Site choices in recreational demand: a matter of utility maximization or regret minimization?

Abstract

This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.

Keywords:

• random regret minimization
• random utility maximization
• site choice probabilities
• recreational demand

Acknowledgements

The authors would like to thank Caspar Chorus, three anonymous reviewers and the participants in the Centro Euro-Mediterraneo per i Cambiamenti Climatici International Workshop on ‘Recent Trends in Non-Market Valuation’, Venice, 3–4 November 2011, for valuable feedback on earlier versions of this paper.

Notes

 1. Using neuroimaging techniques, Coricelli et al. (2005) show that the area of the human brain that is active when decision-makers experience regret after having made a (poor) choice is also highly active split seconds before they make a choice. In their words ‘anticipating regret is a powerful predictor of future choices’.

 2. The compromise effect indicates an anomalous choice behaviour that happens when the addition of an extreme option to the choice set shifts the choice preferences in favour of the compromise option (Chen and Rao Hill 2009). Simonson (1989) has reported that the compromise effect is stronger when people are expected to justify their choices to others, or when they are uncertain about their preference toward specific attribute values. The compromise effect has been frequently observed in consumer choice (Dhar and Simonson 2003) and has had practical implications in areas such as new product introduction, positioning strategy, and product assortments (Simonson and Tversky 1992, Kivetz et al. 2004).

 3. As noted by Thiene et al. (2011), although the RR-MNL paradigm shares with the well-known Regret Theory (Loomes and Sugden 1982, 1983, Quiggin, 1994) its consideration of regret as an important determinant of decisions, the two approaches differ on a number of aspects (for more details see Thiene et al. 2011).

 4. We do not consider the individual level, as in this context we consider the choice level more important for policy-makers and stakeholders.

 5. As stated by Chorus et al. (2008), ‘the word potential is important here, since an attribute's actual contribution to regret depends on (i) whether the considered alternative performs better or worse on the attribute than the alternative it is compared with and (ii) whether regret caused by comparing the alternatives is surpassed or not by comparing the considered alternative with another alternative’.

 6. As indicated by an anonymous reviewer, in contrast with RU-MNL, the negative of the RR-MNL's random error is distributed Extreme Value Type I. Just like the Random Utility Maximization model, the Random Regret Minimization model is capable of modelling random parameters, interaction effects and other sources of variability in parameters. One exception is the use of alternative-specific weights: since the Random Regret Minimization model is built around the notion that differences in attribute-values across alternatives generate regret, it assumes that the weight that is attached to this difference is generic across alternatives.

 7. See Chorus (2010) for a more in-depth discussion of these differences, using numerical examples and formal proofs.

 8. See Chorus (forthcoming) for a more in-depth discussion.

 9. More specifically, the Ben-Akiva and Swait test to compare two non-nested models, A and B, gives an upper bound for the probability that, when model A achieves a better Log-likelihood than model B, A is the correct model. This upper bound can be considered a conservative proxy for the significance of a difference in model fit between two non-nested models A and B.

10. See Ben-Akiva and Lerman (1985) for an in-depth and more formal presentation of the Logsum.

11. This is because the RR-MNL Logsum, being based on regret minimization, is not concerned with consumer surplus, which is indeed the focus of the RU-MNL Logsum (Chorus 2011).

12. Via-ferratas are challenging trails with metal ropes and ladders designed to help climbers to access vantage points or the top of a mountain in order to enjoy viewscapes.

13. For a complete description of the survey, see Hynes et al. (2007, 2008).