An agent learns in continuous time from two information sources, each associated with a hypothesis. If a hypothesis is true, the associated source confirms it at a positive rate. False hypotheses are never confirmed. The agent's optimal learning strategy has two phases. During the first phase,...

Theoretical paper DeMarzo, Vayanos, and Zwiebel (2003) proposes a model of information aggregation in networks when individuals are subject to persuasion bias. The term "persuasion bias" refers to a particular form of boundedly rational behavior when individuals connected into a network do not...

Many experiments demonstrate that an individual's choice decisions are inconsistent. Following Luce (1959) and Block, Marschak, et al. (1960), a random choice approach to this problem has become very popular. It posits the existence of a probabilistic choice function that describes the...

We present a dynamic model that illustrates three forces that shape the effect of overconfidence (overprecision of consumed information) on the amount of collected information. The first force comes from overestimating the precision of the next consumed piece of information. The second force is...

An agent has to choose among a finite set of actions with payoffs depending on an unknown state of the world with two (possibly correlated) binary components, (θ1, θ2), θi∈{0,1}. Before making the choice, the agent can learn about the state by dynamically allocating his limited attention...