Jerome R. Busemeyer

Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment

Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (a) violations of stochastic dominance, (b) violations of strong stochastic transitivity, (c) violations of independence between alternatives, (d) serial position effects on preference, (e) speed-accuracy trade-off effects in decision making, (f) the inverse relation between choice probability and decision time, (g) changes in the direction of preference under time pressure, (h) slower decision times for avoidance as compared with approach conflicts, and (i) preference reversals between choice and selling price measures of preference. The proposed theory is compared with 4 other theories of decision making under uncertainty.

Analysis of multiplicative combination rules when the causal variables are measured with error.

A variety of theories in psychology postulate that the causal variables combine according to a multiplicative rule to determine the value of the dependent variable. To test multiplicative combination rules empirically, applied researchers frequently use an observational method that involves the following procedure: (a) assessment techniques are used to measure the value of each theoretical construct for each individual, (b) product scores are formed by multiplying the measures of the causal variables, and (c) hierarchical regression analysis is used to test the statistical significance of the increment in R contributed by the product term. The purpose of this article is to evaluate the validity of the observational method with respect to two measurement issues: measurement level (i.e., the effects produced by allowing monotonic transformations of the measures), and measurement error (i.e., the effects produced by using unreliable measures of the causal variables). Our evaluation is based on a theoretical distinction between the structural model (the set of equations relating theoretical constructs to each other) and the measurement model (the set of equations relating the theoretical constructs to the observed measures). We conclude that hierarchical regression analysis is inadequate for determining whether the structural model is additive or multiplicative for two reasons. First, an additive structural model may produce multiplicative effects through a nonlinear measurement model. Second, a multiplicative structural model may produce nondetectable multiplicative effects because of multiplicative measurement error. Some alternatives to hierarchical regression analysis are described.

Two-Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence.

The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

A quantum probability explanation for violations of ‘rational’ decision theory

Two experimental tasks in psychology, the two-stage gambling game and the Prisoners Dilemma game, show that people violate the sure thing principle of decision theory. These paradoxical findings have resisted explanation by classical decision theory for over a decade. A quantum probability model, based on a Hilbert space representation and Schrödingers equation, provides a simple and elegant explanation for this behaviour. The quantum model is compared with an equivalent Markov model and it is shown that the latter is unable to account for violations of the sure thing principle. Accordingly, it is argued that quantum probability provides a better framework for modelling human decision-making.

A Quantum Theoretical Explanation for Probability Judgment Errors.

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors, including the anchoring and adjustment model for probability judgments. In the quantum model, a new fundamental concept in cognition is advanced--the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. We conclude that quantum information-processing principles provide a viable and promising new way to understand human judgment and reasoning.

Using Cognitive Models to Map Relations Between Neuropsychological Disorders and Human Decision-Making Deficits

Findings from a complex decision-making task (the Iowa gambling task) show that individuals with neuropsychological disorders are characterized by decision-making deficits that lead to maladaptive risk-taking behavior. This article describes a cognitive model that distills performance in this task into three different underlying psychological components: the relative impact of rewards and punishments on evaluations of options, the rate that the contingent payoffs are learned, and the consistency between learning and responding. Findings from 10 studies are organized by distilling the observed decision deficits into the three basic components and locating the neuropsychological disorders in this component space. The results reveal a cluster of populations characterized by making risky choices despite high attention to losses, perhaps because of difficulties in creating emotive representations. These findings demonstrate the potential contribution of cognitive models in building bridges between neuroscience and behavior.

Can quantum probability provide a new direction for cognitive modeling

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality.

Survey of decision field theory

Abstract This article summarizes the cumulative progress of a cognitive-dynamical approach to decision making and preferential choice called decision field theory. This review includes applications to (a) binary decisions among risky and uncertain actions, (b) multi-attribute preferential choice, (c) multi-alternative preferential choice, and (d) certainty equivalents such as prices. The theory provides natural explanations for violations of choice principles including strong stochastic transitivity, independence of irrelevant alternatives, and regularity. The theory also accounts for the relation between choice and decision time, preference reversals between choice and certainty equivalents, and preference reversals under time pressure. Comparisons with other dynamic models of decision-making and other random utility models of preference are discussed.

An adaptive approach to human decision making: Learning theory, decision theory, and human performance

This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria.

Older adults as adaptive decision makers : Evidence from the Iowa gambling task

Older adults process emotional information differently than younger adults and may demonstrate less of a negativity bias on cognitive tasks. The Iowa Gambling Task designed by A. Bechara, H. Damasio, D. Tranel, and A. R. Damasio (1997) has been used to examine the integration of emotion and cognition in a risky-choice decision task and may give insight into differences in the decision-making strategies in younger and older adults. Eighty-eight younger adults (18-34 years) and 67 older adults (65-88 years) completed the Iowa Gambling Task. Using a theoretical decomposition of the task designed by J. R. Busemeyer and J. C. Stout (2002), the authors found that both groups were successful at solving the task but used very different strategies that reflected each groups strength. For younger adults, that strength was learning and memory. For older adults, that strength was an accurate representation of wins and losses (valence).