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1
Heimer, Carol Anne. Remodeling the garbage can: Implications of the causal origins of items in decision streams. Chicago, Ill: American Bar Foundation, 1998.
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2
The right decision: A mathematician reveals how the secrets of decision theory can help you make the right decision every time. New York: McGraw-Hill, 2010.
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3
Chuvikov, Dmitriy. Models and algorithms for reconstruction and examination of emergency events of road accidents based on logical artificial intelligence. 2nd ed. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1220729.
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The purpose of the monograph is to create a methodology, combined expert and simulation models, as well as algorithms and software-modeling tools for reconstruction and examination of accident events for automating decision-making by an expert center employee.
The methodology of combining and algorithms of joint work of an expert system based on logical artificial intelligence (mivar approach) and a simulation system for solving problems of reconstruction and examination of road accidents are developed; model reconstruction and examination of the accident in the formalism of the knowledge base bipartite oriented mivar nets, including analysis formulas braking qualities of the vehicle, determining the speed of a car's performance in terms of specific DTS, the formula for calculating different occasions: - slip car when braking, driving on curved sections of the road, hitting a car on the pedestrian in uniform motion and unlimited visibility; a method of generation of interfaces for designer expert systems based on the concept of mivar approach; special software in the form of expert systems "Analysis of road accident" in order to reduce the complexity of the process of calculating the disputed accidents, errors in the calculation and improve the accuracy and objectivity of the results obtained and the speed and quality of the calculations.
It can be useful to specialists of expert institutions, insurance companies, educational institutions in the field of expertise, as well as unmanned vehicles in terms of objective analysis and examination of road accidents.
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Naumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.
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The book describes the basic issues concerning consumer behavior on the basis of the simulation of the decision-making process on buying behavior of customers in the sales area of the store and shopping Internet sites.
The classification of models of consumer behavior, based on research in the area of economic, social and psychological theories and empirical evidence regarding decision-making by consumers when purchasing the goods, including online stores. Methods of qualitative and quantitative research of consumer behavior, fundamentals of statistical processing of empirical data.
Attention is paid to the processes of consumers ' perception of brands (brands) and advertising messages, the basic rules for the display of goods (merchandising) and its impact on consumer decision, recommendations on the use of psychology of consumer behavior in personal sales.
Presents an integrated model of consumer behavior in the Internet environment, the process of perception of the visitor of the company, the factors influencing consumer choice of goods online.
Is intended for preparation of bachelors in directions of preparation 38.03.02 "Management", 38.03.06 "trading business" and can be used for training of bachelors in direction of training 43.03.01 "Service", and will also be useful for professionals working in the field of marketing, distribution and sales.
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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".
The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.
Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Massimo, Warglien, and Masuch Michael 1949-, eds. The logic of organizational disorder. Berlin: W. de Gruyter, 1996.
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7
G, March James, and Weissinger-Baylon Roger, eds. Ambiguity and command: Organizational perspectives on military decision making. Boston: Pitman, 1986.
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James G. March (Other Contributor) and Roger Weissinger-Baylon (Other Contributor), eds. Ambiguity and command: Organizational perspectives on military decision making. Pitman, 1986.
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9
Kang, Sun Mo. An interactive organizational choice processing system to support decision making by using a prescriptive garbage can model. 1987.
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10
Jann, Werner. Michael D. Cohen, James G. March, and Johan P. Olsen, “A Garbage Can Model of Organizational Choice”. Edited by Martin Lodge, Edward C. Page, and Steven J. Balla. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199646135.013.4.
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This chapter examines “A Garbage Can Model of Organizational Choice,” a paper authored by Michael D. Cohen, James G. March, and Johan P. Olsen. It first discusses the assumptions of the garbage can model about decision-making in organizations, paying particular attention its three main elements: problematic preferences, unclear technologies, and fluid participation. It then considers four “relatively independent streams” and their interrelations: problems, solutions, participants, and choice opportunities. The chapter also assesses the paper’s main impact by focusing on organization theory and the original formal model before turning to the more specific areas of policy-making, administrative reform, and institutional theory.
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Severn, Sylvia Ann. The selection of cooperating teachers: Organized anarchies and garbage can decision making. 1992.
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13
van den Berg, Gerard J., and Arne Uhlendorff. Economic Job Search and Decision-Making Models. Edited by Ute-Christine Klehe and Edwin van Hooft. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199764921.013.021.
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The economic job search theory is based on the assumption that individuals have imperfect information about jobs and wages. It takes time to find an acceptable job and individuals have to make decisions about their job search behavior. The optimal job search behavior is characterized by the reservation wage, that is, the wage above which job offers are accepted, and by the search effort. Both components depend on factors such as the income during job search and the probability of receiving a job offer. Search effort can be described by the amount of resources used for finding a job, which includes time but can also include the type of search channels. We present the basic models of economic job search theory and selected empirical findings, in which we focus on the job search behavior of unemployed individuals.
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van Gelder, Jean-Louis. Dual-Process Models of Criminal Decision Making. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.8.
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This chapter discusses the application of dual-process and dual-system models to offender decision making. It is argued that these models offer a more accurate account of the decision process than the traditional choice models in criminology, such as rational choice and deterrence models, and can overcome their various limitations. Specific attention is devoted to the hot/cool perspective of criminal decision making, which takes the dual-process hypothesis as a point of departure. This model is rooted in the idea that both “cool” cognition and “hot” affect, or thinking and feeling, guide behavior and that understanding their interaction is fundamental for understanding how people make criminal choices.
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Treiber, Kyle. Biosocial Criminology and Models of Criminal Decision Making. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.4.
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This chapter explores how integrating the science of criminal decision making and contemporary biosocial criminology can benefit our understanding of why people make criminal action decisions and the role of biological factors. It reviews relevant biosocial findings but argues that efforts to link them to criminal decision making are limited by the lack of a strong model of the action process. It then compares how key components of this process—motivation, perception, and choice—are portrayed in models of criminal decision making with what is currently known about their biomechanics. It concludes that models of criminal decision making would benefit from the integration of evidence from the biological sciences and that some common assumptions may need to be reconsidered. It argues that biosocial criminology would benefit from a stronger, more biologically informed model of criminal decision making, which could better explain the role of biological factors in crime causation.
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Jones, Shayne. Personality and Offender Decision Making. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.9.
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This chapter introduces readers to the notion of personality and how it relates to offending through decision making. It also demonstrates the importance of personality to criminological inquiry more generally. The chapter begins with an explanation of what is personality, with a focus on two of the more common structural models used to measure it. In addition, a review of the robust association between personality and offending is provided. This is followed by an explanation of the theoretical and empirical linkages between personality and offender decision making. The final section provides readers with a sense of how personality can be better integrated into criminology and the advantages that can be realized by doing so.
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Birks, Daniel. Simulating Crime Event Decision Making. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.27.
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Crime is the result of numerous interconnected and interdependent crime event decisions made by potential offenders, victims, and crime preventers. The majority of these decisions go unobserved, and the situations within which they take place are difficult to control in support of traditional experimental studies. For these reasons, it can be difficult to identify causal links between individual behavior and observable crime phenomena and, in turn, to assess the veracity of proposed crime event decision calculi. This chapter discusses the use of agent-based modeling (ABM), and particularly agent-based social simulation (ABSS), in studying such crime event decision calculi. It outlines how ABM can be used to construct formal models of crime event actors, the decisions they make, and the interactions that occur among them. Subsequently, it proposes that ABSS provide unique means to formally assess the explanatory capacity of proposed crime event decision calculi in explaining observable characteristics of crime.
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Butz, Martin V., and Esther F. Kutter. Decision Making, Control, and Concept Formation. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0012.
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While attention controls the internal, mental focus of attention, motor control directs the bodily control focus. Our nervous system is structured in a cascade of interactive control loops, where the primary self-stabilizing control loops can be found directly in the body’s morphology and the muscles themselves. The hierarchical structure enables flexible and selective motor control and the invocation of motor primitives and motor complexes. The learning of motor primitives and complexes again adheres to certain computational systematicities. Redundant behavioral alternatives are encoded in an abstract manner, enabling fast habitual decision making and slower, more elaborated planning processes for realizing context-dependent behavior adaptations. On a higher level, behavior can be segmented into events, during which a particular behavior unfolds, and event boundaries, which characterize the beginning or the end of a behavior. Combinations of events and event boundaries yield event schemata. Hierarchical combinations of event schemata on shorter and longer time scales yield event taxonomies. When developing event boundary detectors, our mind begins to develop environmental conceptualizations. Evidence is available that suggests that such event-oriented conceptualizations are inherently semantic and closely related to linguistic, generative models. Thus, by optimizing behavioral versatility and developing progressively more abstract codes of environmental interactions and manipulations, cognitive encodings develop, which are supporting symbol grounding and grammatical language development.
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Bernasco, Wim, Jean-Louis van Gelder, and Henk Elffers, eds. The Oxford Handbook of Offender Decision Making. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.001.0001.
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How offenders make decisions that lead to criminal conduct is a core element of virtually every discussion about crime and law enforcement. What type of information can deter a potential offender? For whom is the prospect of a sanction effective? How can emotions facilitate or impede crime? How does the availability of guns affect behavior in violent conflicts? Do offenders learn to commit crime from the experiences of others? Is crime perpetrated by juveniles always the result of impulsive decisions? How do offenders choose crime targets and locations? The Oxford Handbook of Offender Decision Making covers and integrates contemporary theoretical, methodological, and empirical knowledge about the role of human decision making as it relates to criminal behavior. It provides state-of-the art reviews of the main paradigms in offender decision making, such as rational choice theory and deterrence, but also includes recent approaches such as dual-process models of decision making. It contains up-to-date reviews of empirical research on a wide range of decision types, from criminal initiation and desistance to choice of location, time, target, victim, and modus operandi. It also contains reviews of decision making regarding specific types of crime, including homicide, sexual crime, burglary, and white-collar and organized crime. In addition, it includes comprehensive in-depth treatments of the principal research methods used to study offender decision making, such as experimental designs, observation studies, surveys, offender interviews, and simulations.
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Boland, Lawrence A. Equilibrium Models in Economics. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.001.0001.
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Equilibrium models used in beginning economics classes are based on the equilibrium concept developed by Alfred Marshall, but that concept of an equilibrium does not correspond to the equilibrium concept recognized in modern formal mathematical models taught to graduate students. In both cases, the assumptions needed to produce explanations of economic events are open to question. The assumptions needed to prove the existence of an equilibrium in formal mathematical models are often questioned not only by older model builders but also by today’s formal model builders. This book critically examines both model building cultures by examining the major problematic assumptions employed building equilibrium models with particular attention to the assumptions used to characterize learning, knowledge, and expectations. These assumptions are recognized as essential in any equilibrium model that claims to address the dynamics of decision making. These assumptions are also the object of the critiques provided by those developing evolutionary models and by those promoting the development of complexity economics. Attention is also given to the inadequacies of what is taught to beginning students when it comes to the question of whether equilibrium models can provide a realistic explanation of economic events and objects such as prices, market demands, and market supplies.
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Boland, Lawrence A. Equilibrium models vs. realistic understanding. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.003.0009.
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In the real world, the process of reaching the assumed equilibrium involves decision makers’ knowledge and their awareness of any disequilibrium. Equilibrium attainment also requires their making the correct decisions required for a ‘stable’ equilibrium. Any model which fails to explicitly address the equilibrium process and its requirements is vulnerable to criticism of the model’s realism. This chapter explores, specifically, whether the knowledge required to reach equilibrium can ever be attained by participants, whether the process of obtaining that knowledge can be consistent with the requirements of achieving an equilibrium. It also explores the ‘ignorant consumer’ who has no way of knowing that he or she is not maximizing.
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Howes, Andrew, Xiuli Chen, Aditya Acharya, and Richard L. Lewis. Interaction as an Emergent Property of a Partially Observable Markov Decision Process. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0011.
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In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scenarios, including visual search, interactive search and sense-making. The chapter uses these scenarios to illustrate the explanatory power of POMDPs in HCI. It also shows that POMDPs embrace the embodied, ecological and adaptive nature of human interaction.
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Singer, Donald, and W. David Menzie. Quantitative Mineral Resource Assessments. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195399592.001.0001.
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Policy makers, mineral exploration experts, and regional planners decide how public lands, which may contain undiscovered resources, should be used or whether to invest in exploration for minerals on a regular basis. Decisions are also made concerning mineral resource adequacy, national policy, and regional development. This book makes explicit the factors that can affect a mineral-related decision so that decision-makers can clearly see the possible consequences of their decisions. Based on work done at the US Geological Survey, the authors address the question of the kinds of issues decision-makers are trying to resolve and what forms of information would aid in resolving these issues. The goal of the process discussed is to offer unbiased quantitative assessments in a format needed in decision-support systems so that consequences of alternative courses of action can be examined with respect to land use or mineral-resource development. An integrated approach focuses on three assessment parts and the models that support them. Although the concepts presented are straightforward and understandable, in assessments, carefully listening to the experts in other disciplines leads to better products. Navigating through and making sense of QRA requires not just learning rules and equations, but life experiences and common sense. The judgment required to understand which tools to apply are best learned by example and experience. This will be useful to governmental or industrial policy makers, managers of explorations, planners of regional development, and similar decision-makers.
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Jeffares, Ben, and Kim Sterelny. Evolutionary Psychology. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0020.
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The article presents several models of evolutionary psychology. Nativist evolutionary psychology is built around a most important insight that ordinary human decision-making has a high cognitive load. Evolutionary nativists defend a modular solution to the problem of information load on human decision-making. Human minds comprises of special purpose cognitive devices or modules. One of the modules is a language module, a module for interpreting the thoughts and intentions of others, another is a ‘naive physics’ module for causal reasoning about sticks, stones, and similar inanimate objects, a natural history module for ecological decisions, and a social exchange module for monitoring economic interactions with peers. These modules evolved in response to the distinctive, independent, and recurring problems faced by the ancestors. Domain specific modules handle information about human language, human minds, inanimate causal interactions, the biological world, and other constant adaptive demands faced by human ancestors. Nativist evolutionary psychologists have turned to moral decision making, arguing that cross-cultural moral judgments are invariant in an unexpected way. Natural selection can build and equip a special purpose module only if the information an agent needs to know is stable over evolutionary time. Automatized skills are an alternative means of coping with high-load problems. These skills are phenomenologically rather like modules, but they have very different developmental and evolutionary histories.
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Steinhauser, Karen E., and James A. Tulsky. Defining a ‘good’ death. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0008.
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Although any outcome of an advanced illness can be predicted, in palliative care settings the word ‘prognosis’ usually means the estimated time to death. Prognosis is an important but challenging set of clinical skills for palliative medicine clinicians to master. It is important because patients and families want to know what to expect, it influences clinical decision-making, and it may determine eligibility for services. It is challenging because of the inherent uncertainty of making predictions and because dying is not an easy topic to discuss. Advances in statistical computing have allowed the development of mathematical models and predictive tools that are now more accurate than clinical estimates. A large section of this chapter is devoted to presenting and evaluating several of these models, although prognostic uncertainty remains a significant issue even with them, and survival estimates should never drive clinical decision-making alone.
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Walker, Stephen G., and Mark Schafer. Operational Code Theory: Beliefs and Foreign Policy Decisions. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190846626.013.411.
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The process of foreign policy decision making is influenced in large part by beliefs, along with the strategic interaction between actors engendered by their decisions and the resulting political outcomes. In this context, beliefs encompass three kinds of effects: the mirroring effects associated with the decision making situation, the steering effects that arise from this situation, and the learning effects of feedback. These effects are modeled using operational code analysis, although “operational code theory” more accurately describes an alliance of attribution and schema theories from psychology and game theory from economics applied to the domain of politics. This “theory complex” specifies belief-based solutions to the puzzles posed by diagnostic, decision making, and learning processes in world politics. The major social and intellectual dimensions of operational code theory can be traced to Nathan Leites’s seminal research on the Bolshevik operational code, The Operational Code of the Politburo. In the last half of the twentieth century, applications of operational code analysis have emphasized different cognitive, emotional, and motivational mechanisms as intellectual dimensions in explaining foreign policy decisions. The literature on operational code theory may be divided into four general waves of research: idiographic-interpretive studies, nomothetic-typological studies, quantitative-statistical studies, and formal modeling studies. The present trajectory of studies on operational code points to a number of important trends that straddle political psychology and game theory. For example, the psychological processes of mirroring, steering, and learning associated with operational code analysis have the potential to enrich our understanding of game-theoretic models of strategic interaction.
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Summerson, Samantha R., and Caleb Kemere. Multi-electrode Recording of Neural Activity in Awake Behaving Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0004.
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Systems neuroscience is being revolutionized by the ability to record the activity of large numbers of neurons simultaneously. Chronic recording with multi- electrode arrays in animal models is a critical tool for studies of learning and memory, sensory processing, motor control, emotion, and decision-making. The experimental process for gathering large amounts of neural ensemble data can be very time consuming, however, the resulting data can be incredibly rich. We present a detailed overview of the process of acquiring multichannel neural data, with a particular focus on chronic tetrode recording in rodents.
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Ward, Michael D. Statistical Analysis of International Interdependencies. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.303.
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The origin of the statistical analysis of international relations can be traced back to 1920s with the work of Quincy Wright, who founded the University of Chicago’s Committee on International Relations. He led an interdisciplinary study of war that provided a first compendium of what was then known about the causes of war. Wright's studies and those that came after them were based on the assumption that systematic data were required to advance our knowledge about the causes of violent conflicts, and that an analysis of the dynamics of strategic decision making were essential; in short, systematic data coupled with a theoretical framework that focused on the decision-making calculus. However, debates soon raged over whether this scientific approach was better than the classical approach, which was based on philosophy, history, and law, and did not conform to strict standards of verification and proof. Since then, the literature has evolved into studies with a strong theoretical motivation, often expressed via game theoretical analytics, examined empirically with statistical frameworks that are specifically sculpted to probe those strategic dependencies. As such, existing models have resolved the levels of analysis problem that appeared daunting to earlier generations by actually focusing on the modeling of aspects of world politics that enjoin many different levels simultaneously.
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Panzironi, Francesca. Networks. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.270.
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A network may refer to “a group of interdependent actors and the relationships among them,” or to a set of nodes linked by a web of interdependencies. The concept of networks has its origins in earlier philosophical and sociological ideas such as Jean-Jacques Rousseau’s “general will” and Émile Durkheim’s “social facts”, which adressed social and political communities and how decisions are mediated and ideas are structured within them. Networks encompass a wide range of theoretical interpretations and critical applications across different disciplines, including governance networks, policy networks, public administration networks, social movement networks, intergovernmental networks, social networks, trade networks, computer networks, information networks, and neural networks. Governance networks have been proposed as alternative pluricentric governance models representing a new form of negotiated governance based on interdependence, negotiation and trust. Such networks differ from the competitive market regulation and state hierarchical control in three aspects: the relationship between the actors, decision-making processes, and compliance. The decision-making processes within governance networks are founded on a reflexive rationality rather than the “procedural rationality” which characterizes the competitive market regulation and the “substantial rationality” which underpins authoritative state regulation. Network theory has proved especially useful for scholars in positing the existence of loosely defined and informal webs of experts or advocates that can have a real and substantial influence on international relations discourse and policy. Two examples of the use of network theory in action are transnational advocacy networks and epistemic communities.
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Taliaferro, Jeffrey W. Prospect Theory and Foreign Policy Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.281.
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Prospect theory is one of the most influential behavioral theories in the international relations (IR) field, particularly among scholars of security studies, political psychology, and foreign policy analysis. Developed by Israeli psychologists Daniel Kahneman and Amos Tversky, prospect theory provides key insights into decision making under conditions of risk and uncertainty. For example, most individuals are risk averse to secure gains, but risk acceptant to avoid losses (loss aversion). In addition, most people value items they already posses more than they value items they want to acquire (endowment effect), and tend to be risk averse if they perceive themselves to be facing gains relative to their reference point (risk propensity). Prospect theory has generated an enormous volume of scholarship in IR, which can be divided into two “generations”. The first generation (1990–1999) sought to establish prospect theory’s plausibility in the “real world” by testing hypotheses derived from it against subjective expected-utility theory or rational choice models of foreign policy decision making. The second generation (2000–present) began to incorporate concepts associated with prospect theory and related experimental literature on group risk taking into existing mid-level theories of IR and foreign policy behavior. Two substantive areas covered by scholars during this period are coercive diplomacy and great power intervention in the periphery as they relate to loss aversion. Both generations of prospect theory literature suffer from conceptual and methodological difficulties, mainly around the issues of reference point selection, framing, and preference reversal outside laboratory settings.
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Wiers, Reinout W., Kristen G. Anderson, Bram Van Bockstaele, Elske Salemink, and Bernhard Hommel. Affect, Dual-Processing, Developmental Psychopathology, and Health Behaviors. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190499037.003.0008.
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This chapter discusses dual-process models of (health) behaviors, regarding both their recent criticisms and implications for health interventions. It agrees with critics that impulsive and reflective processes should not be equated with specific brain processes, but that psychological processes are emergent properties of the dynamic unfolding interplay between different neural systems. It maintains that at a psychological level of description, these models can still be useful to understand challenges to health behaviors and possible interventions. Affective processes can influence impulsive decision-making in health, but also reflective processes, when they concern affectively relevant goals. Cognitive training methods, including cognitive bias modification and training of executive control, have shown some success in changing health behaviors, but a critical variable for long-term success appears to be motivation to change.
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Poguntke, Thomas, Susan E. Scarrow, and Paul D. Webb. Political Party Organizations. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.227.
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How political parties organize directly affects who is represented and which policies are prioritized. Political parties structure political choice, which is one of the main functions generally ascribed to them. Their roles as gatekeepers for policies and political careers are closely linked to their nature as membership-based organizations, and to the extent to which they empower members to directly or indirectly influence these crucial choices. Parties also play a crucial role as campaign organizations, whose organizational strength influences their electoral success. The literature often summarizes differences in how parties organize and campaign by identifying major party types, which can be regarded as “classic models” of party organization. Yet, actual parties must adapt to changing environments or risk being supplanted by newer parties or by other political actors. For instance, in recent years one popular adaptation has involved parties opening their decision-making processes by introducing party-wide ballots to settle important questions. Changes like these alter how parties act as intermediaries in representation and political participation. Thanks to the increasing availability of comparable data on party organizations in established and new democracies, and in parliamentary and presidential systems, today’s scholars are better equipped to study the origins and impacts of parties’ organizational differences.
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Keohane, Georgia Levenson. Capital and the Common Good. Columbia University Press, 2017. http://dx.doi.org/10.7312/columbia/9780231178020.001.0001.
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Despite social and economic advances around the world, poverty and disease persist, exacerbated by the mounting challenges of climate change, natural disasters, political conflict, mass migration, and economic inequality. While governments commit to addressing these challenges, traditional public and philanthropic dollars are not enough. Here, innovative finance has shown a way forward: by borrowing techniques from the world of finance, we can raise capital for social investments today. Innovative finance has provided polio vaccines to children in the DRC, crop insurance to farmers in India, pay-as-you-go solar electricity to Kenyans, and affordable housing and transportation to New Yorkers. It has helped governmental, commercial, and philanthropic resources meet the needs of the poor and underserved and build a more sustainable and inclusive prosperity. Capital and the Common Good shows how market failure in one context can be solved with market solutions from another: an expert in securitization bundles future development aid into bonds to pay for vaccines today; an entrepreneur turns a mobile phone into an array of financial services for the unbanked; and policy makers adapt pay-for-success models from the world of infrastructure to human services like early childhood education, maternal health, and job training. Revisiting the successes and missteps of these efforts, Georgia Levenson Keohane argues that innovative finance is as much about incentives and sound decision-making as it is about money. When it works, innovative finance gives us the tools, motivation, and security to invest in our shared future.
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
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Cameron, Charles M., and Lewis A. Kornhauser. Theorizing the U.S. Supreme Court. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.264.
Full textAbstract:
We summarize the formal theoretical literature on Supreme Court decision-making. We focus on two core questions: What does the Supreme Court of the United States do, and how can one model those actions; and, what do the justices of the Supreme Court want, and how can one model those preferences? Given the current state of play in judicial studies, these questions then direct this survey mostly to so-called separation of powers (SOP) models, and to studies of a multi-member (“collegial”) court employing the Supreme Court’s very distinctive and highly unusual voting rule.The survey makes four main points. First, it sets out a new taxonomy that unifies much of the literature by linking judicial actions, modeling conventions, and the treatment of the status quo. In addition, the taxonomy identifies some models that employ inconsistent assumptions about Supreme Court actions and consequences. Second, the discussion of judicial preferences clarifies the links between judicial actions and judicial preferences. It highlights the relationships between preferences over dispositions, preferences over rules, and preferences over social outcomes. And, it explicates the difference between consequential and expressive preferences. Third, the survey delineates the separate strands of SOP models. It suggests new possibilities for this seemingly well-explored line of inquiry. Fourth, the discussion of voting emphasizes the peculiar characteristics of the Supreme Court’s voting rule. The survey maps the movement from early models that ignored the special features of this rule, to more recent ones that embrace its features and explore the resulting (and unusual) incentive effects.
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Colaresi, Michael, and Jude C. Hays. Spatial and Temporal Interdependence. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.301.
Full textAbstract:
Time and space are two dimensions that are likely to provide the paths—either singly or in tandem—by which international policy decisions are interdependent. There are several reasons to expect international relations processes to be interdependent across space, time, or both dimensions. Theoretical approaches such as rational expectations models, bureaucratic models of decision-making, and psychological explanations of international phenomena at least implicitly assume—and in many cases explicitly predict—dependence structures within data. One approach that researchers can use to test whether their international processes of interest are marked by dependence across time, space, or both time and space, is to explicitly model and interpret the hypothesized underlying dependence structures. There are two areas of spatial modeling at the research frontier: spatial models with qualitative and limited dependent variables, an co-evolution models of structure and behavior. These models have theoretical implications that are likely to be useful for international relations research. However, a gap remains between the kinds of empirical models demanded by international relations data and theory and the supply of time series and spatial econometric models that are available to those doing applied research. There is a need to develop appropriate models of temporal and spatial interdependence for qualitative and limited dependent variables, and for better models in which outcomes and structures of interdependence are jointly endogenous.
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Brunner, Ronald D., and Amanda H. Lynch. Adaptive Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.601.
Full textAbstract:
Adaptive governance is defined by a focus on decentralized decision-making structures and procedurally rational policy, supported by intensive natural and social science. Decentralized decision-making structures allow a large, complex problem like global climate change to be factored into many smaller problems, each more tractable for policy and scientific purposes. Many smaller problems can be addressed separately and concurrently by smaller communities. Procedurally rational policy in each community is an adaptation to profound uncertainties, inherent in complex systems and cognitive constraints, that limit predictability. Hence planning to meet projected targets and timetables is secondary to continuing appraisal of incremental steps toward long-term goals: What has and hasn’t worked compared to a historical baseline, and why? Each step in such trial-and-error processes depends on politics to balance, if not integrate, the interests of multiple participants to advance their common interest—the point of governance in a free society. Intensive science recognizes that each community is unique because the interests, interactions, and environmental responses of its participants are multiple and coevolve. Hence, inquiry focuses on case studies of particular contexts considered comprehensively and in some detail.Varieties of adaptive governance emerged in response to the limitations of scientific management, the dominant pattern of governance in the 20th century. In scientific management, central authorities sought technically rational policies supported by predictive science to rise above politics and thereby realize policy goals more efficiently from the top down. This approach was manifest in the framing of climate change as an “irreducibly global” problem in the years around 1990. The Intergovernmental Panel on Climate Change (IPCC) was established to assess science for the Conference of the Parties (COP) to the U.N. Framework Convention on Climate Change (UNFCCC). The parties negotiated the Kyoto Protocol that attempted to prescribe legally binding targets and timetables for national reductions in greenhouse gas emissions. But progress under the protocol fell far short of realizing the ultimate objective in Article 1 of the UNFCCC, “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference in the climate system.” As concentrations continued to increase, the COP recognized the limitations of this approach in Copenhagen in 2009 and authorized nationally determined contributions to greenhouse gas reductions in the Paris Agreement in 2015.Adaptive governance is a promising but underutilized approach to advancing common interests in response to climate impacts. The interests affected by climate, and their relative priorities, differ from one community to the next, but typically they include protecting life and limb, property and prosperity, other human artifacts, and ecosystem services, while minimizing costs. Adaptive governance is promising because some communities have made significant progress in reducing their losses and vulnerability to climate impacts in the course of advancing their common interests. In doing so, they provide field-tested models for similar communities to consider. Policies that have worked anywhere in a network tend to be diffused for possible adaptation elsewhere in that network. Policies that have worked consistently intensify and justify collective action from the bottom up to reallocate supporting resources from the top down. Researchers can help realize the potential of adaptive governance on larger scales by recognizing it as a complementary approach in climate policy—not a substitute for scientific management, the historical baseline.
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Mackay, Ronnie, and Warren Brookbanks, eds. Fitness to Plead. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198788478.001.0001.
Full textAbstract:
While criminalisation may be justified whenever an offender commits a sufficiently serious moral wrong requiring that he or she be called to account, the doctrine of fitness to plead calls this principle into question in the case of a person who lacks the capacity to participate meaningfully in a criminal trial. In light of the emerging focus on capacity-based approaches to decision making and the international human rights requirement that the law should treat defendants fairly, this volume offers a benchmark for the theory and practice of fitness to plead, considering differing perspectives and debate on the future development of a doctrine which has up until now been under-discussed and under-researched. The fitness-to-plead rules stand as an exception to notions of public accountability for criminal wrongdoing yet, despite the doctrine’s long-standing function in criminal procedure, it has proven complex to apply in practice and has given rise to many varied legislative models and considerable litigation in different jurisdictions. Particularly troublesome is the question of what is to be done with someone who has been found unfit to stand trial. Here the law is required to balance the need to protect these defendants who are unable to participate effectively in their own trial, whether permanently or for a defined period, with the need to protect the public from people who may have caused serious social harm as a result of their antisocial behaviour. The challenge for law reformers, legislators, and judges is to create rules that ensure that everyone who can properly be tried is tried, while seeking to preserve confidence in the fairness of the legal system by ensuring that people who cannot properly engage in the criminal trial process are not forced to endure it.
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Gao, Yanhong, and Deliang Chen. Modeling of Regional Climate over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.591.
Full textAbstract:
The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of this information for local decision-making, regional climate models (RCMs) were established in the 1970s. Dynamic and thermodynamic influences of the TP on the East and South Asia summer monsoon have since been widely investigated by model. Besides the heterogeneity in topography, impacts of land cover heterogeneity and change on regional climate were widely modeled through sensitivity experiments.In recent decades, the TP has experienced a greater warming than the global average and those for similar latitudes. GCMs project a global pattern where the wet gets wetter and the dry gets drier. The climate regime over the TP covers the extreme arid regions from the northwest to the semi-humid region in the southeast. The increased warming over the TP compared to the global average raises a number of questions. What are the regional dryness/wetness changes over the TP? What is the mechanism of the responses of regional changes to global warming? To answer these questions, several dynamical downscaling models (DDMs) using RCMs focusing on the TP have recently been conducted and high-resolution data sets generated. All DDM studies demonstrated that this process-based approach, despite its limitations, can improve understandings of the processes that lead to precipitation on the TP. Observation and global land data assimilation systems both present more wetting in the northwestern arid/semi-arid regions than the southeastern humid/semi-humid regions. The DDM was found to better capture the observed elevation dependent warming over the TP. In addition, the long-term high-resolution climate simulation was found to better capture the spatial pattern of precipitation and P-E (precipitation minus evapotranspiration) changes than the best available global reanalysis. This facilitates new and substantial findings regarding the role of dynamical, thermodynamics, and transient eddies in P-E changes reflected in observed changes in major river basins fed by runoff from the TP. The DDM was found to add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation.Although these advantages in the DDM over the TP are evidenced, there are unavoidable facts to be aware of. Firstly, there are still many discrepancies that exist in the up-to-date models. Any uncertainty in the model’s physics or in the land information from remote sensing and the forcing could result in uncertainties in simulation results. Secondly, the question remains of what is the appropriate resolution for resolving the TP’s heterogeneity. Thirdly, it is a challenge to include human activities in the climate models, although this is deemed necessary for future earth science. All-embracing further efforts are expected to improve regional climate models over the TP.