Bounded rationality questions backward induction, which however, does not exclude such reasoning when anticipation is easy. In our stochastic (alternating offer) bargaining experiment, there is a certain first-period pie and a known finite deadline. What is uncertain (except for the final...

Bounded rationality questions backward induction, which however, does not exclude such reasoning when anticipation is easy. In our stochastic (alternating offer) bargaining experiment, there is a certain first-period pie and a known finite deadline. What is uncertain (except for the final...

In this study, we extend the single-period newsvendor problem with stochastic demand into a multi-period and time-dependent one and find a solution for it. We analyze the multi-period newsvendor problem with stochastic demand in a Stackelberg framework where the wholesaler is the leader and the...

People who are members of a group and identify with it behave differently from people who perceive themselves as isolated individuals. This difference depends on two main factors. First, preferences over outcomes change with the degree of identification with the group. Second, this...

Spatial evolutionary games model individuals who are distributed in a spatial domain and update their strategies upon playing a normal form game with their neighbors. We derive integro-differential equations as deterministic approximations of the microscopic updating stochastic processes. This...

We study the problem of assigning indivisible objects to agents where each is to receive one object. To ensure fairness in the absence of monetary compensation, we consider random assignments. Random Priority, also known as Random Serial Dictatorship, is characterized by symmetry, ex-post...

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...

We apply standardized numerical techniques of stochastic optimization (Judd [1998]) to the climate change issue. The model captures the feature that the effects of uncertainty are different with different levels of agent's risk aversion. A major finding is that the effects of stochasticity...

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another...