Expert-Based Information and Policy Subsystems:
A Review and Synthesis
Christopher M. Weible
This article reviews and synthesizes the uses of expert-based information in policy subsystems. The
review begins by summarizing the different uses of information in the multiple streams theory, the
punctuated equilibrium theory, the social construction theory, and the advocacy coalition framework.
Three uses of expert-based information are identified as instrumental, learning, and political. The three
uses of expert-based information are then compared across unitary, collaborative, and adversarial policy
subsystems. This article synthesizes the findings in a set of propositions about the use of expert-based
information in policy subsystems and about the factors that contribute to shifts from one policy
subsystem to another.
KEY WORDS: science, collaborative management, the advocacy coalition framework, policy processes,
policymaking, policy process theories
Introduction
The policy process is often conceptualized as a complex system of inputs and
outputs (Easton, 1965). Among all the inputs and outputs, one of the most important
is information. A deep literature exists about the role of information in various
policymaking contexts (Adams, 2004; DeWitt, 1994; Fischer, 2000; Ingram, Schneider,
& McDonald, 2004; Jenkins-Smith, 1990; Kingdon, 1995; Knorr, 1977; Lee, 1993;
Ozawa, 1991; Pelz, 1978; Rich, 1991; Sabatier, 1987; Weiss, 1979; van Kerkhoff & Lebel,
2006). This literature shows that the use of information in policymaking ranges from
the instrumental, where information directly impacts policy, to the political, where
information is used to argue against an opponent (Knorr, 1977; Pelz, 1978; Rich, 1975).
Over long periods of time, information can also foster learning, belief change, and
policy change (Sabatier, 1987; Weiss, 1977). This article reviews the science and policy
literature to generate propositions about (i) the different uses of expert-based information
across three types of policy subsystems; and (ii) the expected role of expertbased
information in contributing to shifts between policy subsystems.
Expert-based information is defined as content generated by professional, scientific,
and technical methods of inquiry (Adams, 2004; van Kerkhoff & Lebel, 2006).
It is often, but not always, based on accepted analytical approaches as defined by
The Policy Studies Journal, Vol. 36, No. 4, 2008
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0190-292X © 2008 Policy Studies Organization
Published by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford, OX4 2DQ.
professional peers. The sources of expert-based information include the social and
natural sciences, policy analyses, government reports, and research coming from
universities, think tanks, and consulting firms. Likewise, the term “expert” includes
policy analysts, scientists, and researchers in government and nongovernment organizations.
Expert-based information is not restricted to the sciences, but it also
includes information coming from engineering and the humanities. For simplicity,
science and expert-based information will be used interchangeably.
Expert-based information is distinguished from local (or community) information,
which is based on trial-and-error learning about a topic in relation to a specific
place (Adams, 2004; Huntington et al., 2002; van Kerkhoff & Lebel, 2006). Distinguishing
expert-based information from local information is important because the
former takes on a greater force as a legitimizing agent in the policy process. The
legitimizing force of expert-based information derives from popular perceptions that
science is neutral and dispassionate (Ozawa, 1991). Making a distinction between
expert-based information and local information is also useful because it often parallels
divisions between public and private interests (Adams, 2004) and between
cultures (Huntington et al., 2002).
Groups of experts in any policy process are not homogenous. Compared to
scientists from academia, government scientists face different financial and professional
incentives in conducting research, writing reports, making public statements,
and relating to other actors. To complicate matters, many academics have “innerouter”
careers that teeter between academia and government (Kingdon, 1995, p. 56).
Similar arguments can be made about scientists from think tanks, nonprofit and
private organizations, and consulting firms. In addition, research has shown that the
type of information (from a benefit–cost analysis to a financial model) affects its
utilization (Rich, 1991). In this article, I temporarily put aside issues related to the
role played by different types of expert-based information and of the experts’ organizational
affiliations.1
Every review bounds its phenomena. This article relies heavily on the theories of
the policy process (Sabatier, 2007) to derive insights and propositions about the roles
of expert-based information in policy. The referenced policy process theories include
the multiple streams theory (Kingdon, 1995), the punctuated equilibrium theory
(Baumgartner & Jones, 1993), the social construction theory (Ingram, Schneider, &
deLeon, 2007; Schneider & Ingram, 1997), and the advocacy coalition framework
(Sabatier & Jenkins-Smith, 1993). Among policy process theories, the advocacy coalition
framework is used most because it already states hypotheses about the use of
science in policymaking (Sabatier, 1987, 2005; Sabatier & Zafonte, 2001). Beyond the
literature on policy process theories, this review draws heavily on the research
utilization literature including Amara, Ouimet, and Landry (2004), Caplan,
Morrison, and Stanbaugh (1975), Rich (1975, 1991), and Weiss (1977).
This article proceeds inductively in five parts. The first part summarizes the ways
in which expert-based information is integrated in the different theories of the policy
process. The second describes how the uses of expert-based information in policymaking
can be simplified into learning, political, and instrumental uses. The third
identifies unitary, adversarial, and collaborative policy subsystem types, and the
616 Policy Studies Journal, 36:4
fourth describes the role of expert-based information in each. Building on the first
four sections, the final part provides two sets of original propositions about (i) the
use of expert-based information in policy subsystems; and (ii) shifts between subsystem
types.
Part 1. The Use of Expert-Based Information in Four Policy Process Theories
There are many descriptions of expert-based information usage in the policy
process. Dunn (1994), for example, takes the policy cycle and describes how science
is used in each stage. He shows that science can be used to (i) identify problems in
agenda setting; (ii) forecast impacts in policy formulation; (iii) compare alternatives
in policy adoption; and (iv) monitor impacts in implementation and evaluation
(Dunn, 1994, p. 17). Descriptions of expert-based information in the policy cycle tend
to emphasize the instrumental use where science directly impacts policy without
much reference to politics or learning. An instrumental interpretation of expertbased
information in the policy process is not surprising considering the rational,
apolitical, and sequential assumptions of the policy cycle. Fortunately, several theories
have improved our depiction of the policy process beyond the faculties of the
policy cycle (Sabatier, 2007). A summary of four of these theoretical frameworks
provides a more complete depiction of expert-based information in policymaking.2
The Multiple Streams Theory3
The multiple streams theory describes how agenda setting and policy change
result from policy entrepreneurs attempting to open windows of opportunity by
merging previously independent problem, policy, and political streams (Kingdon,
1995; Zahariadis, 2007). The multiple streams theory places expert-based information
in two of its streams. In the problem stream, actors use science to indicate the
seriousness and causes of a problem and to help evaluate the effectiveness of current
policies and programs. In the policy stream, science offers ideas, helps legitimize
ideas, and provides a means for entrepreneurs to advocate for the technical feasibility
of an idea. A principal strategy for policy entrepreneurs is to seek the right
opportunity to utilize expert-based information to place an issue on an agenda,
match an idea to a problem, and advocate for policy change. The implication from
the multiple streams theory is threefold: (i) policy participants use science to identify
problems and evaluate ideas as solutions; (ii) the effect of expert-based information
is contingent on the presence of a skillful entrepreneur; and (iii) entrepreneurs use
expert-based information to shape agendas and policies for political gain.
The Punctuated Equilibrium Theory
The punctuated equilibrium theory argues that policy change is not only mostly
incremental but also marked by sporadic punctuations (Baumgartner & Jones, 1993;
Weible: Expert-Based Information and Policy Subsystems 617
Jones & Baumgartner, 2005). The causal driver in the punctuated equilibrium theory
is the pace with which actors process information, shift their attention, and change
the policy image. Policy images reflect both emotive and empirical social constructions
of an issue (Baumgartner & Jones, pp. 25–7). Incremental policy change arises
because actors resist changing their behavior or beliefs from challenging information,
thereby maintaining a stable policy image. In contrast, punctuated policy change
occurs when actors overcompensate for previous neglect of information and radically
readjust a policy image. Policy images also play an important role in policy
processes by affecting the scope of conflict and the mobilization of allies and opponents.
Actors wanting to maintain the status quo will use science to fortify the
legitimacy of current processes, limit any negative attention, and dampen the mobilization
of potential opponents. Actors wanting punctuations will use science to
challenge the legitimacy of current processes, publicize negative aspects of the policy
issue, and mobilize allies for change (see Pralle, 2006). The seminal example is the
nuclear power industry in the twentieth century (Baumgartner & Jones, pp. 59–82;
Duffy, 1997). It was scientists who helped create the positive image of the industry
immediately after World War II and fostered incremental policy change for nearly 20
years. It was also scientists who leaked safety concerns in the 1960s that eventually
modified the policy image and led to major changes in the industry in the 1970s. The
implication from the punctuated equilibrium is fourfold: (i) the causal driver within
the punctuated equilibrium theory is the pace with which actors process expertbased
information; (ii) disproportionate information processes result in creating,
maintaining, or destroying a policy image; (iii) expert-based information affects the
expansion of conflict expansion and mobilization of political interests; and (iv)
expert-based information can be a contributing factor to both incremental and major
policy change.
Social Construction Theory
The social construction theory, as presented by Ingram et al. (2007) and
Schneider and Ingram (1997), focuses on the interdependence of power and social
constructions of target populations to shape policy designs and to enhance or
degrade democratic processes. The social construction theory depicts the perceived
content and quality of expert-based information as socially constructed phenomena.
The use of expert-based information is contingent on the composition of the scientific
community, prevailing social constructions, and the distribution of power
(Schneider & Ingram, pp. 150–88). Critical factors for understanding science in the
social construction theory include the extent that (i) the scientific community is
divided or united; (ii) the policy community is divided or unified; and (iii) the
scientific and technical information presents risks or opportunities to powerful
groups (Ingram et al., p. 109; Schneider & Ingram, pp. 150–88). The implication from
the social construction theory is that expert-based information depends heavily on
political context, and it can be used to reinforce or challenge prevailing social constructions
of target populations and policy designs.
618 Policy Studies Journal, 36:4
The Advocacy Coalition Framework
The advocacy coalition framework views policy as translations of beliefs from
competing coalitions (Sabatier & Jenkins-Smith, 1993). Expert-based information
affects policy indirectly by slowly altering the beliefs of policy actors in a process
called “policy-oriented learning” (Sabatier, 1987; Sabatier & Zafonte, 2001; Weiss,
1977). During intense conflicts, the advocacy coalition framework predicts that (i)
expert-based information becomes a valuable coalition resource to mobilize allies
and to argue with opponents; and (ii) policy-oriented learning occurs within one
coalition rather than between coalitions (Sabatier, 1987). Learning across coalitions
will more likely occur when conflict is at intermediate levels, when there is a professional
forum, when both coalitions have access to technical resources to engage in
debate, and when discussions focus in part on secondary aspects of their belief
systems (Sabatier, 2005; Sabatier & Weible, 2007, p. 220). Within the advocacy coalition
framework, science indirectly influences policy through learning and belief
change, and it is also dependent on both the level of conflict among coalitions and
the availability of institutional forums enabling discourse among coalitions.
These four theories provide different insight into the use of expert-based
information in policymaking, especially compared to the policy cycle. Expert-based
information serves political uses from reinforcing social constructions of target
populations to fortifying arguments of advocacy coalitions. Science often shapes
policy indirectly by modifying policy images and by changing beliefs through learning.
The multiple streams theory, the advocacy coalition framework, and the social
construction theory emphasize that the role of science is dependent on the policymaking
context.
Part 2. Three Uses of Expert-Based Information
One way to draw lessons from the policy process theories about expert-based
information would be to make a list of all the ways that science is acquired, learned,
and applied in policymaking. Such a list would showcase the nexus between science
and policy but would not necessarily facilitate communication among scholars or
guide research in the area. What is needed is a depiction of expert-based information
in policy that simplifies the complexity. This section describes three uses of science in
the policy process, drawing from the research utilization literature (Caplan et al.,
1975; Dunn, 1994; Knorr, 1977; Pelz, 1978; Weiss, 1979).
Learning
The learning use of expert-based information focuses on the cognitive processes
of policy participants. The learning use is derived from Weiss (1977) who argued that
the accumulation of science slowly and indirectly affects policy by altering decision
makers’ beliefs about the causes of problems and preferred solutions. The argument
is that a single research study or report rarely has a significant impact on the beliefs
of political actors or on any single policy decision; research instead affects policy
Weible: Expert-Based Information and Policy Subsystems 619
indirectly by accumulating, like sedimentation, and gradually altering the belief
systems of the actors involved in a policy process. The learning use is the basis for
policy-oriented learning and one path for belief and policy change in the advocacy
coalition framework (Sabatier, 1987), and it also parallels the long-term effects of
science on government in the multiple streams theory (Kingdon, 1995). In the punctuated
equilibrium theory, the learning use is found in its theory of information
processing, where expert-based information shapes learning disproportionately
through a slow reluctance to accept contradictory information and with dramatic
shifts in attempts to overcompensate for past decisions. From a punctuated equilibrium
perspective, the learning use contributes to the accumulation of information
and the radical shifts in attention and punctuations in policy.
Political
When decision makers rely on expert-based information to legitimize previously
made policy decisions, information is being politicized (Fischer, 2000; Jasonoff, 1990;
Jenkins-Smith, 1990; Ozawa, 1991; Schneider & Ingram, 1997). The political use may
include the distortion and/or the selective use of information. In the policy process
theories, examples of the political use include various strategies: (i) creating, maintaining,
and destroying policy images in the punctuated equilibrium theory and
social constructions in the social construction theory; (ii) convincing coalition allies
to mobilize around a given issue or to counter arguments by opponents in the
advocacy coalition framework; and (iii) attempting to tie policy ideas to problems by
policy entrepreneurs in the multiple streams theory.
Instrumental
The instrumental use occurs when expert-based information directly affects
policymaking. The instrumental use is based on the rational, ideal approach to
problem solving where a problem exists, research is conducted, and the decision
follows the research findings (Amara et al., 2004; Caplan et al., 1975; Weiss, 1979).
Science used in this way might include direct impacts from science on forecasting
impacts, highlighting trade-offs, and evaluating impacts of current policies and programs.
In contrast to the political use, the instrumental use of expert-based information
often requires a willingness to entertain outcomes that conflict with beliefs.
Furthermore, the instrumental use is more observable and possibly more attributable
to one or more information sources, in contrast to learning. Depending on the
decision-making context, the instrumental use of expert-based information can be
found in the policy process theories. For example, the advocacy coalition framework
would predict that the instrumental use of science will more likely occur in professional
forums where coalitions work cooperatively often with scientists. The social
construction theory would predict that the instrumental use of science will more
likely occur when there is consensus among scientists and policy participants. The
instrumental use of expert-based information is not inconsistent with, but is harder
to find in, the punctuated equilibrium theory and multiple streams theory. One could
620 Policy Studies Journal, 36:4
speculate from a multiple streams perspective, for instance, that policy participants
are more likely to use expert-based information instrumentally when the problem
and policy streams are flowing independently from the political stream and less
when entrepreneurs attempt to merge streams.
Part 3. The Three Policy Subsystem Types
If the political context is not considered, the three uses of expert-based information
can oversimplify and misrepresent the nexus between science and policy. The
question is not whether a use exists or does not exist—all three uses occur in a policy
process at any moment in time. Instead, the question is under what contexts will one
usage reinforce or dominate the others (Amara et al., 2004). The next challenge is to
develop an understanding of different types of political contexts to subsequently
draw lessons about the role of expert-based information in each.
The first step in understanding policy processes is to decide on a level of analysis.
This article focuses on policy subsystems, as depicted explicitly in the punctuated
equilibrium theory and the advocacy coalition framework (Baumgartner & Jones,
1993; Sabatier & Jenkins-Smith, 1993). Definitions of policy subsystems vary across
the literature with a number of variants including whirlpools, subgovernments, iron
triangles, and issue networks (see McCool, 1995, pp. 251–411). Borrowing from the
advocacy coalition framework, policy subsystems are semiautonomous decisionmaking
networks of policy participants that focus on a particular policy issue usually
within a geographic boundary (Sabatier, 1987).
Taking a policy subsystem approach to understand political context is not
enough to understand the use of expert-based information in policymaking.
Depending on a person’s perspective, the context of a policy subsystem might
include cultural factors, institutions and rules, socioeconomic or environmental conditions,
relations among actors, social constructions, power, and authority.4
To simplify,
three ideal types of policy subsystems are defined and presented in Table 1.
The first, a unitary policy subsystem, includes a single, dominant coalition that is
similar to an iron triangle (Freeman, 1955) or a policy monopoly (Baumgartner &
Jones, 1993). The second type, a collaborative policy subsystem, involves cooperative
coalitions where conflict is at intermediate levels. The third is an adversarial policy
subsystem, characterized by high conflict among competitive coalitions. Table 1
identifies five attributes of each policy subsystem type and the following paragraphs
define each of these attributes.
1. Coalitions are defined mostly in accordance with the advocacy coalition framework.
The advocacy coalition framework defines coalitions by compatible policy
core beliefs and by similar coordination patterns (Sabatier & Jenkins-Smith,
1993). The advocacy coalition framework would predict high intra-coalition
belief compatibility across all policy subsystem types. At the subsystem level,
belief compatibility among actors refers to the degree of convergence or divergence
in belief systems among all subsystem actors, including those from different
coalitions (Jenkins-Smith, 1990, p. 95). The three subsystem types in
Weible: Expert-Based Information and Policy Subsystems 621
Table 1 vary from high compatibility in beliefs for unitary subsystems where
opponents are largely nonexistent, to low compatibility in beliefs for adversarial
subsystems where coalitions are very competitive, and to intermediate levels of
compatibility where coalitions continue to disagree but agree enough to cooperate
in collaborative subsystems.
Coalitions are also defined by their coordination patterns among allies and
opponents. Coordination includes a range of activities from developing and
executing joint plans to modifying behavior to achieve similar or noninterfering
objectives (Sabatier & Jenkins-Smith, 1999, pp. 138–41). Whereas earlier versions
of the advocacy coalition framework assumed that all coalition members interact,
this assumption has been shown to be unrealistic (Nahrath, 1999; Schlager, 1995).
It is more plausible and still consistent with the framework to assume that
coordination among members will vary based on the centrality of a given issue
to the members’ beliefs and to the members’ resources. Coalition members could
then be classified as either auxiliary or principal members (Hula, 1999; Silva,
2007; Zafonte & Sabatier, 2004). Principal members are central to the coalition
and coordinate the majority of coalition activities. The expectation is that principal
coalition members are directly connected to nearly all coalition members or
separated from other coalition members by one or two other coalition members.
These principal members serve as entrepreneurs for a coalition in providing
leadership and in bearing the transaction costs in coordinating activities. In
Table 1. A Summary of Three Ideal Types of Policy Subsystems
Unitary Subsystems Collaborative
Subsystems
Adversarial Subsystems
1. Coalitions Single coalition with
high intra-coalition
belief compatibility
and high intracoalition
coordination
Cooperative coalitions
with intermediate
inter-coalition belief
compatibility and high
inter and intracoalition
coordination
Competitive coalitions
with low intercoalition
belief
compatibility and high
intra-coalition and
low inter-coalition
coordination
2. Policy images Single Reconciled Debated
3. Degree of
centralization and
interdependence
Authority is centralized
and interdependence
with other subsystems
is ignored
Authority is
decentralized,
fragmented across
policy subsystems, or
both. Coalitions share
access to authority.
Authority is centralized
but fragmented within
the policy subsystem,
fragmented across
policy subsystems, or
both. Coalitions
compete for access to
authority
4. Venues Coalition influences
decisions in one or
two amiable venues
(legislature, agencies)
Coalitions use a variety
of venues, including
ones based on
consensus-based
institutions
Coalitions seek to
influence decisions in
any amiable venue
(courts, legislatures,
agencies)
5. Policy designs Policies distribute
benefits to single
coalition
Policies are voluntary,
win–win, and flexible
in means
Policies are coercive,
win–lose, and
prescriptive in means
622 Policy Studies Journal, 36:4
contrast, auxiliary members are peripheral to a coalition’s network and coordinate
with only a few other coalition members. This modified definition of coordination
patterns within coalitions continues to assume that policy core beliefs
are the glue that binds coalitions together but now assumes that some members
will anchor the coalition as central participants whereas others will serve as
auxiliary members on the periphery.
The three subsystems vary by the degree that coordination patterns are
primarily within coalitions or between coalitions. In unitary and adversarial
subsystems, coordination patterns will primarily occur within a coalition among
allies. In collaborative subsystems, coordination patterns will include more
cross-coalition interactions or a broker that connects opposing coalitions.5
2. Policy images are projected social constructions or public translations of a coalition’s
beliefs that frame events and serve as sound bites, campaign slogans, and
causal stories (Baumgartner & Jones, 1993; Schneider & Ingram, 1997; Stone,
1997). To achieve their belief-driven objectives, coalitions project and defend
policy images to contest an opponent or to attract positive or negative attention
to the policy subsystem. Such a process is consistent with Jenkins-Smith’s (1990)
description of subsystem conflict as including the “struggle over the manipulation
of the shape and content of the policy space” (p. 86). The principal audiences
of a coalition’s policy image include actors in other subsystems, the
general public, and macropolitical actors. Following the logic of the punctuated
equilibrium theory (Baumgartner & Jones, 1993; Pralle, 2006), a stable policy
image serves to insulate policy subsystems from external interference whereas
an unstable policy image can attract the attention of macropolitical actors with
the potential to alter the distribution of resources and authority in the subsystem.
The three policy subsystems vary with unitary policy subsystems
showing a single policy image, collaborative policy subsystems showing a reconciled
policy image, and adversarial policy subsystems showing contested
policy images.
3. Degree of centralization and interdependence. Policies are made by actors with
authority to make decisions and in most industrialized, democratic systems,
authority is fragmented. The fragmentation of authority in a policy subsystem is
a function of two factors: The first is the degree of centralization or devolution
and the second is the degree of interdependence from other policy subsystems.
In federalist systems, for example, as decentralization and devolution increases,
authority shifts from federal government agencies toward state and local government
agencies and from the exclusion to the inclusion of interested and
affected nongovernment actors in decision making. As centralization increases,
authority shifts toward federal government agencies and possibly state agencies.
The implication is that some coalitions gain and others lose, as authority shifts
between centralized and decentralized locales. The extent that centralization
helps a coalition is contingent on the extent that the authority rests with government
agencies sympathetic to the coalition’s objectives or is divided with other
government agencies counter to a coalition’s objectives.
Weible: Expert-Based Information and Policy Subsystems 623
The authority in a policy subsystem is also affected by the degree of interdependence
with other policy subsystems (Scholz & Stiftel, 2005). Policy subsystems
are semiautonomous and always overlap so events in one policy
subsystem will, to various degrees, affect other policy subsystems (Fenger &
Klok, 2001; Zafonte & Sabatier, 1998). Given overlap with multiple policy subsystems,
coalition members tend to pay attention to other policy subsystems
when they can secure political gains or prevent political losses. Thus, the interdependence
of policy subsystems should not be viewed as a politically neutral
phenomenon but instead as a situation where coalitions can gain leverage over
another coalition or where conflict emerges between coalitions from different
policy subsystems.
A winning policy subsystem configuration from a coalition’s perspective can
be found in a unitary policy subsystem where authority is centrally located within
one or just a few government agencies sympathetic to a coalition’s objectives and
where other policy subsystems are ignored. A more confrontational situation can
be found in adversarial policy subsystems where authority is centrally located but
also fragmented across multiple government agencies aligned with competing
coalitions, where conflict between coalitions arises from the intersection with
other policy subsystems, or both. In collaborative policy subsystems, authority
tends to be more decentralized and devolved and the intersection with other
policy subsystems can be a point of tension, but such tensions are also mitigated.
In contrast to adversarial policy subsystems, coalitions in collaborative policy
subsystems are more likely to use consensus-based institutions to help share
access to authority and overcome the fragmentation within and between
subsystems.
4. Venues are decision-making arenas where coalitions attempt to influence decisions
made by each other and, particularly, by government agency officials
(Baumgartner & Jones, 1993; Pralle, 2006; Sabatier & Jenkins-Smith, 1993).
Traditional venues include legislative committees and subcommittees, courts,
executives, and administrative agencies. These traditional venues dominate
adversarial and unitary subsystems and are limited in resolving conflicts because
(i) legislatures tend to sidestep conflicts by writing vague policies; (ii) administrative
agencies respond to vague legislation by making coercive top-down
decisions; and (iii) courts resolve procedural issues, not substantive disputes
(Emerson, Nabatchi, O’Leary, & Stephens, 2003, pp. 5–9). These traditional
venues also limit participation among interested and affected actors and limit
discourse by relying mostly on hearings and testimonies. In contrast, collaborative
subsystems not only include these traditional venues but also venues featuring
a configuration of consensus-based institutions. These consensus-based
institutions usually involve deliberative norms of engagement, inclusive participation,
transparent decision rules, and face-to-face negotiation. The argument is
not that coalitions solely engage in consensus-based institutions. Sometimes, for
example, two cooperative coalitions will work together to pressure officials in a
legislative venue. The argument, instead, is that collaborative policy subsystems
624 Policy Studies Journal, 36:4
will feature a consensus-based institutional venue that fosters communication
and cooperation between coalitions.
5. Policy designs are the content of a policy that “cause agents or targets to do something
they would not do otherwise or with the intention of modifying behavior to
solve public problems or attain policy goals” (Schneider & Ingram, 1997, p. 93).
Policy subsystems vary in the extent that designs are coercive or voluntary and
flexible as well as in their distribution of costs and benefits (Wilson, 1995).
Summarizing the patterns from Table 1, a unitary policy subsystem is one dominated
by a single coalition. This dominant coalition will be supported by a single
policy image that reflects a high degree of belief compatibility among members and
facilitates coordination among members. If opposition exists, they will be unorganized
and lack sufficient resources to pose any threat to the coalition’s position.
Authority in a unitary policy subsystem will be centralized within a few agencies
that serve as principal members of the dominant coalition. The preferred instruments
will distribute benefits to coalition members and costs across society or will be
flexible and voluntary in compliance. The dominant coalition will maintain the status
quo by continuing to assert influence in just a few venues, by making incremental
policy choices, and by dampening internal and external events that might attract the
attention of the general public or macropolitical actors.
Collaborative policy subsystems include cooperative coalitions who continue to
disagree but who are able to find enough common ground to negotiate and work
together. Access to authority is shared among coalitions, and negotiations occur
in venues featuring open participation rules, transparent decision making, and
consensus-based decision rules (Sabatier et al., 2005). Opponents regularly engage
each other face-to-face; and cooperation between opponents is often aided by effective
brokers. The coalitions continue to coordinate with allies but cross-coalition
coordination occurs. Cooperative coalitions prefer policy instruments that are flexible
in means or voluntary in compliance.
Adversarial policy subsystems will include competitive advocacy coalitions
with incompatible beliefs and different patterns of coordination. Authority will be
fragmented between coalitions. Competitive coalitions will usually be anchored by
government agencies or a powerful interest group and have access to sufficient
resources to challenge each other in framing the policy image and in accessing
venues. Inter-coalition conflicts are compounded because coalitions prefer coercive
and prescriptive policies. A competitive coalition trying to change the status quo will
try to expand the scope of conflict outside of the policy subsystem by attracting
attention of supportive macropolitical actors or other subsystem actors (Pralle, 2006).
A competitive coalition wanting to maintain the status quo will try to keep decisions
within a subsystem and dampen conflict escalation (Pralle, 2006).
Part 4. Expert-Based Information and Policy Subsystems
This section describes four attributes of expert-based information across the
three types of policy subsystems.
Weible: Expert-Based Information and Policy Subsystems 625
1. Analytic compatibility. Analytic compatibility is the extent that experts active in a
policy subsystem share similar theories and methods in understanding and
explaining phenomena in a policy subsystem. Jenkins-Smith (1990) described a
similar category called “analytic tractability” in reference to problem attributes.
This article shifts his definition from problem attributes to expert attributes. The
rationale is that complexity or tractability of a phenomenon is based on the state
of existing theory and methods and less on the problem itself (King, Keohane, &
Verba, 1994, p. 10). Analytic compatibility assumes that experts with similar
analytical approaches or with similar “scientific paradigms” will elevate similar
components of a system to study, use similar methods for measuring the chosen
components, and make similar arguments of cause and effect within a system
(Kuhn, 1970; Sabatier & Zafonte, 2001).
In studying experts in the policy process, analytic compatibility can be
defined operationally by matching similar academic disciplines, subdisciplines,
and other fields of study (Barke & Jenkins-Smith, 1993). The fundamental
assumption underlying analytic compatibility is that disciplines represent a
mobilization of bias in comprehending complex phenomena (Kuhn, 1970).6
The
explanatory power of disciplines as an operational measure of analytic compatibility
should not be overemphasized. Individuals are too complex to
assume that the behavior of experts can be boiled down to their disciplinary
training. Instead, the argument is that public policy researchers will be able to
explain a portion of the variance in expert behavior in a policy process by
measuring their discipline and matching the analytical assumptions of a given
discipline to the coalitions’ beliefs. Outside of policy core beliefs, the expectation
is that analytic compatibility will provide a secondary basis for bonding
experts to coalitions. The idea is that a coalition will more likely listen
to experts if the experts’ analytical approach reinforces a coalition’s belief
system.
2. Treatment of uncertainty and risk. Probably the most important attribute in studying
expert-based information in the policy process is the treatment of uncertainty
and risk. Scientific and technical uncertainty in a policy subsystem includes three
dimensions: (i) inability of actors to know the components that define and affect
a subsystem, including problem seriousness, various causes, or plausible actions
and consequences; (ii) inability to measure and understand components of a
subsystem; and (iii) inability to know the links or probabilities between actions
and consequences (Jones, 2001, p. 48; Rowe, 1977). Uncertainties translate into
risk when the probabilities linking actions to consequences are known (Knight,
1921). Both uncertainties and risks are used in subsystem politics. Uncertainty,
for example, provides actors with an opportunity to use their beliefs to interpret
unknowns in a decision situation. By emphasizing unknowns, subsystem actors
can raise anxiety and fear among the general public and macropolitical actors or
counter an opponent with a rival causal story (Stone, 1997). Emphasizing uncertainty
can increase perceptions of formidable transaction costs, thereby threatening
collective action within and between coalitions. Risks are also useful in
626 Policy Studies Journal, 36:4
politics but for different reasons. The political power of risks is the ability to link
a cause with an effect—no matter how probable or improbable.
3. Experts and coalitions. Experts will become members of a coalition based on
shared beliefs and because their information will likely buttress a coalition’s
arguments. Experts join coalitions, especially in unitary and adversarial subsystems,
because their information will be largely ignored otherwise (Sabatier,
1987). Coalitions seek experts as allies because of the legitimacy of expert-based
information in helping to make and implement decisions. Extant research has
shown that scientists can be coalition members by matching the beliefs of scientists
with the coalition’s beliefs and by asking all subsystem actors to identify
information sources, allies, and coordination partners (Weible & Sabatier, 2005).
4. Policy-oriented learning. Policy-oriented learning is defined as “relatively enduring
alternations of thought or behavioral intentions that result from experience
and/or new information and that are concerned with the attainment or revision
of policy objectives” (Sabatier & Jenkins-Smith, 1999, p. 123). Policy-oriented
learning is adaptive learning and involves interpreting mistakes, making strategic
adjustments, and trying new strategies for goal attainment (Sabatier, 1987).
Learning, however, is limited by the actors’ cognitive abilities. Responding to
complex external stimuli, actors must simplify based on previously learned
strategies, by belief heuristics as filters, and by focusing their attention (Jones,
2001). Actors with similar simplification strategies will probably find it easier to
learn among each other. Thus, policy-oriented learning is easier within a coalition
where members share similar belief systems than between coalition where
opponents likely disagree (Sabatier & Jenkins-Smith, 1999, pp. 145–7). Among
experts, another factor affecting policy-oriented learning is analytic compatibility.
The argument is that the higher the analytical compatibility of scientists, the
more likely they will learn from each other. Whether it is learning between
coalitions or between experts, policy-oriented learning is not a politically neutral
process but instead contributes to a policy subsystem’s social constructions
(Schneider & Ingram, 1997), policy images (Baumgartner & Jones, 1993), and
policy spaces (Jenkins-Smith, 1990).
From Tables 1 and 2, the following arguments emerge about the use of
expert-based information in the three subsystem types. In unitary subsystems,
the analytical compatibility will be high because scientific and technical experts
are aligned with the dominant coalition. In such a situation, experts will agree
on which components of the subsystem to recognize, on methodological
approaches, and on perceptions of uncertainties and risks. The political role of
experts will be to report positive news to macropolitical actors and the general
public. Scientists will be fairly central as a source of information but will be on
the periphery as important allies and coordination partners. In unitary subsystems,
learning will be constrained within the dominant coalition, largely
reinforcing preexisting beliefs or the analytical approaches of active scientists.
In collaborative subsystems, subsystem actors will seek to integrate local
information and expert-based information in consensus-based institutional
Weible: Expert-Based Information and Policy Subsystems 627
venues. Actors will recognize the limits of information and proceed adaptively
through joint fact-finding strategies (Dewitt, 1994; Lee, 1993; Norton, 2005;
National Research Council [NRC], 1996). Cooperation across coalitions will coincide
with cooperation across different analytical methods of inquiry. The result
will be interdisciplinary approaches to problem solving. Scientists will continue
to be coalition members but their centrality, especially in ally and opponent
networks, will decrease.7
Policy-oriented learning occurs across coalitions and
disciplines.
In adversarial subsystems, coalitions will diverge in their analytical
approaches to problems solving and in their perceptions of uncertainties and
risks. Coalitions will use uncertainty and risk to boost their preferred policy
image or to challenge a policy image of a rival coalition. Because of the political
value of expert-based information, experts will become central allies in their
coalition. Consequently, experts will also become central opponents to a rival
coalition. Learning will reinforce beliefs within coalitions and among experts
with similar analytical approaches.8
Part 5: Stating Propositions
Given Tables 1 and 2, a number of propositions can be derived about the role of
expert-based information in the policy process. Two sets of propositions are presented.
The first set includes three propositions about the use of expert-based information
in policy subsystems.
1. The political use of expert-based information will be highest in adversarial subsystems.
The rationale for the first proposition derives from the high-value conflicts in
adversarial subsystems, making expert-based information appealing as a political
weapon to argue against opponents.
2. The instrumental use of expert-based information will vary from the highest in collaborative,
to an intermediate level in unitary, and to the lowest in adversarial policy
subsystems. Science will least likely be used instrumentally in adversarial policy
subsystems because actors will primarily be set on defeating opponents and
Table 2. The Use of Expert-Based Information in Three Types of Policy Subsystem
Unitary Subsystems Collaborative Subsystems Adversarial Subsystems
1. Analytic
compatibility
Experts agree on theory,
data, and methods
Experts reconcile
differences in theory,
data, and methods
Experts disagree on
theory, data, and
methods
2. Treatment of
uncertainty
and risk
Uncertainty used for
political gains
Uncertainty acknowledged
and decisions proceed
adaptively
Uncertainty used for
political gains
3. Experts and
coalitions
Experts serve as auxiliary
allies
Experts serve as auxiliary
allies or opponents
Experts serve as principal
allies or opponents
4. Policy-oriented
learning
High intra-coalition
learning and no
inter-coalition learning
High intra-coalition
learning and high
inter-coalition learning
High intra-coalition
learning and low
inter-coalition learning
628 Policy Studies Journal, 36:4
reinforcing their policy positions and not on following the suggestions from
expert-based information. The instrumental use will most likely be found in
collaborative policy subsystems because of the potential for iterative, joint factfinding
to get the right science and to get the science right for decision-making
actors (Ehrmann & Stinson, 1999; NRC, 1996). The instrumental use will be
found in unitary, as long as the science reinforces the status quo of the subsystem,
but will be ignored otherwise because of the homogeneity of beliefs among
members of the dominant coalition.
3. Learning will occur within coalitions or among experts with similar analytical
approaches in all subsystems and will most likely occur across coalitions or across
experts with dissimilar analytical approaches in collaborative subsystems. In adversarial
and unitary subsystems, learning will mostly reinforce existing beliefs or
analytical methods. The intermediate level of conflict and presence of consensusbased
venues in collaborative subsystems makes it the best type of policy
subsystem for learning across coalitions or across different analytical
approaches.9
The second set of propositions summarizes the rationales for shifts from one
subsystem to another and the role of expert-based information in each.
1. A shift from a collaborative subsystem to a unitary subsystem will occur under two
conditions: (i) when there is a decrease over time in the diversity of participants relative
to the diversity of the actors affected by subsystem decisions; and (ii) when there is a
decrease over time in attention given to the subsystem by macropolitical actors and the
general public. The participation within a collaborative policy subsystem can be
reduced by human time constraints, lack of media attention by the media, loss of
subsystem salience, and the alienation of new actors by the emergent vocabularies
and social constructions (Leach, 2004; Norton, 2005; Schneider & Ingram,
1997).
2. A shift from a unitary subsystem to an adversarial subsystem will occur when there is
an increase in participation by macropolitical actors and/or by new actors from the same
or from a competitive policy subsystem. This proposition is a simplified restatement
of the collapse of a policy monopoly (Baumgartner & Jones, 1993; Redford, 1969;
Schattschneider, 1960).
3. A shift from a collaborative subsystem to an adversarial subsystem will occur when new
actors begin to participate from a competing policy subsystem and/or after an internal or
external event alters the balance of power between existing coalitions. This proposition
focuses on the increasing interdependence of competing policy subsystems
(Redford, 1969) or from the reemergence of conflict from an event that radically
shifts subsystem policies (Sabatier & Jenkins-Smith, 1993).
4. A shift from an adversarial subsystem to a collaborative subsystem will occur after a
hurting stalemate when the existing coalitions exhaust the available venues and view the
status quo as unacceptable. This proposition is a restatement from the advocacy
coalition framework (Sabatier & Jenkins-Smith, 1999, p. 150).
Weible: Expert-Based Information and Policy Subsystems 629
Conclusions
This article seeks to understand the use of expert-based information in policy
from the perspective of four major theories of the policy process, adding to the
related literature (e.g., Ingram et al., 2004; Jenkins-Smith, 1990; Kingdon, 1995; Lee,
1993; Ozawa, 1991; Sabatier, 1987; Schneider & Ingram, 1997). Building on previous
works, the emphasis here is on the comparison of different uses of expert-based
information across three types of policy subsystems and by summarizing and deriving
testable propositions.
The findings in Tables 1 and 2 rely mostly on the advocacy coalition framework,
the punctuated equilibrium theory, and the social construction theory. These three
analytical approaches are complementary because they share similar assumptions,
share similar causal logic about the policy process, and can be easily applied to
subsystem politics. The multiple streams theory was of less utility, probably because
its logic applies more to macropolitics than subsystem politics. For instance, the
multiple streams theory assumes fluid participation among participants, which
better characterizes macropolitical behavior than subsystem behavior. However, the
logic in the multiple streams theory about coupling ideas and problems is consistent
with the relationships presented in Tables 1 and 2.
This review augments the advocacy coalition framework in at least four ways.
1. The advocacy coalition framework predicts that conflicts will generally occur
between coalitions within policy subsystems. This article argues that conflicts
can also occur between coalitions from different policy subsystems. While
similar arguments are made by Fenger and Klok (2001) and Zafonte and Sabatier
(1998), conflict between coalitions from different subsystems is better conceptualized
through the expansion of conflict and venue shopping as found in the
punctuated equilibrium theory.
2. The advocacy coalition framework defines coalitions as actors with similar policy
core beliefs and coordination patterns. This article adds to this definition by
formalizing two types of coalition members: principal and auxiliary (Hula, 1999;
Silva, 2007; Zafonte & Sabatier, 2004).
3. Policy images (or social constructions) are among the most important concepts in
the punctuated equilibrium and social construction theories. However, both of
these theories have been unclear with respect to the source of policy images. In
the advocacy coalition framework, policy images become public projections of
belief systems.
4. The advocacy coalition framework connects scientists to coalitions based on
similar beliefs. This article adds analytical compatibility as a secondary bond
between scientists and coalitions.
In outlining the elements and relationships in Tables 1 and 2 and in deriving the
propositions, an effort was made to meet the criteria for generating theories in King
et al. (1994, pp. 99–114) and in Sabatier (1999, pp. 262–6). First, an attempt was made to
630 Policy Studies Journal, 36:4
define clearly the concepts and to make them logically coherent. Second, relationships
in Tables 1 and 2 have two causal drivers: either beliefs from the advocacy coalition
framework or attention from the punctuated equilibrium theory. Third, the propositions
are falsifiable. Fourth, the intended scope is one or more policy subsystems.
Fifth, it offers nonobvious propositions. Sixth, it is based on a review of, and an
attempt to improve, existing theories and frameworks.
To a great extent, this review and synthesis simplifies the use of expert-based
information, the role experts, and policy subsystems. In the face of complexity,
however, the best strategy is to proceed with explicit and clear simplifying strategies
that can aid communication, guide behavior, and—most importantly—be tested and
applied empirically. There is probably no better way of learning from mistakes and
proceeding adaptively as researchers.
Christopher M. Weible is an assistant professor at the University of Colorado
Denver, School of Public Affairs.
Notes
Versions of this article have been presented at the 2007 annual meeting of the American Political Science
Association, the 2007 symposium on Collaborative Governance in the West at Oregon State University,
and the 2008 Arthur M. Sackler Colloquia at the National Academy of Sciences. I wish to thank Austin
Dickson, Edella Schlager, Ed Weber, Brent Steele, Mark Lubell, Paul Sabatier, Dorothy Daley, Richard
Barke, Gordon Kingsley, and two anonymous reviewers for comments.
1. Researchers can look to the work by Barke and Jenkins-Smith (1993), who show that different organizational
affiliations of scientists shape perceptions of risks.
2. Another major policy process theory is the institutional analysis and development framework (Ostrom,
1990, 2005). The IAD framework seeks to explain individual behavior and collective action within
action arenas that are shaped by physical, community, and institutional factors (Ostrom, 1990). In the
IAD framework, information is used by actors mostly to reduce transaction costs, identify important
strategies, and facilitate collective action (Schlager, 2007, p. 301). While the IAD framework mostly
expects direct, instrumental uses of information, it does not preclude the opportunistic use of information
for personal gain (Schlager, 2007). The implication from the institutional analysis and development
framework is twofold: (i) information is typically used instrumentally and affects the extent that
actors can act collectively; and (ii) the opportunistic use of information is possible. The institutional
analysis and development framework is not discussed in this review because it downplays science,
learning, and the role of coalitions and because it is not a subsystem framework.
3. The multiple streams theory has been labeled a framework (Zahariadis, 2007). In this article, I follow
Ostrom’s (2007) definitions of frameworks, theories, and models, and adopt Schlager’s (1995) classification
that multiple streams is better categorized as a theory than a framework. I use similar arguments
for labeling the social construction theory. For simplicity, I often refer to the frameworks and theories
of the policy process as just “theories.”
4. Other researchers have categorized different kinds of policy subsystems (see Howlett & Ramesh, 2003;
McCool, 1995).
5. Two types of coalitions are presented: competitive and cooperative. Competitive coalitions exist in
adversarial policy subsystems and are defined by high levels of distrust, polarized beliefs, and mostly
internal coordination patterns. In contrast, cooperative coalitions exist in collaborative policy
subsystems and have intermediate levels of trust and belief differences and more cross-coalition
interactions.
6. Empirical support for the analytical bias of academic disciplines can be found in Barke and JenkinsSmith
(1993). Recognizing that disciplines create analytical bias is not an original argument (e.g., Snow,
1964). It is, however, a salient topic today. The movement across academia for interdisciplinary eduWeible:
Expert-Based Information and Policy Subsystems 631
cation is an institutional response to the limits of uni-disciplinary education in solving complex
problems. Similarly, a number of recent publications discuss biases in academic disciplines and the
effects on public policy (e.g., Cohen, 2006; Norton, 2005). Cohen (2006), for example, offers a multidisciplinary
framework to help policy actors counter the “deep analytic bias” in their disciplinary
approaches to understanding environmental policy (p. 12).
7. Weible (2007) found that the citations to university scientists and consultant decreased for ally and
opponent networks in two collaborative compared to two adversarial policy subsystems.
8. In collaborative and adversarial policy subsystems, the coalitions might vary in the extent that they rely
on science. Some adversarial and collaborative policy subsystems will involve coalitions with active
scientists as members. Other collaborative and adversarial subsystems will involve a coalition with
members from the scientific community and another without. When a coalition with experts faces a
coalition without experts, issues of technocracy and democratic accountability of expert-based decisions
will likely emerge (see Fischer, 2000; Lach, List, Steele, & Shindler, 2003; Schneider & Ingram,
1997).
9. This proposition builds heavily from the advocacy coalition framework (Sabatier & Jenkins-Smith,
1993). A glance at the relationships in Tables 1 and 2 suggests additional propositions and hypotheses.
For example, Table 2 suggests the following: In collaborative compared to adversarial policy subsystems,
the centrality of experts in coalitions will decrease. One of the advocacy coalition framework’s
hypotheses is in Table 2: “Policy-oriented learning across belief systems is more likely when there is an
intermediate level of informed conflict between the two coalitions” (Sabatier & Jenkins-Smith, 1999,
p. 124).
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