Benefit of adding an alternative to one׳s choice set: A regret minimization perspective

The aim of this paper is to present and test a crucial building block for a regret minimization based choice set formation model, in that it presents a regret based benefit measure for the value associated with adding an alternative to one׳s choice set. By doing so, the paper contributes to existing research which has predominantly adopted a utility-based perspective. I show, using simulations based on a route choice model estimated on stated choice data, that the two perspectives – regret based and utility based – generate markedly different benefits. These are caused by the fact that the regret based perspective takes into account choice set composition effects. For example, in line with its behavioral premises, the regret based model predicts that adding an attractive alternative to the set choice only results in a large reduction in regret when the alternative outperforms existing alternatives in terms of every attribute (i.e., becomes a ‘clear winner’). In general, the benefit of adding a new alternative to one׳s choice set is predicted to be substantially higher by a utility based model, compared to a regret based counterpart. This implies that, to the extent that regret minimization (utility maximization) is an important determinant of decision-making, a utility (regret) based model would overestimate (underestimate) the true size of the decision-maker׳s choice set.

[1]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[2]  Joffre Swait,et al.  Choice set generation within the generalized extreme value family of discrete choice models , 2001 .

[3]  Todd Sarver,et al.  ANTICIPATING REGRET : WHY FEWER OPTIONS MAY BE BETTER , 2008 .

[4]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[5]  M. Ben-Akiva,et al.  Discrete choice models with latent choice sets , 1995 .

[6]  C. Manski The structure of random utility models , 1977 .

[7]  Dmitri Kuksov,et al.  When More Alternatives Lead to Less Choice , 2010, Mark. Sci..

[8]  Michel Bierlaire,et al.  An empirical comparison of travel choice models that capture preferences for compromise alternatives , 2013 .

[9]  B. Schwartz,et al.  Maximizing versus satisficing: happiness is a matter of choice , 2002 .

[10]  Oded Netzer,et al.  Alternative Models for Capturing the Compromise Effect , 2004 .

[11]  H S Mahmassani,et al.  The Econometrics of Search , 1985 .

[12]  A. Chernev When More Is Less and Less Is More: the Role of Ideal Point Availability and Assortment in Consumer Choice This Research Argues That Choices from Different Size Assort- Ments Are a Function of the Degree to Which Consumers Have , 2022 .

[13]  C. Chorus A New Model of Random Regret Minimization , 2010, European Journal of Transport and Infrastructure Research.

[14]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[15]  Anthony Richardson,et al.  SEARCH MODELS AND CHOICE SET GENERATION , 1980 .

[16]  Caspar G. Chorus,et al.  Logsums for utility-maximizers and regret-minimizers, and their relation with desirability and satisfaction , 2012 .

[17]  Moshe Ben-Akiva,et al.  A joint model of travel information acquisition and response to received messages , 2013 .

[18]  R. Sugden,et al.  Regret Theory: An alternative theory of rational choice under uncertainty Review of Economic Studies , 1982 .

[19]  Harry Timmermans,et al.  The value of travel information: Decision strategy-specific conceptualizations and numerical examples , 2006 .

[20]  A. Tversky,et al.  Loss Aversion in Riskless Choice: A Reference-Dependent Model , 1991 .

[21]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[22]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[23]  Hjp Harry Timmermans,et al.  Information gain, novelty seeking and travel: a model of dynamic activity-travel behavior under conditions of uncertainty , 2005 .

[24]  Peter Wright,et al.  Consumer Choice Strategies: Simplifying Vs. Optimizing: , 1975 .

[25]  Donald G. Morrison,et al.  Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data , 1995 .

[26]  Moshe Ben-Akiva,et al.  Incorporating random constraints in discrete models of choice set generation , 1987 .