A seller sets up an advertising policy so as to maximize a flow of discounted utility over time in a scenario characterized by two essential issues. First, the seller is ignorant from the outset of the prospective customers' willingness to buy and runs a Bayesian learning process in parallel to...

Until recently, theorists considering the evolution of human cooperation have paid little attention to institutional punishment, a defining feature of large-scale human societies. Compared to individually administered punishment, institutional punishment offers a unique potential advantage: the...

Research on learning-by-doing has typically been restricted to cases where estimation and control can be treated separately. Recent work has provided convergence results for more general learning problems where experimentation is an important aspect of optimal control. However, the associated...

Until recently, theorists considering the evolution of human cooperation have paid little attention to institutional punishment, a defining feature of large-scale human societies. Compared to individually-administered punishment, institutional punishment offers a unique potential advantage: the...

In this paper we give an overview of algorithms developed to aid catalyst optimization at the Mexican Institute of Petroleum. We present a methodology that uses an evolutionary approach and ideas from tabu search and simulated annealing to guide the search for optimum catalytic materials based...

We present expert system (ES) and Integer Programming (IP) formulations of an NP-completeconstraint satisfaction problem (CSP). The problem involves generating aplan for assigning faculty to courses given a variety of constraints and preferences andother tentative data. The expert system...

Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis...

Textbooks on Design of Experiments invariably start by explaining why one-factor-at-a-time (OAT) is an inferior method. Here we will show that in a model with all interactions a variant of OAT is extremely efficient, provided that we only have non-negative parameters and that there are only a...