We develop a characterization of stochastically stable states for the logit-response learning
dynamics in games, with arbitrary specification of revision opportunities. The result allows us …

We derive a family of probabilistic choice models, including the multinomial logit model, from
a microeconomic model in which the decision maker has to make some effort in order to …

We consider a population of agents, either finite or countably infinite, located on an arbitrary
network. Agents interact directly only with their immediate neighbors, but are able to observe …

This paper presents a survey of research on learning with a special focus on the structure of
interaction between individual entities. The structure is formally modelled as a network: the …

I propose a social learning framework where agents repeatedly take the weighted average
of all agents' current opinions in forming their own for the next period. They also update the …

We model retail price stickiness as the result of costly, error‐prone decision making. Under
our assumed cost function for the precision of choice, the timing of price adjustments and the …

This paper proposes a model in which retail prices are sticky even though firms can always
change their prices at zero cost. Instead of imposing a “menu cost”, we assume that more …

A strategic model of network formation is developed which permits unreliable links and
organizational costs. Finding a connected Nash network which guarantees a given payoff to …

This dissertation explores the importance of the payoff structure and beliefs for
noncooperative games. Chapter 2 considers instances where the payoffs and the objectives …

A non-cooperative model of network formation is developed. Agents form links with others
based on the cost of the link and its assessed benefit. Link formation is one-sided, ie, agents …