A continuous model for dynamic pricing under costly price modifications

This paper presents a heuristic method to solve a dynamic pricing problem under costly price modifications. This is a remarkably difficult problem that is solvable only under very few special cases. The method is applied to a more general form of the problem and is numerically tested for a variety of demand functions in the literature. The results show that the method is quite accurate, approximating the optimal profit within usually much less than 1\%. A more important result is that the accuracy tend to be much greater as the number of price changes increases, precisely when the underlying optimization problem becomes much harder, which makes this approach particularly desirable

An index for dynamic product promotion and the knapsack problem for perishable items

This paper introduces the knapsack problem for perishable items (KPPI), which concerns the optimal dynamic allocation of a limited promotion space to a collection of perishable items. Such a problem is motivated by applications in a variety of industries, where products have an associated lifetime after which they cannot be sold. The paper builds on recent developments on restless bandit indexation and gives an optimal marginal productivity index policy for the dynamic (single) product promotion problem with closed-form indices that yield estructural insights. The performance of the proposed policy for KPPI is investigated in a computational study.Dynamic promotion, Perishable items, Index policies, Knapsack problem, Festless bandits, Finite horizon, Marginal productivity index

Data-Driven Estimation in Equilibrium Using Inverse Optimization

Equilibrium modeling is common in a variety of fields such as game theory and transportation science. The inputs for these models, however, are often difficult to estimate, while their outputs, i.e., the equilibria they are meant to describe, are often directly observable. By combining ideas from inverse optimization with the theory of variational inequalities, we develop an efficient, data-driven technique for estimating the parameters of these models from observed equilibria. We use this technique to estimate the utility functions of players in a game from their observed actions and to estimate the congestion function on a road network from traffic count data. A distinguishing feature of our approach is that it supports both parametric and \emph{nonparametric} estimation by leveraging ideas from statistical learning (kernel methods and regularization operators). In computational experiments involving Nash and Wardrop equilibria in a nonparametric setting, we find that a) we effectively estimate the unknown demand or congestion function, respectively, and b) our proposed regularization technique substantially improves the out-of-sample performance of our estimators.Comment: 36 pages, 5 figures Additional theorems for generalization guarantees and statistical analysis adde

SPATIAL SEARCH IN COMMERCIAL FISHING: A DISCRETE CHOICE DYNAMIC PROGRAMMING APPROACH

We specify a discrete choice dynamic programming model of commercial fishing participation and location choices. This approach allows us to examine how fishermen collect information about resource abundance and whether their behavior is forward-looking.Resource /Energy Economics and Policy,