1.   Coherence and Justification Clusters: Epistemic Metrics for Propositional Knowledge   

      Taken from Interrogative Domains for Epistemic Agents.


  1. Cameron Hughes, Ctest Laboratories

  2. Tracey Hughes, Ctest Laboratories


Interrogative domains are domains where the primary activity consists of one or more people asking questions and one or more people giving answers. A civil or criminal trial is one example of an interrogative domain.  Attorney's present questions to defendants, plaintiffs or witnesses, and the witnesses, plaintiffs, or defendants are charged with providing answers to the questions given. Other examples of an interrogative domain are congressional hearings, interviews and surveys.  The question and answer sets taken from an interrogative domain are potential sources of knowledge.  This potential is a result of the fact that given each question & answer pair entails at least one proposition. That proposition asserts something about the world that is either true or false. The agent(s), those individuals who are charged with making a determination or taking an action based on entailed propositions of the question and answer sets are candidate knowers in the interrogative domain. In other words based on the entailed propositions from the question and answer sets of the interrogative domains, what can the agent be said to know?

At Ctest Laboratories, we take the digital transcripts of interrogative domains, we extract the entailed propositions and then use mining algorithms and deductive /abductive inference analysis against those propositions to see if we can identify coherence and justification among the entailed propositions. That is,  does any given proposition support any other given proposition in the transcript?  How are the propositions in the transcript related or not related? Do any propositions challenge, impeach, or discredit any other propositions in the transcript? How can we characterize, organize and group these propositions and their relationships? We employ visualization techniques during the analysis that result in the propositions being placed into groups of various types and sizes of clusters we call coherence and justification clusters.  We use the justification clusters as part of an epistemic metric that characterizes the integrity, quality and pedigree of any social knowledge that might be inferred from the entailed propositions that were taken from the original question & answer sets.  If  the justification clusters suggest that a set of propositions in the transcript pass the appropriate threshold of coherence then our epistemic agent is said to be committed to or (believe) that set of propositions. Further, if that same set of propositions pass an appropriate threshold of justification, we say that our epistemic agent is justified in its commitment to that set of propositions. With those two conditions in hand and some simplifying assumptions about the original entailed propositions, we talk about an epistemic agent having propositional knowledge taken from the interrogative domain, and we use justification clusters to characterize the validity of that knowledge. Our current research in computational epistemology is motivating us to believe that the digital transcripts of various interrogative domains are fertile sources of social knowledge. In this paper, we explore the use of  coherence and justification clusters as a part of an epistemic metric that can validate whether or not, or to what degree social knowledge can be extracted from digital transcripts of interrogative domains for use by epistemic agents.