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: An online retail platform's rating system is believed to be important in mitigating moral hazards and adverse selection. However, there is a concern that the rating system may be biased if sellers give gifts to buyers. To investigate the effect of gift-giving on an online rating system, We conducted a laboratory experiment with two treatments that differed in whether sellers were allowed to send gifts to buyers. The two treatments are called the gift market and the no-gift market, respectively. Sellers could send buyers gifts before they rate the sellers in the gift market, but gift-giving was not allowed in the no-gift market. We find that allowing gifts did not disrupt the reputation system or impact market efficiency. Moreover, reputation was similarly valuable to buyers under the two treatments. Additionally, buyers placed equal value on the marginal benefits of products and gifts when making purchasing decisions and evaluating sellers. Therefore, sending gifts was less efficient than offering high-value products in attracting buyers, and in building a reputation. Nevertheless, sellers in the gift market sent gifts to buyers very frequently. As a result, the positive relationship between ratings and buyers' earnings was preserved, although gifts were transferred.