It is well-known that subgame-perfect Nash equilibrium does not eliminate incentives for joint-deviations or renegotiations. This paper presents a systematic framework for studying non-cooperative games with group incentives, and offers a notion of equilibrium that refines the Nash theory in a...

This talk will describe a stream of research in experimental economics focusing on the illumination and demonstration of other-regarding preferences (ORPs). Evidence will come primarily from public goods experiments, but also bargaining games (ultimatum, dictator, trust, ...

The no trade principle asserts that risk-neutral agents are not prepared to trade if and only if a common prior exists. The purpose of this article is to provide general versions of this principle. We first study the case when no topological assumption is made on the state space. Bets are...

This paper analyzes how the gossip process can be manipulated by biased people and the impact of such manipulation on information transmission. In this model, a single piece of information is transmitted via a chain of agents with privately known types. Each agent may be either objective or...

Suppose an agent is contemplating an action with state-contingent payoffs, and has a prior belief about the probability of the states. She hires an expert to update her priors before the action. Experts may be both informed as well as uninformed, and are not necessarily truthful. The question...

Within the context of strategic interaction, we provide a unified framework for analyzing information, knowledge, and the "stable" pattern of behavior. We first study the related interactive epistemology and, in particular, show an equivalence theorem between a strictly dominated strategy and a...

In its standard “public choice” form, the mechanism-design framework abstracts from institutional and technological constraints beyond those that the modeler can represent in the definition of states, outcomes, and preferences. This abstraction can create a useful simplification. However,...