A gaze through the lens of decision theory toward knowledge translation science

Research findings become evidence when an individual decides that the information is relevant and useful to a particular circumstance. Prior to that point, they are unrelated facts. For research translation to occur, research evidence needs filtering, interpretation, and application by individuals to the specific situation. For this reason, decision science is complementary to knowledge translation science. Both aim to support the individual in deciding the most appropriate action in a dynamic environment where there are masses of uncensored and nonprioritized information readily available. Decision science employs research theories to study the cognitive processes underpinning the filtering and integration of current scientific information into changing contexts. Two meta-theories, coherence and correspondence theories, have been used to provide alternative views and prompt significant debate to advance the science. The aim of this article is to stimulate debate about the relationship between decision theory and knowledge translation. Discussed is the critical role of cognition in clinical decision making, with a focus on knowledge translation. A critical commentary of the knowledge utilization modeling papers is presented from a decision science perspective. The article concludes with a discussion on the implications for knowledge translation when viewed through the lens of decision science.<br />

Resolving conflicts in fire management using decision theory: asset-protection versus biodiversity conservation

Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Evaluation of Bayesian belief networks for diagnosis of psychotic disorders

The thesis examined the inter-rater reliability and procedural validity of four computerised Bayesian belief networks (BBNs) which were developed to assist with the diagnosis of psychotic disorders. The results of this research indicated that BBNs can significantly improve diagnostic reliability and may represent an important advance over current diagnostic methods. The professional portfolio investigated, through the presentation of case studies and review of literature relevant to each case study, how comorbidity and context of depression may impact on cognitive behavioural therapy treatment.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Making conservation decisions under uncertainty for the persistence of multiple species

Population models for multiple species provide one of the few means of assessing the impact of alternative management options on the persistence of biodiversity, but<br />they are inevitably uncertain. Is it possible to use population models in multiple-speciesconservation planning given the associated uncertainties? We use information-gap decision theory to explore the impact of parameter uncertainty on the conservation decision when planning for the persistence of multiple species. An information-gap approach seeks robust outcomes that are most immune from error. We assess the impact of uncertainty in key model parameters for three species, whose extinction risks under four alternative management scenarios are estimated using a metapopulation model. Three methods are described for making conservation decisions across the species, taking into account uncertainty. We find that decisions based on single species are relatively robust to uncertainty in parameters, although the estimates of extinction risk increase rapidly with uncertainty. When identifying the best conservation decision for the persistence of all species, the methods that rely on the rankings of the management options by each species result in decisions that are similarly robust to uncertainty. Methods that depend on absolute values of extinction risk are sensitive to uncertainty, as small changes in extinction risk can alter the ranking of the alternative scenarios. We discover that it is possible to make robust conservation decisions even when the uncertainties of the multiple-species problem appear overwhelming. However, the decision most robust to uncertainty is likely to differ from the best decision when uncertainty is ignored, illustrating the importance of incorporating uncertainty into the decision-making process.

Corporate social responsibility in professional team sport organisations : towards a theory of decision-making

<b>Research question:&nbsp;</b><div>Corporate social responsibility (CSR) is increasingly important to&nbsp;business, including professional team sport organisations. Scholars focusing on CSR&nbsp;in sport have generally examined content-related issues such as implementation,&nbsp;motives or outcomes. The purpose of this paper is to add to that body of knowledge by&nbsp;focusing on process-related issues. Specifically, we explore the decision-making&nbsp;process used in relation to CSR-related programmes in the charitable foundations of&nbsp;the English football clubs.<br /><br /><b>Research methods: <br /></b>Employing a grounded theory method and drawing on the&nbsp;analysis and synthesis of 32 interviews and 25 organisational documents, this research&nbsp;explored managerial decision-making with regard to CSR in English football.<br /><br /><b>Results and findings: <br /></b>The findings reveal that decision-making consists of four&nbsp;simultaneous micro-social processes (&lsquo;harmonising&rsquo;, &lsquo;safeguarding&rsquo;, &lsquo;manoeuvring&rsquo;&nbsp;and &lsquo;transcending&rsquo;) that form the platform upon which the managers in the charitable&nbsp;foundations of the English football clubs make decisions. These four micro-social&nbsp;processes together represent assessable transcendence; a process that is fortified by&nbsp;passion, contingent on trust, sustained by communication and substantiated by factual&nbsp;performance enables CSR formulation and implementation in this organisational&nbsp;context.<br /><br /><b>Implications: <br /></b>The significance of this study for the sport management literature is&nbsp;threefold: (1) it focuses on the individual level of analysis, (2) it shifts the focus of the&nbsp;scholarly activity away from CSR content-based research towards more processoriented&nbsp;approaches and (3) it adds to the limited number of studies that have utilised&nbsp;grounded theory in a rounded manner.</div>

Consensual commitment: a grounded theory of the meso-level influence of organizational design on leadership and decision-making

This research examined the relationship between organizational design and leadership in decision-making teams. It used a grounded theory-based qualitative research design. The validity of the research was enhanced by data triangulation, wherein quantitative psychometric data augmented the qualitative data that are traditionally used. The research was based upon two organizations within the substantive setting of the knowledge industry. The higher order category of consensual commitment explained effective decision-making. At the meso-level of leadership modeling, organizational design influenced both leadership style and decision-making. Specifically, an organizational design that generated lateral job roles and a relational leadership orientation was found to enhance consensual commitment, and provided a level of assurance against dysfunctional team dynamics. &copy; 2009 Elsevier Inc. All rights reserved.

Judgement, decision, and integrity

Although Aristotle did not mention it, integrity can be understood in an Aristotelian framework. Seeing it in these terms will show that it is an executive virtue which concerns the existential well being of an agent. This analysis is not offered as an exegesis of Aristotle's text, but as an attempt to use an Aristotelian framework to understand a virtue deemed important today. This account will have the benefit of solving some problems relating to motivational internalism and, as such, will contribute to that recent current of thought which has been highlighting the importance of virtue thinking in moral theory. I will distinguish moral judgement from decision and show that moral judgement is dependent upon virtue more strongly than it is upon impartial rationality. I will suggest that integrity is the virtue to which moral judgement gives expression and is the virtue which links judgement to decision so as to overcome akrasia. <br />