Traditional Decision Theory Research Articles

Decision Theory without Luminosity

Abstract Our decision-theoretic states are not luminous. We are imperfectly reliable at identifying our own credences, utilities and available acts, and thus can never be more than imperfectly reliable at identifying the prescriptions of decision theory. The lack of luminosity affords decision theory a remarkable opportunity — to issue guidance on the basis of epistemically inaccessible facts. We show how a decision theory can guarantee action in accordance with contingent truths about which an agent is arbitrarily uncertain. It may seem that such advantages would require dubiously adverting to externalist facts that go beyond the internalism of traditional decision theory, but this is not so. Using only the standard repertoire of decision-theoretic tools, we show how to modify existing decision theories to take advantage of this opportunity. These improved decision theories require agents to maximize conditional expected utility — expected utility conditional upon an agent’s actual decision situation. We call such modified decision theories ‘self-confident’. These self-confident decision theories have a distinct advantage over standard decision theories: their prescriptions are better.

Shock determination in a two-stage decision-making model: The case of COVID-19 in Colombia.

The contributions of this paper are as follows: (a) proposing a two‐stage model to study whether an event has temporary or permanent effects on the probability of choosing a good within a market basket that traditional decision theory cannot explain and (b) studying the effects of COVID‐19 on consumers' decisions in Colombia. Findings suggest that the pandemic has transitory effects on preferences in the short run. If it lasts longer, it could induce to permanent changes. Thus, this model can be used to analyze the temporary or permanent effects of any event, regardless of its nature or geographical region, on consumer's decision.

Modeling Imprecision in Perception, Valuation, and Choice

Traditional decision theory assumes that people respond to the exact features of the options available to them, but observed behavior seems much less precise. This review considers ways of introducing imprecision into models of economic decision making and stresses the usefulness of analogies with the way that imprecise perceptual judgments are modeled in psychophysics—the branch of experimental psychology concerned with the quantitative relationship between objective features of an observer's environment and elicited reports about their subjective appearance. It reviews key ideas from psychophysics, provides examples of the kinds of data that motivate them, and proposes lessons for economic modeling. Applications include stochastic choice, choice under risk, decoy effects in marketing, global game models of strategic interaction, and delayed adjustment of prices in response to monetary disturbances.

204 - Learning from former SOS-Kinderdorf children: health status in early adulthood

Service value is affected by providers, receivers, and/or its environment. To that, value creation model (Ueda et al. 2008) could provide us deep insights on an issue of how to synthesize value. In the meantime, traditional decision theory is not enough to address such a value issue. This is mainly because decision theory is constructed on a probability basis. It implies that the theory cannot deal with co-creating situations and is not intrinsically appropriate for analyzing service decision problems. On the other hand, Gilboa and Schmeidler (1995) proposed Case-based Decision Theory, which describes decision-making in a human’s inductive manner. In this paper, as a case of service decision problems, we discussed value creation model from the viewpoint of Case-based Decision Theory.

Thinking about decisions: An integrative approach of person and task factors

AbstractDecisions vary. They may vary in both content and complexity. People also vary. An important way that people vary is how much they think. Some prior research investigating thinking and decision making largely conflicts with most traditional decision theories. For example, if considering an array of products to choose from, thinking more about the alternative's attributes should lead to a better decision. However, some research indicates that thinking more may also lead to focusing on irrelevant aspects of the decision and a less optimal outcome. We propose that this conflict in the literature exists because of a failure to consider the interaction between the individual and the decision task. To test this, we used separate methodologies that enhance or attenuate a person's thinking. In Study 1, we selected people who were especially high or low in need for cognition and had them complete a robust decision‐making inventory, which included both complex and simple tasks. In Study 2, we manipulated participant's level of glucose, which acts as the brain's fuel to enhance or attenuate thinking ability. Both studies provide insight for understanding our central tenant that more thought leads to better decisions in complex tasks but does not influence simple decisions. These findings show how the individual's thinking can interact with the constructive elements of the task to shape decision choice.

Interpreting value creation model by case-based decision theory

Service value is affected by providers, receivers, and/or its environment. To that, value creation model (Ueda et al. 2008) could provide us deep insights on an issue of how to synthesize value. In the meantime, traditional decision theory is not enough to address such a value issue. This is mainly because decision theory is constructed on a probability basis. It implies that the theory cannot deal with co-creating situations and is not intrinsically appropriate for analyzing service decision problems. On the other hand, Gilboa and Schmeidler (1995) proposed Case-based Decision Theory, which describes decision-making in a human’s inductive manner. In this paper, as a case of service decision problems, we discussed value creation model from the viewpoint of Case-based Decision Theory.

Beyond team-directed reasoning: participatory intentions contribute to a theory of collective agency

Philosophical accounts of collective intentionality typically rely on members to form a personal intention of sorts, viewed as a mental state. This tendency is opposed by recent economic literature on team-directed reasoning (as studied by Bacharach, Gold, and Sugden), which focuses on the reasoning process leading up to the formation of the members’ intentions. Our formal analysis bridges these paradigms and criticizes the team- directed reasoning account on two counts: first, team-directed reasoning is supposed to transcend traditional game and decision theory by adopting a certain collectivistic reasoning method. However, we show that team-directed reasoning yields the same action recommendations as a certain I-mode we-intention type. Accordingly, an important part of we-mode reasoning can be reduced to I-mode reasoning with certain preferences. Second, contrary to the claims of team-directed-reasoning theorists, we refute that team-directed reasoning surpasses pro-group intentions in selecting cooperatively rational solutions. That is, in some scenarios team-directed reasoning fails to guarantee successful cooperation whereas pro-group intentions succeed in doing so. We therefore propose to revise team-directed reasoning and introduce a third we-intention type, called participatory intentions. We prove that participatory intentions guarantee that a best group action is performed whenever either team-directed reasoning or pro-group intentions do.

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.

The behavioral TOPSIS

Abstract This paper updates and expands the TOPSIS that was introduced four decades ago. First, we re-present the logic of TOPSIS according to the traditional decision theory. It shows that TOPSIS also has a built-in multi-attribute value function that is not revealed explicitly. So far, this has been a hidden aspect of TOPSIS. Second, we incorporate the decision maker's (DM) behavioral tendency into TOPSIS. The behavioral TOPSIS accommodates the loss aversion concept in behavioral economics. A DM can set the loss aversion ratio to reflect his/her choice inclination. Two cases are illustrated to show the efficacy of the behavioral TOPSIS.

Neural Mechanisms of Cognitive Dissonance (Revised): An EEG Study.

Cognitive dissonance theory suggests that our preferences are modulated by the mere act of choosing. A choice between two similarly valued alternatives creates psychological tension (cognitive dissonance) that is reduced by a postdecisional reevaluation of the alternatives. We measured EEG of human subjects during rest and free-choice paradigm. Our study demonstrates that choices associated with stronger cognitive dissonance trigger a larger negative frontocentral evoked response similar to error-related negativity, which has in turn been implicated in general performance monitoring. Furthermore, the amplitude of the evoked response is correlated with the reevaluation of the alternatives. We also found a link between individual neural dynamics (long-range temporal correlations) of the frontocentral cortices during rest and follow-up neural and behavioral effects of cognitive dissonance. Individuals with stronger resting-state long-range temporal correlations demonstrated a greater postdecisional reevaluation of the alternatives and larger evoked brain responses associated with stronger cognitive dissonance. Thus, our results suggest that cognitive dissonance is reflected in both resting-state and choice-related activity of the prefrontal cortex as part of the general performance-monitoring circuitry.SIGNIFICANCE STATEMENT Contrary to traditional decision theory, behavioral studies repeatedly demonstrate that our preferences are modulated by the mere act of choosing. Difficult choices generate psychological (cognitive) dissonance, which is reduced by the postdecisional devaluation of unchosen options. We found that decisions associated with a higher level of cognitive dissonance elicited a stronger negative frontocentral deflection that peaked ∼60 ms after the response. This activity shares similar spatial and temporal features as error-related negativity, the electrophysiological correlate of performance monitoring. Furthermore, the frontocentral resting-state activity predicted the individual magnitude of preference change and the strength of cognitive dissonance-related neural activity.

Barriers to Resolution in Ideologically Based Negotiations: The Role of Values and Institutions

While traditional behavioral decision theory as applied to negotiation sheds light on some of the barriers encountered in negotiations, it does not fully account for many of the difficulties and failures to reach settlement in ideologically-based disputes. In this paper, we identify a number of factors that differentiate ideologically-based negotiations from other types of negotiation, and advance a perspective that takes into account the value-laden and institutional contexts in which they occur. We illustrate our ideas by applying them to the organizationally relevant example of environmental disputes.

Binary Regret for Statistically Dependent Risks

Unlike the traditional normative decision theories under risk, we are concerned with a unary relation, interpreted as subjective acceptability judgment, on the set of all joint probability distributions over two-attributed decision outcomes, where the first attribute describes final wealth levels the chosen alternative may yield and the second foregone wealth levels which may be given by the abandoned alternative. We show axioms of the unary relation for the existence of a general binary regret model. Then, we develop and provide two axiomatic characterizations of a simple regret model which additively separates expected intrinsic-utility and regret-rejoicing scale. Our model generalizes Bell's and Loomes and Sugden's classical simple regret model.

Drowsiness Detection by Bayesian-Copula Discriminant Classifier Based on EEG Signals During Daytime Short Nap.

Daytime short nap involves individual physiological states including alertness and drowsiness. In order to have a better understanding of the periodical rhymes of physiological states and then promote a good interpretability of alertness, the aim of this study is to detect drowsiness during daytime short nap. A method of Bayesian-copula discriminant classifier (BCDC) was introduced to detect individual drowsiness based on the physiological features extracted from electroencephalogram (EEG) signals. As an extension of traditional Bayesian decision theory, the BCDC method tries to construct the class-conditional probability density functions by exploiting the theory of copula and kernel density estimation. The proposed BCDC method was validated with experimental dataset and compared with other traditional methods for drowsiness detection. The obtained results showed that our method outperformed other methods in terms of three evaluation criteria. Our proposed method is effective to detect drowsiness with superior performance. Additionally, the BCDC method is relatively robust to different parameter settings on the group-level dataset. The proposed method is likely to be a useful tool to improve the correctness of the estimated class-conditional probability density functions. Since features are extracted from spontaneous EEG recordings, the results of this study can be further generalized to other experimental environment to detect vigilance level or driver drowsiness.

SEXUAL SELECTION. Irrationality in mate choice revealed by túngara frogs.

Mate choice models derive from traditional microeconomic decision theory and assume that individuals maximize their Darwinian fitness by making economically rational decisions. Rational choices exhibit regularity, whereby the relative strength of preferences between options remains stable when additional options are presented. We tested female frogs with three simulated males who differed in relative call attractiveness and call rate. In binary choice tests, females' preferences favored stimulus caller B over caller A; however, with the addition of an inferior "decoy" C, females reversed their preferences and chose A over B. These results show that the relative valuation of mates is not independent of inferior alternatives in the choice set and therefore cannot be explained with the rational choice models currently used in sexual selection theory.

Uncertain expected utility function and its risk premium

Other than traditional decision theory, this paper employs uncertainty theory to handle indeterminacy. Uncertain variables are used to represent uncertain choices. Uncertain expected utility function is defined as an increasing function of uncertain choices. Several mathematical properties of the uncertain expected utility functions are derived using inverse uncertainty distributions. In order to compare two different choices, the first order dominance and second order dominance via uncertain expected utility functions are introduced. We also investigate risk aversion attitude and risk premium. Finally, the relationship between risk premium and risk averse attitude is investigated.

Application of Relation Analysis in Evaluation and Optimization of Pile-Foundation

a list of criteria subject to selection and decision of pile-foundation is produced. Beside general process of traditional method, AHP and FAHP, relation analysis is introduced. Relation analysis could effectively consider the preference criteria, which is in accordance with investors. The relation analysis process is implanted in traditional decision theory by reasoned formulas and matrices. Last process is revealed smoothly and operability.

UNCERTAIN DECISION MAKING AND ITS APPLICATION TO PORTFOLIO SELECTION PROBLEM

In the traditional decision theory, choice with undetermined consequence is usually regarded as random variable, which usually describes objective uncertainty. This paper first considers the human uncertainty in making decisions, and employs uncertain variable to describe the choice. Utility function is also employed in the paper, and expected utility is introduced as a criterion to rank the choices. At last, in order to illustrate the uncertain decision making method, a portfolio selection problem is considered.

THE FUTILE SEARCH FOR TRUE UTILITY

In traditional decision theory, utility is regarded as a mathematical representation of preferences to be inferred from agents’ choices. In the recent literature at the interface between economics, psychology and neuroscience, several authors argue that economists could develop more predictive and explanatory models by incorporating insights concerning individuals’ hedonic experiences. Some go as far as to contend that utility is literally computed by specific neural areas and urge economists to complement or substitute their notion of utility with some neuro-psychological construct. In this paper, I distinguish three notions of utility that are frequently mentioned in debates about decision theory and examine some critical issues regarding their definition and measurability. Moreover, I provide various empirical and conceptual reasons to doubt that economists should base decision theoretic analyses on some neuro-psychological notion of utility.

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd-following, group-think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard-to-envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building "disaster-resilient" communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.

Distinguished Scholar Invited Essay Behavioral Decision Making

The past few decades have witnessed greatly enhanced interest in behavioral decision theory. Unlike traditional decision theory, which is normative or prescriptive and seeks an optimal solution, behavioral decision theory (although it yields important practical implications) is inherently descriptive, seeking to understand how people actually make decisions. This article discusses rationality and its limits, approaches to examination of decision processes, and consequences of limits on rationality. Issues relating to the clinical-actuarial controversy and automatic decision making are then addressed. Two approaches to improving decision making–by use of statistical groups and prediction markets as well as by implementation of forms of paternalistic intervention–are examined. Implications for leadership and organizations are then offered.