A decision inventory approach for improving decision support for climate change impact assessment and adaptation

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 0 ( 2 0 0 7 ) 6 1 0 – 6 2 1

A decision inventory approach for improving decisionsupport for climate change impact assessment andadaptation

Christopher R. Pyke a,*, Britta G. Bierwagen b, John Furlow c, Janet Gamble b,Thomas Johnson b, Susan Julius b, Jordan West b

aCTG Energetics, Inc., 101 N. Columbus Street, Suite 401, Alexandria, VA 22314, USAbGlobal Change Research Program, U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, NW (MC 8601 N),

Washington, DC 20460, USAcClimate Change Program, U.S. Agency for International Development, Ronald Reagan Building, Washington, DC 20523-1000, USA

a r t i c l e i n f o

Published on line 27 June 2007

Keywords:

Decision support

Decision making

Climate change

Climate adaptation

Knowledge management

a b s t r a c t

Assessing and adapting to the impacts of climate change requires balancing social, eco-

nomic, and environmental factors in the context of an ever-expanding range of objectives,

uncertainties, and management options. The term decision support describes a diverse

class of resources designed to help manage this complexity and assist decision makers in

understanding impacts and evaluating management options. Most climate-related decision

support resources implicitly assume that decision making is primarily limited by the

quantity and quality of available information. However, a wide variety of evidence suggests

that institutional, political, and communication processes are also integral to organizational

decision making. Decision support resources designed to address these processes are

underrepresented in existing tools. These persistent biases in the design and delivery of

decision support may undermine efforts to move decision support from research to practice.

The development of new approaches to decision support that consider a wider range of

relevant issues is limited by the lack of information about the characteristics, context, and

alternatives associated with climate-related decisions. We propose a new approach called a

decision assessment and decision inventory that will provide systematic information

describing the relevant attributes of climate-related decisions. This information can be

used to improve the design of decision support resources, as well as to prioritize research

and development investments. Application of this approach will help provide more effective

decision support based on a balanced foundation of analytical tools, environmental data,

and relevant information about decisions and decision makers.

Published by Elsevier Ltd.

avai lable at www.sc iencedi rec t .com

journal homepage: www.e lsev ier .com/ locate /envsc i

1. Introduction

Decision support provides a link between decision making,

scientific information, and analytical tools. The annual

* Corresponding author.E-mail address: [email protected] (C.R. Pyke).

1462-9011/$ – see front matter. Published by Elsevier Ltd.doi:10.1016/j.envsci.2007.05.001

number of publications describing the development or

application of decision support systems has grown steadily

over the last three decades (Fig. 1) with applications spreading

across a broad range of disciplines (Fig. 2). Moreover, these

Fig. 1 – The number of peer-reviewed publications associated with the term ‘‘decision support’’ has expanded rapidly over

the last 30 years (dashed line) (ISI Web of Science). Peer-reviewed publications related to climate change (solid line) appear

10–15 years behind the increasing of the term in other applications. Between 2001 and 2005, climate change-related

applications represented only 2% of ‘‘decision support’’ citations.

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trends underestimates the actual extent of the practice,

because it tracks only the use of the term ‘‘decision support’’

and misses important related activities such as soil or

agricultural extension services. Despite its widespread use,

decision support is not easy to define. Research contributions

self-identifying with ‘‘decision support’’ are dominated by the

fields of computer science, engineering, operations research,

and management science (Eom, 1999; Shim et al., 2002). The

concept is also found under different labels across a range of

Fig. 2 – Distribution of peer-reviewed papers associated with the

in 2004–2005 (only categories with >1% of citations are shown).

physical, biological, and social sciences (Rauscher, 1999; Hipel

et al., 2001; Saunders-Newton and Scott, 2001; Ross et al., 2002;

Stoms et al., 2002; Owens, 2002; Fang et al., 2003; Cash et al.,

2003; Larson and Sengupta, 2004). Decision support also takes

a wide variety of forms including software tools, documents,

and work processes. This breadth reflects the importance of

understanding and assisting decision making processes, and

the profound challenge of understanding its cognitive,

behavioral, and socio-economic dimensions (Brewer and

search term ‘‘decision support’’ for 1290 papers published

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Stern, 2005). In this paper, we take a broad view of decision

support and use the terms decision support tools, systems,

and resources interchangeably. We recognize that these terms

share a common focus on understanding and assisting

decision making processes (Freeling, 1984; Gamble et al., 2004).

Recently, there have been calls for more investment in

decision support related to the issue of climate change (e.g.,

the November 2005 U.S. Climate Change Science Program

workshop, Climate Science in Support of Decision Making),

including greater attention to moving climate-related decision

support tools from research to application (Mahoney et al.,

2001). These priorities reflect a desire to provide decision-

relevant tools; however, there is much to be learned about the

best practices and approaches involved in the provision of

effective decision support.

Many researchers claim or imply that decision support

tools can help improve decision making (Matthies et al., 2007).

However, decision making objectives are rarely directly linked

to the products or outputs of decision support systems, and

the efficacy of environmental decision support systems is

typically not tested. Those engaged in development and

implementation of decision support face a substantial, often

unspoken, gap between information provided by decision

support tools and the needs, values, and, ultimately, actions of

decision makers and their organizations.

Enthusiasm for the potential of decision support for

climate impact assessment and adaptation is tempered by

findings from applications in other fields. Meta-analysis and

large-scale reviews of the effectiveness of medical decision

support systems indicate that only 50% of clinical decision

support systems yield significant changes in measurable

patient outcomes (Wears and Berg, 2005). Moreover, evalua-

tions performed by clinical decision support developers

systematically result in higher estimates of utility than those

performed by independent investigators (Randolph et al.,

1999; Kawamoto and Lobach, 2003). These statistics may hide

some less tangible benefits associated with clinical decision

support, such their value in making decision making pro-

cesses more systematic, transparent, and reproducible (Bem-

mel and Musen, 1997). However, they are also likely to provide

an overly optimistic guide to decision support applications in

environmental fields. Clinical decision support often focuses

on a fairly well-bounded interaction between doctor and

patient—a single decision maker with a clearly specified

beneficiary. Environmental decision making is often more

complicated involving multiple decision makers, a myriad of

stakeholders, a web of constraints, and competing objectives.

Rather than providing an precise analog, comparison between

medical and climate-related decision support underscores the

paucity of information on the real world performance of

environmental decision support and the likelihood of that

many applications may not yield desired results.

This paper reviews perspectives on organizational decision

making and reflects on their implications for the development

of effective decision support systems for assessing and

adapting to the impacts of climate change. The emphasis

on adaptation to climate change reflects the importance and

complexity of adaptive opportunities in areas such as

biological conservation, water resources management, and

air quality protection. We consider climate-related decision

support systems currently in use with respect to their implied

decision making perspectives. We highlight gaps and biases in

current approaches and suggest a number of next steps that

could lead to more effective decision support.

2. Perspectives on decision making anddecision support

The contemporary concept of decision support and its

relationship to decision making has been a topic of discussion

since the early 1970s (Simon, 1977; Brewer and Stern, 2005).

Advances in the fields of computer science, cognitive

psychology, management science, and organizational learn-

ing have stimulated debate about the nature of human

decision making and the role of technology in supporting it

(Simon, 1979; Levitt and March, 1988; Miller, 2002). In this

section, we consider decision support with respect to four

major perspectives on decision making: rational choice,

organizational process, political process, and knowledge

transfer (Alter, 1977; Kling, 1978; Alter, 2004). We focus our

attention on aspects of these perspectives that are most

relevant to organizational decision making (Levitt and March,

1988) – the typical context for climate adaptation (Berkhout

et al., 2006). These four perspectives are not meant to be

mutually exclusive. Rather, we attempt to bound the typical

range of decision support options and illustrate how different

perspectives on decision making may be reflected in decision

support strategies (Fig. 3).

2.1. Rational choice

Classical economic actors make decisions efficiently using

knowledge about present and future states of the world

(Simon, 1979; Camerer, 1995)—a process widely described as

rational choice. This is the decision making style of the

archetypal Homo economicus (March, 1991; Boudon, 2003), and it

suggests that decision makers find the best option by

maximizing utility through the logical consideration of cost-

benefit trade-offs (Edwards and Fasolo, 2001). It draws its

power and analytical elegance from elements of neoclassical

theory, including rationality, equilibrium, competition, and

completeness of markets (Arrow, 1986). This perspective is

supported by a rich set of analytical tools and a myriad of

alternative formulations, many of which relax or elaborate on

its underlying assumptions (e.g., bounded rationality, trans-

action cost theory, regret theory) (Coase, 1960; Loomes and

Sugden, 1982; Jones, 1999). Rational choice is often described

with respect to an individual or unitary concept of a firm or

organization, and, in some formulations, it requires an

intricate understanding of the repercussions of individual

decisions (Smith, 1991). Despite contention about its details,

rational choice often provides a practical description of the

economic behavior of large numbers of people (Blume and

Easley, 1982; McAllister, 1990). Discussion of the breadth of

interpretations of rational choice is far beyond the scope of

this paper (Jones, 1999; Boudon, 2003), and we use it here

simply to describe a perspective on decision making oriented

toward the logical analysis of specified costs and benefits,

typically with the goal of efficiency and utility maximization.

Fig. 3 – Three conceptual models illustrating relationships between four decision support perspectives. (A) A continuum view

conveys a sense of dissimilarity between rational choice perspectives traditionally used in natural and biological science

communities and political processes perspectives common in the social and political sciences. (B) A diffuse boundaries view

reflects the numerous potential gradations between fuzzy categories. (C) An end member view represents categories as

archetypes that can be combined in any combination with knowledge transfer as a component of each type.

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Rational choice decision support involves providing infor-

mation about the costs and benefits associated with decision

alternatives. From this perspective, climate impacts and

adaptive opportunities can be understood, or potentially even

optimized, by providing information necessary to evaluate

changes in utility (Kurtz and Snowden, 2003). In practice, this

perspective often is simplified to the belief that the provision

of more accurate and complete information results in better

decisions (Sarewitz, 2004).

2.2. Organizational process

In the real world, Homo sapiens outnumber Homo economicus

and, more importantly for this review, Homo sapiens acting

together in organizations (Black, 1948; Scott, 2004). Organiza-

tions are the dominant feature of our socio-economic

environment, and they impose constraints on the flow of

information, authority, and resources (Kovoor-Misra, 2002).

Organizations are also influenced by cognitive, social, and

psychological factors that predispose them to outcomes such

as status quo bias, persistent indecision, and incrementalism

(Samuelson and Zeckhauser, 1988; Moon et al., 2003; Masa-

tlioglu and Ok, 2005). Organizations are composed of indivi-

duals who vary in the alignment of their motivations with

conventional assumptions about goals of the organization as a

whole and persistent gaps in motivations between owners,

managers, and workers (Simon, 1979; Simon, 1995). In addition

to internal processes, we recognize that organizations do not

exist in isolation. They are often influenced by key external

institutions or concerns such as legitimacy or consensus

(Prakash, 2001). Consequently, the nature of interactions

within and between organizations (and their members and

leadership) is integral to understanding organizational beha-

vior and performance. As with rational choice, we cannot do

justice to the breadth of considerations that have been made

for organizational decision making (Scott, 2004). Rather, we

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use the concept to highlight a perspective emphasizing the

role of organizational processes, constraints, and configura-

tions in the relevance of climate impacts and adaptive

opportunities.

Organizational process decision support systems work by

integrating relevant information with the goals, expressed

needs, capabilities, constraints, work processes, and culture of

an organization (Rosenheck, 2001). They are most effective

when they are tailored to specific organizational configura-

tions (Gravenhorst et al., 2003) with explicit consideration for

their psychological, cognitive, and social processes (Wears and

Berg, 2005). These systems explicitly assume that the assess-

ment of climate impacts or the consideration of adaptive

options is not only limited by the absolute quality and quantity

of available information (as in the Rational Choice perspective),

but also by an organization’s ability to assimilate, understand,

and apply it (Rogers, 1995). Consequently, adaptive changes in

organizational behavior require close integration of informa-

tion technologies with work processes required to accom-

modate new information and integrate with existing goals and

practices (Levitt and March, 1988).

2.3. Political process

Decision making is always associated with a degree of control

and influence on the course of events or the allocation of

resources (Jones, 2001). A political process perspective on

decision support emphasizes the importance of resources,

power, and influence (Kling, 1978; Zucker, 1987). This

perspective recognizes that the importance of climate impacts

and the motivation and resources required for adaptation

result from a blending of scientific information with psycho-

logical, social, cultural, and political considerations (Becker,

1983; Fredriksson and Svensson, 2003). These factors deter-

mine the relative importance of issues and the feasibility of

potential alternatives. In many cases, it is challenging to

develop or apply objective criteria to compare dissimilar

environmental goals, such as the level of resources to devote

to the protection of endangered species versus children’s

health. In practice, such trade-offs are made through political

processes that are informed and influenced by a variety of

actors, including scientists and technical experts (Slovic,

1999). Resulting priorities vary between countries, such as

documented differences in acceptable versus unacceptable

water quality, air quality, and support for environmental

protection (Inglehart, 1995). For our purposes, a political

process perspective recognizes that some aspects of decision

making reflect the interests, ambitions, and constraints

associated with individuals, interest groups, and constitu-

encies (Besley and Case, 2003). In practice, this perspective

may incorporate elements of rational choice or organizational

process perspective, yet it often rests on different body of

intellectual knowledge with distinct epistemologies.

Political process decision support informs decision makers

about the importance of climate impacts or adaptive oppor-

tunities and their potential effects on interest groups and

constituencies. This perspective recognizes that understand-

ing impacts or achieving adaptive outcomes requires demon-

strating the relative importance of the issue and, ideally,

identifying benefits for key interest groups and stakeholders.

In this context, decision support systems may also help

decision makers recognize opportunities to gain resources

(e.g., funds for public works) while meeting the needs of their

constituents (e.g., maintaining clean water under combina-

tions of climate and land use change) (Munduate and Grave-

nhorst, 2003).

2.4. Knowledge transfer

Knowledge about climate impacts and adaptive alternatives is

not usually produced by managers and decision makers.

Consequently, knowledge must be conveyed from producers

to consumers (Hjorland, 2003; Hivon et al., 2005). There are

many models representing this process, and it has been widely

discussed under labels such as knowledge management and

risk communication (Fig. 3) (Leiss, 1996; Gallupe, 2001;

Gurabardhi et al., 2004). The path between knowledge

producers and consumers is often convoluted, multi-faceted,

and subject to many types of resistance (Argote et al., 2000). In

this context, we draw a distinction between information, as a

synonym for data, and knowledge, as the result of learning

and experience (Dretske, 1983). For example, information can

be easily conveyed as bits and bytes of data, but a user must

interact with and interpret information to gain knowledge

(Argote et al., 2000). Decision support systems can help

facilitate this transformation by describing the most relevant

aspects of a problem, illustrating alternative solutions, or

providing trusted tools or information (Cross and Sproull,

2004). We recognize that knowledge transfer may be an

implicit part of each of the other decision making perspec-

tives. However, as with the political process perspective, it

reflects a set of theory, evidence, and literature that cuts

across the other perspectives.

Knowledge transfer decision support systems help deci-

sion makers learn about potential impacts and adaptive

opportunities (Rayner et al., 2005). These decision support

resources can take many forms, ranging from human

educators (e.g., an agricultural extension agent) to software

tools (e.g., web pages). These decision support resources do

not ‘‘create’’ new information (e.g., through experimentation,

observation, or simulation); rather, they package and convey

information and facilitate its transformation into relevant

knowledge. The challenge is to design knowledge transfer

decision support resources tailored to the behavioral, cogni-

tive, cultural, and psychological needs of target users.

3. Characteristics of effective decision support

These perspectives represent different aspects of organiza-

tional decision making (Table 1 provides a summary). In some

cases, it may be possible to use them as testable hypotheses

about the nature of decision making in a particular situation

(Woods, 1998; Kaplan, 2001). This might entail the systematic

evaluation of the outcomes associated with the use of decision

support that allows one to evaluate the proposition, ‘‘If

decisions are made this way . . . then these are the most

appropriate decision support practices.’’

Such explicit empirical performance testing is very rare for

environmental decision support systems, but empirical

Table 1 – Summary of typical challenges and approaches associated with each decision making perspective (see Section 2for details)

Decision making perspective Typical decision making challenge Typical decision support approach

Rational choice Lack of information about costs, benefits,

and future conditions

Provide information about costs,

benefits, and future conditions

Organizational process Lack of understanding of organizational,

social, cultural, and bureaucratic constraints

Improve or complement organizational

processes, develop strategies for relaxing

organizational constraints

Political process Lack of recognition of issue or its relative

importance, lack of information about implications

for key constituencies and stakeholders

Provide information on relative importance

and implications for key constituencies

and stakeholders

Knowledge transfer Barriers to the recognition, exchange, and

understanding of information and the

development of relevant knowledge

Enable or facilitate more effect

communication and learning

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evidence is available from applications of decision support

associated with clinical medicine (Hunt et al., 1998; Kawamoto

and Lobach, 2003; Garg et al., 2005). Studies suggest that

elements of each decision making perspective are important

for the development and application of effective decision

support systems, including rational choice (e.g., information

quantity, quality, and timeliness), organizational processes

(e.g., work flows, lines of authority, and technology), political

process (e.g., organizational priorities, user buy-in, and patient

benefits), and knowledge transfer (e.g., awareness, training,

and feedback) (van der Meijden et al., 2003). Experience in

clinical setting suggests that the most effective decision

support tools view the workplace as a complex system with

strong interactions between information needs, technology,

human factors, and organizational routines (Wears and Berg,

2005). The result is a ‘‘sociotechnical’’ system designed by

keen observers of organizational goals and processes and,

ideally, independently tested to achieve specific aspects of

organizational change (Randolph et al., 1999). This analogy

may be directly relevant for some climate-related applica-

tions, but, in many cases, it would need to be extended to

encompass the broader set of issues and stakeholders

typically associated with climate change-related concerns.

4. Existing decision support systems

Given these observations and theoretical perspectives on

decision making and decision support, we next consider how

these concepts relate to existing tools for decision support

related to climate impact assessment and adaptation. The goal

is provide a sense of the state-of-practice for climate-related

decision support, while recognizing that the examples are

limited and the universe of climate-related decision support

systems is broad, poorly defined, and rapidly expanding.

4.1. Assessments

The most well-known decision support resources associated

with climate impacts and adaptation include scientific

assessments, such as the Intergovernmental Panel on Climate

Change (IPCC) assessment reports (www.ipcc.ch), the first U.S.

National Assessment of the Potential Consequences of

Climate Variability and Change (www.usgcrp.gov), and the

U.S. Climate Change Science Program (CCSP) Synthesis and

Assessment products (www.climatescience.gov). These

reports are extensive interdisciplinary syntheses of technical

information on many aspects of climate change, and they are

explicitly designed as decision support resources for policy

makers (Mahoney et al., 2001). They summarize published

research findings on climatic processes, impacts, and, for

selected systems, describe potential approaches to adapta-

tion. The reports emphasize improving the quantity and

quality of available information and implicitly reflect a

rational choice perspective.

4.2. Decision aids

General findings from climate change assessments have been

summarized into a variety of simplified decision aids, including

rule or matrix-based tools for screening adaptation options. For

example, the Adaptation Decision Matrix uses subjective

scoring to compare the relative cost-effectiveness of alternative

adaptation measures (Benioff and Warren, 1996), and the

RamCo system uses a series of structured questions to a

decision matrix to illustrate adaptive opportunities for coastal

zone management (www.riks.nl). These kinds of simplified

tools recognize that a number of barriers exist between the

research community to decision makers. Their developers have

responded with an emphasis on increasing the supply of

information with attention to knowledge transfer.

4.3. Models

Complex, interdisciplinary climate change issues seem to lend

themselves naturally to modeling of physical, biological, and

socioeconomic processes. Many groups have turned such

models into tools for evaluating impacts and, sometimes,

considering adaptation options. There is no way to sharply

delineate exploratory models from decision support tools, but

we can consider some illustrative climate-related examples

that have been described as decision support resources (Smith

et al., 1999). These tools include data access systems, climate-

related calculations, early warning and monitoring systems,

and forecasting models. Access systems include tools like the

Crop Assessment Data Retrieval and Evaluation (CADRE)

system that integrates climate data, crop models, and data

extraction software to determine crop production statistics.

Many calculation-oriented tools are associated with agricul-

tural applications, such as the Agricultural Water Resources

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and Decision-Support tool (AWARDS) and the U.N. Food and

Agriculture Organization’s CROPWAT model. These tools use

climate information to provide estimates of demand as a

function of agricultural practices, crop type, and climatic

conditions. A variety of climate-related decision support tools

augment monitoring systems and provide early warning for

environmental hazards. These systems include the AIRNow

Air Quality Index that provides real-time air quality predic-

tions for over 300 U.S. cities, the Harmful Algal Bloom Mapping

System that predicts and displays conditions in the Gulf of

Mexico, the Coral Reef Early Warning System (CREWS) which

uses expert system rules to provide daily alerts on the

likelihood and potential severity of coral bleaching events,

and the Heat-Watch System that predicts local excess

mortality associated with weather events (Sheridan and

Kalkstein, 2004). The last general type of model-based decision

support system involves quantitative forecasting tools, such

as the Invasive Species Forecasting System (ISFS). ISFS applies

predictive models and remotely sensed data to create

regional-scale assessments of invasive species patterns and

vulnerable habitats.

4.4. Scenarios

Some decision support systems do not attempt to offer any

particular view of the future; rather, they are designed to

generate and evaluate user-defined ‘‘what if’’ scenarios

(Peterson et al., 2003). For example, the Fire-Climate-Society

(FCS-1) model uses an analytical–hierarchy-process to visua-

lize fire risk associated with different combinations of future

climate, land use, and stakeholder preferences. The Water

Evaluation and Planning (WEAP) model allows users to explore

the implications of different water resource management

practices on multiple social, economic, and ecological end-

points. The Tool for Environmental Assessment and Manage-

ment (TEAM) uses a multi-criteria approach that allows users

to consider alternative adaptation strategies and evaluate

tradeoffs (Julius and Scheraga, 2000). These decision support

systems place greater emphasis on user interaction, sensitiv-

ity analysis, and capabilities for the generation of customized

reports and visualizations.

This is only a small sample of decision support systems, but

it is sufficiently representative to illustrate a pervasive

emphasis on rational choice and knowledge transfer decision

making perspectives (Sarewitz et al., 2000; Sarewitz, 2004).

These decision support systems link models and environ-

mental data to create information products (reports, maps,

visualizations) that are designed to be read or interpreted by

decision makers. This reflects an underlying hypothesis that

information makes decision makers more aware of potential

impacts and adaptive opportunities and this will lead to some

(typically unspecified) desirable outcome. However, this

reasoning is usually not stated explicitly or tied to any

particular information about constraints and opportunities

facing potential user communities. Moreover, none of these

tools report systematic testing of their effectiveness in terms

of contributions toward decision making or outcomes. In fact,

there appear to be few published tests of the performance or

cost-effectiveness of any climate-related decision support

system (but see Farrell and Jager, 2005).

This is a cause for concern because experience with

decision support systems in other fields indicates that many

systems do not yield significant changes in performance (Garg

et al., 2005). This suggests that it is possible, even likely, that

many climate-related decision support systems are not

actually helping decision makers understand climate change

impacts or adaptive opportunities. We can observe that the

developers of these tools rarely emphasize rigorous efforts to

understand the needs of decision makers, test the efficacy of

their tools with respect to decision making outcomes, or

describe the role of particular decision support tools or

products within organization work processes. Yet, experience

with decision support systems in other fields indicates that

these are essential attributes of successful decision support

applications (van der Meijden et al., 2003; Edmundson, 2003).

5. Discussion

5.1. Improving decision support

The current state-of-the-art, climate-related decision support

resource is a piece of information technology linking analy-

tical tools with environmental data or climate scenarios.

These tools have been used to help identify and describe

climate change impacts. They have demonstrated their utility

in opening lines of communication between knowledge

producers in the scientific community and decision makers.

However, this approach also reflects a number of biases and

limitations. Most existing decision support resources reflect

an implicit preference for rational choice and knowledge

transfer perspectives based on the hypothesis that more,

higher quality information will lead to better outcomes.

However, we have described contrasting and complementary

perspectives on decision making that require different

approaches to decision support. A mismatch between the

design of decision support resources and the needs and

capabilities of decision makers results in a persistent gap

between an understanding of impacts and the development of

practical, actionable alternatives. In part, this gap reflects the

lack of conceptual understanding and empirical data about the

context and characteristics of adaptive decisions.

5.2. Prioritizing among decisions

Understanding the nature of decision support resources and

their relationship to decision making processes is a necessary

foundation for effective decision support, but it is not

sufficient for the widespread use of decision support for

impact assessment and adaptation. It is relatively easy to

show that providing decision support in forms that match the

needs of decision makers is more likely to result in specific

outcomes (Freeling, 1984); however, it is also important to

understand which decisions are most likely to benefit from

decision support in the first place. This question is usually

beyond the scope of decision support development projects.

Many climate-related decision support systems are demon-

strations or prototypes (Edwards and Fasolo, 2001). Conse-

quently, the decisions chosen as the focus for decision support

are determined by the selection of a study area or set of tools.

Fig. 4 – Conceptual diagrams illustrating the difference between (a) current decision support practices and (b) a more

balanced approach to the integration of scientific information, decision analysis, and information about decision context

and characteristics.

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In other words, they are not a point of inquiry; rather, they are

a given.

This approach is compatible with research and develop-

ment, but it does not provide a foundation for a more

comprehensive or strategic view of adaptation to climate

change and climate variability. Ubiquitous resource limita-

tions and urgency for action require that we prioritize among

decisions based on their relative value for adaptation and the

likelihood that decision support will be useful to decision

makers. Decision support must be not only technically

suitable to the problem at hand (e.g., the right data and

analytical tools) but also be feasible and acceptable to

stakeholders (e.g., applied to the right decision made by the

approximate organization) (Miller, 2002; Prime Minister’s

Strategy Unit, 2004). An ad hoc approach to the selection of

subjects for the development of decision support overlooks

substantial research on the qualities of organizations that

make them interested in and capable of change (Meyer et al.,

1993; Miller, 2002; Gravenhorst et al., 2003). It is possible to

identify and prioritize opportunities among individual deci-

sions and across organizations (Hambrick and Abrahamson,

1995).

6. Next steps toward effective decisionsupport

We believe that aligning tools with user needs and identifying

promising opportunities requires new information about the

characteristics of decisions and decision makers. There is no

technological quick fix for this problem, because it is rooted in

a lack of empirical information about humans and institutions

(Simon, 1979). We can move forward by considering three

questions: (1) How are decisions made? (2) What data do we

need to collect? (3) How should we compile, organize, and

analyze the information?

6.1. Decision making processes

The most fundamental step forward is to embrace the notion

that decision making processes matter to the effectiveness of

decision support. This concept is hardly newsworthy in the

context of the expansive organizational and policy literature

on the subject of decision making. However, it appears that it

has not been adequately considered among those engaged in

climate change-related assessments and decision support

(Farrell and Jager, 2005). At the most basic level, it is important

for tool builders, often scientists and engineers, to recognize

relevant elements of general decision making theory, such as

policy process cycles, as well as specific concepts, such as

policy windows (Kingdon, 1984; Hart and Victor, 1993; Howlett,

1998). These ideas inform both the design of a decision support

resource and the likelihood that any resource will influence

outcomes. Consequently, information about decision making

processes should form the foundation of any credible decision

support research and development project.

6.2. Information needs

Future decision support systems should be tailored to the

needs of decision makers, and, ideally, reflect a more balanced

combination of rational choice, organizational process, poli-

tical process, and knowledge transfer perspectives (van der

Meijden et al., 2003; Wears and Berg, 2005) (Fig. 4). In practice,

this will require going beyond simply asking decision makers

what they want. Decision makers cannot always be expected

to understand or be able to articulate the implications of

climate change for their issues (Altalo et al., 2003a,b). Decision

support developers must interpret decision maker needs with

respect to the goals, objectives, and capabilities of a specific

organization. This requires understanding what the organiza-

tion actually strives to do, which in many cases will suggest

different decision support resources from those decision

makers initially claim to desire (Altalo et al., 2003a,b). The

information underlying this approach starts with three types

of data:

� D

� D

dependencies, technologies, access to capital, availability of

alternatives.

� D

environmental.

This information provides a picture of the characteristics

and context of a decision and the associated decision maker.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 0 ( 2 0 0 7 ) 6 1 0 – 6 2 1618

This will help decision support developers understand the

relevance of a given decision for adaptation, evaluate

constraints and opportunities facing decision makers, and

develop tools that fit a decision’s organizational, political,

legal, and socioeconomic context.

6.3. Decision inventory

Given the scope of the data collection that we are advocating, it

is important to understand how a decision inventory could be

developed, managed, and applied. As an example, consider

the way the now ubiquitous greenhouse gas (GHG) inventories

are used to support decisions about climate mitigation. GHG

inventories are databases that describe the emissions of GHG

across political and economic subdivisions. These data are

coupled with information about the technological and socio-

economic aspects of these emission sources to provide a

foundation for mitigation policy and emissions management

that helps decision makers to identify the most important

emission sources and prioritize mitigation efforts (Winiwarter

and Schimak, 2005). No equivalent resource currently exists

for impact assessment or adaptation, but an inventory of

adaptive decisions could be used in an analogous way to

identify adaptive opportunities. A decision inventory would

combine information about the climate sensitivity and

adaptive value of decisions along with constraints and

opportunities associated with alternative actions. It could be

organized so that information could be summarized by

geographic and economic subdivisions. For example, a

decision maker might be able to identify the ‘‘Top 3’’ adaptive

opportunities for water quality management in a given region

or associated with a particular environmental problem (e.g.,

coastal wetland restoration). Like existing GHG inventories, an

inventory of adaptive decisions could provide the basis for

strategic adaptive policy and management. Such inventories

could be conducted for any logical institutional or geographic

subdivision, such as organizations, states, regions, or agen-

cies. This would represent an actionable step forward for the

adaptation research community and progress toward a more

systematic and effective approach to decision support.

7. Conclusion

The provision of effective decision support for climate impact

assessment and adaptation is a challenging goal. Current

approaches are dominated by systems designed to improve

the quantity or quality of information available to decision

makers. However, theory and practical experience suggest

that decision support systems are more likely to lead to

desired outcomes when they balance the provision of

information with concern for organizational and political

processes. These considerations reflect an underdeveloped

dimension to existing decision support tools. A more balanced

approach will require new data on the characteristics and

context surrounding decisions and decision makers. This new

information can be used to improve the delivery of decision

support, as well as help identify sensitive decisions and

valuable adaptive opportunities. Progress in these areas will

represent an important contribution toward the long term goal

of encouraging the effective use of decision support for

adaptation to climate change.

Acknowledgements

The views expressed in this paper are those of the authors and

do not necessarily reflect the views or policies of the U.S.

Environmental Protection Agency. The authors thank Joel

Scheraga, Anne Grambsch, Thomas Wilbanks, William Clark,

and two anonymous reviewers for constructive comments

during the preparation of this manuscript.

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Dr. Chris Pyke is the Director of Climate Change Services for CTGEnergetics, a consulting and technology company organizedaround the principles of a sustainable built environment. Dr. Pykeworks on issues associated with land use and climate change. Hispractice applies science-based approaches to understand andmitigate greenhouse gas emissions and achieve sustainabilitygoals under changing climatic conditions. Prior to joining CTGEnergetics, Dr. Pyke was a physical scientist with the U.S. EPA’sGlobal Change Research Program and a David H. Smith AppliedConservation Research Fellow with the National Center for Eco-logical Analysis and Synthesis.

Dr. Britta Bierwagen is a physical scientist in the U.S. Environ-mental Protection Agency’s Office of Research and Development,National Center for Environmental Assessment. Her research inEPA’s Global Change Research Program focuses on climate andland use change effects on aquatic ecosystems and water quality.Current assessments include examining effects on biological indi-cators and aquatic invasive species, modeling land use changesnationally with an examination of ecosystem and water resourceconsequences, and developing adaptation responses (manage-ment strategies) that maximize socio-ecological resilience to cli-mate change. She also participates in activities of the U.S. ClimateChange Science Program.

John Furlow leads the climate change adaptation program at theUS Agency for International Development. USAID is working tomake development projects in areas such as agriculture, coastaldevelopment, land-use management, and infrastructure moreresilient to the impacts of climate change. Prior to joining USAID,John worked at the US Environmental Protection Agency on theGlobal Change Research Program.

Janet Gamble is an economist in the U.S. Environmental ProtectionAgency’s Global Change Research Program. Dr. Gamble’s researchfocuses on assessing the human dimensions of global change andtheir associated adaptation strategies. Currently Dr. Gamble isserving as the convening lead author on a U.S. Climate ChangeScience Program (CCSP) Synthesis and Assessment Product (SAP4.6) analyzing the impacts of climate variability and change onhuman health, human welfare, and human settlements. Otherrecent projects include an analysis of climate change impacts on

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 0 ( 2 0 0 7 ) 6 1 0 – 6 2 1 621

aeroallergens and the quality of life impacts of vector-borne dis-eases.

Thomas Johnson is a physical scientist with the U.S. Environmen-tal Protection Agency’s Office of Research and Development,National Center for Environmental Assessment. Dr. Johnson’sresearch interests include the study and management of climateand land use change impacts on water resources.

Susan Julius is an analyst with the U.S. EPA’s Global ChangeResearch Program. She applies a risk assessment approach tounderstand and evaluate the risks posed by climate change toaquatic ecosystems and species and to develop potential manage-ment responses that increase ecosystems’ resilience to projectedchanges. Prior to joining the Global Change Research Program,

Susan was with EPA’s Office of Policy working on socio-economicand ecological impacts of climate change, and with EPA’s Office ofAir and Radiation evaluating the ecological benefits of Title IV ofthe Clean Air Act.

Dr. Jordan West is Special Assistant for Ecology in the U.S. Envir-onmental Protection Agency’s Office of Research and Develop-ment, National Center for Environmental Assessment. Herresearch in EPA’s Global Change Research Program focuses on:assessments of climate change impacts on ecosystem services ofwatersheds and coastal ecosystems; and the development ofadaptation responses (management strategies) that maximizesocio-ecological resilience to climate change. She also participatesin activities of the U.S. Climate Change Science Program and theU.S. Coral Reef Task Force.