The rapid growth of online retail in the last decade has led to widespread use of consumer-generated ratings. This paper theoretically and experimentally identifies influences that drive consumers to rate products and examines how those factors can create distortions in product ratings. By...

We analyse pricing, effort and tipping decisions at the online service ‘Google Answers’. Users set a price for the answer to their question ex ante, and they can additionally tip the researcher who provided the answer ex post.

We develop a Bayes–Nash analysis of the generalized second-price (GSP) auction, the multi-unit auction used by search engines to sell sponsored advertising positions. Our main result characterizes the efficient Bayes–Nash equilibrium of the GSP and provides a necessary and sufficient...

Expert advice is often biased in ways that benefit the advisor. We demonstrate how self-deception helps advisors be biased while preserving their self-image as ethical and identify limits to advisors' ability to self-deceive. In experiments where advisors recommend one of two investments to a...

In the presence of self-interested parties, mechanism designers typically aim to implement some social-choice function in an equilibrium. This paper studies the cost of such equilibrium requirements in terms of communication. While a certain amount of information x needs to be communicated just...

A growing literature analyzes revenue-maximizing contracts for situations in which agents can acquire private information before they decide whether to join the contract. It is conjectured that the results also apply to the more natural scenario where information can be acquired either before or...

Rule learning posits that decision makers, rather than choosing over actions, choose over behavioral rules with different levels of sophistication. Rules are reinforced over time based on their historically observed payoffs in a given game. Past works on rule learning have shown that when...

This paper investigates the possibility that people discover effective heuristics when playing similar perfect information games of varying complexity. We call this discovery experience Eureka Learning. We use a change-point analysis to identify 35 percent of our subjects as Eureka Learners.