This paper analyzes the formation of networks in which each agent is assumed to possess some information of value to the other agents in the network. Agents derive payoff from having access to the information of others through communication or spillovers via the links between them. Linking...

We develop a dynamic framework of strategic information transmission through cheap talk in a social network. Privately informed agents have different preferences about the action to be implemented by each agent and repeatedly communicate with their neighbors in the network. We first characterize...

I present a model of observational learning with payoff interdependence. Agents, ordered in a sequence, receive private signals about an uncertain state of the world and sample previous actions. Unlike in standard models of observational learning, an agent's payoff depends both on the state and...

We propose two novel axioms for qualitative probability spaces (Bernstein, 1917; de Finetti, 1937; Koopman, 1940; Savage, 1954): (i) unlikely atoms, which requires that there is an event containing no atoms that is at least as likely as its complement; and (ii) third-order atom-swarming, which requires that...

A learning rule is uncoupled if a player does not condition his strategy on the opponent's payoffs. It is radically uncoupled if a player does not condition his strategy on the opponent's actions or payoffs. We demonstrate a family of simple, radically uncoupled learning rules whose...

We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but...

I provide axiomatic foundations for a model of taste uncertainty with endogenous learning through consumption. In this setting, uncertainty is over an unobservable, subjective state space. Preference over lottery–menu pairs is sufficient to identify the state space and the learning process. In...