Philippe Delquié

Extended Framework for Modeling Choice Behavior

We review the case against the standard model of rational behavior and discuss the consequences of various ‘anomalies’ of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.

The Value of Information and Intensity of Preference

Previous research has documented the lack of clear relationships between the value of information and characteristics of the decision problem. This paper shows that the intensity of the decision makers preference toward the prior choice alternatives can be regarded as the primary determinant of information value. The value of an information source, measured in utility units, is maximal when the decision maker is indifferent vis-a-vis the prior alternatives, and it is lower as the preference for one alternative over the others gets stronger. This holds under quite general conditions, which are made explicit, and for any decision makers utility function, wealth, and background risks. The maximum buying price of information follows the same pattern for linear utility and, with a restriction, exponential utility, and this may hold approximately for other types of utility functions. The result provides a general, concise, and intuitive explanation for seemingly ill-behaved variations in the value of information. It indicates that nonmonotonic, quasi-concave relationships should generally be expected between value of information and parameters of the decision problem.

Optimal Conflict in Preference Assessment

Conflict arises indecision making when the choice alternatives present strong advantages and disadvantages over one another, that is, when the trade-offs involved are large. Conflict affects human response to choice, in particular, it increases decision difficulty and response unreliability. On the other hand, larger trade-offs, i.e., higher conflict, reveal more information about an individuals preferences and mitigate the influence of measurement unreliability on preference model estimation. This suggests, somewhat counterintuitively, that there may exist some optimal level of conflict for efficient measurement of preferences. How to determine this level? This issue is examined from behavioral and analytical angles. We outline a general analysis of the interaction between trade-off size and modeling accuracy, and demonstrate its application on a simple example. The kind of analysis developed here can be conveniently implemented in a computer spreadsheet, and would be especially valuable when large amounts of preference data are to be collected, as in consumer preference studies, experimental research, and contingent valuation surveys.

Interpretation of the Risk Tolerance Coefficient in Terms of Maximum Acceptable Loss

Users (or would-be users) of exponential expected utility often seek a concrete, intuitive meaning for the risk tolerance coefficient (RT) that they can grasp and explain to others easily. This paper shows an interpretation of RT as the maximum loss the decision maker is willing to be exposed to at a stated probability level, regardless of the upside potential. As an example, if you are facing a portfolio of projects having a 1 in 20 chance of total loss and you indicate that L is the maximum loss you would tolerate, then your risk tolerance is L/3. Other such examples, which may be better suited to other situations, are presented. In some contexts, this interpretation may be congruent with the way individuals naturally think about their risk-taking propensity, for example, as willingness to be exposed to losses or as in value-at-risk. This interpretation can also be helpful in determining whether assuming exponential utility is adequate for the situation being analyzed, and in eliciting the value of RT. The merits of this approach for thinking about RT are discussed.

Mean-Risk Analysis with Enhanced Behavioral Content

We study a mean-risk model derived from a behavioral theory of Disappointment with multiple reference points. One distinguishing feature of the risk measure is that it is based on mutual deviations of outcomes, not deviations from a specific target. We prove necessary and sufficient conditions for strict first and second order stochastic dominance, and show that the model is, in addition, a Convex Risk Measure. The model allows for richer, and behaviorally more plausible, risk preference patterns than competing models with equal degrees of freedom, including Expected Utility (EU), Mean–Variance (M-V), Mean-Gini (M-G), and models based on non-additive probability weighting, such as Dual Theory (DT). In asset allocation, the model allows a decision-maker to abstain from diversifying in a positive expected value risky asset if its performance does not meet a certain threshold, and gradually invest beyond this threshold, which appears more acceptable than the extreme solutions provided by either EU and M-V (always diversify) or DT and M-G (always plunge). In asset trading, the model provides no-trade intervals, like DT and M-G, in some, but not all, situations. An illustrative application to portfolio selection is presented. The model can provide an improved criterion for mean-risk analysis by injecting a new level of behavioral realism and flexibility, while maintaining key normative properties.

From the Editors---Probability Scoring Rules, Ambiguity, Multiattribute Terrorist Utility, and Sensitivity Analysis

This issues “From the Editors” column is coauthored with all the associate editors, to emphasize their major role in the leadership of the journal. We first review this years operations and thank our editorial board and referees. Our first article, by David J. Johnstone, Victor Richmond R. Jose, and Robert L. Winkler, presents “Tailored Scoring Rules for Probabilities,” which take into account the decision makers specific situation. Next, in “Do Bayesians Learn Their Way Out of Ambiguity?,” Alexander Zimper addresses how people perceive ambiguous probabilities and how they update their perceptions. In our third article, Chen Wang and Vicki M. Bier examine “Target-Hardening Decisions Based on Uncertain Multiattribute Terrorist Utility.” The final article, by Stephen P. Chambal, Jeffery D. Weir, Yucel R. Kahraman, and Alex J. Gutman, is on “A Practical Procedure for Customizable One-Way Sensitivity Analysis in Additive Value Models.”

Risk Measures from Risk-Reducing Experiments

This paper introduces the concept of risk-reducing experiments as a basis for designing risk measures. A risk-reducing experiment provides the option to mitigate the impact of less favorable outcomes in a gamble, and the gambles risk is measured as the increase in value brought about by such an experiment. Two examples are presented, including one based on the concept of expected value of perfect information. Both examples yield familiar risk measures, and extensions of them are discussed. A risk measure derived from a risk-reducing experiment makes explicit the sense in which the riskiness of a gamble is captured. Risk-reducing experiments offer a new approach for conceiving, or choosing among, risk measures.

Estimation of Risk and Time Preferences: Response Error, Heterogeneity, Adaptive Questionnaires, and Experimental Evidence from Mortgagers

We develop a methodology for the measurement of the parameters of cumulative prospect theory and time discounting models based on tools from the preference measurement literature. These parameters are typically elicited by presenting decision makers with a series of choices between hypothetical alternatives, gambles or delayed payments. We present a method for adaptively designing the sets of hypothetical choices presented to decision makers, and a method for estimating the preference function parameters which capture interdependence across decision makers as well as response error. We apply our questionnaire design and estimation methods to a study of the characteristics of homeowners who owe more on their mortgage than the current value of the underlying real estate asset. Our estimates indicate that such homeowners have larger discount rates and present bias than others, but do not differ in their risk preferences.