Responsibility and blame: a structural-model approach

Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We extend the definition of causality introduced by Halpern and Pearl [2001] to take into account the degree of responsibility of A for B. For example, if someone wins an election 11--0, then each person who votes for him is less responsible for the victory than if he had won 6--5. We then define a notion of degree of blame, which takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent

Staying true with the help of others: doxastic self-control through interpersonal commitment

I explore the possibility and rationality of interpersonal mechanisms of doxastic self-control, that is, ways in which individuals can make use of other people in order to get themselves to stick to their beliefs. I look, in particular, at two ways in which people can make interpersonal epistemic commitments, and thereby willingly undertake accountability to others, in order to get themselves to maintain their beliefs in the face of anticipated “epistemic temptations”. The first way is through the avowal of belief, and the second is through the establishment of collective belief. I argue that both of these forms of interpersonal epistemic commitment can function as effective tools for doxastic self-control, and, moreover, that the control they facilitate should not be dismissed as irrational from an epistemic perspective

Exploiting the similarity of non-matching terms at retrieval time

In classic information retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as 'term mismatch', has been recognised for a long time by the information retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity can enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed

Queries, rules and definitions as epistemic statements in concept languages

Concept languages have been studied in order to give a formal account of the basic features of frame-based languages. The focus of research in concept languages was initially on the semantical reconstruction of frame-based systems and the computational complexity of reasoning. More recently, attention has been paid to the formalization of other aspects of frame-based languages, such as non-monotonic reasoning and procedural rules, which are necessary in order to bring concept languages closer to implemented systems. In this paper we discuss the above issues in the framework of concept languages enriched with an epistemic operator. In particular, we show that the epistemic operator both introduces novel features in the language, such as sophisticated query formulation and closed world reasoning, and makes it possible to provide a formal account for some aspects of the existing systems, such as rules and definitions, that cannot be characterized in a standard first-order framework

Tracking epistemic beliefs and sensemaking in collaborative information retrieval

Collaborative information retrieval is an area of increasing interest. However, the wider sensemaking context in which it occurs is understudied. Furthermore, the role of users’ beliefs about the domain they are querying, its structure, stability, complexity, and justifications – their epistemic beliefs – has been little studied in either individual or collaborative IR software development contexts. Here we discuss a tool to combine the knowledge mapping tool Cohere, with reference management capabilities through which the iterative, epistemically germane, potentially (but not necessarily) collaborative IR process may be ‘brought out’ both for sensemaking, and research purposes