Decision by sampling

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision's context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.     

Money does not stink: Using unpleasant odors as stimulus material changes risky decision making

Odors are strong elicitors of affect, and they play an important role in guiding human behavior, such as avoiding fire or spoiled food. However, little is known about how risky decision making changes when stimuli are olfactory. We investigated this question in an experimental study of risky decision making with unpleasant odors and monetary losses in a fully incentivized task with real outcomes. Odor and monetary decisions were matched so that monetary losses corresponded to the amount of money participants were willing to pay to avoid smelling an odor. Hierarchical Bayesian analyses using prospect theory show that participants were less sensitive to probabilities when gambling with odors than when gambling with money. These results highlight the importance of taking the sensory modality into account when studying risky decision making.     

风险决策过程验证：补偿/非补偿模型之争的新认识与新证据

风险决策是人类赖以生存和发展的重大决策。如何进行风险决策是人类不断认识和改造世界过程中遇到的未解之谜。主流风险理论认为, 风险决策是一个补偿性的、期望值最大化的过程; 而非主流的风险理论则认为, 风险决策是非补偿性的, 并不遵循期望法则所假设的加权求和等过程。这一谜团为何一直没有得以破解, 或许是因为我们未找到揭示其心理过程的令人信服的证据。近10年来, 我们针对风险决策过程, 借助行为实验、眼动记录法、事件相关电位、功能磁共振成像等技术, 系统探索风险决策的心理和神经机制, 为回答“风险决策是否遵循补偿性规则”这一问题提供了汇聚性证据。本文依据期望法则所假设的概率函数推导、加权过程、加权求和过程、总分最大化等步骤, 对这些研究进行梳理与回顾, 以期加深人们对风险决策机制的理解, 并为建立、健全与风险决策相关的政策、法律法规提供理论依据。     

遗传因素在风险决策加工中的作用

风险决策是指个体对不同选项及其概率进行权衡之后做出决定的过程。它是一个复杂的加工过程, 需要平衡奖赏选项的诱惑和损失选项的忧虑。风险决策能力是人脑最重要的高级功能之一。在现实生活中, 风险决策能力具有非常大的个体差异, 而遗传和环境各自都在其中起着关键的作用。在这篇文章中, 我们首先综述近年来风险决策研究领域中探讨遗传影响风险决策加工的研究, 包括双生子研究与分子遗传学研究。在介绍分子遗传学研究时, 按照基因所属神经递质系统, 分别介绍了多巴胺递质系统相关基因(如COMT、DAT等)、五羟色胺递质系统相关基因(如SLC6A4、TPH1等)和其他基因(如BDNF)对风险决策能力个体差异的影响。随后, 我们探讨了环境对风险决策个体差异的影响, 以及基因-环境交互作用对风险决策个体差异的影响。接下来, 我们介绍了将脑的结构与功能作为内表型来考察基因和环境对风险决策个体差异的影响的最新进展。在文章的最后, 我们指出今后对风险决策个体差异的研究应该同时考虑遗传和环境, 并考察脑的结构和功能在其中的中介作用。     

A generalized distance function for preferential choices

Many cognitive theories of judgement and decision making assume that choice options are evaluated relative to other available options. The extent to which the preference for one option is influenced by other available options will often depend on how similar the options are to each other, where similarity is assumed to be a decreasing function of the distance between options. We examine how the distance between preferential options that are described on multiple attributes can be determined. Previous distance functions do not take into account that attributes differ in their subjective importance, are limited to two attributes, or neglect the preferential relationship between the options. To measure the distance between preferential options it is necessary to take the subjective preferences of the decision maker into account. Accordingly, the multi‐attribute space that defines the relationship between options can be stretched or shrunk relative to the attention or importance that a person gives to different attributes describing the options. Here, we propose a generalized distance function for preferential choices that takes subjective attribute importance into account and allows for individual differences according to such subjective preferences. Using a hands‐on example, we illustrate the application of the function and compare it to previous distance measures. We conclude with a discussion of the suitability and limitations of the proposed distance function.     

MDFT account of decision making under time pressure

In this paper, decision making under time pressure for multiattribute choice alternatives in a risky environment is investigated. A model, multiattribute decision field theory (MDFT), is introduced that describes both the dynamic and the stochastic nature of decision making and accounts for the observed changes in choice probabilities, including preference reversals as a function of time limit. An experiment in which five different time limits were imposed on the decision maker is presented to test the predictions of the model. It is shown that MDFT is able to account for the complex decision behavior observed in the data. Furthermore, MDFT is compared with the predictions of decision field theory (Busemeyer & Townsend, 1993; Roe, Busemeyer, & Townsend, 2001).     

Risk taking under the influence: A fuzzy-trace theory of emotion in adolescence

Fuzzy-trace theory explains risky decision making in children, adolescents, and adults, incorporating social and cultural factors as well as differences in impulsivity. Here, we provide an overview of the theory, including support for counterintuitive predictions (e.g., when adolescents “rationally” weigh costs and benefits, risk taking increases, but it decreases when the core gist of a decision is processed). Then, we delineate how emotion shapes adolescent risk taking—from encoding of representations of options, to retrieval of values/principles, to application of those values/principles to representations of options. Our review indicates that: (i) gist representations often incorporate emotion including valence, arousal, feeling states, and discrete emotions; and (ii) emotion determines whether gist or verbatim representations are processed. We recommend interventions to reduce unhealthy risk taking that inculcate stable gist representations, enabling adolescents to identify quickly and automatically danger even when experiencing emotion, which differs sharply from traditional approaches emphasizing deliberation and precise analysis.     

DECISION AFFECT THEORY:

Abstract— How do people feel about the outcomes of risky options? Results from two experiments demonstrate that the emotional reaction to a monetary outcome is nor a simple function of the utility of that outcome Emotional responses also depend on probabilities and unobtained outcomes Unexpected outcomes have greater emotional impact than expected outcomes Furthermore any given outcome is lets pleasant if an unobtained outcome is better We propose an account of emotional experiences associated with outcomes of decisions called decision affect theory. It incorporates utilities expectations and counterfactual companions into hedonic responses. Finally, we show that choices between risky options can be described as the maximization of expected emotional experiences as predicted by decision affect theory That is people choose the risky option for which they expect to feel better on average.     

What a Feeling: The Role of Immediate and Anticipated Emotions in Risky Decisions

The risk‐as‐feelings hypothesis argues that many risky decisions are not only predicted by anticipated emotions, as most consequentialistic decision making theories would presume, but also by immediate emotions. Immediate emotions refer to the “hot” visceral feelings people feel as they contemplate a specific decision option at the cusp of making a decision, whereas anticipated emotions are those emotions that people forecast that they will feel once they experience possible consequences of that decision. Four studies focused on the role of both types of emotions in decisions under risk and uncertainty. Decisions were substantively predicted by immediate emotional states beyond anticipated emotions or the subjective probability attached to outcomes. Thus, risky choices may be prompted, in part, by how people feel about the “riskless” portion of the decision—specifically, the various decision options they are contemplating—rather than the potential outcomes those options may produce. Copyright © 2011 John Wiley & Sons, Ltd.     

The dynamics of deferred decision

Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.     

Games of luck and games of chance: the effect of luck- versus chance-orientation on gambling decisions

Chance and luck are conceived as two distinct causal agents that effect different results. The present study examined the proposition that persons who habitually attribute the outcome of random events to chance (chance-oriented persons) and those who prefer to attribute such outcomes to luck (luck-oriented persons) cope differently with decision making under uncertainty. Chance-oriented persons decide according to given or estimated odds that define the decision problem. Luck-oriented persons, on the other hand, rely on self-attributions of personal luck, and ignore the probabilities of decision outcomes. The hypothesized qualitative difference between the approaches of chance- and luck-oriented persons to decision making under uncertainty was supported substantially by the findings. © 1998 John Wiley & Sons, Ltd.     

Gambling for Gatorade: risk-sensitive decision making for fluid rewards in humans

Determining how both humans and animals make decisions in risky situations is a central problem in economics, experimental psychology, behavioral economics, and neurobiology. Typically, humans are risk seeking for gains and risk averse for losses, while animals may display a variety of preferences under risk depending on, amongst other factors, internal state. Such differences in behavior may reflect major cognitive and cultural differences or they may reflect differences in the way risk sensitivity is probed in humans and animals. Notably, in most studies humans make one or a few choices amongst hypothetical or real monetary options, while animals make dozens of repeated choices amongst options offering primary rewards like food or drink. To address this issue, we probed risk-sensitive decision making in human participants using a paradigm modeled on animal studies, in which rewards were either small squirts of Gatorade or small amounts of real money. Possible outcomes and their probabilities were not made explicit in either case. We found that individual patterns of decision making were strikingly similar for both juice and for money, both in overall risk preferences and in trial-to-trial effects of reward outcome on choice. Comparison with decisions made by monkeys for juice in a similar task revealed highly similar gambling styles. These results unite known patterns of risk-sensitive decision making in human and nonhuman primates and suggest that factors such as the way a decision is framed or internal state may underlie observed variation in risk preferences between and within species.     

Opposite effects of feedback contingency on the process of risky decisions-making

The main aim of this study was to look into the effect of feedback contingency on decision making in complex risky situations, using a driving context. Participants had to decide braking or not in a set of risky traffic situations. After the response, a negative non-contingent or contingent feedback was used. The results highlight the importance of feedback contingency upon safer decision making in risky contexts, as they showed how a contingent feedback led to faster and safer responses than a control group without any feedback; whereas a non-contingent one gives rise to slower responses and, more importantly, an enhanced risk-taking behavior that could be the cause of undesirable effects on road safety. The feedback effect was even more evident in the appearance of an opposite response bias as a function of contingency and enhanced by learning. These results accord with the theoretical accounts based upon the feedback influence on the threshold level of decision making. Moreover, the effects of feedback may be explained by new proposals focusing on the importance of attentional factors as well as of the mental models people build to react in complex risky scenarios, as a product of feedback and learning. Finally, this research may increase our understanding of the role of feedback in the process of learning safer behavior in complex risky situations.     

Process models deserve process data: comment on Brandstätter, Gigerenzer, and Hertwig (2006)

Comments on the article by E. Brandst?tter, G. Gigerenzer, and R. Hertwig. Resolution of debates in cognition usually comes from the introduction of constraints in the form of new data about either the process or representation. Decision research, in contrast, has relied predominantly on testing models by examining their fit to choices. The authors examine a recently proposed choice strategy, the priority heuristic, which provides a novel account of how people make risky choices. The authors identify a number of properties that the priority heuristic should have as a process model and illustrate how they may be tested. The results, along with prior research, suggest that although the priority heuristic captures some variability in the attention paid to outcomes, it fails to account for major characteristics of the data, particularly the frequent transitions between outcomes and their probabilities. The article concludes with a discussion of the properties that should be captured by process models of risky choice and the role of process data in theory development.     

Anticipated Emotions as Guides to Choice

When making decisions, people often anticipate the emotions they might experience as a result of the outcomes of their choices. In the process, they simulate what life would be like with one outcome or another. We examine the anticipated and actual pleasure of outcomes and their relation to choices people make in laboratory studies and real-world studies. We offer a theory of anticipated pleasure that explains why the same outcome can lead to a wide range of emotional experiences. Finally, we show how anticipated pleasure relates to risky choice within the framework of subjective expected pleasure theory.     

Modeling the Dynamics of Risky Choice

Individuals make decisions under uncertainty every day. Decisions are based on incomplete information concerning the potential outcome or the predicted likelihood with which events occur. In addition, individuals' choices often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a 2-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system can be employed to capture the dynamics of human decision making under uncertainty (i.e., multistability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlinear complexities of human decision making are discussed as well as the degree to which the theory of nonlinear dynamical systems might offer an alternative framework for understanding human decision making processes.