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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 âa 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.â 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 usersâ 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- 33
34 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 âdecision supportâ has recently come into common use in the climate context, but the underlying idea is far from new. The core ideaâmaking scientific knowledge useful for practical decision makingâis 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 âon the ground,â 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) Decisionsâ Climate-related, or climate-sensitive, decisions are choices by individuals or organizations, the
EFFECTIVE DECISION SUPPORT 35 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 Kâ12 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 interactionsâbut 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
36 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 makersâ 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 humansâ 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 Productsâ 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 Servicesâ Decision support services are activities, consul- tations, or other forms of interaction that enable decision makers to make
EFFECTIVE DECISION SUPPORT 37 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 Systemsâ 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 Supportâ 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. Improved 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 JÃ¤ger, 2006; National Research Council, 2006b, 2007a, 2008c, 2008d; Newig, 2007; Rowe et al., 2008).
38 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 authorsâ 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).
EFFECTIVE DECISION SUPPORT 39 BOX 2-1 Sources of Evidence About Effectiveness in Decision Support Public health: Valente and Schuster, 2002; Totlandsdal et al., 2007; Jackson and Shields, 2008 Hazards management: Quarantelli, 1991; Cutter, 1994; Mileti, 1999; Drew, Nyerges, and Leschine, 2004; Morss et al., 2005 Natural resource management: Feller et al., 1984; Healey and Ascher, 1995; McDaniels, Gregory, and Fields, 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: Lemmons and Brown, 1995; Sexton et al., 1999; Steel et al., 2004; Francis et al., 2005; Stoll-Kleemann, 2005 Land use planning: James, 1999; Forester, 1999; Dortmans, 2005; Francis et al., 2004; Richardson, 2005; Szaro, Boyce, and Puchlerz, 2005; Lejano, 2008 Environmental risk communication: National Research Council, 1989; Covello, McCallum, and Pavlova, 1989; Kasperson and Kasperson, 2005 Sustainability science and promotion of sustainable behavior: Gardner and Stern, 1996; National Research Council, 1999c; McKenzie-Mohr 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: Haas, 1992; Liftin, 1994; Glasser, 1995; Alcamo, Kreileman, and Leemans, 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: Berkes and Folke, 1998; Cash, 2001; Jacobs, 2002; Pulwarty and Melis, 2001; 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.,
40 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 usersâ needs: Decision support activities should be driven by usersâ 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 constituenciesâcollections 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 inferiorâalthough 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-
EFFECTIVE DECISION SUPPORT 41 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 Usersâ 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 obviousâto 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 (1997â2001), 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).
42 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 groupsâ 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 othersâ 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 âa shift from a view of knowledge as a âthingâ that can be
EFFECTIVE DECISION SUPPORT 43 transferred to viewing knowledge as a âprocess of relatingâ that involves negotiation of meaning among partnersâ (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 âboundary organizationsâ 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). NOAAâs RISA centers are an example in the climate area; other federally sponsored boundary organizations, such as the EPAâs Great Lakes National Program Office, which are already performing linking functions, can expand their work on climate. Boundary organizations are commonly defined as âinsti- tutions that straddle the shifting divide between politics and science. They draw their incentives from and produce outputs for principals in both
44 informing decisions in a changing climate domains and thus .â.â. facilitate the transfer of useful knowledge between science and policyâ (Guston et al., 2000:7). Ideally, the functions that boundary organizations perform include link- ing science and practice while keeping them distinct, creating mechanisms of mutual accountability, and using the process of creating reports, models, assessments, and other products to facilitate and focus interaction (e.g., Cash et al., 2003; Cash, Borck, and Patt, 2006; National Research Coun- cil, 2006b). When scientists and decision makers are not already skilled in working with each other, boundary organizations may be necessary to produce fruitful interactions and effective decision support. Ongoing communication is also important because most climate-related decisions involve multiple and shifting arrays of interested and affected groups (Lindell et al., 1997). Decision support systems are generally more effective if participants recognize and accommodate group differences, en- courage transparency and accountability, and thereby build the trust that is essential to constructive intergroup coordination, information exchange, and risk-taking (e.g., Earle and Cvetkovich, 1998; Rupesh, Murphy, and McIntosh, 2003; Drew, Nyerges, and Leschine, 2004). Both formal and informal arrangements can effectively bridge these differences (National Research Council, 2002a; Moser and Dilling, 2007; Jackson and Shields, 2008). Allowing all participants to interact directly can help manage group differences, if this is done carefully (see National Research Council, 2008c). It is important that decision support systems at least consider the diversity of influences on a given decision, including the beliefs, attitudes, values, perceptions, social norms, and resource allocation tradeoffs of the affected parties. Build Connections Across Disciplines and Organizations Well-informed responses to climate change almost always require com- bining information from different disciplines, and decision makers are increasingly asking for knowledge and information that integrates across disciplines (e.g., Adger et al., 2003; Aram, 2004; Brechin et al., 2002; Brewer, 2007; Cimorelli and Stahl, 2005; Cundill, Fabricius, and Marti, 2005; Edwards and Steins, 1999; Jacobs, Garfin, and Lenart, 2005; Kinzig et al., 2000; Malone and Yohe, 2002; National Research Council, 1999a, 2004c; Quinlan and Scogings, 2004; van Kerkhoff, 2005). For example, re- ducing greenhouse gas emissions requires knowledge not just of how these gases affect the climate system, but also about the available technological and policy options at different scales of decision making and their likely economic and societal costs and benefits. Adaptation questions raise similar challenges of integration across disciplines, sectors, and scales. It takes time and care for multidisciplinary teams to come together and
EFFECTIVE DECISION SUPPORT 45 collaborate productively (e.g., Brewer, 1999; National Research Council, 2004c; Cummings and Kiesler, 2005; Pulwarty, Simpson, and Nierenberg, 2009). Decision support efforts that do not take the time and care needed for multi- and interdisciplinary collaboration may produce considerable frustration among scientists and partial or misleading information for deci- sion makers. It is similarly important to connect decision makers and stakeholders from different sectors or organizations to enable collaboration and informa- tion exchange. In governments, this typically means collaboration among different agencies. Even when individual agency representatives bring the necessary motivation, initiative, and leadership, formal interdepartmental and interagency mechanisms or reorganizations are frequently needed to permanently bridge divisions caused by different enabling laws, regulations, missions, procedures, budgets, and agency âculturesâ (e.g., Galloway, 1996; Jacobs et al., 2005; Moser, 2006a; Miles et al., 2006; Corringham et al., 2008; Pulwarty, Simpson, and Nierenberg, 2009). In seeking the needed integration, it is critical not to exclude or undermine decision makersâ and stakeholdersâ sense of ownership, processes of engagement, or valuable local knowledge (e.g., Farrelly, 2005). This process takes care and time, andâif ignoredâis likely to undermine the decision-making processes (and by extension, the intent of decision support). It is also important to build connections across geographic scales and levels of social and political organization. For example, integrative scientific assessments at the global or national levels do not provide detailed under- standing of local environmental, climatological, or social processes and are therefore not typically very useful for local decision making (e.g., Berkes, 2002; Cash and Moser, 2000; Cash et al., 2006; Jacobs, Garfin, and Lenart, 2005; Ludwig and Stafford Smith, 2005; Mitchell et al., 2006; Reid et al., 2007; Wilbanks, 2007; Young, 2002). Similarly, although policy mecha- nisms at higher levels provide broad frameworks, incentives, guidance, or mandates (e.g., setting a cap on carbon emissions or providing funds for reducing vulnerability to climate change impacts), local and regional juris- dictions still need to find effective ways to act within those frameworks. It can be helpful or necessary to build cross-scale engagement mechanisms or networks to remove barriers to action and to produce the scientific under- standing, collaborative learning, and information sharing, innovation, and relationship and trust building needed to achieve a productive linking of policies and practices at different levels of organization. Seek Institutional Stability Research and experience shows that decision support is more effective when it continues over time. Formal institutionalization of new entities is
46 informing decisions in a changing climate not always necessary for success: pilot projects or small, short-lived decision support interactions can help get a process started, and decision support can rely on existing boundary organizations, such as federally supported exten- sion services or private-sector service providers. But when well-established organizations do not exist to provide decision support, it can be difficult to attract funding, to convey a sense of longevity to potential information users, to establish trust among information users, or to get the institutional support needed in academic settings for space, time, scientific staffing, and instrumentation. Thus, in many instances, formal institutionalization of decision support will be critical to its longevity, recognition, and success. The model of establishing focused decision support centers within or affiliated with academic institutions has often been judged successful at the regional level (e.g., RISA centers, the Great Lakes Regional Assessment, the International Research Institute for Climate and Society, cooperative extension services, and many others cited in National Research Council, 2006b). Other decision support institutions may be formalized to serve a particular temporary policy purpose (e.g., specifically appointed advisory councils in support of state or national policy decisions) or to provide long- lived technical support for international policy regimes. An example is the Technology and Economic Assessment Panel (TEAP), which was initially in- strumental in building consensus around technological solutions to replace ozone-depleting substances and has become a permanent technical advisory body to the parties of the Montreal Protocol. Box 2-2 describes various models of institutionalized decision support, but the universe of potentially effective decision support models is not restricted to these few. BOX 2-2 Selected Models of Institutionalized Decision Support Cooperative Extension The USDAâs cooperative extension system provides almost a century of experi- ence in decision support process that is directly relevant to emerging climate-re- lated needs, even if not yet concerned with climate change. The system benefits from multitiered organization and the public mandate of land-grant universities. Over time, it has faced challenges common to all decision support activities: cul- tural and institutional differences and barriers between science and decision mak- ing, undue influence of specific interest groups, funding limitations, and changing communication technologies and interaction. The roots of the system go back to 1862, but it formally began with the 1914 Smith-Lever Act, which established cooperative extension services in every state (National Research Council, 1995; McDowell, 2001; Comer et al., 2006). Today,
EFFECTIVE DECISION SUPPORT 47 BOX 2-2â continued the federal Cooperative State Research, Education, and Extension Service funds state and local extension programs through a network of state, regional, and county extension offices in every state and territory and more than half of all U.S. counties. The target audiences for extension now include not only farmers, but also a broader range of individuals, households, businesses, and governments (National Research Council, 1996a; Kellogg Committee, 1999; McDowell, 2001; U.S. Department of Agriculture, 2008). Extension incorporates diverse and mutually reinforcing personnel and fund- ing mechanisms. States provide matching and additional funds for research and extension, and most counties also provide support. Thousands of extension staff, mainly based at land-grant universities, conduct outreach activities and respond to public inquiries in person, by telephone, and through internet, print, video, or other formats. They collaborate with colleagues at land-grant universities; other research- ers; federal, state, county, and local government agencies; nonprofit associations; professional and business organizations; private industry; citizen groups; founda- tions; the military; and other groups (U.S. Department of Agriculture, 2008). Land-grant universities typically hire faculty with significant extension obliga- tions, often in lieu of classroom teaching. Extension faculty and staff are often generalists rather than specialists, to meet diverse and changing public needs. Many aim to foster two-way communications between decision makers and re- searchers and two-way relationships between inquiry and application (National Research Council, 1996a; Kellogg Committee, 1999; McDowell, 2001). With the advent of the internet and web-based communication technologies, relationships between extension agents and their clients continue to change. The NOAA Regional Integrated Sciences and Assessments Program NOAA established the RISA Program in the mid-1990s to help ârealign our nationâs climate research to better serve societyâ by supporting âresearch that addresses complex climate sensitive issues of concern to decision makers and policy planners at a regional levelâ (see http://www.climate.noaa.gov/cpo_pa/risa/). RISA teams are typically based at universities, though some team members may come from government research facilities, nonprofit organizations, and private- sector entities. The teams commonly conduct research on climate-sensitive as- pects of fisheries, water, and wildfire management; agriculture; and, more recently, public health and coastal management issues. This small NOAA Program, with an annual budget of only around $3 million, currently sponsors nine RISA centers. Some are well into their second decade of existence, including the Climate Impacts Group in the Pacific Northwest and the Climate Assessment of the Southwest; others are relatively new, including the Southern Climate Impacts Planning Program and the Carolinas Integrated Sciences and Assessments; at least one, in New England was not sustained. RISA projects typically feature participatory problem framing and problem solv- ing; strong stakeholder involvement; an emphasis on ensuring that scientists live in the regions where they are conducting assessments; team building to integrate physical and social science experts; and starting with pilot projects that engender collaboration among scientists and decision makers. Fundamental to RISA Pro- grams is the notion that decisions are improved both through the incorporation
48 informing decisions in a changing climate BOX 2-2â continued of scientific information and through developing and sustaining knowledge-action networks. RISA Programs have been important test beds for learning how to apply the principles of effective decision support in informing decisions about adapting to climate change. The Montreal Protocol A well-organized decision support system contributed significantly to the suc- cess of the Montreal Protocol of 1987, the treaty that orchestrated international efforts to reduce the atmospheric concentration of substances that deplete strato- spheric ozone. Understanding that information and understanding would not be static, the authors of the protocol included provisions for regular assessments of the relevant information and review of the control provisions. These assessments are organized under the Technology and Economic Assessment Panel (TEAP; see Canan and Reichman, 2002) and combine disciplinary knowledge from chemi- cal engineering to economics. With scientifically grounded estimates of both the costs and benefits of control, deliberations have focused on solving the problem of ozone depletion. More than two-thirds of TEAP members have been from affected industries, with most of the others from government organizations and universities. Including technologists from the private sector has had several positive effects: it ensured that the reports contained upâto-date information on alternative technologies; it led to sharing of technological information among people in industry who could facilitate the changes necessary to reduce use of ozone-depleting substances (ODSs); and it added to the credibility of the reports in industry. TEAP reports have informed the parties to the protocol about which control measures were feasible and sped the global introduction of low or non-ODS-emitting technologies. Involve- ment of industry experts in TEAP, as well as their partnerships in developing ozone science, provided their firms with a good understanding of the issues involved, lowered resistance to change, and developed industry support for the international process as well as national implementation of the protocolâs ODS restrictions. Consensus among the TEAP members created reasonable assurance that businesses would comply with rules structured around those agreements. In this way, changes in the protocol in response to new information reflected rigorous science and gained political acceptability. The TEAP model, while reflecting the principles of effective decision support described in this chapter, cannot simply be copied for responding to climate change, because the latter problem is much more complicated in its technological and economic aspects, and the political issues are far more difficult to address. Design for Learning As Chapter 1 describes, the climate is continually changing and in- teracting with a world that is changing independently; the science of climate change is also rapidly changing and will continue to change; and
EFFECTIVE DECISION SUPPORT 49 the climate policy environment is evolving. Decision makers and decision support systems must respond effectively in this uncertain, continuously evolving decision environment or risk perpetuating errors and becoming obsolete. The needed learning orientation has several dimensions. First, decision makers need to consider multidisciplinary scientific information along with various other inputs, political factors, leadership directives, and priorities (Clark, Mitchell, and Cash, 2006; Morss et al., 2005). Both scientists and decision makers need to understand the necessity of tradeoffs across sectors, concerns, interests, and temporal and spatial scales (Carbone and Dow, 2005; Pulwarty, Simpson, and Nierenberg, 2009). Second, information products created in isolation from specific deci- sion contexts will not necessarily meet decision makersâ needs, especially in rapidly changing decision environments. Useful information reflects at- tention to the different dimensions of the decision support interaction and decision context; to emerging opportunities; to the strengths, advantages, and capacities of those involved; and to situational constraints. Third, certain events create âpolicy windowsâ for actionâthat is, convergences of problem recognition and pressure for policy solutions (see, e.g., Sabatier and Jenkins-Smith, 1993; Solecki and Michaels, 1994; Kingdon, 2002; Moser, 2005a). For climate-related decision support, recent first-hand experience of a disaster, such as a heat wave, drought, storm, or flood, can dramatically increase decision makersâ desire for and openness to new information and action, at least for a short period of time (Russell et al., 1995; Birkland, 1997). Individuals involved in decision support systems can prepare for and respond to such windows of opportunity by establishing relationships, communication channels, information products, and policy proposals in advance, then using relationships and making in- formation widely available in the wake of a trigger event (Mileti, 1999). If they are not prepared in these ways, the chance to provide decision support and implement change may pass as political pressure produces demands to âget back to normalâ as quickly as possible (e.g., Moser, 2005a). Maintenance of learning networks for decision support, especially for vulnerable groups, remains important in noncrisis times (e.g., Mileti and Peek, 2002). Participants need to remain aware of changing conditions and opportunities while choosing to be selectively responsive (e.g., Lee, 1993; Gunderson, Holling, and Light, 1995; Pulwarty, Simpson, and Nierenberg, 2009). Choices about funding, personnel, and research priorities should re- flect the need for continued learning, effective delivery of promised decision support, maintaining good relationships, and providing strong leadership over time (see Chapter 3).
50 informing decisions in a changing climate DECISION SUPPORT SERVICES We emphasize above that decision support includes more than provid- ing information tools and products. In this section we discuss what those engaged in effective decision support actually do. To begin, it is important to recognize that decision support activities and services vary over the course of a decision support relationship and differ depending on the phase of the policy or decision-making process. Different Services at Different Stages in the Decision Process A rich and varied literature conceives of policy making and decision making as progressing in stages or phases (e.g., Brewer, 1973; Brewer and deLeon, 1992; Birkland, 2005). These stage models vary in detail and by context, but for understanding the needs of decision support, certain simi- larities among the models are particularly important. One is that decision makers need different kinds of knowledge and information at different stages, and so the most appropriate input from science is stage dependent. Most stage models also note that decision making is an iterative and on- going process, which implies that decision support relationships should be ongoing to be effective and should be prepared to revisit old questions from time to time. Some of the phase models also emphasize that informa- tion and influence flows not only from scientists to decision makers, but also from decision makers and affected parties to science, in such forms as framing decision-relevant questions, expressing values and concerns to be considered, and providing specialized knowledge of local conditions (Na- tional Research Council, 1996b, 2008c). In the early stages of a process, scientists may play a principal roleâ as they have with climate changeâin detecting a problem and framing it to initiate a policy debate or decision process. Among the principal tasks for decision support at that time may be raising awareness, providing basic education about the problem, and translating scientific knowledge into lay language (e.g., Ogunseitan, 2000, 2003; Schreurs et al., 2001; Pielke, 1997). At later stages, decision support may focus on exploring the local effects of climatic events, systematically assessing policy alterna- tives, along with their costs and implications, or developing models and incentives for behavioral change. During policy implementation, decision support may focus on developing tools that help routinize decisions, defining professional ethics, and training and skills building for decision makers. In the appraisal or evaluation stage of decision making, decision support may emphasize monitoring and assessing, leading to termination, a change in decisions, or another round of decision making in an iterative process (Vogel et al., 2007; see also Chapter 3). Engagement of various
EFFECTIVE DECISION SUPPORT 51 audiences is critical in all phases, although their specific concerns, needs, and potential contributions vary. Those involved in providing decision support may change over time because different expertise is required to meet different decision support needs. Box 2-3 describes some options for beginning a decision support relationship and describes how that relation- ship may change over time. Because needs change with the phases of a decision process, continual engagement between researchers and decision makers is needed to identify what support is most needed, when, and by whom. Sometimes new science will be needed, while at other times the greatest need is for translating and interpreting existing knowledge to make it more accessible and useful to policy and management. Effective decision support addresses not only deci- sion makersâ expressed concerns and needs, but also the potential concerns they, or affected parties, might raise if they had more knowledge, under- standing, and capacity to participate. Communication Communication services include facilitating dialogue about the issues of concern, framing the problem, translating and visualizing existing knowl- edge, and interpreting it for different audiences. What is needed at any one time depends on the phase of the decision, the people involved, and their needs and current understanding of the issues at hand. Communication research shows that generally, communication is more effective when targeted to specific rather than generic audiences and when it includes specific information about vulnerabilities and alternative response options (e.g., Turner et al., 1979; Key, 1986; Bolton and Orians, 1992; Moser and Dilling, 2007). Including multiple information sources can help reach multiple audiences, since different groups trust different sources (e.g., Key, 1986). Different information sources and formats are perceived dif- ferently by different audiences in terms of their relevance, clarity, certainty, and trustworthiness, and so are differentially effective (e.g., Turner et al., 1986; Vaughan and Nordenstam, 1991; Lindell and Perry, 1992; Mileti and Fitzpatrick, 1993; Vaughan, 1995; Gutteling and Weigman, 1996; Mitchell et al., 2006). Perceptions of irrelevance, inconsistency, confusion, or doubt can delay action. Awareness of a climate-related hazard does not necessarily lead to spe- cific risk-averse decisions and behavior (e.g., Drabek, 1986; Slovic, 1989, 2000; Redman, Spencer, and Sanson-Fisher, 1990; Weinstein and Nicolich, 1993; Lindell and Perry, 2004; Folke et al., 2005). Decision makers are more likely to act to reduce vulnerability if they perceive the need for, and appropriateness of, such measures through processes of discovery that are informed by expert knowledge, not externally imposed (Mileti, 1999).
52 informing decisions in a changing climate BOX 2-3 Initiating and Changing Decision Support Relationships In principle, there are at least four ways to open communication channels between scientists and decision makers for decision support. They may be used sequentially or selectively (National Research Council, 2008d): (1) Informal conversations initiated by scientists, decision makers, spon- soring agencies or boundary organizations. Informal consultations help by identifying potential ways to meet information needs with existing or new information and by building essential relationships and trust. (2) Formal needs assessments through surveys and semi-structured inter- views, conducted by personnel of existing decision support teams, independent third parties, or agencies interested in sponsoring decision support (e.g., Moser and Tribbia 2006/2007; Tribbia and Moser, 2008; Corringham et al., 2008). Such assessments produce insight into what information decision makers currently use, how is it used, what rules and regulations govern its use, and what real or potential barriers exist to changing existing procedures (e.g., Rayner, Ingram, and Lach, 2005), as well as insight into what additional information would be useful. (3) Workshops that bring together a range of scientific experts with in- dividuals, organizations, or existing networks of potential users of climate- related information to identify climate-related issues that are important to a sector, region, or community, characterize the range of information needs, and determine to what extent existing information can provide that information or new scientific research is needed. Such workshops can be done periodically to reassess changing information needs. (4) Small-scale pilot projects that create or enhance knowledge-action net- works and test decision support in a climate-affected sector or region (see Georgakakos et al., 2005, for a description of the INFORM project testing alterna- tive management procedures and information inputs in California water reservoir operations; see also the water planning exercise described in Box 4-2). These initial modes of contact serve the purpose of mutual education, not just information elicitation from one side of the knowledge-action continuum. Effective facilitation of any of these processes can be essential in establishing good work- ing relationships, developing useful insights, and engendering a spirit of curiosity and collaboration. They also make clear which decision support services are most needed initially. Decision support collaborations change over time and, if well designed for learning, develop increasingly effective mechanisms and practices for engage- ment. Common, overlapping phases of growth can be distinguished. In the initial phase, decision support teams select and integrate researchers across disci- plines, define a few key issue areas, develop cooperative relationships with con- stituencies, and start accumulating relevant knowledge bases, methodologies, and datasets. Later, teams clarify critical issues, learn more about decision prob- lems, identify key vulnerabilities, and begin to produce integrated assessments and other decision-relevant information. When teams have established functional networks, communication processes, and norms for integration, they can produce information and knowledge products and tools of practical utility. Teams then have greater capacity to engage in more open and cross-sectoral dialogues and proj- ects (Lemos and Morehouse, 2005; Pulwarty, Simpson, and Nierenberg, 2009).
EFFECTIVE DECISION SUPPORT 53 Thus, effective public health and hazards-related decision support strategies often do not direct audiences to take specific actions; rather, they offer risk- related information that responds to public questions and concern, then offer clear decision options with associated costs and benefits (Morgan and Henrion, 1990; Nathe, 2000; Mileti, 2003). This approach allows decision makers to develop a sense of ownership of an issue and process and manage their own learning, mobilization, and action in full concert with the learn- ing, mobilization, and action of important peers or stakeholders (Evans and Stoddart, 1994; Patrick and Wickizer, 1995; Mileti and Peek, 2002). Risk-related information is not always understood in the ways its purveyors intend. For instance, there are well-known biases in humansâ understandings of risk information, as well as difficulties in the communica- tion process (e.g., Turner, Nigg, and Paz, 1986; Vaughan and Nordenstam, 1991; Lindell and Perry, 1992; Mileti and Fitzpatrick, 1993; Vaughan, 1995; Gutteling and Weigman, 1996; National Research Council, 2006a; Moser and Dilling, 2007). Messages that emphasize the negative potential of hazards without advising on practical responses can increase public apa- thy, avoidance, and denial (e.g., Lopes, 1992; Moser, 2007b). In contrast, conveying a sense of personal efficacy and realistic avenues to reducing risk and vulnerability makes action more likely (Rogers, 1983; Weinstein and Sandman, 1992; Mulilis and Duval, 1995; Vaughan, 1995; Gardner and Stern, 1996; Lindell and Whitney, 2000). Effective decision support ac- tivities have to address these challenges in producing relevant information, making it available through mediated and direct communication channels, and fostering its appropriate interpretation and use. Communication research shows that information is most effectively delivered when repeated in clear, consistent, and incremental messages over time; when it includes honest articulation of uncertainties; and when it is made easily available through multiple channels and formats, including mediated, print, and personal contacts (Sorensen, 1983; Bolton and Orians, 1992; Mileti and Fitzpatrick, 1994; Nathe et al., 1999; Nathe, 2000; Na- tional Research Council, 2006a). Disseminating strategic and coordinated messages requires close communications and planning across knowledge- action networks. Unidirectional communicationsâsuch as through the mass mediaâcan help to raise public awareness of an issue and influence attitudes and be- liefs, but interpersonal contacts are typically necessary to effect changes in human behavior (Lazarsfeld et al., 1948; Eulau, 1980; Hornik, 1989; Redman, Spencer, and Sanson-Fisher, 1990; Valente et al., 1996; Campbell et al., 2000; Dunwoody, 2007). Social relationships filter, screen, interpret, and validate the messages and information that people receive. Even if awareness increases, human attitudes can be deeply entrenched and so deter action (Katz, 1960; Rogers, 1983; Turner et al., 1986; Mileti, 1999; Ajzen,
54 informing decisions in a changing climate 1991). Peer approval and social norms can strongly influence behavior when outcomes are uncertain, as is often the case in climate change-related con- texts (McCay and Acheson, 1987; Valente and Saba, 1998; Schultz, 2002). These findings add further weight to the central importance of social and professional networks in communicating decision-relevant information. Decision support systems can build on these findings when providing specific communication services. For example, because facts do not have unambiguous meanings and action implications, it is important to frame messages in ways that connect with the concerns of the audiences. Expertise in science writing and visual communication can help in connecting with audiences. Visual presentations can be very powerful aids, although they are vulnerable to manipulation (see Sheppard, 2005). Visual presentation of climate-related phenomena deserves further research as a tool for deci- sion support. âRoad showsâ involving repeated, yet slightly varied delivery of presentations to different audiences can be important for reaching and maintaining the knowledge base of all affected groups, even if time con- suming and unattractive to some scientists. The need for extensive outreach to various groups illustrates the advantages of a team-based approach in which responsibilities are shared and in which dedicated staff support the preparation of outreach materials and are responsible for maintaining com- munication networks. Finally, both public and private forums for dialogue among scientists, decision makers, and other parties can provide important decision sup- port services. The right formats for such forums depend on the intended goal (Forester, 1999; Rowe and Frewer, 2000; Creighton, 2005; National Research Council, 2008c). The uncertainties and high stakes involved in some climate-related decisions place particular demands on communica- tion. Decision makersâ understandings of climate-related uncertainties are affected by their experiences, perceptions, capacities, and interests, as well as their familiarity with concepts of uncertainty and risk (Carbone and Dow, 2005; National Research Council, 2006a; Pulwarty, Simpson, and Nierenberg, 2009). Mediation and Brokerage In some instances, the most important decision support service is to es- tablish or âbrokerâ the connection between existing information and those whose decisions may be improved by it. Brokering can involve convening decision makers and stakeholders and helping them to identify and clarify their respective interests and goals, negotiate decision criteria, and deter- mine acceptable outcomes (e.g., Cash et al., 2003; Kramer and Wells, 2005; Richardson, 2005). As decision makers may want information that science cannot provide, an important decision support service is to help match
EFFECTIVE DECISION SUPPORT 55 what they want with what science can deliver for specified timetables. Such mediation helps mobilize science for decision support while helping its credibility with decision makers. According to Cash et al. (2003:8,088), mediation works âby enhancing the legitimacy of the process through in- creasing transparency, bringing all perspectives to the table, providing rules of conduct, and establishing criteria for decision making.â Some RISA centers and other boundary organizations make this sort of decision support their primary service. Establishing trust among par- ticipants is essential to success, as is the trust and âcredibility that comes through long-term, sustained engagementâ (McNie, Pielke, and Sarewitz, 2007:16). The likelihood of success also increases if researchers and prac- titioners have a sense of shared responsibility for their interaction and for the use of knowledge in decision making and both sides are fully aware of the larger systems of power and knowledge in which they function (van Kerkhoff and Lebel, 2006). Brokering also involves overcoming various cognitive, institutional, and political barriers to information use. It is especially difficult for decision makers to modify policies and decisions in light of new scientific informa- tion when the potential consequences are significant, uncertainty is high, experience is limited, or equity issues are a principal concern. In such situ- ations, decision makers need assistance in the critical consideration and assessment of different knowledge claims and the practical integration of in- formation in decision processes. Getting decision makers to pay active and considered attention to the policy implications of new information may re- quire deliberative involvement in decision forums by scientists or boundary organizations (Jacobs, Garfin, and Lenart, 2005; Lemos and Morehouse, 2005; Pulwarty, Simpson, and Nierenberg, 2009). It may require research teams to acquire new skill sets or to involve individuals with experience in this set of decision support services, and it may call on decision makers to take more risks, rely more heavily on personal judgment, or operate more iteratively (Jacobs, Garfin, and Lenart, 2005). Research to Generate Decision-Relevant Information When decision makers begin to think about the relevance of climate change to their decisions, they may ask scientific questions that scientists have not yet investigated. An important decision service is to answer some of these questions through what has been called use-inspired research (Stokes, 1997). Such research may be focused on a very specific question, such as how to design an appliance energy efficiency label to convey its information most effectively, or on a much more basic research question, such as how to measure the vulnerability of communities to sea-level rise. Use-inspired research typically responds to the questions of decision mak-
56 informing decisions in a changing climate ers or stakeholders, but is designed and carried out by scientists, sometimes incorporating specific âlocal knowledgeâ from nonscientists; it is vetted for its scientific quality through peer review and for its usefulness and salience by decision makers. Use-inspired research questions sometimes arise from formal or in- formal needs assessments that explore the concerns, responsibilities, and decisions of climate-affected individuals or groups. Needs assessments may involve narrowly or broadly focused stakeholder engagement processes, which require experience and expertise in participatory processes to be conducted effectively (e.g., Rowe and Frewer, 2000; Kasemir et al., 2003; Stringer et al., 2006; Newig, 2007; National Research Council, 2008c). At other times, research questions arise from integrative assessments that draw on a wide range of data and findings from many sources to help identify important decision-related knowledge gaps (Pulwarty, Simpson, and Nierenberg, 2009; also see Appendix A). They may also arise from scientific reviews that focus on research needs for decision making (e.g., National Research Council, 2005a; Stern and Wilbanks, 2008). This sort of research often requires contributions from multiple disciplines to most usefully inform decisions. User-driven questions have led to advances in basic scientific under- standing. An often cited example is the work the Climate Impacts Group (the Pacific Northwest RISA center at the University of Washington-Seattle) undertook in response to resource managersâ desire to better understand the link between salmon fishery management and climate variability. The research led to the discovery of the Pacific Decadal Oscillation and opened a line of research that both informed decisions and generated basic scientific advances. Given the complexity of the climate and the Earth system, it is extremely likely that use-inspired questions will continue to require basic research in many fields of environmental, ecological, social, economic, and physical science, and in engineering. We discuss the link between decision support needs and scientific research in detail in Chapter 4. Decision Structuring An overwhelming emphasis on climate modeling and information prod- ucts has drawn attention away from an extensive body of relevant knowledge from the decision sciences that shows that poor decisions come not just from a lack of good technical information (e.g., Kahneman et al., 1982; Kahneman and Tversky, 2000; Plous, 1993; Simon, 1956; Slovic et al., 1977; Tversky and Kahneman, 1981). In addition, judgment varies with how information is presented and with contextual and experiential cues that are available to people during the decision-making process (Arvai et al., 2006b; Payne et al., 1992; Slovic, 1995; Slovic and Lichtenstein, 2006).
EFFECTIVE DECISION SUPPORT 57 Given these potential problems, getting the best available knowledge and making it accessible to decision makers and other affected groups may not be sufficient. It may also be necessary to organize decision processes so that the most relevant science will be done and so that it can be interpreted coherently and constructively (National Research Council, 1996a). Deci- sion structuring is therefore an important decision support service. Recent analytic and behavioral research on decision making provides much-needed guidance on how to structure decisions about responses to climate change. This work emphasizes qualitative discussions about how climate change might affect the operations of a particular system or about the feasibility and likely results of various possible responses to climate change (some recent studies include Arvai, Gregory, and McDaniels, 2001; Gregory, Arvai, and McDaniels, 2001; Gregory, McDaniels, and Fields, 2001; McDaniels, Gregory, and Fields, 1999; Edwards, Miles, and von Winterfeldt, 2007). These discussions have five basic elements: 1. Defining the boundaries of the problem: What is the question being addressed? What factors are included and excluded from consideration? 2. Defining the objectives: What is trying to be achieved? What would constitute success for all involved? 3. Laying out the alternative options: What alternatives are available to achieve the objectives? 4. Estimating the consequences of each alternative option by certain criteria: What can be expected to happen if a given option is adopted? What is unknown about each course of action? How will the expected outcomes match the objectives? 5. Evaluating the tradeoffs among the options: What may be gained and lost by choosing one option over another? Specialists draw on insights from the decision sciences to help inform and guide decision processes through these elements, with ongoing input from decision makers and stakeholders. Discussions may lead to calls for research on specific options and their consequences. For example, research on relationships among policy regimes and goals (e.g., interactions between the U.N. Framework Convention on Climate Change and the Montreal Protocol or the Biodiversity Convention; integration of sustainability, mil- lennium development goals, and climate change) may provide critical input into the design of new or modified international agreements. Decision structuring, by providing more thorough consideration of the parts of a decision problem, can result in greater clarity about a problem and affect the ways decision participants see it and its possible solutions. Framing and clarifying decisions can also build and mobilize some constituencies and disenfranchise others. These effects may be difficult to discern, especially
58 informing decisions in a changing climate immediately, but may have a lasting impact on the decision environment (Birkland, 2005; Mitchell et al., 2006). Evaluation Any continuing decision support effort has to respond to changing demands, decision environments, and scientific knowledge. Some decision support providers assist in conducting explicit assessments of how well the effort is doing; others may involve less formal evaluations. Thus, a final set of decision support services concerns evaluation of the systemsâ own internal workings and external effects (deliberate learning and evaluation are discussed further in Chapter 3). In the past, formal evaluations have not commonly been undertaken as part of decision support efforts, but they are increasingly recognized as an important element of deliberate ef- forts to improve decision support services. For example, the RISA Program was at first experimental, in the sense that different RISA teams developed activities at different scales and engaged with constituents through different decision venues, organizational structures, and mechanisms. A handful of evaluative activities and publications on these experiments have produced a small literature on lessons learned. They also have identified âevaluationâ as maybe the most prominent gap in RISA activities to date (e.g., Lemos and Morehouse, 2005; McNie, Pielke, and Sarewitz, 2007; McNie, 2008; Pulwarty, Simpson, and Nierenberg, 2009). Most of the available evidence on the results from the RISA centers takes the form of experience-based judgments by RISA funders, staffs, and users. Some of the usersâ judgments have been strongly positive, and the positive judgments appear to be associated with recognition that the pro- grams have followed principles of effective decision support, particularly beginning with usersâ needs and linking information producers and users. For example, the Western Governorsâ Association passed a resolution in May 2007 to âgive a high priority to funding for federal programs, such as the RISAs that provide the translation function between basic scientific re- â Some decision researchers have demonstrated that in a range of difficult decision contexts, nonscientists and their organizations use fairly simple cognitive heuristics quite effectively to arrive at decisions (e.g., Gigerenzer and Selten, 2001; Zsambok and Klein, 1997; Hodgkinson and Starbuck, 2008). We are not persuaded that this skill will apply well to climate-related decisions for the foreseeable future because of certain characteristics of climate change: It in- volves events outside anyoneâs experience, proceeds at an accelerating rate, evolves on a very long time horizon, and has consequences that are uncertain not only in magnitude but also in kind. The most obvious simple heuristics, such as relying on past experience and climate aver- ages, are more likely to be misleading than helpful as bases for estimating the consequences of possible actions. Of course, the efficacy of simple heuristics is an empirical question. We return to this issue in Chapter 4.
EFFECTIVE DECISION SUPPORT 59 search on climate variability and change and the application of that research to real-world water management situations at the regional, state, and local levelsâ (see http://www.westgov.org/wswc/050407%20risa%20resolution. pdf; emphasis in original). One reason formal evaluation is often neglected may be that program goals, which must be measurable to make formal evaluation possible, are not often articulated clearly enough for measurement, especially at the outset. Resources (e.g., funding, staff time, and evaluation expertise) are also scarce. Finally, weak evaluation results can increase a programâs vul- nerability to budget cuts and staff reallocations (Jacobs, Garfin, and Lenart, 2005; Pulwarty, Simpson, and Nierenberg, 2009). Evaluation is a challenge when the ultimate results of a decision are not obvious or are delayed (e.g., Global Environmental Assessment Project, 1997; Parson, 2003; Lemos and Morehouse, 2005; Pulwarty, Simpson, and Nierenberg, 2009). Moreover, some dimensions of âeffectivenessâ are difficult to assess, and different evaluators will judge effectiveness from different perspectives and by different criteria (Jacobs, Garfin, and Lenart, 2005; National Research Council, 2006b; Pulwarty, Simpson, and Nierenberg, 2009). It is critical but difficult for evaluation to assess can- didly the partnership between scientists and decision makers and the qual- ity of relationships. Sometimes, the greatest value of evaluation is not to provide the equivalent of a final grade, but to elicit qualitative feedback (Jacobs, Garfin, and Lenart, 2005) that can be shared with those involved in order to enhance transparency and legitimacy, build trust, and foster the ongoing collaboration. In short, evaluation may be most useful as part of a learning process, to facilitate the evolution of decision support efforts and inform leaders about how to promote needed change (Lemos and Morehouse, 2005; McNie, Pielke, and Sarewitz, 2007; Pulwarty, Simpson, and Nierenberg, 2009). In Chapter 3, we discuss the role of evaluation in learning in greater detail. BARRIERS TO EFFECTIVE DECISION SUPPORT The scientists and practitioners who interact with each other around climate decisions do so outside the boundaries of familiar disciplinary, in- stitutional, and professional expectations, and occasionally at considerable personal and professional expense. Working through boundary organiza- tions may reduce some of these costs, but that can involve its own challenges and resource commitments (Cash et al., 2003; Sarewitz, 2004). Successful interactions between scientists and decision makers face persistent institu- tional, organizational, and cultural barriers. We turn here to a discussion of these barriers and then to some strategies to overcome them.
60 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 areaâs 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, âthe federal re- search support system is geared more toward knowledge generation than problem solving.â Such barriersâwhether formalized or implicitâ
EFFECTIVE DECISION SUPPORT 61 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-Ã -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 âfitâ 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 âstandardâ 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 todayâs or tomorrowâs 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.
62 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.
EFFECTIVE DECISION SUPPORT 63 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 nationâs 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
64 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 âbusiness as usualâ is over for the worldâs 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).
EFFECTIVE DECISION SUPPORT 65 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 stateâs 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
66 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 nationâs decision support capacity (see also Chapter 4), training, internships, and information exchange among
EFFECTIVE DECISION SUPPORT 67 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 Â 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 usersâ 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 NOAAâs 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 usersâ 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.
68 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. â¢ 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. â¢ The Environmental Protection Agency (EPA) should expand its climate-related decision support programs to serve more regional and sectoral constituencies. â¢ Other federal agencies should take similar steps for their climate- affected constituencies. â¢ 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 NOAAâs 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
EFFECTIVE DECISION SUPPORT 69 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 NOAAâs 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 publicâprivate 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.