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Article

Jon Green, Jonathon Kingzette, and Michael Neblo

Defined expansively as the exchange of politically relevant justifications, political deliberation occurs at many sites in the democratic system. It is also performed by several different types of actors. Here, we review political deliberation based on who is deliberating and what role these deliberations play in making binding decisions. First, ordinary citizens frequently deliberate in informal settings. While these discussions often fail to live up to the standards outlined by deliberative theorists, they typically correlate with other democratic goods, such as increased political participation. Second, there have been several attempts in recent years to construct the conditions necessary for quality deliberation among citizens by organizing small-group discussions in semi-formal settings. Proponents of such discussions argue that they promote a variety of democratic goods, such as political knowledge and better-justified political decisions, and as such should be incorporated into the formal policymaking process. However, critics of these procedural innovations hold that a more deliberative society is unrealistic or, alternatively, that deliberation is not without drawbacks on its own terms. Third, in a limited number of cases, citizens’ deliberations are formally embedded in democratic institutions, serving to advise voters and politicians or directly leading to binding decisions. Finally, political elites deliberate frequently. Opinion leaders attempt to and often succeed in shaping the discourse around issues, while elected officials, bureaucrats, and judges formally deliberate before making almost every binding decision. Surprisingly, though these deliberations happen frequently and likely have substantial effects on policy, they are probably the least studied in the political system, though recent breakthroughs in text analysis offer a path forward to analyzing deliberation among elites more systematically.

Article

In such a complex and well-researched domain as decision support systems (DSS), with a long history of authors making insightful contributions since the 1960’s, it is critical for researchers, especially those less experienced, to have a broad knowledge of the seminal work that has been carried out by prior generations of researchers. This can serve to avoid proposing research questions which have been considered many times before, without having consideration for the answers which have been put forward by previous scholars, thereby reinventing the wheel or “rediscovering” findings about the life of organizations that have been presented long before. The study of human and managerial decision-making is also characterized by considerable depth and seminal research going back to the beginning of the 20th century, across a variety of fields of research including psychology, social psychology, sociology or indeed operations research. Inasmuch as decision-making and decision support are inextricably linked, it is essential for researchers in DSS to be very familiar with both stream of research in their full diversity so they are able to understand both what activity is being supported and how to analyze requirements for developing decision support artefacts. In addition, whilst the area of decision support has sometimes been characterized by technology-based hype, it is critical to recognize that only a clear focus on the thinking and actions of managers can provide decisive directions for research on their decision support needs. In this article, we consider first the characteristics of human cognition, before concentrating on the decision-making needs of managers and the lessons that can be derived for the development of DSS.

Article

The idea of satisficing as a decision rule began with Herbert Simon. Simon was dissatisfied with the increasingly dominant notion of individuals as rational decision makers who choose alternatives that maximize expected utility on two grounds. First, he viewed the maximizing account of decision making as unrealistic given that individuals have cognitive limitations and varying motivations that limit cognitive ability and effort. Second, he argued that individuals do not even choose alternatives as if they are maximizing (i.e., that the maximizing account has predictive validity). Instead, he offered a theory of individuals as satisficers: decision makers who consider a limited number of alternatives, expending limited cognitive effort, until they find one that is “good enough.” At this point, he argued, the consideration of alternatives stops. The satisficing decision rule has influenced several subfields of political science. They include elite decision making on military conflicts, the economy, and public policy; ideas of what the mass public needs to know about politics and the extent to which deficits in political knowledge are consequential; and understanding of survey responses and survey design. Political and social psychologists have also taken Simon’s idea and argued that satisficing rather than maximizing is a personality trait—stable characteristics of individuals that make them predisposed toward one or other type of alternative search when making decisions. Research in these subfields additionally raises normative questions about the extent to which satisficing is not only a common way of making decisions but a desirable one. Satisficing seems superior to maximizing in several respects. For example, it has positive effects on aspects of decision makers’ well-being and is more likely to result in individuals voting their interests in elections. There are, however, a number of directions in which future research on satisficing could be taken forward. These include a fuller incorporation of the interaction of affect and cognition, clearer tests of alternative explanations to satisficing, and more focus and understanding on the effects of the Internet and the “information age.”

Article

The poliheuristic theory of decision making is a framework for explaining both the outcome of a choice and the process by which the decision maker arrives at that outcome. It posits a two-stage process in which individuals first apply a noncompensatory principle, eliminating those options that negatively impact the decision maker on one or more critical dimensions. In the second stage, the decision maker employs a conventional decision rule, such as expected utility theory or the lexicographic decision rule, to choose among the remaining alternatives. The poliheuristic theory of decision has been used by scholars of political science and international relations to explain decisions in hundreds of scenarios—these include foreign policy in democratic and autocratic states, the organizational and operational decisions of terrorist leaders, and the behavior of groups—attesting to its explanatory and predictive power. Other scholars have used experimental methods to demonstrate the theory’s ability to accurately describe the decision process. Analysts can apply poliheuristic principles when using the Applied Decision Analysis methodology to explain or predict a decision. The analyst can use his or her knowledge of a decision to construct a matrix of possible choices (alternatives) and decision criteria (dimensions). The analyst can then assign weights to the various dimensions and determine which should be regarded as noncompensatory. By eliminating the alternatives that result in a negative impact on the critical dimensions and then choosing among the remaining options in an analytic way, the analyst can then predict (or explain) the decision maker’s choice.

Article

A decision strategy is a set of mental and physical operations that a decision maker uses to reach a choice among two or more alternatives. Once the alternatives have been identified, a decision strategy involves gathering information about at least some of the different alternatives under considerations and making judgments about them. A decision strategy will include a mechanism for selecting the best alternative—for example, select the alternative with the highest probability of success. Decision strategies differ along two primary dimensions: how much information is gathered, and how comparable that information is across alternatives. Four major types of decision strategies include classic rational choice (relatively deep search, equally distributed across alternatives), confirmatory motivated reasoning (relatively deep search, unequally distributed across alternatives), fast and frugal (relatively shallow search, equally distributed across alternatives), and heuristic-based intuitive (shallow search, unequally distributed across alternatives). Although standard rating scales have been developed to help ascertain which strategies a decision maker prefers, the best method for determining which strategy is being employed is to directly observe information gathering while the decision is being made. An important task for future research is to more clearly explicate the situations when different decision strategies perform particularly well or particularly poorly.

Article

Ciara Heavin and Frederic Adam

Since the 1960s, information technology (IT)/information systems (IS) professionals, data practitioners, and senior managers have focused on developing decision support capabilities to enhance organizational decision making. Initially, this quest was mostly driven by successive generations of technological advances. However, in the last decade, the pace at which large volumes of diverse data can be collected and processed, new algorithmic advances, and the development of computational infrastructure such as graphics processing units (GPUs) and tensor processing units (TPUs) have created new opportunities for global businesses in areas such as financial services, manufacturing, retail, sports, and healthcare. At this point, it seems that most industries and public services could potentially be revolutionized by these new techniques. The word analytics has replaced the previous individual components of computerized decision support technologies that have been developed under various labels in the past (). Much of the traditional researcher and practitioner communities who were concerned with decision support, decision support systems (DSSs), and business intelligence (BI) have reoriented their attention to innovative tools and technologies to derive value from new data streams through artificial intelligence (AI) and analytics. Identifying the main areas of focus for decision support and analytics provides a stimulus for new ideas for researchers, managers, and IS/IT and data professionals. These stakeholders need to undertake new empirical studies that explain how analytics can be used to develop and enhance new forms of decision support while considering the dilemmas that may arise due to the data capture and analyses of new digital data streams.

Article

In recent years, scientists have identified cognitive processes that short-circuit our deliberative faculties. In the domain of climate change in particular, a number of psychological barriers and biases may disrupt typical discourse and reflection and may even prevent those who are aware of climate change from taking action to mitigate or reduce its impact. These processes include the use of heuristic versions of calculation-based decisions to reduce processing load, which can make climate change judgments responsive to situational factors in the immediate decision context. Recent research in the decision sciences provides insight into how common biases in judgment inhibit rational deliberation about climate change, which may lead to the gap between society’s recognition of environmental problems and society’s frequent failure to address them appropriately. These insights involve the finite nature of human attention and cognitive resources, the complex interactions of personal experience and emotion, the challenges that uncertainty and risk place on behavior, and the profoundly social nature of human action. Understanding these barriers and systematic biases have led to a set of potential interventions, which demonstrate how practitioners can put research insights into practice in order to address a variety of sustainability challenges. One important direction for these interventions involves changing the decision context in ways that account for decision bias (e.g., using green defaults) and triggering more adaptive decisions as a result.

Article

Marcel Zeelenberg

Regret is the prototypical decision related emotion. It is felt when the outcome of a non-chosen alternative is better than the outcomes obtained. Regret is a functional emotion that helps people to correct mistakes. It is also functional because people can anticipate regret beforehand, then choose in such a way as to avoid regret from happening. Researchers in economics proposed regret theory, an alternative to rational choice theory, which takes into account the anticipation of regret and its influence in choice. Researchers in psychology studied how anticipations of regret influence decision making in a variety of domains, including health behaviors. The findings suggest that interventions can be developed that are based on the idea that people are regret averse.

Article

Megan A.K. Peters

The human brain processes noisy information to help make adaptive choices under uncertainty. Accompanying these decisions about incoming evidence is a sense of confidence: a feeling about whether a decision is correct. Confidence typically covaries with the accuracy of decisions, in that higher confidence is associated with higher decisional accuracy. In the laboratory, decision confidence is typically measured by asking participants to make judgments about stimuli or information (type 1 judgments) and then to rate their confidence on a rating scale or by engaging in wagering (type 2 judgments). The correspondence between confidence and accuracy can be quantified in a number of ways, some based on probability theory and signal detection theory. But decision confidence does not always reflect only the probability that a decision is correct; confidence can also reflect many other factors, including other estimates of noise, evidence magnitude, nearby decisions, decision time, and motor movements. Confidence is thought to be computed by a number of brain regions, most notably areas in the prefrontal cortex. And, once computed, confidence can be used to drive other behaviors, such as learning rates or social interaction.

Article

The field of judgment and decision making has seen an explosion of research and analyses since the 1990s, notably in five closely related fields: Rational choice and its variants, the concept of intuition, “dual process” theories, the “heuristics and biases” literature, and the concept of “naturalistic” decision making. Yet none of these theories captures—by design or because of the limits of the approach—the actual mechanism by which emergent judgment occurs on complex decisions. Such decisions are non-optimizable and guided by multiple and often conflicting objectives and values; their outcomes will flow from the nonlinear interaction of many variables whose causal relationships are poorly understood. As a result, critical assumptions of many classical decision making models cannot be met in such situations, and the default approach relies not so much on calculative decision making as on instinctive judgment. This term implies a mechanism that is less calculative and consequentialist that it is imaginative, creative, and unconscious. Emergent, largely intuitive judgment is the only mechanism appropriate to such complex, nonlinear situations in which both an objective maximization of utilities and an accurate assessment of likely consequences are impossible. The concept of judgment broadly defined, as a form of unconscious, emergent, and imaginative interpretation of facts and events, offers the best model for how decision makers approach non-optimizable situations.

Article

Akan Malici

Syria is in tatters. A brutal dictator, vicious terrorist groups, and a raging civil war have led to the death of hundreds of thousands and the displacement of millions. Given the ongoing Syrian conflict, President Barack Obama’s rather restrained foreign policy toward the Bashar al-Assad regime has been described as “feckless,” “flawed,” and “clueless.” In August 2012, however, President Obama issued a strong warning when he famously said the “red line” for the United States in terms of stepping up a military offensive would be if “we started seeing a whole bunch of chemical weapons moving around or being utilized.” Beginning in 2013, the Damascus regime did utilize chemical weapons against the Syrian people, perhaps most shockingly in August in the Ghouta suburbs of Damascus. Yet President Obama ultimately held back from intervening militarily, and his decision has since received strong and persistent criticism. After retiring from his post as secretary of defense, Robert Gates judged the president’s decision a “serious mistake.” It allowed the tragedy to continue and American credibility to be hurt. The president himself, however, disagreed. About the decision not to act militarily he said later: “I’m very proud of this moment.” He was convinced that the United States could not successfully affect the situation and that he had kept the country out of another quagmire. It is indeed the case that good decisions can avoid wars or win them while bad decisions can start wars or lose them, and these consequences are just one reason why the study of foreign policy mistakes is so important. Yet it can, of course, not be the case that an evaluation of foreign policy decisions is rendered to the subjective eyes of the beholder. Instead, what is needed is an objective framework by which to identify and analyze foreign policy mistakes. Foreign policy mistakes are procedural errors concerning the diagnostic or the prescriptive level of the foreign policy decision-making task. They can be mistakes of omission or commission and can occur in regard to a threat or to an opportunity. Of special importance is the question how foreign policy mistakes can be avoided. This question can be answered through a decision-making framework defined by (a) the level of information a leader can have about a foreign policy challenge and (b) the potential consequences of a decision. Because many, if not most, challenges with respect to questions of international security are “ill-defined,” the myopic strategy of disjointed incrementalism is ideal. It avoids mistakes by making reversible (disjointed) and relatively small (incremental) moves away from the status quo. In the case of Syria, President Obama followed a strategy of disjointed incrementalism. He could not rely on much certain or reliable information regarding the situation and correctly understood that a military engagement could have potentially very adverse consequences in terms of casualties and a general escalation of the situation. Contrary to often repeated judgments that his decision not to engage militarily was a mistake, it is the case that he indeed avoided a mistake.

Article

Understanding how individuals make political decisions in a complex and ever-changing world requires recognition of the dynamic nature of the environment, as well as theoretical and methodological strategies to address these complications. As the scholarly understanding of the limits of human cognition expands, researchers can no longer rely on decision-making models that assume unlimited time, resources, and/or abilities of voters. Fortunately, dynamic process tracing models demonstrate the information processing component of decision-making, turning the focus away (slightly) from the decision outcome and toward the ways that people come to these decisions. These models derive from weaker, but more accurate, assumptions about the cognitive abilities of humans and provide critical insight into both the factors that voters consider when making decisions and the ways voters incorporate those factors into their decisions. In addition, thanks to the work of Lau and Redlawsk, these processes are directly observable with their Dynamic Process Tracing Environment (DPTE). Researchers relying on dynamic process tracing models are now able to assess the influence of political and demographic factors on the pattern, content, and amount of information voters access and rely on when making political decisions. These models offer a more realistic view of voter abilities than rational choice models, as well as providing greater insight into the process of decision-making (rather than the outcome of the process) than much of the work deriving from the Michigan model of public opinion. Additionally, the DPTE offers advantages over earlier static information board studies. Rather than seeing one’s self in conflict with decades of public opinion research, however, scholars in the dynamic process tracing tradition would be wise to consider their work as complementary. A focus on political variables as outcomes misses a crucial cognitive step: the evaluation of environmental stimuli through the lenses of short- and long-term predispositions. As scholars seek to understand why voters possess certain attitudes, they should ask how those attitudes were formed in the first place. Dynamic process tracing models allow for theorizing about and empirically testing components of the decision-making process previously left uninvestigated.

Article

Traditional models of political decision making tend to focus on the subject’s information levels or information-processing strategy. One of the most common conceptions of political decision making assumes that voters who are informed by a store of factually accurate policy information make more optimal decisions—that is, decisions more in line with their supposed political interests—than those who lack such information. However, this traditional view of political decision making minimizes the roles of affect and social influence on judgment. No phenomenon underscores the primary place of these constructs more so than the meteoric rise of online social media use. Indeed, scholars working at the intersection of social media use and political judgment have made important revisions to the traditional model of political decision making. Specifically, the popularity of online social networks as a tool for exchanging information, connecting with others, and displaying affective reactions to stimuli suggest that new models of competent political decision making which take into account social, affective, and cognitive elements are replacing older, information-based and rational choice models. In this essay, I review some of the pertinent literature on social media use and decision-making and argue that motivation, emotion, and social networks are key components of political judgment and are in fact more relevant to understanding political decisions than political knowledge or political sophistication. I also propose that new models of political decision-making would do well to take into account automaticity, social approval, and the role of information in both rationalizing preferences and persuading others.

Article

Cigdem V. Sirin and José D. Villalobos

Numerous empirical works document that discrete emotions have substantive and differential effects on politically motivated processes and outcomes. Scholars have increasingly adopted a discrete-emotions approach across various political contexts. There are different theoretical paths for studying discrete emotions. Appraisal theories contend that cognition precedes emotion, where distinct cognitive appraisal tendencies elicit discrete emotional reactions associated with specific coping mechanisms. Affective Intelligence Theory, another dominant paradigm in the study of discrete emotions in politics, argues for affective primacy. Others are more concerned with the level of analysis issue than the emotion-cognition sequence. For instance, Intergroup Emotions Theory calls for differentiating between individual-level and group-based discrete emotions, asserting that the latter form is a stronger predictor of collective political actions. Scholars also need to consider which methodological strategies they should employ to deal with a range of issues that the study of discrete emotions brings about. For instance, one issue is how to effectively induce a specific emotional state such as hope without also triggering other related yet discrete emotions such as enthusiasm in an experimental setting. Beyond these theoretical and methodological choices, there are various opportunities to diversify the field of study. Above all, the field needs more cross-national replications and extensions of U.S.-based findings to help resolve the debate over the universality versus contextuality of discrete emotions. The field would also benefit from the study of a wider array of emotional states by expanding beyond its main focus on negative discrete emotions. Contemporary developments—such as the increasing use of social media by the public and political actors—further offer novel platforms for investigating the role of discrete emotions.

Article

The representativeness heuristic was defined by Kahneman and Tversky as a decision-making shortcut in which people judge probabilities “by the degree to which A is representative of B, that is, by the degree to which A resembles B.” People who use this cognitive shortcut bypass more detailed processing of the likelihood of the event in question but instead focus on what (stereotypic) category it appears to fit and the associations they have about that category. Simply put: If it looks like a duck, it probably is a duck. The representativeness heuristic usually works well and provides valid inferences about likelihood. This is why political scientists saw it as an important part of a solution to an enduring problem in their field: How can people make political decisions when so many studies show they lack even basic knowledge about politics? According to these scholars, voters do not need to be aware of all actions and opinions of a political candidate running for office. To make up their mind on who to vote for, they can rely on cues that represent the performance and issue position of candidates, such as the party they are affiliated with, their ranking in the polls, and whether (for instance) they act/appear presidential. In other words, they need to answer the question: Does this candidate fit my image of a successful president? The resulting low-information rationality provides voters with much confidence in their voting decision, even though they do not know all the details about the history of each candidate. Using heuristics allows relatively uninformed citizens to act as if they were fully informed. Despite this optimistic view of heuristics at their introduction to the discipline, they originated from research showing how heuristic use is accompanied by systematic error. Tversky and Kahneman argue that using the representativeness heuristic leads to an overreliance on similarity to a category and a neglect of prior probability, sample size, and the reliability and validity of the available cue. Kuklinsky and Quirk first warned about the potential effect of these biases in the context of political decision-making. Current research often examines the effects of specific cues/stereotypes, like party, gender, race, class, or more context-specific heuristics like the deservingness heuristic. Another strand of research has started exploring the effect of the representativeness heuristic on decision-making by political elites, rather than voters. Future studies can integrate these findings to work toward a fuller understanding of the effects of the representativeness heuristic in political decision-making, more closely consider individual differences and the effects of different contexts, and map the consequences that related systematic biases might have.

Article

The cognitive and emotional mechanics of the human brain have profound effects on when and what people and political leaders learn, and this can have significant effects on their causal beliefs, preferences, and policies. The existence of the availability heuristic and its biasing effects on political judgment is one of the most robust findings from decades of research in cognitive psychology. The core mechanism involves people being more likely to learn from the phenomena that are most easily recalled by memory, which tend to be dramatic and vivid events, rather than other, often more normatively probative sources. Most applications of this insight to foreign policy decision-making also tend to assume that an actor’s personal experiences will impact what tends to be more or less easily recalled and thus better predict who learns which lesson from which event. This heuristic enables leaders to deal with the vast amount of extant information but also can cause systematic biases in causal inference. Documenting the availability heuristic and its effects on political decision-making requires (usually archival) data on leaders beliefs’ over long periods of time, from their formative political lessons through decisions and nondecisions when in power, in order to reliably clarify which lessons were in fact learned, when and why a leader learned which lesson from what data point, why that data point happened to be cognitively available, and whether these lessons influenced policy. Ideally, studies should also assess these leaders’ associates where possible to determine whether they learned similar lessons from the same events. Studies can also apply statistical analysis to larger populations of leaders who are likely to have found different events cognitively available. This article focusses on decisions in the realm of foreign policy and international security, although availability certainly plays a role in other domains as well. Decades of scholarship have now shown the relevance of the availability heuristic in U.S., Soviet, Indian, Chinese, and Pakistani grand strategy and foreign policy, approaches to nuclear weapons, and extant alliances and threat perceptions. But much work remains to be done in these cases and elsewhere, as well as in other fields like international political economy and comparative politics.

Article

The application of evolutionary theories or models to explain political decision making is quickly maturing, fundamentally interdisciplinary, and irreducibly complex. This hybridization has yielded significant benefits, including real progress toward understanding the conditions under which cooperation is possible, and a clearer understanding of the apparently “irrational” drivers of political violence. Decision making requires a nervous system that conditions motivation and behavior upon adaptively relevant cues in the environment. Such systems do not exist because they maximize utility, enlightenment, or scientific truth; they exist because on average they led to outcomes that were reproductively beneficial in ancestral environments. The reproductive challenges faced by our ancestors included not only ecological problems of predator avoidance but also political problems such as inter-group threat and the distribution of resources within groups. Therefore, evolutionary approaches to political decision making require direct and deep engagement of the logic whereby natural selection builds adaptations. This view of human psychology yields valuable insights on the domain specificity of political decision making as well as the psychological consequences of mismatch between modern and ancestral environments. In other words, there is accumulating evidence that many of the complex adaptations of the human brain were designed to solve the many problems of ancestral politics. This discussion begins by distinguishing evolutionary approaches from other frameworks used to explain political decision making, such as rational choice, or realism in international relations. Since evolutionary models of political decision making have now produced decades of original theoretical and empirical contributions, we are in a useful position to take stock of this research landscape. Doing so crystalizes the promises, perils, and scope of evolutionary approaches to politics.

Article

Kazuhisa Takemura

Behavioral decision theory is a descriptive psychological theory of human judgment, decision making, and behavior that can be applied to political science. Behavioral decision theory is closely related to behavioral economics and behavioral finance. Behavioral economics is an attempt to understand actual human economic behavior, and behavioral finance studies human behavior in financial markets. Research on people’s decision making represents an important part of these fields, in which various aspects overlap with the scope of behavioral decision theory. Behavioral decision theory focuses on the decision-making phenomena that are broadly divisible into those under certainty, those under risk, and others under uncertainty that includes ambiguity and ignorance. What are the theoretical frameworks that could be used to explain the decision-making phenomenon? Although numerous theories related to decision making have been developed, they are, in essence, often broadly divided into two types: normative theory and descriptive theory. The former is intended to support rational decision making. The latter describes how people actually make decisions. Both normative and descriptive theories reflect the nature of actual human decision making to a degree. Even descriptive theory seeks a certain level of rationality in actual human decision making. Consequently, the two are mutually indistinguishable. Nonetheless, a major example of normative theory is regarded as the system of utility theory that is widely used in economics. A salient example of descriptive theory is behavioral decision theory. Utility theory has numerous variations, such as linear and nonlinear utility theories. Most theories have established axioms and mathematically developed principles. In contrast, behavioral decision theory covers a considerably wide range of variations of theoretical expressions, including theories that have been developed mathematically (such as prospect theory) and those expressed only with natural language (such as multiattribute decision-making process models). Behavioral decision theory has integrated the implications of the normative theory, descriptive theory, and prescriptive theory that help people to make better decisions.

Article

To understand how policy is made, one must understand not only the individuals who make the decisions, but also the role of bureaucratic politics and the goals of the institutions themselves. Graham Allison’s classic Essence of Decision created the bureaucratic politics model and was the catalyst for a rich research agenda on decision-making. Using Allison as a starting point, researchers have expanded the understanding of the role of bureaucracies in deliberation and decision-making, particularly during times of crisis. Typically, institutions fill the day-to-day “politics as usual” role of decision-making, but their actions during crisis, by definition an abnormal event, allow bureaucracies to pursue their own objectives by way of a new opportunity to exert influence and to reshape the power structure of the political landscape. The research agenda on individuals and decision-making has also made great strides since the 1970s and helps to illuminate when the bureaucratic politics model has great explanatory power and when it is less useful. The level of influence bureaucracies have is dependent upon where they sit within the system and how they are utilized by the executive branch of government. Leaders, such as the President of the United States, hold a significant amount of power, and the ways in which they hold onto power, or allocate it to other actors, which is a function of their leadership style, can either empower or disempower bureaucracies. In other words, the importance of bureaucracies connected to the executive branch of government fluctuates with an individual’s personality characteristics and leadership style. Specifically, a leader’s personal need for power, their expertise, and their personal interest in policymaking, as well as their cognitive complexity, the amount of differing information they want and are capable of cognitively processing, influence the way in which the leader will delegate decision-making. Leaders like Lyndon B. Johnson relied heavily upon expert advisers and allocated decision-making to lower-level agencies. Alternatively, some leaders (e.g., Richard Nixon) have experience, particularly in foreign policy, and believe they are their own expert adviser; thus, they are involved in nuanced decision-making and rely upon only a very small number of advisers (in Nixon’s case, just Henry Kissinger). A common normative criticism of bureaucratic politics, and group decision-making in general, is the collective cognitive conformity, commonly known as groupthink. The general assumption is that individuals within a group will seek conformity and avoid the conflict caused by raising alternatives during policy deliberation. However, bureaucratic politics mitigates groupthink by bringing in a greater number of actors with differing goals and perspectives, making deliberation more open. Again, this is significantly influenced by how the leader utilizes advisers and their respective bureaucracies. Where Kennedy was very open-minded and actively sought various perspectives during the Cuban missile crisis, George W. Bush created an insulated decision-making environment after 9/11 and leading up to the invasion of Iraq. As society continues to change, particularly with regard to reliance upon technological adaptations, such as nuclear energy, new crises will occur. These crises will require the cooperation of more bureaucracies and occasionally new bureaucracies. Through these crises, bureaucracies will compete for political influence, and the power structure of the political landscape will inevitably change and affect policy decision-making.

Article

There are lots of ways that emotions have been studied in psychology and various ways that their use has been examined in the context of foreign policy. Perhaps one of the most useful ways to examine the influence of emotion on foreign policy is through the lens of risk and threat assessment. Some approaches to emotion tend to categorize emotions as valence-based, in terms of broad-based positivity or negativity. Certainly, elements of this kind of approach can be useful, particularly in terms of thinking about the ways in which political conservatives appear to have a negativity bias. However, an investigation of discrete emotions allows a more sophisticated and nuanced exploration of the effect of emotion on risk analysis and threat assessment, in particular the effect of fear, anger, and disgust on decision-making under conditions of risky threat. Genetic, as well as environmental, circumstances can influence individual variance in the experience and expression of such emotions, and any comprehensive approach to understanding the influence of emotion on decision-making should take all these factors into account.