The following book has just been published:
Edmonds, B. & Meyer, R. (eds.) (2013) Simulating Social Complexity - a
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10 Apr 2013
Paper: Warren Thorngate and Bruce Edmonds on "Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis "
Thorngate, Warren and Edmonds, Bruce (2013) 'Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis' Journal of Artificial Societies and Social Simulation 16 (2) 4 <http://jasss.soc.surrey.ac.uk/16/2/4.html>.
- 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.
- Ordinal, Goodness-Of-Fit, Statistics, Evidence, Validity, Predictions, Observations
Slides and abstract from invited talk at Gronigen on "The Scandal of Generic Models in the Social Sciences"
The Scandal of Generic Models in the Social Sciences
Abstract: Despite overwhelming evidence that many aspects of human cognition are highly context-dependent, generic (that is models that are supposed to hold across different contexts) abound, including: most models of rationality and decision making, and most models that are based on statistically fitting equations to data. Context itself, especially social context, has been systematically by-passed by both quantitative and qualitative researchers. Quantitative researchers claim to be only interested in those patterns that are cross-context. Qualitative researchers only deal with accounts within context. Neither tackle the nature of context itself: how it works, in what ways it impacts upon behaviour.
Dealing with context is notoriously hard: the concept is slippery and its effects hard to identify. However, I claim it is not impossible to research. A combination of rich datasets and newer computational methods could help (a) identify some social contexts and (b) relate what happens within a context to how contexts are collectively constructed. Such a step could help relate quantitative and qualitative evidence in a way that is better founded and hence, perhaps, open the way to the unification of the social sciences as a coherent discipline.
Slides available at: http://www.slideshare.net/BruceEdmonds/scandal-of-generic-models-gronigen-v3