Skilloscopy: Bayesian modeling of decision makers' skill

Di Fatta, G. and Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 (2013) Skilloscopy: Bayesian modeling of decision makers' skill. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 43 (6). pp. 1290-1301. ISSN 0018-9472 doi: 10.1109/TSMC.2013.2252893

Abstract/Summary

This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers. In an increasingly sophisticated, connected and information-rich world, decision making is becoming both more important and more difficult. At the same time, modelling decision-making on computers is becoming more feasible and of interest, partly because the information-input to those decisions is increasingly on record. The aims of Skilloscopy are to rate and rank decision makers in a domain relative to each other: the aims do not include an analysis of why a decision is wrong or suboptimal, nor the modelling of the underlying cognitive process of making the decisions. In the proposed method a decision-maker is characterised by a probability distribution of their competence in choosing among quantifiable alternatives. This probability distribution is derived by classic Bayesian inference from a combination of prior belief and the evidence of the decisions. Thus, decision-makers’ skills may be better compared, rated and ranked. The proposed method is applied and evaluated in the gamedomain of Chess. A large set of games by players across a broad range of the World Chess Federation (FIDE) Elo ratings has been used to infer the distribution of players’ rating directly from the moves they play rather than from game outcomes. Demonstration applications address questions frequently asked by the Chess community regarding the stability of the Elo rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The method of Skilloscopy may be applied in any decision domain where the value of the decision-options can be quantified.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/29423
Identification Number/DOI	10.1109/TSMC.2013.2252893
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Bayesian inference, chess, decision making, skill evaluation, skilloscopy
Publisher	IEEE
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