Towards a Computational Analysis of Status and Leadership Styles on FDA Panels
Decisions by committees of technical experts are increasingly impacting society. These decision-makers are typically embedded within a web of social relations. Taken as a whole, these relations define an implicit social structure which can influence the decision outcome. Aspects of this structure are founded on interpersonal affinity between parties to the negotiation, on assigned roles, and on the recognition of status characteristics, such as relevant domain expertise. This paper build upon a methodology aimed at extracting an explicit representation of such social structures using meeting transcripts as a data source. Whereas earlier results demonstrated that the method presented here can identify groups of decision-makers with a contextual affinity (i.e., membership in a given medical specialty or voting clique), we now can extract meaningful status hierarchies, and can identify differing facilitation styles among committee chairs. Use of this method is demonstrated on the transcripts of U.S. Food and Drug Administration (FDA) advisory panel meeting transcripts; nevertheless, the approach presented here is extensible to other domains and requires only a meeting transcript as input.
KeywordsLinguistic analysis Bayesian inference committee decision-making
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