In this chapter we study dynamic incentive models in which risk sharing is endogenously limited by the presence of informational or enforcement frictions. We comprehensively overview one of the most important tools for the analysis such problems -- the theory of recursive contracts. Recursive...

This paper proposes a tractable way to model boundedly rational dynamic programming. The agent uses an endogenously simplified, or "sparse," model of the world and the consequences of his actions and acts according to a behavioral Bellman equation. The framework yields a behavioral version of...

This paper uses a dynamic optimization model to estimate the welfare gains that a small open economy can derive from insuring against natural disasters with catastrophe (CAT) bonds. We calibrate the model by reference to the risk of earthquakes, floods and storms in developing countries. We find...

This paper introduces a nonlinear certainty equivalent approximation method for dynamic stochastic problems. We first use a novel, stable and efficient method for computing the optimal policy functions for deterministic dynamic optimization problems, and then use them as certainty-equivalent...

This paper formulates a structural dynamic programming model of preschool investment choices of altruistic parents and then empirically estimates the structural parameters of the model using the NLSY79 data. The paper finds that preschool investment significantly boosts cognitive and...

A nonlinear programming formulation is introduced to solve infinite horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to avoid inefficient discretization. Our numerical results show that this nonlinear...

We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is...

We apply numerical dynamic programming to multi-asset dynamic portfolio optimization problems with proportional transaction costs. Examples include problems with one safe asset plus two to six risky stocks, and seven to 360 trading periods in a finite horizon problem. These examples show that it...

Numerical dynamic programming algorithms typically use Lagrange data to approximate value functions over continuous states. Hermite data is easily obtained from solving the Bellman equation and can be used to approximate value functions. We illustrate this method with one-, three-, and...

A key open question in economics is the practical, portable modeling of bounded rationality. In this short note, I report ongoing progress that is more fully developed elsewhere. I present some results from a new model in which the decision-maker builds a simplified representation of the world....