We study whether information about imminent future dividends can abate bubbles in experimental asset markets. Using the seminal design of Smith et al. (1988) we find that markets where traders are asymmetrically informed about future dividends have smaller, and shorter, bubbles than markets with...

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...

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...

This article reviews so-called screening in simulation; i.e., it examines the search for the really important factors in experiments with simulation models that have very many factors (or inputs).The article focuses on a most e¢ cient and e¤ective screening method, namely Sequential...

We investigate minimax Latin hypercube designs in two dimensions for several distance measures. For the l-distance we are able to construct minimax Latin hypercube designs of n points, and to determine the minimal covering radius, for all n. For the l1-distance we have a lower bound for the...

We experimentally study how people form predictive models of simple data generating processes (DGPs), by showing subjects data sets and asking them to predict future outputs. We find that subjects: (i) often fail to predict in this task, indicating a failure to form a model, (ii) often cannot...