.e., the negative correlations between the different predictions or models).
While positive dissensus precludes by definition the possibility of a decision by
consensus, this kind of disagreement nevertheless prepares the way for an epistemically
fruitful aggregation of judgments. By contrast, we propose to call “negative dissensus”
the opposite of positive dissensus, namely the kind of disagreement that is not based on
reasons and does not lead to an increase in individual predictive accuracy.
38
A remaining question is: In predictive contexts, once positive dissensus has
prepared the ground for epistemically fruitful judgment aggregation, what aggregative
procedure is best? We have throughout this paper implicitly assumed a simple averaging
out of the individual predictions (of which majority rule would be an instanciation), that
is an equal weighing of all judgments (one man, one vote). There might be situations,
however, in which giving more weight to some individual models is epistemically more
fruitful.
Conclusion
In this paper, we aimed to offer a more discriminating approach to the question of
whether consensus, rational, overlapping or otherwise, is a worthy normative ideal for
deliberative democracy. We answered this question by considering exclusively the
epistemic properties of consensus versus those of greater or lesser disagreement. We have
also tried to identify conditions under which disagreement might prove epistemically
fruitful. We conclude that the value of consensus depends in part on the kind of task
deliberators set out to perform—whether it is solving a problem or making a collective
prediction.
If the point is to solve a problem and under the assumption that there exists an oracle
revealing the true value of proposed solutions, the goal of a Habermasian, rationally
motivated consensus—in which individuals agree on an outcome for the same underlying
reasons—remains normatively valid. In practice, and particularly if the group is large
and/or the time constraints tight, unanimous consensus will rarely if ever be achieved as
such but greater convergence on an outcome remains a prime face worthy goal.
39
More often than not, however, deliberators will need to engage in a different, often
additional task, that of evaluating the value of different alternatives. When a politician
proposes a change in say tax policy, the effects of that policy aren’t known. The decision
as to whether to adopt the proposal therefore largely rest on the predictive models that
decision makers and veto players bring to the table. Thus, the deliberation morphs from
a problem-solving situation to a predictive situation.
In predictive contexts, we argue that consensus should generally not be the goal. It
might be the case that converging on one single prediction is the epistemically smart
thing to do for the group but this will be a rare exception (which assumes some kind of
oracle revealing the most accurate model or predictor). In the limit case of Deliberation
of degree zero, where individuals have beliefs or signals but they lack the ability to
communicate their reasoning, seeking consensus or at least converging towards the mean
of the pre-deliberative predictions cannot have any positive expected value. It is just a
waste of time. For Deliberation of degree 1, 2, and 3, consensus might retain some value
but deliberators will generally be better off pursuing the goal of a “positive dissensus”—
which can result in more disagreement post- than pre-deliberation—rather than that of a
greater convergence on a prediction, let alone a unanimous agreement on it.
The amount of emergent dissensus will depend on the nature of the predictive task.
Contexts that are more complex or more uncertain will sustain greater disagreement.
This pluralism has long term robustness advantages by keeping ideas on the table that
may prove useful as data improves. In general, in transient environments, greater
diversity provides both greater robustness and more responsiveness (Page 2010).
40
Agreeing to disagree transcends mere respect for opinion. It produces better predictions
now and better predictions later.
Our notion of positive dissensus shares features with the notions of meta-agreement
and meta-consensus, which suggest agreement on underlying reasons for a given view,
although not on their importance. When one turns to the practical matter of translating
reasons into a prediction, the ability to paint a coherent picture that includes all accepted
reasons may not be possible for multiple reasons: lack of data, computational constraints,
incommensurability of processes or time scales, and so forth. Therefore, different people
may pick and choose distinct subsets of reasons and produce a positive dissensus. That
dissensus results not necessarily from individual differences in import for the various
rational, but could instead arise from an awareness that the context is sufficiently large
that it requires an ensemble approach.
All in all, our conclusion is thus that deliberative disagreement can be not just
procedurally but epistemically appealing as well. Agonistic pluralists have thus a point
when they criticize deliberative democrats’ almost exclusive emphasis on rational
consensus. However, to the extent that agonistic pluralists are right to celebrate
disagreement, they are wrong not to be more discriminating. Not every kind of
disagreement is good. In fact we offer a distinction between positive and negative
dissensus. Positive dissensus is the kind of deliberative disagreement that results from
people refining their individual predictive models based on reasons ranging from
superficial (accuracy of signals, correlation between predictions) to deep (exchange of
variables/data/models) and may very well lead them to disagree more rather than less
with each other’s predictions. Negative dissensus, by contrast, is not based on
41
deliberation or is exacerbated by deliberation but for the wrong epistemic reasons and
generally will not cash out into any epistemic improvement at the collective level.
42
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