Evaluating a dual-proces model of risk: Affect and cognition as determinants of risky choice

In three studies we addressed the impact of perceived risk and negative affect on risky choice. In Study 1, we tested a model that included both perceived risk and negative affect as predictors of risky choice. Study 2 and Study 3 replicated these findings and examined the impact of affective versus cognitive processing modes. In all the three studies, both perceived risk and negative affect were shown to be significant predictors of risky choice. Furthermore, Study 2 and Study 3 showed that an affective processing mode strengthened the relation between negative affect and risky choice and that a cognitive processing mode strengthened the relation between perceived risk and risky choice. Together, these findings show support for the idea of a dual-process model of risky choice

Following wrong suggestions: self-blame in human and computer scenarios

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.Comment: To be published in the Proceedings of IFIP Conference on Human-Computer Interaction (INTERACT)201

The risk perceptions of individual investors

Risk perceptions of individual investors are studied by asking experimental questions to 2,226 members of a consumer panel. Their responses are analyzed in order to find which risk measures they implicitly use. We find that most investors implicitly use more than one risk measure. For those investors who systematically perceive risk according to the same risk measure, semi-variance of returns is most popular. Semi-variance is similar to variance, but only negative deviations fro the mean or another benchmark are taken into account. Stock investors implicitly choose for semi-variance as a risk measure, while bond investors favor probability of loss. Investors state that they consider the original investment to be the most important benchmark, followed by the risk-free rate of return, and the market return. However, their choices in the experimental questionnaire study reveal that the market return is the most important benchmark

Random regret minimization for consumer choice modeling: assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM-RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts

"If only I had taken the other road...": Regret, risk and reinforced learning in informed route-choice

This paper presents a study of the effect of regret on route choice behavior when both descriptional information and experiential feedback on choice outcomes are provided. The relevance of Regret Theory in travel behavior has been well demonstrated in non-repeated choice environments involving decisions on the basis of descriptional information. The relation between regret and reinforced learning through experiential feedbacks is less understood. Using data obtained from a simple route-choice experiment involving different levels of travel time variability, discrete-choice models accounting for regret aversion effects are estimated. The results suggest that regret aversion is more evident when descriptional information is provided ex-ante compared to a pure learning from experience condition. Yet, the source of regret is related more strongly to experiential feedbacks rather than to the descriptional information itself. Payoff variability is negatively associated with regret. Regret aversion is more observable in choice situations that reveal risk-seeking, and less in the case of risk-aversion. These results are important for predicting the possible behavioral impacts of emerging information and communication technologies and intelligent transportation systems on travelers' behavior. © 2012 Springer Science+Business Media, LLC

Revisiting consistency with random utility maximisation: theory and implications for practical work

While the paradigm of utility maximisation has formed the basis of the majority of applications in discrete choice modelling for over 40 years, its core assumptions have been questioned by work in both behavioural economics and mathematical psychology as well as more recently by developments in the RUM-oriented choice modelling community. This paper reviews the basic properties with a view to explaining the historical pre-eminence of utility maximisation and addresses the question of what departures from the paradigm may be necessary or wise in order to accommodate richer behavioural patterns. We find that many, though not all, of the behavioural traits discussed in the literature can be approximated sufficiently closely by a random utility framework, allowing analysts to retain the many advantages that such an approach possesses