Results are reported of a laboratory experiment aimed at examining whether strategic substitutability and strategic complementarity have an impact on the tendency to cooperate in two-player dominance-solvable games with a Pareto-inefficient Nash equilibrium. We find that there is significantly...

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...

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the...

We create an experimental asset market in which we conduct share repurchases and share issues. Although the intrinsic value of the shares is independent of the quantity outstanding, the interventions result in changes in asset price. Specifically, we find the following. (1) A repurchase of...

This paper selectively surveys some of the more prominent laboratory experimental studies on asset market behavior. The strands of literature considered are market microstructure, pari-mutuel betting markets, characteristics of participants, the effect of information release, and studies of the...

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...

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...

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design fixes the input...

This tutorial reviews the design and analysis of simulation experiments. These experiments may have various goals: validation, prediction, sensitivity analysis, optimization (possibly robust), and risk or uncertainty analysis. These goals may be realized through metamodels. Two types of...