2
Effective Decision Support: Definitions, Principles, and Implementation

Recognizing the trend of climatic changes described in Chapter 1, the U.S. Climate Change Science Program (CCSP) (2003:3) has adopted as its guiding purpose the vision of “a nation and the global community empowered with the science-based knowledge to manage the risks and opportunities of change in the climate and related environmental systems.” This vision casts the program as a decision support program: to provide knowledge that people need to make better decisions and to do so in ways that enable and empower decision makers to use it appropriately.

We see this vision as entirely appropriate for the federal research program, and we believe it could apply and be adopted more broadly for the nation, including for decision support activities at other levels of government and in the private and civic sectors. However, the program has not in fact been organized so as to implement this vision. As this and subsequent chapters make clear, realizing this vision will require significant changes both in the federal program and in the modus operandi of many other research and decision-making institutions. The most important of these changes is to put users’ needs at the center of the processes of decision support. That means, in turn, paying close attention to those processes, in addition to the products provided.

This chapter explains what we mean by climate-related decisions and by decision support. We draw on a wide range of literature to distill six key principles that characterize effective decision support systems and to document the benefits of following them. The chapter identifies the types of services or activities decision support systems provide, the barriers that can prevent effective implementation of the principles, and strategies for over-



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2 Effective Decision Support: Definitions, Principles, and Implementation R ecognizing the trend of climatic changes described in Chapter 1, the U.S. Climate Change Science Program (CCSP) (2003:3) has adopted as its guiding purpose the vision of “a nation and the global commu- nity empowered with the science-based knowledge to manage the risks and opportunities of change in the climate and related environmental systems.” This vision casts the program as a decision support program: to provide knowledge that people need to make better decisions and to do so in ways that enable and empower decision makers to use it appropriately. We see this vision as entirely appropriate for the federal research pro- gram, and we believe it could apply and be adopted more broadly for the nation, including for decision support activities at other levels of govern- ment and in the private and civic sectors. However, the program has not in fact been organized so as to implement this vision. As this and subsequent chapters make clear, realizing this vision will require significant changes both in the federal program and in the modus operandi of many other research and decision-making institutions. The most important of these changes is to put users’ needs at the center of the processes of decision support. That means, in turn, paying close attention to those processes, in addition to the products provided. This chapter explains what we mean by climate-related decisions and by decision support. We draw on a wide range of literature to distill six key principles that characterize effective decision support systems and to document the benefits of following them. The chapter identifies the types of services or activities decision support systems provide, the barriers that can prevent effective implementation of the principles, and strategies for over- 

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE coming the barriers. We end with a set of conclusions and recommendations meant to inform the initiation, design, and implementation of decision sup- port activities sponsored by federal agencies and others. DEFINITIONS AND KEY CONCEPTS The term “decision support” has recently come into common use in the climate context, but the underlying idea is far from new. The core idea—making scientific knowledge useful for practical decision making—is evident in many fields, ranging from public health to risk assessment, soft- ware development, resource management, and many more. Decision sup- port is often narrowly understood as an activity that provides data, tools, and other types of information products that make scientific information more accessible to decision makers: for example, translating it into nontech- nical language. In this spirit, the CCSP has made major efforts to enhance the technical and modeling basis on which climate-related risk management decisions may be based. This focus on information products can also be found in other federal agencies, at other levels of government, in the private sector, and in other countries. Yet there is a broader view of decision support which is increasingly being adopted in some federal agencies and nongovernmental efforts and is also reflected in studies of science-practice interactions and of decision support needs (see, e.g., National Research Council, 2007a, 2008d). In this view, decision support consists of a set of processes intended to create the conditions for the production of decision-relevant information and for its appropriate use. Ongoing communication between the producers and users of information is at the center of these processes, and information products are one result, but not the exclusive one. This view stems from decision support activities “on the ground,” including some that are sponsored by federal agencies, such as the Global Change Research Program of the Envi- ronmental Protection Agency (EPA) (in particular, its ongoing Great Lakes Regional Assessment); the Regional Integrated Sciences and Assessments (RISA) Program and Science Applications and Research Program (SARP) at the National Oceanic and Atmospheric Administration (NOAA); and the Forest and Agricultural Extension Services at the U.S. Department of Agriculture (USDA) (see National Research Council, 2006b, for additional examples), as well as in activities at the state and local levels in the private and public sectors. We adopt this broader understanding of decision sup- port to include both products and processes. The rest of this section elabo- rates our usage of concepts and terms fundamental to this report. Climate-Related (or Climate-Sensitive) Decisions Climate-related, or climate-sensitive, decisions are choices by individuals or organizations, the

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 EFFECtIVE DECISIOn SUPPORt results of which can be expected to affect climate change or to be affected by climate change and its interactions with ecological, economic, and so- cial systems. Choices to mitigate or adapt to climate change are obviously included, but also included are decisions about matters that may be only indirectly related to climate (e.g., changing educational requirements for grades K–12 in ways that may better enable the next generation to deal with climate change challenges). One important implication of this defini- tion deserves special emphasis. Decisions are climate sensitive regardless of whether or not decision makers recognize them as such at the time of deci- sion making. Many decisions and decision-making routines that were well suited to past climatic conditions will be less so under future conditions of climate and climate-society interactions—but not all the affected decision makers may yet realize it. Although decision support can potentially help all climate-affected decision makers get better results, a decision maker who does not yet realize that a decision at hand is climate sensitive will not perceive a need for such support. Thus, one of the challenges of decision support is to identify climate-sensitive decisions that are not being treated as such, help decision makers realize how climate change may affect them, and then support subsequent climate-cognizant decisions. Decision-Relevant Knowledge (or Information) Knowledge or informa- tion is decision relevant if it yields deeper understanding of a choice or if, incorporated in making a choice, it yields better expected results for deci- sion makers and their constituencies than would be achieved if the choice were made without that knowledge or information. We note that decision- relevant information is useful for decisions only when it is also accessible and understandable to decision makers and in a timely manner. It is important to make explicit that decision-relevant information for climate-related decisions is not only about climate. It may also include information about: 1. basic characteristics of climate variability and change and the im- plications of these processes for climate-related decisions and for things people value; 2. the expected effects of climate change on hydrological, ecological, and other biophysical systems at particular places and times; 3. the social and economic processes that drive climate change; 4. the socioeconomic and human-environmental processes that alter the vulnerability of human or ecological systems to climate variability and change (e.g., changes in the numbers and socioeconomic characteristics of people living in vulnerable areas); 5. the expected effects of climatic processes on human systems tak- ing into account other ongoing environmental, economic, and social

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE processes (sometimes called multiple stresses, such as potential property damage to coastal homes considering changes in both climate and regional development); 6. the range of strategies available at different scales for mitigation (technologies, policy options, market mechanisms, etc.) and for coping or adaptation (e.g., engineering, economic, behavioral, etc.); 7. the likely costs and consequences of potential policies and other actions to respond to climate change (e.g., ecological effects of developing biofuels, economic effects of different options to protect against hazards, and co-benefits of increasing the resilience of vulnerable regions, sectors, or communities); and 8. the barriers to success for potential responses to climate change and ways to overcome them. As discussed in Chapter 4, meeting these various information needs will require a considerable expansion of the national scientific effort in the areas described by 3 through 8 above. Climate-Related Decision Support Climate-related decision support in- volves organized efforts to produce, disseminate, and encourage the use of information that can improve climate-related decisions. It includes vari- ous kinds of activities, products, and services, including efforts to iden- tify decision makers’ information needs; production of decision-relevant information; creation of information products based on this information; dissemination of these products; efforts to encourage the use of decision- relevant information; ongoing communication among producers and users of decision support products and services to evaluate and improve the qual- ity of information, relationships between information producers and users, and ultimate decisions; and development of organizations, networks, and institutions to serve those purposes. Decision support cannot lower actual risks directly or immediately, but it can influence humans’ awareness of and responses to risk in ways that can, over time, mitigate threats from the natural world, as well as the vulnerability resulting from human exposure to threats. Decision Support Products Decision support products are the tangible deliverables developed in the course of decision support (including data, maps, projections, images, tools, models, or documents) that contain infor- mation intended to be useful for decision making. The media or channels developed to deliver this information (brochures, web pages, etc.) may also be considered decision support products. Decision Support Services Decision support services are activities, consul- tations, or other forms of interaction that enable decision makers to make

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 EFFECtIVE DECISIOn SUPPORt better use of decision-relevant information and decision support products, including formal and informal efforts to identify information needs, educate those involved in the decision process, and facilitate or evaluate decision support processes. Decision support services may be less visible to outsiders than decision support products, but they are equally important. The most appropriate services vary with the specific situation at hand, the larger deci- sion context, and the phase in the decision process. Decision Support Systems Sometimes also called knowledge-action systems or networks, decision support systems comprise the individuals, organiza- tions, communication networks, and supporting institutional structures that provide and use decision support products and services. They include the people and organizations that develop the knowledge needed to pro- duce those products and services, as well as the knowledge, information products, and services. Effective Decision Support The effectiveness of decision support can be judged by the extent to which it increases the likelihood that decision- relevant information is produced and enables and empowers decision mak- ers to use it appropriately. The many elements of effective decision support can be usefully grouped under three categories: 1. Increased usefulness of information. Decision support is effective to the extent that the information provided is considered by the intended users as credible, legitimate, actionable, and salient in terms of their deci- sion deadlines and other concerns (e.g., Jones, Fischhoff, and Lach, 1999; Cash et al., 2003; Mitchell et al., 2006; National Research Council, 1999b, 2008d; Reid et al., 2007). 2. Improed relationships between knowledge producers and users. Decision support is effective when it engages scientists and decision makers in mutual learning and the coproduction of knowledge that could not have emerged from either side alone and when it yields increased mutual under- standing, respect, and trust (see, e.g., Jasanoff, 1987; Gunderson, Holling, and Light, 1995; National Research Council, 1996b; Global Environmental Assessment Project, 1997; Cvetkovich and Lofstedt, 1999; Sidaway, 2005; Hahn et al., 2006; McNie, 2008). 3. Better decisions. Decision support is effective when the resulting decisions have the qualities of good decisions identified in Chapter 1 (in- cluding productive problem definitions and clear objectives) and when the decision makers and key constituencies view the decision as having been improved by the support received (e.g., Haas, Keohane, and Levy, 1993; Coglianese and Snyder Bennear, 2003; Clark, Mitchell, and Cash, 2006; Farrell and Jäger, 2006; National Research Council, 2006b, 2007a, 2008c, 2008d; Newig, 2007; Rowe et al., 2008).

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8 InFORmIng DECISIOnS In A ChAngIng ClImAtE The effectiveness of decision support is thus a multidimensional con- struct. Consequently, tradeoffs may be necessary if some objectives are con- sidered paramount. Moreover, participants in decision support systems may differ in their judgments of the attributes of information (salience, legiti- macy, credibility, and efficacy) and of the quality of relationships, processes, or decision outcomes. These differences help shape the political context in which decision making takes place and in which decision support systems strive to function effectively. In general, however, long-term engagements with deliberate efforts to learn and improve interactions achieve these ob- jectives more fully than limited interactions. Processes for enhancing such learning are discussed in more detail in Chapter 3. PRINCIPLES OF EFFECTIVE DECISION SUPPORT As noted above, decision support has a long history in fields other than climate, including public health; hazards management; natural resource management; environmental management and policy making; land use planning; environmental risk communication; sustainability science and promotion of sustainable behavior; stratospheric ozone, air quality, and climate change mitigation; and specific efforts in coping with and adapt- ing to climate variability and change in various sectors or regions: see Box 2-1. To understand what makes decision support systems effective, we examined empirical research from these fields and also from studies of the relation of science to its uses in policy, resource management, and society. Some of these rely heavily on the authors’ judgment and some are synthe- ses of extensive bodies of research and experience (e.g., Gibbons et al., 1994; National Research Council, 1996b, 2005a, 2008c; Jasanoff, 1990; Nowotny et al., 2001; Pielke, 2007; Pohl, 2005; Slaughter and Rhoades, 2005; Stokes, 1997). We also examined core social science theory and re- search on communication, decision making, organizational behavior, and social change (e.g., Bell, Raiffa, and Tversky, 1988; Drabek, 1986; Brewer and deLeon, 1992; Gutteling and Weigman, 1996; Kahneman and Tversky, 2000; Rogers, 2003; Edwards, Miles, and von Winterfeldt, 2007). We have also drawn on a limited body of observational research and the experiences of professionals and scientists engaged in climate-related decision support, including those working on decision support efforts supported by NOAA, EPA, other federal agencies, state and local governments, and the private sector. A noteworthy source of insights specific to climate-related decision support is a recent NOAA review of efforts to provide decision support related the use of information on seasonal-to-interannual climate variation in the water resources sector (U.S. Climate Change Science Program and Subcommittee on Global Change Research, 2008).

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 EFFECtIVE DECISIOn SUPPORt BOX 2-1 Sources of Evidence About Effectiveness in Decision Support Public health:     alente  and  Schuster,  2002; Totlandsdal  et  al.,  2007;  Jackson  and  Shields,  V 2008 Hazards management:     uarantelli,  1991;  Cutter,  1994;  Mileti,  1999;  Drew,  Nyerges,  and  Leschine,  Q 2004; Morss et al., 2005 Natural resource management:     eller et al., 1984; Healey and Ascher, 1995; McDaniels, Gregory, and Fields,  F 1999; Wondolleck and Yaffee, 2000; Jacobs and Pulwarty, 2004; Mascarenhas  and  Scarce,  2004;  Power,  Sadler,  and  Nicholls,  2005;  Rayner,  Ingram,  and  Lach,  2005;  Nyerges  et  al.,  2006;  Corringham, Westerling,  and  Morehouse,  2008 Environmental management and policy making:     emmons and Brown, 1995; Sexton et al., 1999; Steel et al., 2004; Francis et  L al., 2005; Stoll-Kleemann, 2005 Land use planning:     ames, 1999; Forester, 1999; Dortmans, 2005; Francis et al., 2004; Richardson,  J 2005; Szaro, Boyce, and Puchlerz, 2005; Lejano, 2008 Environmental risk communication:      ational  Research  Council,  1989;  Covello,  McCallum,  and  Pavlova,  1989;  N Kasperson and Kasperson, 2005 Sustainability science and promotion of sustainable behavior:     ardner and Stern, 1996; National Research Council, 1999c; McKenzie-Mohr  G and Smith, 1999; Kates et al., 2001; Clark and Dickson, 2003; Kasemir et al.,  2003; Kaufmann-Hayoz and Gutscher, 2001; van Kerkhoff and Lebel, 2006 Stratospheric ozone, air quality, and climate change mitigation:     aas,  1992;  Liftin,  1994;  Glasser,  1995;  Alcamo,  Kreileman,  and  Leemans,  H 1996; Shackley, 1997; Social Learning Group, 2001; Parson, 2003; Bergin et  al., 2005; Cimorelli and Stahl, 2005; Engel-Cox and Hoff, 2005; Grundmann,  2006; Gupta and van Asselt, 2006; Crutzen and Oppenheimer, 2008 Coping with and adapting to climate variability and change in various sec- tors or regions:     erkes and Folke, 1998; Cash, 2001; Jacobs, 2002; Pulwarty and Melis, 2001;  B Pulwarty, 2003; Georgakakos et al., 2005; Jacobs, Garfin, and Lenart, 2005;  Lemos  and  Morehouse,  2005;  Cash,  Borck,  and  Patt,  2006;  Moser,  2006a,  2007a; Welp et al., 2006; Tribbia and Moser, 2008 Several recent attempts have been made to integrate this wealth of practical insights and the more theoretical literature to accelerate and foster learning throughout the research community (e.g., Cash et al., 2003; van Kerkhoff, 2005; McNie, 2007; Mitchell et al., 2006; National Research Council, 2005b, 2006b, 2008c, 2008d; Singh et al., 2002; Vogel et al.,

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0 InFORmIng DECISIOnS In A ChAngIng ClImAtE 2007; Welp and Stoll-Kleemann, 2006). Drawing on the primary sources and these integrative efforts, we note a convergence of these literatures on six general principles for designing effective decision support systems that are appropriate for all phases of decision support efforts. We believe the principles apply to climate-related decision support. Additional research is needed to learn how to implement those broad principles effectively in specific climate decision contests. (We discuss the need for research on decision support in more detail in Chapter 4.) Decision support efforts are more likely to be judged effective when they follow the principles, which we summarize here and discuss in more detail in the next sections. 1. Begin with users’ needs: Decision support activities should be driven by users’ needs, not by scientific research priorities. These needs are not always known in advance, and they should be identified collaboratively and iteratively in ongoing two-way communication between knowledge producers and decision makers. The latter can usefully be thought of as constituencies—collections of decision makers who face the same or similar climate-related events or choices and therefore have similar information needs. 2. Give priority to processes over products: To get the right products, start with the right process. Decision support is not merely about produc- ing the right kinds of information products. Without attention to process, products are likely to be inferior—although excessive attention to process without delivery of useful products can also be ineffective. To identify, produce, and provide the appropriate kind of decision support, processes of interaction among and between decision support providers and users are essential. 3. Link information producers and users: Decision support systems require networks and institutions linking information producers and us- ers. The cultures and incentives of science and practice are different, for good reason, and those differences need to be respected if a productive and durable relationship is to be built. Some ways to accomplish this rely on networks and intermediaries, such as boundary organizations (see below). 4. Build connections across disciplines and organizations: Decision support services and products must account for the multidisciplinary char- acter of the needed information, the many organizations that share decision arenas, and the wider decision context. 5. Seek institutional stability: Decision support systems need stable support. This can be achieved through formal institutionalization, less for- mal but long-lasting network building, establishing new decision routines, and mandates, along with committed funding and personnel. Stable deci-

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 EFFECtIVE DECISIOn SUPPORt sion support systems are able to obtain greater visibility, stature, longevity, and effectiveness. 6. Design for learning: Decision support systems should be structured for flexibility, adaptability, and learning from experience. Begin with Users’ Needs Effective decision support needs to begin with collaborative problem definition, including all the parties involved, and to support interactions and learning among them. The rationale is obvious—to identify which knowledge is needed by decision makers (and when and how) and what is feasible for science to deliver. Yet much research that is intended to be decision relevant is begun and conducted without consultation with the envisioned end users (e.g., McNie, 2007; Sarewitz and Pielke, 2007). Nu- merous reviews have found that effective assessment and decision-making efforts related to hazards and other scientifically complex issues require communicative and iterative interactions between scientific and decision- making groups (see National Research Council, 1996b, 2007a, 2008c, and sources cited there). Such ongoing interaction, two-way communication, and collaboration allow scientists and decision makers to get to know each other; develop an understanding of what decision makers need to know and what science can provide; build trust; and, over time, develop highly productive relationships as the basis for effective decision support. One- time or sporadic interactions do not usually yield these benefits; ongoing relationships can do so. An extensive literature on social trust in relation to risk management indicates that when people lack direct experience with a risk, their judgments of risks are strongly affected by trust in the authorities who are responsible for managing them, and that trust is in turn affected by characteristics of the interactions between authorities and those poten- tially affected (e.g., Siegrist and Cvetkovich, 2000; Kasperson et al., 2003). The literature suggests the value of participatory processes that address the principal values and concerns of those involved. The intensity and form of communication and collaboration may vary over time and across situations, but it is essential for problem defini- tion. The First U.S. National Assessment of the Potential Consequences of Climate Variability and Change (1997–2001), for example, began with regional and sectoral scoping workshops in which scientists, stakeholders, and program sponsors jointly defined the potential challenges to be further investigated. After such initial problem definition workshops, interactions ranged from occasional updates, to involvement in the review of the emerg- ing science, to data sharing and collaborative research, and to joint dis- semination of results (Moser, 2005b; National Research Council, 2008c; see also Appendix A).

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE Provided that appropriate boundaries between science and policy mak- ing are maintained, collaborative interactions to define problems, identify information needs, share data, investigate options, and review and commu- nicate results can increase the credibility, relevance, and perceived legitimacy of the scientific information, enable decision makers to make decisions or solve problems (perceived efficacy) and increase public understanding of risks, uncertainties, and action alternatives (see, e.g., Holling, 1978; Walters and Holling, 1990; Scheraga and Smith, 1990; Crowfoot and Wollondeck, 1990; Cash et al., 2003; Jacobs, Garfin, and Lenart, 2005; Mitchell et al., 2006; National Research Council, 2007c; Chilvers, 2008). Give Priority to Processes Over Products Interpersonal interactions are critical to effective decision support. If ignored or poorly managed, the resulting disconnects can reduce the quality of relationships between users and producers of information, the usefulness and ultimate use of the information, and even the quality of decisions (Global Environmental Assessment Project, 1997; Mitchell et al., 2006; National Re- search Council, 1989, 1996b, 2007a, 2008c, 2008d; Reid et al., 2007). Perhaps the most important interpersonal processes involve relation- ship building and maintenance, which require time, patience, care, and social skill. Prior experience in collaboration across apparent divides is also useful in building relationships among decision support providers, among decision makers, and between these two groups. Having staff whose time is dedicated to managing and coordinating these relationships and to ex- ternal communication can be particularly helpful (Pulwarty, Simpson, and Nierenberg, 2009). Decision makers who participate in relationship build- ing can effectively facilitate and extend outreach to other decision-making groups if they are linked into these networks, understand those groups’ cultures and languages, and are trusted there (Jacobs, Garfin, and Lenart, 2005; McNie, Pielke, and Sarewitz, 2007). Another process that is key to effective decision support is the develop- ment of a culture of learning among decision support participants (for fur- ther discussion, see Chapter 3). Individuals generally hold expertise in their respective fields and spheres of responsibility but lack expertise in others’ fields. To communicate effectively, they need to learn from each other. Pilot projects, exploratory research, and interactions about short-term needs can hasten such learning (Lemos and Morehouse, 2005; National Research Council, 2006b; Pulwarty, Simpson, and Nierenberg, 2009). For many with experience in decision support, this ongoing opportunity to learn and grow is itself a benefit (Moser, 2005b). Two-way communication is an essential process for decision support which involves “a shift from a view of knowledge as a ‘thing’ that can be

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 EFFECtIVE DECISIOn SUPPORt transferred to viewing knowledge as a ‘process of relating’ that involves negotiation of meaning among partners” (Roux et al., 2006:1). It cannot be overstated how critically the success of an entire decision support enterprise depends on the quality of communication among all involved. Leadership is also necessary for the effective functioning of decision support systems. Leaders serve as flag bearers, advertisers, sources of credibility and legitimacy, conflict mediators, seekers of funding, role models, mentors, and innovators. Leaders set expectations and tones, develop rules and demand delivery on obligations, and engender trust (e.g., Zand, 1997; National Research Council, 2006b). Leaders can also be essential in fostering the trial and spread of new practices, keeping difficult processes going, and learning from inevitable mistakes (e.g., Valente, 1995; Rogers, 2003). Absent or ineffective leadership has ob- structed the successful establishment of climate-related decision support efforts (Grundmann, 2006; McNie, Pielke, and Sarewitz, 2007; Pulwarty, Simpson, and Nierenberg, 2009). Link Information Producers and Users Science and decision making have different purposes, concerns, lan- guages, and norms (e.g., Jasanoff, 1986; Rhodes, 1997; Guston, 2001; Blockstein, 2002; Dabelko, 2005; Nagda, 2006). Decision makers are ac- countable to particular agencies, publics, stakeholders, or shareholders. Scientists, by contrast, are primarily accountable to their funders and their academic institutions and disciplines, and their social contract with society is implicit (e.g., Lubchenco, 1998; Gibbons, 1999; Kellogg Committee, 1999; McDowell, 2001; Slaughter and Rhoades, 2005). To collaborate with each other, these communities need to respect these differences, find forums and ways to mediate between them, and, if necessary, involve organizations or individuals that can cross and yet maintain and manage the boundary between science and practice. Specialized “boundary organizations” have sometimes proved instru- mental in enabling scientists and users of scientific information to work productively together by improving communication, translation, and me- diation between the two communities and establishing useful rather than antagonistic tension between them (Fennell and Alexander, 1987; Guston, 1999, 2001; Gieryn, 1995, 1999; Cash, 2001; Cash et al., 2003). NOAA’s RISA centers are an example in the climate area; other federally sponsored boundary organizations, such as the EPA’s Great Lakes National Program Office, which are already performing linking functions, can expand their work on climate. Boundary organizations are commonly defined as “insti- tutions that straddle the shifting divide between politics and science. They draw their incentives from and produce outputs for principals in both

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0 InFORmIng DECISIOnS In A ChAngIng ClImAtE Resistances to Change In Chapter 1 we note several aspects of climate change that are chal- lenging for decision making, including the difficulty of seeing climate change signals against a background of variability, the need to consider risks from potentially unprecedented events, the long time horizon before these events may arise, and the deep uncertainties associated with forecasts and projec- tions of climate change. These attributes of climate change provide multiple justifications for inaction, such as attributing climate-related events to vari- ability rather than change or waiting for unequivocal evidence of climate change or scientific unanimity. People can readily use these justifications to postpone the search for decision support and discount information that might require a change of practices. In addition, well-funded interests have long engaged in concerted efforts to bolster the justifications for inaction by disputing scientific evidence of climate change, its current impacts, and its likely consequences. The results in the United States have included rela- tively weak and slow public policy responses to climate change and a focus of the climate science agenda on demonstrating with very high confidence that climate change is happening and is anthropogenic to the exclusion of efforts to find the best ways to reduce the risks of climate change or to inform responses to those risks. The legacy of these efforts can be seen in some of the other barriers to decision support listed below. Institutional and Legal (Structural) Barriers Institutions and organizations and their associated formal and in- formal norms and rules impose powerful constraints on the interaction between researchers and decision makers. These constraints reflect pro- fessional performance standards, job descriptions, promotion criteria, ethical norms of conduct, contractual obligations, administrative pro - cedures, decision protocols and schedules, and legal requirements for inclusion or exclusion of certain considerations (e.g., National Research Council, 2006b; Moser, 2006a). For example, scientific information about an area’s vulnerability to storm damage, if it is collected with a pledge of confidentiality, may become publicly available only if there is a legal showing that the public interest in the information outweighs the loss to property owners who face decreased values of their hold- ings due to climate-related risk. As already noted, collaboration among agencies can be impeded by different enabling laws, opposing missions, or incompatible budgetary rules. As claimed in the National Research Council (2006b:15) report, for many federal agencies, “the federal re- search support system is geared more toward knowledge generation than problem solving.” Such barriers—whether formalized or implicit—

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 EFFECtIVE DECISIOn SUPPORt can lead to disconnects, conflicts, and turf battles rather than productive cooperation. As we also note in Chapter 1, few decision-making organizations are well matched to the long time scales and the multiple spatial dimensions of climate change. For example, there are very few organizations that are tasked to take responsibility for the consequences of their actions decades or centuries in the future or that can act at the levels of ecosystems or the Earth system. These mismatches bring into focus questions about how to effectively link institutional mechanisms established at one level to policy frameworks at another (e.g., the emerging regional and national cap-and- trade systems vis-à-vis an effective global climate treaty), how to establish mechanisms for enforcement at all levels, and how to link policy instru- ments across levels of organization and across time. These misfits between problem and response create disincentives to act and therefore to seek and use relevant decision support. Organizational and Cultural Barriers Organizations, and the people in them, are slow to change. Past prac- tices, disciplinary and agency perspectives, and organizational cultures and the norms and rules that underlie them are remarkably resistant to change. Rapidly evolving and emerging decision contexts are set against a backdrop of organizational inertia, presenting a challenge to any efforts to improve decisions. Decision support practitioners need to constantly assess the “fit” between situational realities and decision processes. They also need to consider organizational styles, norms, priorities, and expectations; priorities regarding whose insights and interests are considered important; and attitudes about science, all of which can resist change. Cultural barriers, reflecting differences in such organizational character- istics, exist between organizations in academia, in the policy and business worlds, and among these worlds. Box 2-4 presents a concrete example of these kinds of barriers. It is not uncommon for scientists to give “standard” scientific talks to resource managers, apparently and incorrectly assuming that the decision makers will absorb the information they need and make logical, science-based decisions. When this happens, science and scientists have failed to cross the threshold of salience, learning is thwarted, and ste- reotypes are reinforced that practitioners do not care about science and that scientists pursue their own interests without regard to practical concerns. Most decision makers must focus on solving today’s or tomorrow’s prob- lems, and they pay much less attention to long-term issues, the focus of most climate research, unless they are strongly linked to near-term decisions.

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE BOX 2-4 Barriers to Effective Decision Support: The Case of the Florida Everglades   Resource management is organized around efficient exploitation or protection  of  a  resource,  while  science  is  organized  around  producing  valid  knowledge  of  the  natural  and  social  worlds.  Although  there  is  no  conflict  between  these  mis- sions,  and  effective  resource  management  in  fact  depends  on  sound  science,  each produces demands on the other that require mutual understanding, learn- ing, and a willingness to adjust efforts and attitudes in order to connect science  and  management  effectively.  Efforts  to  restore  the  Florida  Everglades  illustrate  the challenges of overcoming the persistent tensions between the organizational  cultures of resource management and environmental science.    In  response  to  complaints  from  resource  managers  about  the  irrelevance  of  research  to  their  information  needs,  science  managers  at  the  U.S.  Geological  Survey (USGS) made a concerted effort to meet with Fish and Wildlife Service  and National Park Service leaders to identify science information that would be  useful in their decisions. What resulted was a list of short-term, tactical issues that  required  very  short-term  tactical  scientific  approaches  that  were  not  consistent  with  most  research  programs  in  the  USGS.  The  needs  of  the  managers  were  real and important, but the scientific work and human resources needed to meet  them were not readily available without changes that seemed likely to weaken the  future quality of needed science (Mitchell et al., 2006). For example, although de- cision makers need timely information, scientific observations cannot be rushed,  and there may be too few historical observations to provide a clear indication of  long-term trends. Although scientists are cautious in expressing judgments in the  absence of statistically reliable data, managers must address urgent issues and  meet deadlines, and need informed judgment even, or especially, when conclusive  findings are not available. The need to act on the best information available, how- ever imperfect, underlines the importance of decision structuring and facilitation  as elements of decision support.   The  Science  Impact  Program  at  the  USGS,  designed  to  increase  the  use  of  science  in  decision-making,  encountered  challenges  on  several  levels  within  the  organization.  Some  research  scientists  and  science  managers  questioned  the  value  of  time-consuming  meetings  that  mixed  scientific  and  other  issues,  concluded that agency managers were uninterested in the main scientific issues,  and resisted redirecting some of their scientific objectives to meet more tactical  needs  and  taking  on  decision  support  functions.  The  USGS  has  nevertheless  made significant efforts to increase the relevance of its science to resource and  environmental management issues and the awareness of decision makers of the  availability of information developed by USGS research and monitoring programs.  Although  there  has  been  progress,  the  need  remains  to  better  understand  how  the agency can best inform decision makers. 

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 EFFECtIVE DECISIOn SUPPORt Omissions in Professional Training and Education Most climate and environmental experts still do not receive adequate training, mentoring, or incentives for working across disciplines, across is- sue areas, or at the science-practice interface (except for some communica- tions training). They are typically unaware of the lessons learned by those examining such transdisciplinary interactions and are often hesitant to get involved with policy and decision makers (e.g., Hartz and Chappell, 1997; Moser, 2006b; The Royal Society, 2006). Similarly, policy makers do not re- ceive adequate training prior to or in the course of their professional careers in climate and related social and environmental sciences. In some instances, there are also challenges from constraints in hiring practices and lack of interest in or incentives to innovate (National Research Council, 2006b). In light of the rapidly changing climate and policy contexts, these omissions in professional education and training will lead to a situation where human resource constraints seriously undermine the nation’s ability to respond to the rapidly growing demand for climate-related decision support. Time Constraints Versus Urgency Ideally, decision support efforts are anticipatory and forward looking, ahead of needs. Reality is far from that ideal. The key problems include ever-changing decision needs, lack of needed knowledge, and changing scientific understandings of what was previously not known or thought to be well understood. For example, the rapid melting of the great ice sheets is leading to fundamental shifts in glaciology. With global climate rapidly moving into uncharted territory, many decisions will need to be made without well-established scientific input. This growing urgency stands against the fact that collaborative relationships require careful building and long-term maintenance (Jacobs, Garfin, and Lenart, 2005; Lemos and Morehouse, 2005; McNie, Pielke, and Sarewitz, 2007; Pulwarty, Simpson, and Nierenberg, 2009). Meanwhile, specific decisions may require informa- tion on very short notice, on specified schedules, or for time horizons and spatial scales that science is unable to deliver (Carbone and Dow, 2005; Cash et al., 2006; Jacobs, Garfin, and Lenart, 2005; Lemos and Morehouse, 2005; McNie, Pielke, and Sarewitz, 2007; Corringham et al., 2008). Lack of Funding and Other Resources Shortages of funding for all kinds of science are frequently bemoaned. However, the situation for climate-related decision support is arguably more extreme than most. With the growing demand for decision support comes increased demand for answers for scientific questions that were never

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE a major part of federally supported research on climate change. (We discuss this point in more detail in Chapter 4.) In addition, needs for decision sup- port and stakeholder engagement activities, which include the implementa- tion and monitoring of decision outcomes, will only become more pressing as the consequences of climate change become more evident. More funding and better use of existing funding and resources are needed to enhance training in decision support skills; to support relatively neglected, but much needed scientific inquiries (see Chapter 4); to establish additional decision support institutions and equip them adequately; and to advance formal evaluation of decision support activities. Funding barriers can also be critical for decision makers. For example, to overcome institutional separation, improve the sharing of information, and enhance collaboration, government agencies and other organizations may decide to form interdepartmental, interagency, or multi-institutional working groups. In addition to the other barriers mentioned here, these coordinating mechanisms may be constrained from innovating or may not even receive basic financial support (National Research Council, 2006b, 2007b). STRATEGIES TO OVERCOME BARRIERS Several strategies for overcoming the above barriers logically emerge from the foregoing discussion. Leadership Leadership and effective organizational management by top-level in- dividuals in government institutions and in business, as well as at all other levels, is necessary to effectively overcome the deeply engrained barriers to effective decision support and to carry out the daily work of decision support: define scopes of work, maintain project momentum, attend to administrative tasks, initiate efforts to bridge decision-research gaps, maintain independence and integrity, and sustain internal and external relationships. Leadership is also needed to overcome barriers to change and initiate innovative practices. At a time when “business as usual” is over for the world’s climate, for tradi- tional decision-making processes, and for science (see Chapter 1), leadership will be indispensible, even if its value and importance are often unrecognized or underestimated in academia and even in some decision-making organiza- tions (Carbone and Dow, 2005; Jacobs, Garfin, and Lenart, 2005; Lemos and Morehouse, 2005; Clark and Holliday, 2006; McNie, Pielke, and Sarewitz, 2007; Pulwarty, Simpson, and Nierenberg, 2009).

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 EFFECtIVE DECISIOn SUPPORt Mandates Mandates to provide information, outreach, technical support, and extension services can create an institutional environment in academia that pushes science outside the ivory tower. Similarly, policy mandates that require decision makers to consider relevant climate and related science in planning or implementation contexts create an information demand that brings practitioners to experts. For example, a 2006 California law that established the goals of reducing the state’s greenhouse gas emissions to 1990 levels by 2020 and to 80 percent below that by 2050 has created an enormous demand for technical information to create reliable greenhouse gas inventories; establish practical yet verifiable accounting systems; imple- ment technological, market, and behavioral strategies to reduce emissions; and estimate costs and possible savings for each option. A 2008 California law requires disclosures of greenhouse gas emissions in the state environ- mental review process and thereby creates a new information need for regulatory agencies and regulated entities, some of whom may supply this information for themselves. Business can be affected both by such new laws and by shareholder resolutions that require that certain types of scientific or technical infor- mation or concerns be considered in long-term planning and investment decisions. Legal requirements can also have a powerful impact in forging channels of communication, exchange, and collaboration. Mandates are powerful, but they may be insufficient by themselves. Mandates are more likely to be effective when they are aligned with job expectations and reward systems and are supported with adequate fund- ing, staffing, and training to enable individuals to carry out new mandated responsibilities. Institutional Changes and Institution Building If scientists and decision makers are to change familiar patterns of professional behavior, they must have incentives to do so (e.g., professional recognition), protection from disincentives to work at the science-practice interface, and overt support (e.g., training, support staff, other resources). Often clear institutional changes in the rules of conduct, job descriptions, and agency missions are needed. To foster greater cross-disciplinary and cross-organizational integra- tion, intellectual, attitudinal, and institutional changes may be necessary. For example, organizations might be more easily engaged in decision sup- port if they are organized around decision problems rather than disciplines or issues. Making the needed linkages and supporting the needed commu- nication and interaction across the usual divides requires more integrative

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 InFORmIng DECISIOnS In A ChAngIng ClImAtE and holistic perspectives and management approaches. Doing so will not only affect scientific analyses and management choices; it will also broaden the circle of stakeholders. Sometimes integration is more easily achieved with a regional focus that includes attention to connections across scale, sectors, governance mechanisms, and issues. One advantage of such a focus is that regional specificity of knowledge products can engender greater constituent support and interest, long-term engagement, credibility, and acceptance (Jacobs, Garfin, and Lenart, 2005; Carbone and Dow, 2005; Corringham et al., 2008, Pulwarty, Simpson, and Nierenberg, 2009). Experience with decision support efforts in climate, agriculture, fisheries, coastal management, public health, and hazards suggests that creating, strengthening, and promoting institutions that provide decision support for regions or sectors not only helps overcome organizational barriers, but can also stimulate awareness of, and interest in obtaining, information to support decisions. Moving from informal to more formal institutional arrangements for decision sup- port can help gain visibility, name recognition, stature, and legitimacy for decision support efforts. When interactions between scientists and decision makers are not yet established or the decision context is highly contentious, it may be useful to draw on boundary organizations to facilitate the exchange and collabo- ration across the science-practice divide. Getting researchers and decision makers to agree to work with and through a boundary organization, and establishing trust in this collaboration, may take time; however, success- ful boundary organizations lower the transaction costs of working at the science-practice interface. Funding for Decision Support A careful assessment of financial needs, expenditures, and impacts may help redirect available funds toward effective decision support. Funding is essential for interactive processes in the decision support system, for deci- sion support services, for decision support products, and for supportive research. Chapter 4 elaborates on these funding needs for specific types of information that has been relatively neglected. As funding insecurities from one budget cycle to the next can be detrimental to the process of establish- ing ongoing science-practice relationships, possibilities for creative financ- ing over longer periods with local partners can be explored. Training, Education, and Exchange of Experiences To speed the development of the nation’s decision support capacity (see also Chapter 4), training, internships, and information exchange among

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 EFFECtIVE DECISIOn SUPPORt decision support providers and guidance and support from a concerted national decision support effort will be indispensible. To achieve efficiencies and greater effectiveness, it could be useful to draw on capacity in areas heretofore unconnected to climate and to promote closer connections and information exchange across regional decision support teams (Lemos and Morehouse, 2005; Pulwarty, Simpson, and Nierenberg, 2009). Linking networks of extension agents, public health service providers, and haz- ards managers, as well as other networks of relevant professionals (e.g., planners, engineers, educators), could further extend and rapidly build national decision support capacity. A national clearinghouse of decision support activities in the public and private sectors will further speed up the learning. CONCLUSIONS AND RECOMMENDATIONS Conclusion 3: The most effective decision support efforts are organized around six principles: begin with users’ needs; give priority to processes over products; link information producers and users; build connections across disciplines and organizations; seek institutional stability; and design processes for learning. Following these principles improves the likelihood of achieving the three main objectives of decision support: increased usefulness of infor- mation, improved relationships between knowledge producers and users, and better decisions. Decision support systems promote these objectives by engaging in activities and providing services related to communication, mediation and brokerage, and research and observation to produce deci- sion-relevant information, decision structuring, and evaluation. Some deci- sion support efforts, including some of NOAA’s RISA centers, are already striving to implement the principles of effective decision support in the climate response context and fulfill the main functions of decision support programs. These and other promising efforts serve as viable working mod- els for new and broader programs. Recommendation 1: Government agencies at all levels and other orga- nizations, including in the scientific community, should organize their decision support efforts around six principles of effective decision sup- port: (1) begin with users’ needs; (2) give priority to process over prod- ucts; (3) link information producers and users; (4) build connections across disciplines and organizations; (5) seek institutional stability; and (6) design for learning.

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8 InFORmIng DECISIOnS In A ChAngIng ClImAtE Recommendation 2: Federal agencies should develop or expand deci- sion support systems needed by the climate-affected regions, sectors, and constituencies they serve. • The National Oceanic and Atmospheric Administration (NOAA) should expand its Regional Integrated Sciences and Assessments (RISA) Program and Sectoral Applications Research Program (SARP) centers to serve the full range of regions and sectors of the nation where NOAA has natural constituencies. • The Environmental Protection Agency (EPA) should expand its climate-related decision support programs to serve more regional and sectoral constituencies. • Other federal agencies should take similar steps for their climate- affected constituencies. • The federal government should selectively support state and lo- cal governments and nongovernmental organizations to expand their efforts to provide effective decision support to their climate-affected constituencies. In developing new decision support activities or expanding programs to serve new constituencies, a useful way to begin is with dialogues about deci- sions that affect or are affected by climate change (see National Research Council, 2008d). Such dialogues can be organized for constituencies defined regionally, in terms of an affected sector or decision type, or in terms of a policy development that requires new responses. The dialogues should function to identify major climate-affected decisions facing the constitu- ency; identify information needed to inform the decisions, and advise the sponsoring agencies about priorities for research and information develop- ment. Dialogues might focus initially on near-term decisions with long-term consequences that climate change will affect, such as investments in physi- cal infrastructure and adoption of planning and development policies. Dialogues should include agency officials, relevant decision-making authorities, scientists, other sources of decision-relevant information, and individuals and organizations that might serve as effective communica- tion links between information providers and users. Dialogues should be designed to continue over time and to identify new climate-related decision issues as they emerge. Dialogues already established under NOAA’s RISA and SARP Programs, and dialogues begun as part of the 2001 National Assessment of the Consequences of Climate Change, can serve as models for how dialogues could be organized. The Aspen Institute, another ex- ample, conducts its meetings and seminars as moderated dialogues using small group settings in which participants from various backgrounds and perspectives learn from each other through an interactive discussion of

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 EFFECtIVE DECISIOn SUPPORt specific readings. Successful dialogues might develop into pilot programs and eventually into networks or formal organizations linking information providers and users. Federal agencies can begin their efforts to develop decision support systems for their constituencies by adopting the mechanisms identified in Box 2-3 (above), building from initial dialogues, needs assessments, or workshops to pilot projects and then to larger or more permanent activi- ties as judged appropriate, roughly as has been advised for NOAA’s SARP activity (National Research Council, 2008d). The national decision support initiative we propose (described in Chapter 5) would include a program of grants to nonfederal groups, both governmental and nongovernmental, to initiate development of climate-related decision support systems for their constituencies, following a similar developmental process beginning with dialogues, workshops, or needs assessments and moving to pilot projects and beyond. Such a program would allow for innovative efforts, including web-based communication networks and centralized or interactive informa- tion systems for particular constituencies; coordination of networks; and public–private partnerships. Applicants would be asked to demonstrate that their activities would provide new, needed, and more useful climate information to an identified constituency; contribute to the development of lasting decision support networks or other institutions; and, for pilot projects, have a likelihood of becoming self-supporting.

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