Classical Decision Theory Research Articles

How to Save Pascal (and Ourselves) From the Mugger

Abstract In this article, we re-examine Pascal's Mugging, and argue that it is a deeper problem than the St. Petersburg paradox. We offer a way out that is consistent with classical decision theory. Specifically, we propose a “many muggers” response analogous to the “many gods” objection to Pascal's Wager. When a very tiny probability of a great reward becomes a salient outcome of a choice, such as in the offer of the mugger, it can be discounted on the condition that there are many other symmetric, non-salient rewards that one may receive if one chooses otherwise.

Evidential Markov decision-making model based on belief entropy to predict interference effects

Some cognitive and decision making experiments have demonstrated the classical decision theory may be violated. Recently, the interference effects of quantum theory have attracted a strong interest in applying some fields outside physics. It can be also used to explain the paradox in decision models. Existing some experiments and studies attribute the main reason for the existence of interference effects to uncertain information in decision process. Dempster-Shafer evidence theory extends the framework of discernment to power sets so it can describe unknown and imprecise information. This paper proposes evidential Markov decision-making model based on belief entropy to quantitatively predict and determine the value of interference effects. In new model, the frame of discernment is extended by introducing hesitant or unknown states which could be hidden by participants. Moreover, new model assumes there is no input of any information at initial states so it has the most chaotic states and is determined according to the maximum belief entropy. Finally, this paper discusses the effectiveness of new model by comparing with other methods as studying the interference effects of decision process.

Psychological frameworks augment even classical decision theories.

Johnson, Bilovich, and Tuckett set out a helpful framework for thinking about how humans make decisions under radical uncertainty and contrast this with classical decision theory. We show that classical theories assume so little about psychology that they are not necessarily in conflict with this approach, broadening its appeal.

Anomaly Detection via Estimated Mutual Information and Its Relationship to the GLRT

The problem of detecting an anomaly based on two sets of data, the first one that is assumed to represent a quiescent condition, and the second that may contain an anomaly, is addressed. Using estimated mutual information as a discriminating indicator of change, a detector is configured and interpreted, with the changing parameter modeled as the outcome of a random variable. The relationship of the proposed detector to the standard generalized likelihood ratio test is also examined. It is found that the resulting approach merges concepts in information theory, for which a Bayesian assumption is made for the underlying change parameter, with classical decision theory, for which a frequentist assumption for the change parameter is utilized.

Evaluating collaborative rationality-based decisions: a literature review

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions.

Decisions, decisions, decisions in an uncertain environment

AbstractDecision‐makers abhor uncertainty, and it is certainly true that the less there is of it the better. However, recognizing that uncertainty is part of the equation, particularly for deciding on environmental policy, is a prerequisite for making wise decisions. Even making no decision is a decision that has consequences, and using the presence of uncertainty as the reason for failing to act is a poor excuse. Statistical science is the science of uncertainty, and it should play a critical role in the decision‐making process. This opinion piece focuses on the summit of the knowledge pyramid that starts from data and rises in steps from data to information, from information to knowledge, and finally from knowledge to decisions. Enormous advances have been made in the last 100 years ascending the pyramid, with deviations that have followed different routes. There has generally been a healthy supply of uncertainty quantification along the way but, in a rush to the top, where the decisions are made, uncertainty is often left behind. In my opinion, statistical science needs to be much more pro‐active in evolving classical decision theory into a relevant and practical area of decision applications. This article follows several threads, building on the decision‐theoretic foundations of loss functions and Bayesian uncertainty.

Quantum decision making in automatic driving

The behavior intention estimation and interaction between Autonomous Vehicles (AV) and human traffic participants are the key problems in Automatic Driving System (ADS). When the classical decision theory studies implicitly assume that the behavior of human traffic participants is completely rational. However, according to the booming quantum decision theory in recent years and actual traffic cases, traffic behaviors and other human behaviors are often irrational and violate the assumptions of classical cognitive and decision theory. This paper explores the decision-making problem in the two-car game scene based on quantum decision theory and compares it with the current mainstream method of studying irrational behavior-Cumulative Prospect Theory (CPT) model. The comparative analysis proved that the Quantum Game Theory (QGT) model can explain the separation effect which the classical probability model can’t reveal, and it has more advantages than CPT model in dealing with game scene decision-making. When two cars interact with each other, the QGT model can consider the interests of both sides from the perspective of the other car. Compared with the classical probability model and CPT model, the QGT is more realistic in the behavior decision-making of ADS.

A Boundedly Rational Decision-Making Model Based on Weakly Consistent Preference Relations

Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions.

Research on quantum cognition in autonomous driving

Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

Understanding the boundary decision of digital platform enterprises

PurposeBoundary decision is an important but underexplored theme in digital platform research. The boundary decision of digital platform enterprises (DPEs) differs from traditional organizations because of cross-side network effects (CNEs). This study intends to investigate whether transaction cost economics (TCE) and resource-based view (RBV), as classical organization boundary mechanisms, are still applicable for DPEs.Design/methodology/approachTo unfold the research problem, this study conducts a fuzzy-set qualitative comparative analysis (fsQCA) on the samples of 21 platform business units.FindingsThe results show that the classical boundary decision theory still applies in the context of DPEs, but the cross-side network effects will affect boundary decision of DPEs.Originality/valueThis study provides a new framework – integrates TCE, RBV and CNEs – to analyze boundary decision of DPEs. This paper also contributes to research on both organization boundary decision and platform governance.

Behavioral Decision Making in Normative and Descriptive Views: A Critical Review of Literature

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications).

RATIONALITY OR IRRATIONALITY? DECISION TO QUIT A JOB TO START A BUSINESS

The paper discusses rationality and decision making with a critical reflection on decisions by employees to quit jobs and set up fragile, micro, and rival businesses. This is a common phenomenon in management consulting businesses in especially emerging economies, where the demand for consultancy services is ballooning. The concepts of decision making and factors that influence decision making were discussed. Rationality and irrationality within the context of economics were briefly defined and theories such as (1) The utility theory , (2) The utility maximization theory and (3)The classical decision theory were used to explain the phenomenon. It was found that reasons for quitting or staying in a management consulting job is not only motivated by preparedness or readiness, rather a complex array of factors, some of which are observable and explainable while others may be attributable to complex cognitive processes that cannot be easily observed and explained. Deciding to leave a job is a complex but intentional rational decision that is influenced by many factors, some of which are observable, environmental, and cognitive. It is in the nature of the economic man to always decide, consequences of which might be sometimes predictable or influenced by events outside of the decision-makers control.

Individualized Decision Making Under Partial Identification: ThreePerspectives, Two Optimality Results, and One Paradox

Unmeasured confounding is a threat to causal inference and gives rise to biased estimates. In this article, we consider the problem of individualized decision-making under partial identification. Firstly, we argue that when faced with unmeasured confounding, one should pursue individualized decision-making using partial identification in a comprehensive manner. We establish a formal link between individualized decision-making under partial identification and classical decision theory by considering a lower bound perspective of value/utility function. Secondly, building on this unified framework, we provide a novel minimax solution (i.e., a rule that minimizes the maximum regret for so-called opportunists) for individualized decision-making/policy assignment. Lastly, we provide an interesting paradox drawing on novel connections between two challenging domains, that is, individualized decision-making and unmeasured confounding. Although motivated by instrumental variable bounds, we emphasize that the general framework proposed in this article would in principle apply for a rich set of bounds that might be available under partial identification.

Discovering better theories

Cognitive Science Theories of human decision-making have proliferated in recent years. However, these theories are often difficult to distinguish from each other and offer limited improvement in accounting for patterns in decision-making over earlier theories. Peterson et al. leverage machine learning to evaluate classical decision theories, increase their predictive power, and generate new theories of decision-making (see the Perspective by Bhatia and He). This method has implications for theory generation in other domains. Science , abe2629, this issue p. [1209][1]; see also abi7668, p. [1150][2] [1]: /lookup/doi/10.1126/science.abe2629 [2]: /lookup/doi/10.1126/science.abi7668

Using large-scale experiments and machine learning to discover theories of human decision-making.

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

DECAS: a modern data-driven decision theory for big data and analytics

ABSTRACT Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.

Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans

Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. Here, we characterize the neurocognitive processes underlying such voluntary decisions by integrating cognitive modelling of behavioral responses and EEG recordings in a probabilistic reward task. Human participants performed binary choices between pairs of unambiguous cues associated with identical reward probabilities at different levels. Higher reward probability accelerated RT, and participants chose one cue faster and more frequent over the other at each probability level. The behavioral effects on RT persisted in simple reactions to single cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the probability and preference effects were independently associated with changes in the speed of evidence accumulation, but not with visual encoding or motor execution latencies. Time-resolved MVPA of EEG-evoked responses identified significant representations of reward certainty and preference as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials modulated accumulation rates on a trial-by-trial basis. Our findings suggest that reward probability and spontaneous preference collectively shape voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior.

Improving decision-making effectiveness in crisis situations: developing intuitive expertise at the workplace

Design/Methodology/ApproachThis review examines the role of intuition as a cognitive tool to better manage complex crises. The paper draws on a case study in the aviation industry, the Hudson river incident, to advance the potency and value of intuitive expertise in crisis situations.PurposeCrisis managers operating in safety critical domains are often faced with difficult and exceptional conditions that may challenge their expertise and cause them to rely more heavily on their experiential knowledge. This review therefore provides insights into intuitive thinking and demonstrates its importance in crisis decision-making.FindingsEvidence suggests that intuition arguably offers a better cognitive option to decision-makers in high staked and time-pressured crisis situations. The Hudson River case study further highlights why organizations should aim to train their personnel to become better intuitive thinkers.Originality/valueThis review challenges conventional classical decision theory, outlining its limitations in typical fast paced crises environments. The paper instead positions intuition as a scientific construct that holds important value for crisis managers in extreme conditions.

Partial-overall dominance three-way decision models in interval-valued decision systems

Three-way decisions are a generalization of classical decision theory and receive increasing attentions from various fields to handle decision-making problems, especially when involving in incomplete information. An interval is a typical notion of information representation with incompleteness and uncertainty. To measure the dominance degree of one interval dominating or being dominated by another is a hot issue. In this paper, a novel dominance measure is constituted by considering both lengths and locations of intervals. The proposed dominance measure distinguishes two overlapped or coincided intervals, and is able to quantize the separation degree of disjoint intervals. A notion of variable precision overall dominance relation is introduced by integrating both the attribute-wise evaluation information and overall dominance degree of objects. Based on the constituted dominance relation, two three-way decision models are presented in interval-valued decision systems with categorical and interval-valued decision attributes, respectively. Numerical examples of three-way decisions and simulated experiments on classification over two synthetic and four benchmark datasets are presented. Experimental results indicate that the proposed model can produce lower classification errors in comparison with existing methods.

Investigating the origin and consequences of endogenous default options in repeated economic choices.

Classical value-based decision theories state that economic choices are solely based on the value of available options. Experimental evidence suggests, however, that individuals' choices are biased towards default options, prompted by the framing of decisions. Although the effects of default options created by exogenous framing-such as how choice options are displayed-are well-documented, little is known about the potential effects and properties of endogenous framing, that is, originating from an individual's internal state. In this study, we investigated the existence and properties of endogenous default options in a task involving choices between risky lotteries. By manipulating and examining the effects of three experimental features-time pressure, time spent on task and relative choice proportion towards a specific option-, we reveal and dissociate two features of endogenous default options which bias individuals' choices: a natural tendency to prefer certain types of options (natural default), and the tendency to implicitly learn a default option from past choices (learned default). Additional analyses suggest that while the natural default may bias the standard choice process towards an option category, the learned default effects may be attributable to a second independent choice process. Overall, these investigations provide a first experimental evidence of how individuals build and apply diverse endogenous default options in economic decision-making and how this biases their choices.