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Article

John P. Kastellec

Crucial to understanding the behavior of judges and the outputs of courts is the institutional context in which they operate. One key component of courts’ institutional structure is that the judiciary is organized as a hierarchy, which creates both problems and opportunities for judges. For instance, one problem for judges at the top of a hierarchy is how to best exercise oversight of lower court judges, whose decisions are often not reviewed by higher courts. One opportunity is that higher courts can reverse errors by lower courts; another is that, as new legal issues emerge, hierarchy provides opportunities for judges to learn from one another. Scholars of the judicial hierarchy have pursued two broad approaches. The “team perspective” begins by assuming that all judges in a hierarchy have the same values or principles, and thus care only about achieving the correct outcome in a given case. In the team approach, the key problem in adjudication is informational. All judges agree on the correct outcome of a case, conditional on understanding the relevant facts, but may lack this understanding due to resource constraints or informational advantages enjoyed by litigants. The agency approach, by contrast assumes that judges in the hierarchy have differing preferences, and the key problem is how higher courts can ensure compliance by lower courts. Despite these different foundational assumptions, the team and agency approaches have both been employed successfully to study core questions regarding the judicial hierarchy, including: why hierarchy exists; how higher courts can best oversee lower courts; how learning takes place both within and across the levels of the judiciary; and how collegiality influences judicial decision-making. Yet, while our understanding of the judicial hierarchy has greatly increased in recent years, many questions remain, such as how judges learn and how to measure legal doctrine.

Article

Much of the literature on international democratic diffusion appeals to mechanisms—competition, learning, emulation or socialization, and coercion—that typically are treated as competing and theoretically separate. All four, however, fit within a coevolutionary framework, that is, one integrating the concepts of variety, retention, and selection of traits (in this case, regime type). Competition, learning, and emulation are not mutually exclusive and all find support in the large literature on cultural and social evolution. Coercion may seem anti-evolutionary, inasmuch as it implies design and implementation by a powerful rational actor (state, international institution, etc.), but co-evolution can accommodate coercion as well. In co-evolution, agent and environment evolve together: an agent shapes its environment (engages in niche construction), and that reshaped environment alters the fitness of the agents’ traits. A powerful democracy can alter its social and material environment so as to increase the fitness of its own regime. Co-evolution can provide a framework to integrate mechanisms by which democracy and other regime types spread and contract across time and space, and hence can aid empirical research on the effects of global power shifts, including the rise of China, on the fate of democracy in various regions around the world.

Article

Jonathan Pierce and Katherine Hicks

The advocacy coalition framework (ACF) was developed to explain policy processes where contentious coalitions of actors seek to translate competing belief systems into public policy. Advocacy coalitions may include interest groups, members of the media, scientists and academics, and government officials that share beliefs about a public issue and coordinate their behavior. These advocacy coalitions engage in various strategies using resources to influence policy change or stasis. As part of this process, advocacy coalition members may learn within and/or across coalitions. This framework is one of the most prominent and widely applied approaches to explain public policy. While it has been applied hundreds of times, in over 50 different countries, the vast majority of ACF applications have sought to explain domestic policy processes. A reason for the paucity of applications to foreign policy is that some ACF assumptions may not seem congruent to foreign policy issues. For example, the ACF uses a policy subsystem as the unit of analysis that may include a territorial dimension. Yet, the purpose of the territorial dimension is to limit the scope of the study. Therefore, this dimension can be substituted for a government body that has the authority or potential authority to make and implement foreign policy. In addition, the ACF assumes a central role for technical and scientific information in the policy process. Such information makes learning across coalitions more conducive, but the ACF can and should also be applied to normative issues, such as those more common among foreign policy research. This article introduces the ACF; provides an overview of the framework, including assumptions, key concepts and theories, and transferability of the ACF to foreign policy analysis; and discusses four exemplary applications. In addition, it proposes future research that scholars should explore as part of the nexus of the ACF and foreign policy analysis. In the final analysis, the authors suggest the ACF can and should be applied to foreign policy analysis to better understand the development of advocacy coalitions and how they influence changes and stasis in foreign policy.

Article

An improved understanding of foreign policy learning necessitates a clarification of what foreign policy learning is, who learns, and how such learning occurs. Cognitive and social psychologists, sociologists, and political scientists situated in a variety of subfields have contributed to the understanding of foreign policy learning, a multidisciplinary area of inquiry. Learning theorists seek to show how a change in an actor’s beliefs due to experience or observation can lead to changes at other units, such as organizations and within the government. This cognitive dimension is important because actors may pursue a new course of action for politically expedient reasons rather than having genuinely “learned”—a distinction referred to as “complex” vs. “simple” learning. Foreign policy learning can be internal or external. The former type of learning entails what individuals, governments, or organizations learn from their prior experience. Learning theorists who focus on the individual level of analysis borrow insights from political psychology in an effort to shed light on the personal characteristics, the belief structures, and the cognitive psychological mechanisms of political actors that can better inform policymaking. Leaders whose cognitive structures are described as relatively open and complex—like Soviet leader Mikhail Gorbachev, whose learning brought about the dramatic changes that ultimately led to the demise of the Soviet Union—are more likely to alter their beliefs than their cognitively closed and simple counterparts. Yet external learning occurs as well. Policy diffusion studies show that learning can result from demonstration effects. Foreign policy learning via diffusion is not instrumental, but instead occurs through osmosis. Privatization in the former communist states, China’s Foreign Direct Investment liberalization, and the diffusion of environmental norms in the European Union are examples of learning that is contagious, not chosen. A more conscious mode of learning than diffusion is policy transfer, which entails policymakers’ transferring ideas from one country and implementing them in another. Technological innovations, unlike lessons that involve political ideology, are generally easier lessons to transfer—for example, Japan’s success in applying lessons from the West to modernize its army in the second half of the 19th century. The constraints to foreign policy learning are formidable. Decision makers are not always open to reconsidering views that challenge their beliefs. Leaders tend to resort to, and misuse, analogies that prevent learning. Even a change in a decision maker’s beliefs may not lead to foreign policy change, given the myriad political pressures, bureaucratic hurdles, and economic realities that often get in the way of implementing new ideas. Indeed, foreign policy learning and foreign policy change are not synonymous. Scholars face significant obstacles in studying foreign policy learning. There is no consensus on the definition of learning, on what constitutes learning, on how actors learn, when they learn, or on how to assess whether learning has taken place. Despite attempts to make sense of the confusion, scholars face the daunting challenge of improving understanding of how learning is shaped and funneled through the interaction of agents and the structures in which they are situated, as well as the relationship between learning and foreign policy change.

Article

Diana Panke and Ingo Henneberg

The interplay between states and international organizations has received a lot of scholarly attention, largely because the number of international organizations has increased considerably within the last century. State-of-the-art scholarship on the foreign policies of international organizations and states is presented here, as are rationalist and constructivist accounts of how the foreign policies of states impact international organizations (bottom-up perspective), as well as how, in turn, international organizations impact member-state foreign policies (top-down perspective). Thereby, the polity, politics, and policy dimensions of both states and international organizations are examined in order to explain the changes states’ foreign policies can induce, under what scope conditions, in the international organizations’ structure (polity), procedures (politics), and policy outcomes. Vice versa, also explained are the changes international organizations can induce, under what scope conditions, in the foreign policy apparatus of states (polity), foreign policy decision-making procedures (politics), and states’ foreign policies. As is illustrated, the theme “International Organizations and Foreign Policy” is not an established foreign policy subfield per se but is covered here in multiple approaches and theories. In line with the development of international relations, the bottom-up perspective has received much more scholarly attention than the top-down perspective. Furthermore, bottom-up research evidences a tendency toward the strong influence of states’ foreign policies on the policy and polity of international organizations, while the top-down influence of international organizations on states’ foreign policy apparatus, procedures, and policies is usually much more limited. Finally, an outlook into fruitful future avenues for research is outlined.

Article

While a phenomena dating back to antiquity, it wasn’t until the 1960s that American and European social scientists began seriously discussing occurrences in which it appeared as if localities, states and nations in close proximity were adopting similar policies and programs. These early diffusion studies led to a new field that has variously been referred to under titles such as policy transfer, lesson drawing, policy translations, and policy mobility. While having different focuses and agendas, all of these studies attempt to address issues associated with the movement (or active rejection of a possible movement) of ideas, information, policies, and programs from one political system to another. While all transfer studies have helped focus social scientists’ attention on the processes and actors involved in the transfer of ideas, techniques, policies, information, and programs, a better link to the knowledge utilization and learning literatures would help advance the usefulness of transfer studies. At a minimum, by considering the insights from the learning and utilization literatures, social scientists should begin understanding some of the outlook changes that individuals involved in transfer undertake that impact individual and institutional long-term understanding of the process and results. It will also start to help opening up the policymaking process to further scrutiny, particularly in relation to where information is flowing and how it is being used as a policy develops and changes.

Article

Institutional amnesia can be defined in simple terms as an organization’s inability to recall and use historical knowledge for present-day purposes. However, the concept requires to be defined more expansively so that its causes and effects can be fully understood in relation to crises and crisis management. This means conceptualizing institutional amnesia in broader terms as something that influences individual crisis managers, the formal institutional aspects of crisis management agencies, the cultural dimensions of those agencies, and the wider systemic location within which both actors and agencies reside. The analysis of the effects of amnesia in each of these areas reveals the profound effects that it can have on various aspects of crisis management. Institutional amnesia can affect the performance of crisis management policies and the politics of crises more generally. In particular, memory loss can be seen to influence crisis decision-making that relies upon historical analogy, crisis learning which demands that learned lessons are formally institutionalized across time, and meaning-making efforts, which draw upon recollections of the past to justify political projects in the present. The effects that institutional amnesia has on these three important areas illuminate its relevance to crisis analysis. Yet amnesia, and to some extent memory, continue to be concepts that are neglected, or referred to tangentially, by mainstream crisis scholars.

Article

Wouter van Atteveldt, Kasper Welbers, and Mariken van der Velden

Analyzing political text can answer many pressing questions in political science, from understanding political ideology to mapping the effects of censorship in authoritarian states. This makes the study of political text and speech an important part of the political science methodological toolbox. The confluence of increasing availability of large digital text collections, plentiful computational power, and methodological innovations has led to many researchers adopting techniques of automatic text analysis for coding and analyzing textual data. In what is sometimes termed the “text as data” approach, texts are converted to a numerical representation, and various techniques such as dictionary analysis, automatic scaling, topic modeling, and machine learning are used to find patterns in and test hypotheses on these data. These methods all make certain assumptions and need to be validated to assess their fitness for any particular task and domain.

Article

Zachary R. Lewis, Kathryn L. Schwaeble, and Thomas A. Birkland

The September 11 terrorist attacks on the United States were a focusing event that greatly increased attention to particularly large acts of terrorism as a threat to the United States and to particular interests. One of these interests is the aviation industry. The September 11 attacks exploited features of the aviation industry that made it prone to attack and that made an attack on this industry particularly vivid and attention-grabbing. The September 11 attacks led to policy changes in the United States and around the world with respect to aviation security, but those changes were not made in a vacuum. The changes that followed the September 11 attacks were made possible by efforts to learn from the range of aviation security incidents and challenges that have faced commercial aviation throughout its history. While the September 11 attacks were shocking and seemed novel, prior experience with aviation security crises provided those working in the aviation security policy realm with potential responses. The responses were drawn from a set of politically feasible responses that addressed the lapses in security demonstrated by terrorist attacks. The history of policy changes related to terrorism in aviation parallel the changes to policies that were made across the board in response to the elevation of terrorism on the agenda.

Article

Konstantinos V. Katsikopoulos

Polymath, and also political scientist, Herbert Simon dared to point out that the amounts of time, information, computation, and other resources required for maximizing utility far exceed what is possible when real people have to make real decisions in the real world. In psychology, there are two main approaches to studying actual human judgment and decision making—the heuristics-and-bias and the fast-and-frugal-heuristics research programs. A distinctive characteristic of the fast-and-frugal-heuristics program is that it specifies formal models of heuristics and attempts to determine when people use them and what performance they achieve. These models rely on a few pieces of information that are processed in computationally simple ways. The information and computation are within human reach, which means that people rely on information they have relatively easy access to and employ simple operations such as summing or comparing numbers. Research in the laboratory and in the wild has found that most people use fast and frugal heuristics most of the time if a decision must be made quickly, information is expensive financially or cognitively to gather, or a single/few attributes of the problem strongly point towards an option. The ways in which people switch between heuristics is studied in the framework of the adaptive toolbox. Work employing computer simulations and mathematical analyses has uncovered conditions under which fast and frugal heuristics achieve higher performance than benchmarks from statistics and machine learning, and vice versa. These conditions constitute the theory of ecological rationality. This theory suggests that fast and frugal heuristics perform better than complex optimization models if the available information is of low quality or scarce, or if there exist dominant options or attributes. The bias-variance decomposition of statistical prediction error, which is explained in layperson’s terms, underpins these claims. Research on fast and frugal heuristics suggests a governance approach not based on nudging, but on boosting citizen competence.

Article

A structural understanding of the contextualized behavior of states is introduced and operationalized. Context is a central theme of the discipline of geography and identifies context specific, rather than universal, social behavior. Social behavior is both defined by and creates contexts in a constant recursive interaction. Context is defined through a geographic perspective on world-systems analysis, and we focus on the behavior of states. States are central actors because, through territorial sovereignty, they are able to define key social relations and economic flows. The idea of context is developed in a way that extends the key International Relations (IR) concepts of milieu and opportunity and willingness. The recursive interaction between agency and context is conceptualized in a relational way as maneuver, the process by which the aggregate behavior of elites define state-level choices and behaviors that are made by considering the contextual position relative to all other states in the capitalist world-economy. In turn, the decision by any one state changes the behavior of other states so that context and state-level decisions interact and are constantly in flux. The elements of context include the position of a state in the hierarchy of the capitalist world-economy as well as regional and local interstate relations, some of which may display path dependency. The operationalization of maneuver requires an understanding of states as signaling and learning entities and a set of modeling techniques that identify: (1) the degree of change within the system as a whole—or the degree of stability in the number and identity of states within particular positions in the hierarchy of the capitalist world-economy; (2) the maneuver of particular states—or which states change position (or not) within the hierarchy; and (3) the explanatory power of variables measuring political and economic interstate relations in explaining the maneuver behavior of particular states.

Article

Managing critical infrastructures presents a specific set of challenges to crisis managers. These systems include electrical power; communications; transportation; and water, wastewater, and gas line distribution systems. Such infrastructures undergird the continued operation of communities in a modern society. Designed for efficiency, these technical systems operate interdependently, which makes them vulnerable to the stress of extreme events. Changes in population, demographics, land use, and economic and social conditions of communities exposed to hazards have significantly increased the number of people dependent on critical infrastructures in regions at risk. Although advances in science, technology, and engineering have introduced new possibilities for the redesign, maintenance, and retrofit of built infrastructure to withstand extreme events, the complexity of the task has exceeded the capacity of most public and private agencies to anticipate the potential risk and make investments needed to upgrade infrastructures before damage occurs. A mix of public and private ownership of infrastructure systems further complicates the task of ensuring public safety and security in crisis. Public agencies cannot protect communities alone. FEMA has developed a “whole of nation” approach to strengthen cross-jurisdictional linkages with state, county, and municipal emergency managers as well as private and nonprofit organizations. Computational modeling facilitates the exploration of alternative approaches to managing risk generated among a range of actors, interdependent infrastructures, and types of hazard events. Advanced uses of sensors, telemetry, and graphic display of changing performance for critical infrastructure provide timely, accurate information to reduce uncertainty in crisis events. Such technologies enable crisis managers to track more accurately the impact of extreme events on the populations and infrastructures of communities at risk, and to anticipate more reliably the likely consequences of future hazardous events. A basic shift has occurred in the assessment of risk. The focus is no longer on calculating the damage from past events, but on anticipating and reducing the consequences of future hazards, based on sound, scientific evidence as well as local experience and knowledge. Recognizing communities as complex, adaptive systems, crisis managers strive to create a continual learning process that enables residents to monitor their changing environment, use systematically collected data as the basis for analysis and change, and modify policies and practice based on valid evidence from actual environments at risk. Visualization constitutes a key component of collective learning. In complex settings, people comprehend visual images more readily than written or aural directions. Using graphic technologies to display emerging risk at multiple levels simultaneously provides an effective means to guide particular decisions at intermediate (meso) and local levels of operation. For communities seeking to reduce risk, investment in information technologies to enable rapid, community-wide access to interactive communication constitutes a major step toward building capacity not only for managing risk to critical infrastructure but also in maintaining continuity of operations for the whole community in extreme events.

Article

Kumail Wasif and Jeff Gill

Bayes’ theorem is a relatively simple equation but one of the most important mathematical principles discovered. It is a formalization of a basic cognitive process: updating expectations as new information is obtained. It was derived from the laws of conditional probability by Reverend Thomas Bayes and published posthumously in 1763. In the 21st century, it is used in academic fields ranging from computer science to social science. The theorem’s most prominent use is in statistical inference. In this regard, there are three essential tenets of Bayesian thought that distinguish it from standard approaches. First, any quantity that is not known as an absolute fact is treated probabilistically, meaning that a numerical probability or a probability distribution is assigned. Second, research questions and designs are based on prior knowledge and expressed as prior distributions. Finally, these prior distributions are updated by conditioning on new data through the use of Bayes’ theorem to create a posterior distribution that is a compromise between prior and data knowledge. This approach has a number of advantages, especially in social science. First, it gives researchers the probability of observing the parameter given the data, which is the inverse of the results from frequentist inference and more appropriate for social scientific data and parameters. Second, Bayesian approaches excel at estimating parameters for complex data structures and functional forms, and provide more information about these parameters compared to standard approaches. This is possible due to stochastic simulation techniques called Markov Chain Monte Carlo. Third, Bayesian approaches allow for the explicit incorporation of previous estimates through the use of the prior distribution. This provides a formal mechanism for incorporating previous estimates and a means of comparing potential results. Bayes’ theorem is also used in machine learning, which is a subset of computer science that focuses on algorithms that learn from data to make predictions. One such algorithm is the Naive Bayes Classifier, which uses Bayes’ theorem to classify objects such as documents based on prior relationships. Bayesian networks can be seen as a complicated version of the Naive Classifier that maps, estimates, and predicts relationships in a network. It is useful for more complicated prediction problems. Lastly, the theorem has even been used by qualitative social scientists as a formal mechanism for stating and evaluating beliefs and updating knowledge.