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36
Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues
, 1996
"... . A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the DempsterShafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections fo ..."
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Cited by 118 (10 self)
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. A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the DempsterShafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections focuses on one of these paradigms. It first introduces the basic concepts by showing how they can be applied to a relatively simple user modeling problem. It then surveys systems that have applied techniques from the paradigm to user or student modeling, characterizing each system within a common framework. The final main section discusses several aspects of the usability of these techniques for user and student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results. Key words: numerical uncertainty management, Bayesian networks, DempsterShafer theory, fuzzy logic, user modeling, student modeling 1. Introdu...
Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation
 Journal of Prediction Markets
, 2002
"... In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals. ..."
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Cited by 114 (5 self)
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In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals.
Talking Probabilities: Communicating Probabilistic Information With Words And Numbers
 International Journal of Approximate Reasoning
, 1999
"... The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide ..."
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Cited by 39 (5 self)
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The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. The use of verbal probability expressions as an additional method of eliciting probabilistic information may to some extent remove this obstacle. In this paper, we review studies that address the communication of probabilities in words and/or numbers. We then describe our own experiments concerning the development of a probability scale that contains words as well as numbers. This scale appears to be an aid for researchers and domain experts during the elicitation phase of building a belief network and might help users understand the output of the network.
Aggregating disparate estimates of chance
, 2004
"... We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We ad ..."
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Cited by 22 (5 self)
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We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.
Exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks
 In Proceedings of Sixteenth Conference on Uncertainty in Artificial Intelligence
, 2000
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Eliminating Incoherence from Subjective Estimates of Chance
 In: Proceedings of the 8th International Conference on the Principles of Knowledge Representation and Reasoning (KR
, 2002
"... Human judgment is an essential source of Bayesian probabilities but is plagued by incoherence when complex or conditional events are involved. We consider a method for adjusting estimates of chance over Boolean events so as to render them probabilistically coherent. The method works by searching for ..."
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Cited by 8 (5 self)
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Human judgment is an essential source of Bayesian probabilities but is plagued by incoherence when complex or conditional events are involved. We consider a method for adjusting estimates of chance over Boolean events so as to render them probabilistically coherent. The method works by searching for a sparse distribution that approximates a target set of judgments. (We show that sparse distributions suce for this purpose.) The feasibility of our method was tested by randomly generating sets of coherent and incoherent estimates of chance over 30 to 50 variables.
Coherent probability from incoherent judgment
 Journal of Experimental Psychology: Applied
, 2001
"... People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estim ..."
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Cited by 7 (4 self)
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People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estimates of chance. This method was applied to the chances of simple and complex meteorological events, as estimated by college undergraduates. No judge responded coherently, but the optimization method found close (coherent) approximations to their estimates. Moreover, the approximations were reliably more accurate than the original estimates, as measured by the quadratic scoring rule. Methods for correcting incoherence facilitate the analysis of expected utility and allow human judgment to be more easily exploited in the construction of expert systems. Suppose you think the probability that the Internet will expand next year is.90. Suppose you also think the probability that the Internet will expand and PC makers will be profitable is.91. Then you have assigned a greater chance to a conjunction rather than to one of its conjuncts; hence, your judgments are incoherent. You may, nonetheless, prove to be more insightful than someone with
Developing a methodology for eliciting subjective probability estimates during expert evaluations of safety interventions: application for bayesian belief networks, Aviation Human Factors Division, October, from hhttp:// www.humanfactors.uiuc.edui
, 2005
"... The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to deve ..."
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Cited by 5 (0 self)
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The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to develop methods for evaluating the potential safety benefit of each of these intervention products so that resources can be effectively invested to produce the biggest benefit to flight safety. Of interest to the present project is the process of using expert judgments to develop Bayesian Belief Networks (BBN’s) that model the potential impact that specific interventions may have. Specifically, the present report summarizes methodologies for improving the elicitation of probability estimates during expert evaluations of AvSP products for use in BBN’s. The work involved joint efforts between Professor James Luxhoj from Rutgers University and researchers at the University of Illinois. The Rutgers ’ project to develop BBN’s received funding by NASA under contract NAS103057 entitled &quot;Probabilistic Decision Support
Managerial Decision Making Under Risk and Uncertainty
"... Abstract—This paper focuses on managerial decision making under risk and uncertainty. Since no one, so far, has studied managers ´ risk attitudes in parallel with their actual behavior when handling risky prospects the area still remains relatively murky. Interviews have been done with 12 managers i ..."
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Cited by 5 (1 self)
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Abstract—This paper focuses on managerial decision making under risk and uncertainty. Since no one, so far, has studied managers ´ risk attitudes in parallel with their actual behavior when handling risky prospects the area still remains relatively murky. Interviews have been done with 12 managers in the Swedish forest industry concerning how they define risk, how they handle risk, how they make risky decisions, and how the organizational context affects the decisionmaking process. Problems that have been identified in this study are the lack of information and precise objective data, that risk and probability estimations made by the managers are often based on inadequate information and intuition, that no formal analysis is carried out, that no computer based decision tools are used in the decision making processes, and therefore most decisions are based on intuition and gut feeling. Index Terms—Risk taking, decision making, computer based decision tools.
Measure selection: Notions of rationality and representation independence
 Proceedings of the 14th conference on Uncertainty in Artificial Intelligence
, 1998
"... We take another look at the general problem of selecting a preferred probability measure among those that comply with some given constraints. The dominant role that entropy maximization has obtained in this context is questioned by arguing that the minimum information principle on which it is based ..."
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Cited by 4 (1 self)
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We take another look at the general problem of selecting a preferred probability measure among those that comply with some given constraints. The dominant role that entropy maximization has obtained in this context is questioned by arguing that the minimum information principle on which it is based could be supplanted by an at least as plausible “likelihood of evidence ” principle. We then review a method for turning given selection functions into representation independent variants, and discuss the tradeoffs involved in this transformation. setI(J) 1