[Show abstract][Hide abstract] ABSTRACT: This paper introduces the Prince incentive system for measuring preferences. Prince clarifies consequences of decisions and incentive compatibility of experimental choice questions to subjects. It combines the efficiency and precision of matching with the improved clarity and validity of choice questions. It helps distinguish between (a) genuine deviations from classical economic theories (such as the endowment effect) and (b) preference anomalies due to fallible measurements (such as preference reversals). Prince avoids the opaqueness in Becker-DeGroot-Marschak and strategic behavior in adaptive experiments. It helps reducing violations of isolation in the random incentive system. Using Prince we shed new light on willingness to accept and the major components of decision making under uncertainty: utilities, subjective beliefs, and ambiguity attitudes.
[Show abstract][Hide abstract] ABSTRACT: This paper presents the Metric-Frequency Calculator (MF Calculator), an online ap-plication to analyze similarity. The MF Calculator implements the MF similarity al-gorithm developed by Sales and Wakker (2009) for the quantitative assessment of sim-ilarity in ill-structured data sets. It is widely applicable as it can be used when there is little prior control of the variables to be observed, with regard to either their num-ber or their content (qualitative or quantitative). A simulated example illustrates the implementation of the MF Calculator. An example with real data (n=150) of food con-sumer communication behavior in social media is also presented, in order to illustrate the potential of combining the MF Calculator with further statistical analysis. The MF Calculator is a user-friendly tool available free of charge. It can be downloaded from http://mfcalculator.celiasales.org/Calculator.aspx, and it can be used by non-experts from a wide range of social sciences.
[Show abstract][Hide abstract] ABSTRACT: This paper presents the metric-frequency calculator (MF Calculator), an online application to analyze similarity. The MF Calculator implements a metric-frequency similarity algorithm for the quantitative assessment of similarity in ill-structured data sets. It is widely applicable as it can be used with nominal, ordinal, or interval data when there is little prior control over the variables to be observed regarding number or content. The MF Calculator generates a proximity matrix in CSV, XML or DOC format that can be used as input to traditional statistical techniques such as hierarchical clustering, additive trees, or multidimensional scaling. The MF Calculator also displays a graphical representation of outputs using additive similarity trees. A simulated example illustrates the
implementation of the MF calculator. An additional example with real data is presented, in order to illustrate the potential of combining the MF Calculator with cluster analysis. The MF Calculator is a user-friendly tool available free of charge. It can be accessed from http://mfcalculator.celiasales.org/Calculator.aspx, and it can be used by non-experts from a wide range of social sciences.
[Show abstract][Hide abstract] ABSTRACT: In their famous 1982 paper in this Journal, Loomes and Sugden introduced regret theory. Now, more than 30 years later, the case for the historical importance of this contribution can be made.
The Economic Journal 03/2015; 125(583). DOI:10.1111/ecoj.12200 · 1.95 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Uncertainty pervades most aspects of life. From selecting a new technology to choosing a career, decision makers rarely know in advance the exact outcomes of their decisions. Whereas the consequences of decisions in standard decision theory are explicitly described (the decision from description (DFD) paradigm), the consequences of decisions in the recent decision from experience (DFE) paradigm are learned from experience. In DFD, decision makers typically overrespond to rare events. That is, rare events have more impact on decisions than their objective probabilities warrant (overweighting). In DFE, decision makers typically exhibit the opposite pattern, underresponding to rare events. That is, rare events may have less impact on decisions than their objective probabilities warrant (underweighting). In extreme cases, rare events are completely neglected, a pattern known as the “Black Swan effect.” This contrast between DFD and DFE is known as a description–experience gap. In this paper, we discuss several tentative interpretations arising from our interdisciplinary examination of this gap. First, while a source of underweighting of rare events in DFE may be sampling error, we observe that a robust description–experience gap remains when these factors are not at play. Second, the residual description–experience gap is not only about experience per se but also about the way in which information concerning the probability distribution over the outcomes is learned in DFE. Econometric error theories may reveal that different assumed error structures in DFD and DFE also contribute to the gap.
[Show abstract][Hide abstract] ABSTRACT: A central question in many debates on paternalism is whether a decision analyst can ever go against the stated preference of a client, even if merely intending to improve the decisions for the client. Using four gedanken-experiments, this paper shows that this central question, so cleverly and aptly avoided by libertarian paternalism (nudge), cannot always be avoided. The four thought experiments, while purely hypothetical, serve to raise and specify the critical arguments in a maximally clear and pure manner. The first purpose of the paper is, accordingly, to provide a litmus test on the readers’ stance on paternalism. We thus also survey and organize the various stances in the literature. The secondary purpose of this paper is to argue that paternalism cannot always be avoided and consumer sovereignty cannot always be respected. However, this argument will remain controversial.
Theory and Decision 03/2014; 76(3). DOI:10.1007/s11238-013-9375-2 · 0.48 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper provides necessary and sufficient preference conditions for average utility maximization over sequences of variable length. We obtain full generality by using a new algebraic technique that exploits the richness structure naturally provided by the variable length of the sequences. Thus we generalize many preceding results in the literature. For example, continuity in outcomes, a condition needed in other approaches, now is an option rather than a requirement. Applications to expected utility, decisions under ambiguity, welfare evaluations for variable population size, discounted utility, and quasilinear means in functional analysis are presented.
Operations Research 02/2014; 62(1). DOI:10.1287/opre.2013.1230 · 1.74 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Prospect theory is the most popular theory for predicting decisions under risk. This paper investigates its predictive power for decisions under ambiguity, using its specification through the source method. We find that it outperforms its most popular alternatives, including subjective expected utility, Choquet expected utility, and three multiple priors theories: maxmin expected utility, maxmax expected utility, and a-maxmin expected utility.
Journal of Risk and Uncertainty 02/2014; 48(1). DOI:10.1007/s11166-014-9185-0 · 1.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Doyle’s (2013) theoretical survey of discount functions criticizes two parametric families abbreviated as CRDI and CADI families. We show that Doyle’s criticisms are based on a mathematical mistake and are incorrect.
Judgment and decision making 09/2013; 2013(8):630-631. · 2.62 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Uncertainty pervades most aspects of life. From selecting a new technology to choosing a career, decision makers often ignore the outcomes of their decisions. In the last decade a new paradigm has emerged in behavioral decision research in which decisions are “experienced” rather than “described”, as in standard decision theory. The dominant finding from studies using the experience-based paradigm is that decisions from experience exhibit "black swan effect", i.e. the tendency to neglect rare events. Under prospect theory, this results in an experience-description gap. We show that several tentative conclusions can be drawn from our interdisciplinary examination of the putative experience-description gap in decision under uncertainty. Several insights are discussed. First, while the major source of under-weighting of rare events may be sampling error, it is argued that a robust experience-description gap remains when these factors are not at play. Second, the residual experience-description gap is not only about experience per se, but also about the way in which information concerning the probability distribution over possible outcomes is learned.Additional econometric and empirical work might be required to fully flech out these tentative conclusions. However, there was a consensus that an initially polemical literature turns out to be constructive in drawing researcher towards greater rapprochements.
[Show abstract][Hide abstract] ABSTRACT: Behavioral conditions such as compound invariance for risky choice and constant decreasing relative impatience for intertemporal choice have surprising implications for the underlying decision model. They imply a multiplicative separability of outcomes and either probability or time. Hence the underlying model must be prospect theory or discounted utility on the domain of prospects with one nonzero outcome. We indicate implications for richer domains with multiple outcomes, and with both risk and time involved.
[Show abstract][Hide abstract] ABSTRACT: This paper presents preference axiomatizations of expected utility for nonsimple lotteries while avoiding continuity constraints. We use results by Fishburn (1975), Wakker (1993), and Kopylov (2010) to generalize results by Delbaen et al. (2011). We explain the logical relations between these contributions for risk versus uncertainty, and for finite versus countable additivity, indicating what are the most general axiomatizations of expected utility existing today.
[Show abstract][Hide abstract] ABSTRACT: This paper uses decision-theoretic principles to obtain new insights into the
assessment and updating of probabilities. First, a new foundation of
Bayesianism is given. It does not require infinite atomless uncertainties as
did Savage s classical result, AND can therefore be applied TO ANY finite
Bayesian network.It neither requires linear utility AS did de Finetti s
classical result, AND r ntherefore allows FOR the empirically AND normatively
desirable risk r naversion.Finally, BY identifying AND fixing utility IN an
elementary r nmanner, our result can readily be applied TO identify methods OF
r nprobability updating.Thus, a decision - theoretic foundation IS given r nto
the computationally efficient method OF inductive reasoning r ndeveloped BY
Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments
are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability
assessments, AND FOR an alternative r nto the Dempster - Shafer belief
functions that avoids the reduction TO r ndegeneracy after multiple updatings.r
[Show abstract][Hide abstract] ABSTRACT: Experiments frequently use a random incentive system (RIS), where only tasks that are randomly selected at the end of the
experiment are for real. The most common type pays every subject one out of her multiple tasks (within-subjects randomization).
Recently, another type has become popular, where a subset of subjects is randomly selected, and only these subjects receive
one real payment (between-subjects randomization). In earlier tests with simple, static tasks, RISs performed well. The present
study investigates RISs in a more complex, dynamic choice experiment. We find that between-subjects randomization reduces
risk aversion. While within-subjects randomization delivers unbiased measurements of risk aversion, it does not eliminate
carry-over effects from previous tasks. Both types generate an increase in subjects’ error rates. These results suggest that
caution is warranted when applying RISs to more complex and dynamic tasks.
KeywordsRandom incentive system–Incentives–Experimental measurement–Risky choice–Risk aversion–Dynamic choice–Tremble–Within-subjects design–Between-subjects design
[Show abstract][Hide abstract] ABSTRACT: This paper presents a general technique for comparing the concavity of different utility functions when probabilities need not be known. It generalizes: (a) Yaariʼs comparisons of risk aversion by not requiring identical beliefs; (b) Kreps and Porteusʼ information-timing preference by not requiring known probabilities; (c) Klibanoff, Marinacci, and Mukerjiʼs smooth ambiguity aversion by not using subjective probabilities (which are not directly observable) and by not committing to (violations of) dynamic decision principles; (d) comparative smooth ambiguity aversion by not requiring identical second-order subjective probabilities. Our technique completely isolates the empirical meaning of utility. It thus sheds new light on the descriptive appropriateness of utility to model risk and ambiguity attitudes.
Games and Economic Behavior 07/2012; 75(2):481–489. DOI:10.1016/j.geb.2012.01.006 · 0.83 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Time discounting and quality of life are two important factors in evaluations of medical interventions. The measurement of these two factors is complicated because they interact. Existing methods either simply assume one factor given, based on heuristic assumptions, or invoke complicating extraneous factors, such as risk, that generate extra biases. The authors introduce a method for measuring discounting (and then quality of life) that involves no extraneous factors and that avoids distorting interactions. Their method is considerably simpler and more realistic for subjects than existing methods. It is entirely choice based and thus can be founded on economic rationality requirements. An experiment demonstrates the feasibility of this method and its advantages over classical methods.
Medical Decision Making 06/2012; 32(4):583-93. DOI:10.1177/0272989X12451654 · 3.24 Impact Factor