We consider bargaining in a bipartite network of buyers and sellers, who can only trade with the limited number of people with whom they are connected. Such networks could arise due to proximity issues or restricted communication flows, as with information transmission of job openings, business...

We demonstrate that one should not expect convergence of the proposals to the subgame perfect Nash equilibrium offer in standard ultimatum games. First, imposing strict experimental control of the behavior of the receiving players and focusing on the behavior of the proposers, we show...

We examine learning behavior in auctions and Fair division games with independent private values under two different price rules, first and second price. Participants face these four games repeatedly and submit complete bid functions rather than single bids. This allows us to examine whether...

We study the role of norm violations and learning behavior in multi- and single-worker gift-exchange games and find that working with co-workers leads to a twofold effect. First, flexible wages yield moderately higher efforts than in the single-employee treatment. The data suggests that this is...

Payments and discounts incentivize participation in many transactions about which people know little, but can learn more --- payments for medical trial participation, signing bonuses for job applicants, or price rebates on consumer durables. Who opts into the transaction when given such...

People often conform to others either pro-socially or self-interestedly. By conducting modified dictator game experiments where subjects observe others’ behaviors, this study analyzes how observational learning influences subjects’ conforming/non-conforming and pro-social/self-interested...

The central question of this paper is how to enhance supervised learning algorithms with fairness requirement ensuring that any sensitive input does not "`unfairly"' influence the outcome of the learning algorithm. To attain this objective we proceed by three steps. First after introducing...