Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative goodness-of-fit strategy, Ordinal Pattern Analysis (OPA) that will (we argue) be more appropriate for judging the goodness-of-fit of simulations in many situations. OPA is based on matches and mismatches among the ordinal properties of predictions and observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In addition, OPA provides a means to assess prediction-observation fits case-by-case prior to aggregation, and to map domains of validity of competing simulations. We provide examples to illustrate how OPA can be employed to assess the ordinal fit and domains of validity of simulations of share prices, crime rates, and happiness ratings. We also provide a computer programme for assisting in the calculation of OPA indices.
['Warren Thorngate', 'Bruce Edmonds']
Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis