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...

Are productivity increases from small amounts of stress (or shocks) in an economic task dependent upon the content of the shock? It has been found small amounts of stress can lead to an increase in memory. We examine if the same is true with productivity in an economic experiment and whether it...

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...

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately,...

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...

We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding...