Research Topics

Publication

Abstract: Quantiles are used for decision making in investment analysis and in the mining, oil and gas industries. However, it is unknown how common quantile-based decision making actually is among typical individual decision makers. This paper describes an experiment that aims to (1) compare how common is decision making based on quantiles relative to expected utility maximization, and (2) estimate risk attitude parameters under the assumption of quantile preferences. The experiment has two parts. In the first part, individuals make pairwise choices between risky lotteries, and the competing models are fitted to the choice data. In the second part, we directly elicit a decision rule from a menu of alternatives. The results show that a quantile preference model outperforms expected utility for 30%-55%, of participants, depending on the metric. The majority of individuals are risk averse, and women are more risk averse than men, under both models.

Working Papers

Abstract: Traffic apps are becoming ever more widely used. Their use has raised concerns about congestion on secondary roads. This is potentially a serious concern if individuals overreact to the information provided on the app. We conduct a laboratory experiment to study the influence of traffic information on congestion. The experiment exogenously varies the fraction of drivers that have traffic information. The hypotheses for the experiment are drawn from the Experience Weighted Attraction-lite (EWA-lite) model of learning. We find that in early periods, congestion is lowest when only some drivers have information, while in the long run, providing it to some or all is equally efficient and better than providing it to no drivers. An additional treatment in which the willingness-to-pay for information is elicited shows that the value of information to drivers is largely independent of the fraction of others who also have the information. The results support the notion that gradual adoption of traffic apps in preferable than sudden adoption by all drivers.

Abstract: How gifts impact the informativeness of the online reputation system and market efficiency is unclear, as theory offers limited insight. To address this issue, we conducted a laboratory experiment using an infinitely repeated game between buyers and sellers under two treatments: one allowing and one prohibiting gifts. Results show that allowing gifts does not compromise the informativeness of the reputation system: a positive correlation between sellers' previous average ratings and the quality of the offered products was preserved under both treatments. Sellers used gifts to compensate for higher prices and compete with more reputable sellers. Since sellers adopted this strategy over 90% of the time when permitted, gifts did not undermine the integrity of the reputation system. Additionally, gift-giving did not significantly influence purchasing decisions or product quality in transactions and, therefore, had no impact on market efficiency. Interestingly, sellers persistently offered high-value gifts (45%--66% of sales profits) even though enhancing product quality was a more efficient strategy for reputation-building and profit-maximizing.