Death of Paradox: The Killer Logic Beneath the Standards of Proof

The prevailing but contested view of proof standards is that factfinders should determine facts by probabilistic reasoning. Given imperfect evidence, they should ask themselves what they think the chances are that the burdened party would be right if the truth were to become known; they then compare those chances to the applicable standard of proof. I contend that for understanding the standards of proof, the modern versions of logic — in particular, fuzzy logic and belief functions — work better than classical probability. This modern logic suggests that factfinders view evidence of an imprecisely perceived and described reality to form a fuzzy degree of belief in a fact’s existence; they then apply the standard of proof in accordance with the theory of belief functions, by comparing their belief in a fact’s existence to their belief in its negation. This understanding explains how the standard of proof actually works in the law world. It gives a superior mental image of the factfinders’ task, conforms more closely to what we know of people’s cognition, and captures better what the law says its standards are and how it manipulates them. One virtue of this conceptualization is that it is not a radically new view. Another virtue is that it nevertheless manages to resolve some stubborn problems of proof, including the infamous conjunction paradox

Decision-making: a laboratory-based case study in conceptual design

The engineering design process may be seen as a series of interrelated operations that are driven by decisions: each operation is carried out as the consequence of an associated decision. Hence, an effective design process relies heavily upon effective decision-making. As a consequence, supporting decision-making may be a significant means for achieving design process improvements. This thesis concentrates on how to support selection-type decision-making in conceptual engineering design. [Continues.

Risk-based design for multidisciplinary complex systems

The ultimate goal of large-scale design organizations are mainly to reduce costs and improve reliability and performance of systems while assessing how much risk (cost, schedule, scope) they can take and still remain competitive. To achieve this goal they need to develop tools to reach the most preferred design product while reducing the time of decision making during the design process, time to market and total costs, as well as increasing reliability, safety, satisfactory, performance. In addition, they should understand attitudes toward risk; know where more information is needed and identify critical factors and assumptions underlying decisions to aid in the design and development cycle of complex systems. To address these needs, this research introduces design organizations can capture, assess, and efficiently and effectively communicate uncertainty through their design processes, and as a result, improve their capacity for delivering complex systems that meet cost, schedule, and performance objectives

Abduction, Explanation and Relevance Feedback

Selecting good query terms to represent an information need is difficult. The complexity of verbalising an information need can increase when the need is vague, when the document collection is unfamiliar or when the searcher is inexperienced with information retrieval (IR) systems. It is much easier, however, for a user to assess which documents contain relevant information. Relevance feedback (RF) techniques make use of this fact to automatically modify a query representation based on the documents a user considers relevant. RF has proved to be relatively successful at increasing the effectiveness of retrieval systems in certain types of search, and RF techniques have gradually appeared in operational systems and even some Web engines. However, the traditional approaches to RF do not consider the behavioural aspects of information seeking. The standard RF algorithms consider only what documents the user has marked as relevant; they do not consider how the user has assessed relevance. For RF to become an effective support to information seeking it is imperative to develop new models of RF that are capable of incorporating how users make relevance assessments. In this thesis I view RF as a process of explanation. A RF theory should provide an explanation of why a document is relevant to an information need. Such an explanation can be based on how information is used within documents. I use abductive inference to provide a framework for an explanation-based account of RF. Abductive inference is specifically designed as a technique for generating explanations of complex events, and has been widely used in a range of diagnostic systems. Such a framework is capable of producing a set of possible explanations for why a user marked a number of documents relevant at the current search iteration. The choice of which explanation to use is guided by information on how the user has interacted with the system---how many documents they have marked relevant, where in the document ranking the relevant documents occur and the relevance score given to a document by the user. This behavioural information is used to create explanations and to choose which type of explanation is required in the search. The explanation is then used as the basis of a modified query to be submitted to the system. I also investigate how the notion of explanation can be used at the interface to encourage more use of RF by searchers