A Bayesian agent evaluates a stream of information over a finite period before deciding on which of two alternatives to adopt. At any point, the agent is free to convert information into an informative, binary signal. When information arrives at a roughly constant rate, an agent who frequently...

We develop a theoretical framework for studying the effects of interaction on the quaJity of decision-making by monetary policy committees. We show that interaction, i.e. increasing one's expertise through an exchange of views, is most likely not to result in interdependent voting...

When do groups and societies choose to be uninformed? We study a committee that needs to vote on a reform which will give every member a private state- dependent payoff. The committee can vote to learn the state at no cost. We show that the committee decides not to learn the state when...

This study investigates a potential mechanism to promote coordination. With theoretical guidance using a belief-based learning model, we conduct a multi-period, binary-choice, and weakest-link laboratory coordination experiment to study the effect of gradualism - increasing the required levels...

We test whether deviations from Nash equilibrium in rent-seeking contests can be explained by the slow convergence of payoff-based learning. We identify and eliminate two sources of noise that slow down learning. The first source of noise is present because each action is evaluated against a...

We study hedonic games with heterogeneous player types that reflect her nationality, ethnic background, or skill type. Agents' preferences are dictated by status-seeking where status can be either local or global. The two dimensions of status define the two components of a generalized constant...

Evolutionary algorithms have attracted more and more the attention of economists in recent years. Repeatedly it is claimed that they are an adequate tool to describe learning processes within a population of individuals. The present paper examines this claim. To this end, a learning model is set...

This study examines whether developers learn from their experience and from interactions with peers in OSS projects. A Hidden Markov Model (HMM) is proposed that allows us to investigate (1) the extent to which OSS developers actually learn from their own experience and from interactions with...

We study learning and influence in a setting where agents communicate according to an arbitrary social network and naïvely update their beliefs by repeatedly taking weighted averages of their neighbors' opinions. A focus is on conditions under which beliefs of all agents in large societies...

This paper examines the learning dynamics of boundedly rational agents, who are asked to voluntarily contribute to a discrete public good. In an incomplete information setting, we discuss contribution games and subscription games, the latter including a money-back guarantee in case of provision...