- Email: [email protected]
The cognitive underpinnings of policy process studies: Introduction to a special issue of Cognitive Systems Research
Available online at www.sciencedirect.com
ScienceDirect Cognitive Systems Research 45 (2017) 48–51 www.elsevier.com/locate/cogsys
The cognitive underpinnings of policy process studies: Introduction to a special issue of Cognitive Systems Research Bryan D. Jones a, Herschel F. Thomas b,⇑ b
a University of Texas at Austin, United States University of Texas at Arlington, United States
Received 20 April 2017; accepted 23 April 2017 Available online 9 May 2017
Abstract This article introduces the special issue of Cognitive Systems Research on public policy processes. We begin with a discussion of the cognitive foundations of public policy that stem from the complexity of human cognition and emotion. Next, we provide an overview of the articles in the special issue, which occur at the edge of a public policy-cognitive systems boundary. We then turn to a discussion of promising new work in the study of public policy that explores—or may benefit from—the cognitive systems perspective. Ó 2017 Elsevier B.V. All rights reserved.
Keywords: Public policy; Human cognition; Behavioral model of choice; Information processing; Problem definition
1. Introduction With the support of Cognitive Systems Research Editor Peter Erdi, we developed this special issue on public policy processes to summarize and extend the existing literature on the cognitive foundations of policymaking systems. The seven articles included in the special issue—as well as a response by Baumgartner—present a sampling of some of the major work linking a general cognitive systems approach to public policy studies.1 While the articles connect systems of human action focused on policymaking to the dynamic and diverse cognitive systems perspective, they only scratch the surface of both the realities and the potentials of this approach. In this introduction, we outline the cognitive foundations of public policy based in the complexity of human cognition and emotion, and then offer ⇑ Corresponding author.
E-mail address: [email protected] (H.F. Thomas). Special issue available online at: http://www.sciencedirect.com/science/journal/13890417/vsi/10PBJ5583CG 1
http://dx.doi.org/10.1016/j.cogsys.2017.04.003 1389-0417/Ó 2017 Elsevier B.V. All rights reserved.
an overview of each piece in the special issue. We conclude with a discussion of promising new studies in public policy and how they relate to the cognitive systems perspective. 2. Cognitive foundations of policymaking As it concerns human cognition, policymaking takes place in a strange region. On the one hand, it is the ultimate of frontal cortex activities at the societal level: planning, judging outcomes based on the incentives of actors, and the calculation of costs versus benefits of a course of proposed action. On the other hand, because policymaking relies on collective action and because any course of action creates winners and losers, it arouses the strongest of emotions. Any cognitive action by an individual involves emotion (Damasio, 1994). Yet the standard approach by many economists and political scientists of the neo-institutional persuasion is to hold to a rational-analytic framework in which costs and benefits are judged according to a probability calculus, and the best choice of action is the one that
Editorial / Cognitive Systems Research 45 (2017) 48–51
leads to the largest payoff. In the standard rational analysis, each individual has fixed preferences that are maximized according to the probabilistic framework. Where do those preferences come from? In social action, this matters a great deal. If the preferences are not based in facts (and there is no requirement that they be), then people holding such preferences are a version of what Sen (1977) called ‘‘rational fools.” A major preoccupation of public choice economists and neo-institutional political scientists is how diverse individual preferences are combined to form a ‘‘social welfare function,” that is, collective decisions on public policies. These scholars ground their analyses in no normative model—all preferences are equal. Further, they fail for the most part to appreciate the dynamics involved in the prioritization of preferences, treating prioritization among various policy objectives and a position on any one objective as a package. Policy scholars treat these two components of preferences as operating via different causal processes, at least partially. If that is the case, we expect far more instability in the social welfare function than public choice scholars do. How do cognitive and emotional facets of individual actors combine to construct public policies if we allow for the separation of priorities from positions? If preferences are contingent on priorities and position, then is it not the case that the problems facing a political system are important? Newell and Simon (1972) distinguished between the problem-space and the solutionspace in their studies of human problem-solving, finding that individuals had more difficulties in re-evaluating the problem-space than in the solution-space. This is likely to be true in collectives as well. In any case, the notion of distinct problem and solution spaces influenced by different causal processes has become central to policy process studies since the work of Cohen, March, and Olsen (1972) who treat the connection between the two as subject to the activation of attention. Unlike the analytical framework deployed by most economists and neo-institutionalists in political science, most policy process scholars admit the complexity of human cognitive and emotional architectures into their models. Simon (1947) pioneered that approach, and the field has maintained its commitment to it over the years. Simon insisted that public administration and later public policy be grounded in a psychological theory of decisionmaking. He first termed that notion ‘‘bounded rationality,” to capture the limits of a rational framework in explaining human behavior in organizations. Later he and others contributed to developing a more robust and positive model of human action in policymaking, which he termed ‘‘behavioral rationality.” Its major premise is that humans do have priorities and goals, but they are not generally effective in judging the connections between those goals and the complex reality they face. For the most part, that remains the cognitive foundation of policy process studies in political science and elsewhere.
49
3. An overview of the special issue In constructing the special issue, we concentrated on areas we think are at the edge of the public policycognitive systems boundary. Each included article uniquely explores this disciplinary boundary between policy studies and cognitive systems. For example, the authors examine the difficulties in connecting the micro-level analyses of cognitive studies to the systems level activity in policymaking (Jones); the contagion and diffusion that occurs among individual actors within policy systems (Thomas); the performance of different organizational forms in adapting to changing circumstances in a complex world with cognitively-limited decision-makers (Epp); the evolutionary basis of cooperative behavior that is necessary to achieve collective action (Leech and Cronk); information processing in policymaking systems (Workman, Shafran, and Bark); the role of cognitive load and complex problems in the search processes of policymaking systems (Shaffer); and, organizational learning and neural networks (Hegelich). In ‘‘Behavioral Rationality as a Foundation for Public Policy Studies,” Jones discusses links between individual behavior and aggregate patterns in collective organizations—noting progress in the integration of the cognitive sciences into behavioral models. He suggests that our understanding of these microfoundations in political institutions is at a ‘‘turning point” wherein the rational model of human choice is replaced with the behavioral model. In doing so, Jones provides a foundation on which scholars can further develop a cognitive systems approach to policy studies. Drawing heavily on the behavioral model of choice, Thomas explores how cue-taking behavior among policymakers contributes to rapid shifts in their aggregate attention to policy issues over time. In ‘‘Modeling Contagion in Policy Systems,” Thomas uses an agent-based modeling approach to examine how the presence of cue-taking behavior, dense communication networks, and sub-divisions among actors affect patterns in the activity of a simulated policy system. With a simple model of individual-level attention, he shows how cue-taking behavior can generate disjointed patterns that are akin to those widely documented in policy systems at various levels of governance. Epp, in ‘‘Public Policy and the Wisdom of Crowds,” examines differences between government organizations and group systems—such as markets—from the collective intelligence perspective. Given the limited cognitive capacity of any one decision-maker, Epp tests hypotheses about the extent to which group systems or organizations can overcome limitations in the processing of information. Across the empirical cases he studies, Epp shows that organizational systems are less responsive and informationally efficient than group systems. These findings contribute to our understanding of disjointed change in public policymaking by mapping how the collective intelligence of groups systems may be superior to the organized hierarchies of government.
50
Editorial / Cognitive Systems Research 45 (2017) 48–51
Leech and Cronk, in ‘‘Coordinated Policy Action and Flexible Coalitional Psychology: How Evolution Made Humans So Good at Politics,” focus on the psychological and cognitive underpinnings of coordinated policy action in contemporary US politics. They suggest that a ‘‘coalitional psychology” drives the flexibility of political actors to create—and break—coalition ties when circumstances shift. This psychology, they argue, is unique to humans and supports the forming and reforming of political coalitions with previous opponents to achieve policy goals. Leech and Cronk explain how gene-cultural evolution and a process of cultural group selection support such flexibility, and then document cases at the level of countries, groups, and individuals. In ‘‘Problem Definition and Information Provision by Federal Bureaucrats,” Workman, Shafran, and Bark draw on models of congressional decision-making to examine when federal bureaucrats are called upon to provide information about policy problems. They theorize that the uncertainty that coincides with the definition of a problem leads the US Congress to seek information from higher numbers of bureaucrats, to do so early in the hearing process, and to draw from those with the most experience (e.g., career bureaucrats). In studying the appearances of over 25,000 witnesses in Congressional hearings on business and financial regulation, Workman, Shafran, and Bark, find support for these claims and conclude that bureaucrats play a central role in structuring policy debates in Congress during periods of uncertainty. Shaffer makes the argument that existing approaches to understanding attention to public policy issues do not fully address ideas about individual cognition. In ‘‘Cognitive Load and Issue Engagement in Congressional Discourse,” Shaffer develops a novel measurement strategy for computationally analyzing the issue content and dimensionality of hearings held by the US Congress. With the full-text transcripts of a sample of hearings on business and financial regulation, Shaffer identifies intense problem dimensionality during recent periods of economic instability and crisis. At the level of members of Congress, Shaffer documents the interconnectedness of individual-level behavior and cognitive patterns. Hegelich, in ‘‘Deep Learning and Punctuated Equilibrium,” also pursues a computational approach. Hegelich maps the conceptual model underpinning deep learning algorithms to policymaking under a major theoretical framework of the policy process. Specifically, he connects neural networks and the algorithmic notion of ‘‘backpropagation” to the study of multiple layers of inputs and outputs associated with budgetary change and policymaking under Punctuated Equilibrium Theory. 4. Promising new work in policy studies Research in public policy studies is often conducted at the systems level without much in the way of reference to the cognitive capacities of the decision-makers who inhabit
those systems. However there are other areas of policy that are trending in the direction of boundary exploration that bear mention here. First, we would note the work on policy narratives (M. Jones, Shanahan, & McBeth, 2014). Narratives, stories that carry both cognitive and emotional content and which are transmitted among broad groups of people, are shared understandings, and thus serve as a connection between individual cognitive and emotional architectures and systems level action. In addition, economic narratives are becoming a feature of behavioral economics (Ackerlof & Shiller, 2009), providing a potentially unifying approach. Moreover, scholars in this area are moving directly toward exploring the boundary with an increasingly rigorous scientific approach. Second, in recent years the so-called ‘‘streams” approach to policymaking, born in the work of Cohen et al. (1972) is in the process of reinvigoration (Zahariadis, 2014). This work continues to hew to the attention-based framework set in the original papers, but it has not come as far in testing the boundary between cognitive systems and policy studies as the other approaches we examine here. But surely it is ripe for further exploration. Finally, we note the literature on policy design and the social construction of target populations as an area where the policy studies field could benefit from a more explicit cognitive studies foundation. Proponents of the approach highlight the emotional content in how target populations—those receiving the benefits or bearing the burdens of policy action—are constructed in either positive or negative ways. These constructions lead to different policy designs (Schneider, Ingram, & DeLeon, 2014). It is harder to design policies for policy targets that are viewed negatively than those viewed positively, but these images do change, indicating that more work on the cognitive and emotional components of policy design need further addressing. Our broader point is that many areas of policy studies are not tied to rigid and overly-simplified models of actors, but sometimes these foundations are not as welldeveloped as they might be. Acknowledgement We thank Samuel Workman for his feedback on this introduction to the special issue. References Ackerlof, George A., & Shiller, Robert J. (2009). Animal spirits. Princeton: Princeton University Press. Baumgartner, Frank R. (2017). Endogenous disjoint change. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017.04.001. Cohen, Michael D., March, James G., & Olsen, Johan P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25. Damasio, Antonio (1994). Descartes error: Emotion, reason, and the human brain. New York: Putnam. Epp, Derek (2017). Public policy and the wisdom of crowds. Cognitive Systems Research, 43, 53–61.
Editorial / Cognitive Systems Research 45 (2017) 48–51 Hegelich, Simon (2017). Deep learning and punctuated equilibrium. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017. 02.006. Jones, Bryan D. (2017). Behavioral rationality as a foundation for public policy studies. Cognitive Systems Research, 43, 63–75. Jones, Michael D., Shanahan, Elizabeth A., & McBeth, Mark K. (Eds.). The science of stories: Applications of the narrative policy framework in public policy analysis. New York: Palgrave Macmillan. Leech, Beth L., & Cronk, Lee. (2017). Coordinated policy action and flexible coalitional psychology: How evolution made humans so good at politics. Cognitive Systems Research, 43, 89–99. Newell, Allen, & Simon, Herbert A. (1972). Human problem-solving. Englewood Cliffs, NJ: Prentice Hall. Schneider, Anne, Ingram, Helen, & DeLeon, Peter (2014). Democratic policy design. In Paul A. Sabatier & Christopher M. Weible (Eds.), Theories of the policy process (3rd ed., pp. 105–150). Boulder, CO: Westview.
51
Sen, Amayrta (1977). Rational fools: A critique of the behavioral foundations of economic theory. Philosophy and Public Affairs, 6, 317–344. Shaffer, Robert (2017). Cognitive load and issue engagement in congressional discourse. Cognitive Systems Research. http://dx.doi.org/ 10.1016/j.cogsys.2017.03.006. Simon, Herbert A. (1947). Administrative behavior. New York: The Free Press. Thomas, Herschel F. (2017). Modeling contagion in policy systems. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017. 03.003. Workman, Samuel, Shafran, JoBeth, & Bark, Tracey (2017). Problem definition and information provision by federal bureaucrats. Cognitive Systems Research, 43, 104–152. Zahariadis, Nikolaos (2014). Ambiguity and multiple streams. In Paul A. Sabatier & Christopher M. Weible (Eds.), Theories of the policy process (3ed ed., pp. 25–58). Boulder, CO: Westview.
ScienceDirect Cognitive Systems Research 45 (2017) 48–51 www.elsevier.com/locate/cogsys
The cognitive underpinnings of policy process studies: Introduction to a special issue of Cognitive Systems Research Bryan D. Jones a, Herschel F. Thomas b,⇑ b
a University of Texas at Austin, United States University of Texas at Arlington, United States
Received 20 April 2017; accepted 23 April 2017 Available online 9 May 2017
Abstract This article introduces the special issue of Cognitive Systems Research on public policy processes. We begin with a discussion of the cognitive foundations of public policy that stem from the complexity of human cognition and emotion. Next, we provide an overview of the articles in the special issue, which occur at the edge of a public policy-cognitive systems boundary. We then turn to a discussion of promising new work in the study of public policy that explores—or may benefit from—the cognitive systems perspective. Ó 2017 Elsevier B.V. All rights reserved.
Keywords: Public policy; Human cognition; Behavioral model of choice; Information processing; Problem definition
1. Introduction With the support of Cognitive Systems Research Editor Peter Erdi, we developed this special issue on public policy processes to summarize and extend the existing literature on the cognitive foundations of policymaking systems. The seven articles included in the special issue—as well as a response by Baumgartner—present a sampling of some of the major work linking a general cognitive systems approach to public policy studies.1 While the articles connect systems of human action focused on policymaking to the dynamic and diverse cognitive systems perspective, they only scratch the surface of both the realities and the potentials of this approach. In this introduction, we outline the cognitive foundations of public policy based in the complexity of human cognition and emotion, and then offer ⇑ Corresponding author.
E-mail address: [email protected] (H.F. Thomas). Special issue available online at: http://www.sciencedirect.com/science/journal/13890417/vsi/10PBJ5583CG 1
http://dx.doi.org/10.1016/j.cogsys.2017.04.003 1389-0417/Ó 2017 Elsevier B.V. All rights reserved.
an overview of each piece in the special issue. We conclude with a discussion of promising new studies in public policy and how they relate to the cognitive systems perspective. 2. Cognitive foundations of policymaking As it concerns human cognition, policymaking takes place in a strange region. On the one hand, it is the ultimate of frontal cortex activities at the societal level: planning, judging outcomes based on the incentives of actors, and the calculation of costs versus benefits of a course of proposed action. On the other hand, because policymaking relies on collective action and because any course of action creates winners and losers, it arouses the strongest of emotions. Any cognitive action by an individual involves emotion (Damasio, 1994). Yet the standard approach by many economists and political scientists of the neo-institutional persuasion is to hold to a rational-analytic framework in which costs and benefits are judged according to a probability calculus, and the best choice of action is the one that
Editorial / Cognitive Systems Research 45 (2017) 48–51
leads to the largest payoff. In the standard rational analysis, each individual has fixed preferences that are maximized according to the probabilistic framework. Where do those preferences come from? In social action, this matters a great deal. If the preferences are not based in facts (and there is no requirement that they be), then people holding such preferences are a version of what Sen (1977) called ‘‘rational fools.” A major preoccupation of public choice economists and neo-institutional political scientists is how diverse individual preferences are combined to form a ‘‘social welfare function,” that is, collective decisions on public policies. These scholars ground their analyses in no normative model—all preferences are equal. Further, they fail for the most part to appreciate the dynamics involved in the prioritization of preferences, treating prioritization among various policy objectives and a position on any one objective as a package. Policy scholars treat these two components of preferences as operating via different causal processes, at least partially. If that is the case, we expect far more instability in the social welfare function than public choice scholars do. How do cognitive and emotional facets of individual actors combine to construct public policies if we allow for the separation of priorities from positions? If preferences are contingent on priorities and position, then is it not the case that the problems facing a political system are important? Newell and Simon (1972) distinguished between the problem-space and the solutionspace in their studies of human problem-solving, finding that individuals had more difficulties in re-evaluating the problem-space than in the solution-space. This is likely to be true in collectives as well. In any case, the notion of distinct problem and solution spaces influenced by different causal processes has become central to policy process studies since the work of Cohen, March, and Olsen (1972) who treat the connection between the two as subject to the activation of attention. Unlike the analytical framework deployed by most economists and neo-institutionalists in political science, most policy process scholars admit the complexity of human cognitive and emotional architectures into their models. Simon (1947) pioneered that approach, and the field has maintained its commitment to it over the years. Simon insisted that public administration and later public policy be grounded in a psychological theory of decisionmaking. He first termed that notion ‘‘bounded rationality,” to capture the limits of a rational framework in explaining human behavior in organizations. Later he and others contributed to developing a more robust and positive model of human action in policymaking, which he termed ‘‘behavioral rationality.” Its major premise is that humans do have priorities and goals, but they are not generally effective in judging the connections between those goals and the complex reality they face. For the most part, that remains the cognitive foundation of policy process studies in political science and elsewhere.
49
3. An overview of the special issue In constructing the special issue, we concentrated on areas we think are at the edge of the public policycognitive systems boundary. Each included article uniquely explores this disciplinary boundary between policy studies and cognitive systems. For example, the authors examine the difficulties in connecting the micro-level analyses of cognitive studies to the systems level activity in policymaking (Jones); the contagion and diffusion that occurs among individual actors within policy systems (Thomas); the performance of different organizational forms in adapting to changing circumstances in a complex world with cognitively-limited decision-makers (Epp); the evolutionary basis of cooperative behavior that is necessary to achieve collective action (Leech and Cronk); information processing in policymaking systems (Workman, Shafran, and Bark); the role of cognitive load and complex problems in the search processes of policymaking systems (Shaffer); and, organizational learning and neural networks (Hegelich). In ‘‘Behavioral Rationality as a Foundation for Public Policy Studies,” Jones discusses links between individual behavior and aggregate patterns in collective organizations—noting progress in the integration of the cognitive sciences into behavioral models. He suggests that our understanding of these microfoundations in political institutions is at a ‘‘turning point” wherein the rational model of human choice is replaced with the behavioral model. In doing so, Jones provides a foundation on which scholars can further develop a cognitive systems approach to policy studies. Drawing heavily on the behavioral model of choice, Thomas explores how cue-taking behavior among policymakers contributes to rapid shifts in their aggregate attention to policy issues over time. In ‘‘Modeling Contagion in Policy Systems,” Thomas uses an agent-based modeling approach to examine how the presence of cue-taking behavior, dense communication networks, and sub-divisions among actors affect patterns in the activity of a simulated policy system. With a simple model of individual-level attention, he shows how cue-taking behavior can generate disjointed patterns that are akin to those widely documented in policy systems at various levels of governance. Epp, in ‘‘Public Policy and the Wisdom of Crowds,” examines differences between government organizations and group systems—such as markets—from the collective intelligence perspective. Given the limited cognitive capacity of any one decision-maker, Epp tests hypotheses about the extent to which group systems or organizations can overcome limitations in the processing of information. Across the empirical cases he studies, Epp shows that organizational systems are less responsive and informationally efficient than group systems. These findings contribute to our understanding of disjointed change in public policymaking by mapping how the collective intelligence of groups systems may be superior to the organized hierarchies of government.
50
Editorial / Cognitive Systems Research 45 (2017) 48–51
Leech and Cronk, in ‘‘Coordinated Policy Action and Flexible Coalitional Psychology: How Evolution Made Humans So Good at Politics,” focus on the psychological and cognitive underpinnings of coordinated policy action in contemporary US politics. They suggest that a ‘‘coalitional psychology” drives the flexibility of political actors to create—and break—coalition ties when circumstances shift. This psychology, they argue, is unique to humans and supports the forming and reforming of political coalitions with previous opponents to achieve policy goals. Leech and Cronk explain how gene-cultural evolution and a process of cultural group selection support such flexibility, and then document cases at the level of countries, groups, and individuals. In ‘‘Problem Definition and Information Provision by Federal Bureaucrats,” Workman, Shafran, and Bark draw on models of congressional decision-making to examine when federal bureaucrats are called upon to provide information about policy problems. They theorize that the uncertainty that coincides with the definition of a problem leads the US Congress to seek information from higher numbers of bureaucrats, to do so early in the hearing process, and to draw from those with the most experience (e.g., career bureaucrats). In studying the appearances of over 25,000 witnesses in Congressional hearings on business and financial regulation, Workman, Shafran, and Bark, find support for these claims and conclude that bureaucrats play a central role in structuring policy debates in Congress during periods of uncertainty. Shaffer makes the argument that existing approaches to understanding attention to public policy issues do not fully address ideas about individual cognition. In ‘‘Cognitive Load and Issue Engagement in Congressional Discourse,” Shaffer develops a novel measurement strategy for computationally analyzing the issue content and dimensionality of hearings held by the US Congress. With the full-text transcripts of a sample of hearings on business and financial regulation, Shaffer identifies intense problem dimensionality during recent periods of economic instability and crisis. At the level of members of Congress, Shaffer documents the interconnectedness of individual-level behavior and cognitive patterns. Hegelich, in ‘‘Deep Learning and Punctuated Equilibrium,” also pursues a computational approach. Hegelich maps the conceptual model underpinning deep learning algorithms to policymaking under a major theoretical framework of the policy process. Specifically, he connects neural networks and the algorithmic notion of ‘‘backpropagation” to the study of multiple layers of inputs and outputs associated with budgetary change and policymaking under Punctuated Equilibrium Theory. 4. Promising new work in policy studies Research in public policy studies is often conducted at the systems level without much in the way of reference to the cognitive capacities of the decision-makers who inhabit
those systems. However there are other areas of policy that are trending in the direction of boundary exploration that bear mention here. First, we would note the work on policy narratives (M. Jones, Shanahan, & McBeth, 2014). Narratives, stories that carry both cognitive and emotional content and which are transmitted among broad groups of people, are shared understandings, and thus serve as a connection between individual cognitive and emotional architectures and systems level action. In addition, economic narratives are becoming a feature of behavioral economics (Ackerlof & Shiller, 2009), providing a potentially unifying approach. Moreover, scholars in this area are moving directly toward exploring the boundary with an increasingly rigorous scientific approach. Second, in recent years the so-called ‘‘streams” approach to policymaking, born in the work of Cohen et al. (1972) is in the process of reinvigoration (Zahariadis, 2014). This work continues to hew to the attention-based framework set in the original papers, but it has not come as far in testing the boundary between cognitive systems and policy studies as the other approaches we examine here. But surely it is ripe for further exploration. Finally, we note the literature on policy design and the social construction of target populations as an area where the policy studies field could benefit from a more explicit cognitive studies foundation. Proponents of the approach highlight the emotional content in how target populations—those receiving the benefits or bearing the burdens of policy action—are constructed in either positive or negative ways. These constructions lead to different policy designs (Schneider, Ingram, & DeLeon, 2014). It is harder to design policies for policy targets that are viewed negatively than those viewed positively, but these images do change, indicating that more work on the cognitive and emotional components of policy design need further addressing. Our broader point is that many areas of policy studies are not tied to rigid and overly-simplified models of actors, but sometimes these foundations are not as welldeveloped as they might be. Acknowledgement We thank Samuel Workman for his feedback on this introduction to the special issue. References Ackerlof, George A., & Shiller, Robert J. (2009). Animal spirits. Princeton: Princeton University Press. Baumgartner, Frank R. (2017). Endogenous disjoint change. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017.04.001. Cohen, Michael D., March, James G., & Olsen, Johan P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25. Damasio, Antonio (1994). Descartes error: Emotion, reason, and the human brain. New York: Putnam. Epp, Derek (2017). Public policy and the wisdom of crowds. Cognitive Systems Research, 43, 53–61.
Editorial / Cognitive Systems Research 45 (2017) 48–51 Hegelich, Simon (2017). Deep learning and punctuated equilibrium. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017. 02.006. Jones, Bryan D. (2017). Behavioral rationality as a foundation for public policy studies. Cognitive Systems Research, 43, 63–75. Jones, Michael D., Shanahan, Elizabeth A., & McBeth, Mark K. (Eds.). The science of stories: Applications of the narrative policy framework in public policy analysis. New York: Palgrave Macmillan. Leech, Beth L., & Cronk, Lee. (2017). Coordinated policy action and flexible coalitional psychology: How evolution made humans so good at politics. Cognitive Systems Research, 43, 89–99. Newell, Allen, & Simon, Herbert A. (1972). Human problem-solving. Englewood Cliffs, NJ: Prentice Hall. Schneider, Anne, Ingram, Helen, & DeLeon, Peter (2014). Democratic policy design. In Paul A. Sabatier & Christopher M. Weible (Eds.), Theories of the policy process (3rd ed., pp. 105–150). Boulder, CO: Westview.
51
Sen, Amayrta (1977). Rational fools: A critique of the behavioral foundations of economic theory. Philosophy and Public Affairs, 6, 317–344. Shaffer, Robert (2017). Cognitive load and issue engagement in congressional discourse. Cognitive Systems Research. http://dx.doi.org/ 10.1016/j.cogsys.2017.03.006. Simon, Herbert A. (1947). Administrative behavior. New York: The Free Press. Thomas, Herschel F. (2017). Modeling contagion in policy systems. Cognitive Systems Research. http://dx.doi.org/10.1016/j.cogsys.2017. 03.003. Workman, Samuel, Shafran, JoBeth, & Bark, Tracey (2017). Problem definition and information provision by federal bureaucrats. Cognitive Systems Research, 43, 104–152. Zahariadis, Nikolaos (2014). Ambiguity and multiple streams. In Paul A. Sabatier & Christopher M. Weible (Eds.), Theories of the policy process (3ed ed., pp. 25–58). Boulder, CO: Westview.