This dissertation addresses the question of choosing the best information alternative in engineering decisions. The decision maker maximizes his expected utility under uncertainty where both the action he takes and the state of the environment determines the payoff earned. The decision maker has an opportunity to gather information about the decision environment a priori at a certain cost. There might be different information alternatives, and the decision maker has to determine which alternative offers "better" prospects for improving the decision. Any decision environment that is characterized by a finite number of outcomes and a discrete probability distribution over the set of outcomes is a lottery. We analyze the value of information on a single outcome and determine the attributes in each piece of information that maximizes its value. Information is valuable when the decision is changed after gathering information. We show that if the number of optimal actions taken under different outcomes scenarios is finite, the decision maker does not require the perfect information. Further, we analyze the relation between the value of information and its determinants, and show a monotonic relation exists for a restricted class of information bundles and utility functions. We use different approaches to evaluate information and analyze the cases where preference reversals occur between different approaches. We observe that a priori pricing of information does not necessarily induce the same ranking with the expected utility approach, however both approaches agree on whether a given piece of information is valuable or not. The second part of this dissertation evaluates information in both static and dynamic coinsurance problems. In static insurance decisions, we analyze the case where the decision maker gathers information about the severity of the risk events and perform ranking of information bundles in a specific class. In dynamic insurance problems, we make a case study to analyze different physical risks that the production facilities are exposed to. The information in dynamic insurance problems involves more detail with regard to the timing of the multiple risk events. We observe that information on events that pose relatively good scenarios for the decision maker have value, however, their value may diminish as their probability of occurance decreases. The decision maker purchases more information as the profitability of the product increases and less information as the initial wealth increases. Furthermore, the decrease cost of insurance does not necessarily make information more valuable as the value is directly related to the change in the decisions rather than the cost of taking a specific action.

It is widely acknowledged that design (and development) teams increasingly include participants from different domains who must explore and integrate their specialized knowledge in order to create innovative and competitive artefacts and reduce design and development costs. Thus communication, integration of specialized knowledge, and negotiation of differences among domain specialists has emerged as a fundamental component of the design process. This paper presents thirteen communication roles that emerged during four multi-disciplinary design situations in the USA and Europe. These roles supported knowledge exploration and integration, collaboration, and task and project completion by filtering and providing information and negotiating differences across organizational, task, discipline, and personal boundaries. Implications for design methods, tools and education are discussed.

Utilization of complex statistical data has come at great cost to individual researchers, the information community, and to the national information infrastructure. Dissatisfaction with the traditional approach to information system design and information services provision, and, by implication, the theoretical bases on which these systems and services have been developed has led librarians and information scientists to propose that information is a user construct and therefore system designs should place greater emphasis on user-centered approaches. This article extends Dervinâ s and Morris's theoretical framework for designing effective information services by synthesizing and integrating theory and research derived from multiple approaches in the social and behavioral sciences. These theoretical frameworks are applied to develop general design strategies and principles for information systems and services that rely on complex statistical data. The focus of this article is on factors that contribute to error in the production of high quality scientific output and on failures of communication during the process of data production and data utilization. Such insights provide useful frameworks to diagnose, communicate, and learn from error. Strategies to design systems that support communicative competence and cognitive competence emphasize the utilization of information systems in a user centered learning environment. This includes viewing cognition as a generative process and recognizing the continuing interdependence and active involvement of experts, novices, and technological gatekeepers.

This article chronicles more than 30 years of public opinion, politics, and law and policy on privacy and confidentiality that have had far-reaching consequences for access by the social research community to administrative and statistical records produced by government. A hostile political environment, public controversy over the decennial census long form, media coverage, and public fears about the vast accumulations of personal information by the private sector were catalysts for a recent proposal by the U.S. Bureau of the Census that would have significantly altered the contents of the 2000 census Public Use Microdata Sample (PUMS). These events show clearly that science does not operate independently from the political sphere but may be transformed by a political world where powerful interests lead government agencies to assume responsibility for privacy protection that can result in reducing access to statistical data.

As the richness and diversity of information available to us in our everyday lives has expanded, so the need to manage this information grows. The lack of effective information management tools has given rise to what is colloquially known as the information overload problem. Intelligent agent technologies have been explored to develop personalised tools for autonomous information retrieval (IR). However, these so-called adaptive information agents are still primitive in terms of their learning autonomy, inference power, and explanatory capabilities. For instance, users often need to provide large amounts of direct relevance feedback to train the agents before these agents can acquire the users' specific information requirements. Existing information agents are also weak in dealing with the serendipity issue in IR because they cannot infer document relevance with respect to the possibly related IR contexts. This thesis exploits the theories and technologies from the fields of Information Retrieval (IR), Symbolic Artificial Intelligence and Intelligent Agents for the development of the next generation of adaptive information agents to alleviate the problem of information overload. In particular, the fundamental issues such as representation, learning, and classjfication (e.g., classifying documents as relevant or not) pertaining to these agents are examined. The design of the adaptive information agent model stems from a basic intuition in IR. By way of illustration, given the retrieval context involving a science student, and a query "Java", what information items should an intelligent information agent recommend to its user? The agent should recommend documents about "Computer Programming" if it believes that its user is a computer science student and every computer science student needs to learn programming. However, if the agent later discovers that its user is studying "volcanology", and the agent also believes that volcanists are interested in the volcanos in Java, the agent may recommend documents about "Merapi" (a volcano in Java with a recent eruption in 1994). This scenario illustrates that a retrieval context is not only about a set of terms and their frequencies but also the relationships among terms (e.g., java Λ science → computer, computer → programming, java Λ science Λ volcanology → merapi, etc.) In addition, retrieval contexts represented in information agents should be revised in accordance with the changing information requirements of the users. Therefore, to enhance the adaptive and proactive IR behaviour of information agents, an expressive representation language is needed to represent complex retrieval contexts and an effective learning mechanism is required to revise the agents' beliefs about the changing retrieval contexts. Moreover, a sound reasoning mechanism is essential for information agents to infer document relevance with respect to some retrieval contexts to enhance their proactiveness and learning autonomy. The theory of belief revision advocated by Alchourrón, Gärdenfors, and Makinson (AGM) provides a rigorous formal foundation to model evolving retrieval contexts in terms of changing epistemic states in adaptive information agents. The expressive power of the AGM framework allows sufficient details of retrieval contexts to be captured. Moreover, the AGM framework enforces the principles of minimal and consistent belief changes. These principles coincide with the requirements of modelling changing information retrieval contexts. The AGM belief revision logic has a close connection with the Logical Uncertainty Principle which describes the fundamental approach for logic-based IR models. Accordingly, the AGM belief functions are applied to develop the learning components of adaptive information agents. Expectationinference which is characterised by axioms leading to conservatively monotonic IR behaviour plays a significant role in developing the agents' classification components. Because of the direct connection between the AGM belief functions and the expectation inference relations, seamless integration of the information agents' learning and classification components is made possible. Essentially, the learning functions and the classification functions of adaptive information agents are conceptualised by and q d respectively. This conceptualisation can be interpreted as: (1) learning is the process of revising the representation K of a retrieval context with respect to a user's relevance feedback q which can be seen as a refined query; (2) classification is the process of determining the degree of relevance of a document d with respect to the refined query q given the agent's expectation (i.e., beliefs) K about the retrieval context. At the computational level, how to induce epistemic entrenchment which defines the AGM belief functions, and how to implement the AGM belief functions by means of an effective and efficient computational algorithm are among the core research issues addressed. Automated methods of discovering context sensitive term associations such as (computer → programming) and preclusion relations such as (volcanology ⁄→ programming) are explored. In addition, an effective classification method which is underpinned by expectation inference is developed for adaptive information agents. Last but not least, quantitative evaluations, which are based on well-known IR bench-marking processes, are applied to examine the performance of the prototype agent system. The performance of the belief revision based information agent system is compared with that of a vector space based agent system and other adaptive information filtering systems participated in TREC-7. As a whole, encouraging results are obtained from our initial experiments.

Geographic Information Retrieval (GIR) systems provide users with functionalities of representation, storage, organisation of and access to various types of electronic information resources based on their textual and geographic context. This thesis explores various aspects of the development, evaluation and application of GIR systems. The first study focuses upon the extraction and grounding of geographic information entities. My approach for this study consists of a hierarchical structure-based geographic relationship model that is used to describe connections between geographic information entities, and a supervised machine learning algorithm that is used to resolve ambiguities. The proposed approach has been evaluated on a toponym disambiguation task using a large collection of news articles. The second study details the development and validation of a GIR ranking mechanism. The proposed approach takes advantage of the power of the Genetic Programming (GP) paradigm with the aim of finding an optimal functional form that integrates both textual and geographic similarities between retrieved documents and a given user query. My approach has been validated by applying it to a large collection of geographic metadata documents. The third study addresses the problem of modelling the GIR retrieval process that takes into account both thematic and geographic criteria. Based on the Spreading Activation Network (SAN), the proposed model consists a two-layer associative network that is used to construct a structured search space; a constrained spreading activation algorithm that is used to retrieve and to rank relevant documents; and a geographic knowledge base that is used to provide necessary domain knowledge for network. The retrieval performance of my model has been evaluated using the GeoCLEF 2006 tasks. The fourth study discusses the publishing, browsing and navigation of geographic information on the World Wide Web. Key challenges in designing and implementing of a GIR user interface through which online content can be systematically organised based on their geospatial characteristics, and can be efficiently accessed and interrelated, are addressed. The effectiveness and the usefulness of the system are shown by applying it to a large collection of geo-tagged web pages.

Thesis (Ph.D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 177-192).

Thesis (Ph.D.)--Syracuse University, 2008. / "Publication number: AAT 3323071."

Thesis (Doctor of Library Science)--Columbia University, 1992. / Cover title. Includes bibliographical references (p. 216-224).

Thesis (Ph. D.)--University of California, Los Angeles, 1992. / Vita. Includes bibliographical references (leaves 288-300).