Credibility Models

Abstract

We present a general hierarchical Bayesian model where Intelligence Sources make Reports about events or states in the world, which we call Hypotheses. The underlying multi-entity Bayes net for even a simple scenario has hundreds of nodes. We hide the details via Wigmore diagrams and a Google Maps GUI. Our application domain is Intelligence data fusion in asymmetrical warfare (terrorism). Some Hypotheses - like whether a village is a threat – may be abstract or un-observable. For these, we define Indicators – more observable Hypotheses whose value has some bearing on the target Hypothesis. The hierarchy can be arbitrarily deep, and Reports can provide evidence at any level. Furthermore, all Sources have credibility models. Traditional Sources are physical sensors with well-known error models. Non-traditional Sources include humans, websites, news, etc. For these Sources, our credibility models include Hypotheses about unknown factors like objectivity, competence, accuracy, reliability, and veracity. Every Report by a Source provides evidence about those factors. So, for example, successful ad hominem attacks against one Source can undermine his assurances that a village is safe, and lead us to believe it is hostile after all.