Dynamic multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving these problems, including nested Benders decomposition. In this method, recently shown to be...

Multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving the deterministic equivalent forms of these dynamic problems, including the simplex and interior...

A model of learning and adaptation is used to analyze the coevolution of strategies in the repeated Prisoner's Dilemma game under both perfect and imperfect reporting. Meta-players submit finite automata strategies and update their choices through an explicit evolutionary process modeled by a...

This paper deals with artificial agents buying and selling products in a virtual market of goods that may be substituted for each other. On the demand side the market features a homogenous group of agents whose dynamics are determined by three different scenarios. The supply side, on the other...

We present an investment process that: (i) decomposes securities into risk factors; (ii) allows for the construction of portfolios of assets that would selectively expose the manager to desired risk factors; (iii) perform a risk allocation between these portfolios, allowing for tracking error...

This paper defines the components and characteristics of an important class of models called weighted assignment models and identifies these elements in a number of existing and potential applications. The weighted assignment model represents problems with the following characteristics: A set of...

Sufficient conditions have been developed for testing the optimality of solutions to all-integer and mixed-integer linear programming problems after coefficient changes in the right hand side and the objective function, or after introduction of new variables. The same conditions can be used as...

New iterative separable programming techniques based on two-segment, piecewise-linear approximations are described for the minimization of convex separable functions over convex sets. These techniques have two advantages over traditional separable programming methods. The first is that they do...