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121
Eliciting Informative Feedback: The PeerPrediction Method
 Management Science
, 2005
"... informs ® doi 10.1287/mnsc.1050.0379 ..."
Statistical Methods for Eliciting Probability Distributions
 Journal of the American Statistical Association
, 2005
"... Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting the experience of statisticia ..."
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Cited by 39 (2 self)
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Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively, and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful, i.e. what criteria should be employed? Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true ” in some objectivistic sense, and cannot be judged that way. We see elicitation as simply part of the process of statistical modeling. Indeed in a hierarchical model it is ambiguous at which point the likelihood ends and the prior begins. Thus the same kinds of judgment that inform statistical modeling in general also inform elicitation of prior distributions.
Deciding Between Competition and Collusion
 Review of Economics and Statistics
"... Abstract—We develop an approach to identify and test for bid rigging in procurement auctions. First, we introduce a general auction model with asymmetric bidders. Second, we study the problem of identi � cation in our model. We state a set of conditions that are both necessary and suf � cient for a ..."
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Cited by 37 (4 self)
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Abstract—We develop an approach to identify and test for bid rigging in procurement auctions. First, we introduce a general auction model with asymmetric bidders. Second, we study the problem of identi � cation in our model. We state a set of conditions that are both necessary and suf � cient for a distribution of bids to be generated by a model with competitive bidding. Third, we discuss how to elicit a prior distribution over a � rm’s structural cost parameters from industry experts. Given this prior distribution, we use Bayes’s theorem to compare competitive and collusive models of industry equilibrium. Finally, we apply our methodology to a data set of bidding by construction � rms in the Midwest. The techniques we propose are not computationally demanding, use � exible functional forms, and can be programmed using most standard statistical packages. I.
Water Quality Prediction and Probability Network Models
 Canadian Journal of Fisheries and Aquatic Sciences
, 1998
"... It is a common strategy in surface water quality modeling to attempt to remedy predictive inadequacies by incorporating additional mechanistic detail into the model. This approach reflects the reasonable belief that enhanced scientific understanding of basic processes can be used to improve predicti ..."
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Cited by 23 (4 self)
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It is a common strategy in surface water quality modeling to attempt to remedy predictive inadequacies by incorporating additional mechanistic detail into the model. This approach reflects the reasonable belief that enhanced scientific understanding of basic processes can be used to improve predictive modeling. However, nature is complex, and even the most detailed simulation model is extremely simple in comparison. At some point, additional detail exceeds our ability to simulate and predict with reasonable error levels. In those situations, an attractive alternative may be to express the complex behavior probabilistically, as in statistical mechanics, for example. This viewpoint is the basis for consideration of Bayesian probability networks for surface water quality assessment and prediction. To begin this examination of Bayes nets, some simple water quality examples are used for the illustration of basic ideas. This is followed by discussion of a set of proposed probability network ...
Subjective probability assessment in decision analysis: Partition dependence and bias toward the ignorance prior, Management Science
, 2005
"... doi 10.1287/mnsc.1050.0409 ..."
Costbenefit tradeoff analysis using bbn for aspectoriented riskdriven development
 Accepted for the International Conference on Engineering of Complex Computer System (ICECCS2005) in
, 2005
"... Security critical systems must perform at the required security level, make effective use of available resources, and meet endusers expectations. Balancing these needs, and at the same time fulfilling budget and timetomarket constraints, requires developers to design and evaluate alternative secu ..."
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Cited by 19 (12 self)
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Security critical systems must perform at the required security level, make effective use of available resources, and meet endusers expectations. Balancing these needs, and at the same time fulfilling budget and timetomarket constraints, requires developers to design and evaluate alternative security treatment strategies. In this paper, we present a development framework that utilizes Bayesian Belief Networks (BBN) and AspectOriented Modeling (AOM) for a costbenefit tradeoff analysis of treatment strategies. AOM allows developers to model pervasive security treatments separately from other system functionality. This ease the tradeoff by making it possible to swap treatment strategies in and out when computing Return on Security Investments (RoSI). The tradeoff analysis is implemented using BBN, and RoSI is computed by estimating a set of variables describing properties of a treatment strategy. RoSI for each treatment strategy is then used as input to choice of design.
Evaluating and combining subjective probability estimates
 Journal of Behavioral Decision Making
, 1997
"... This paper concerns the evaluation and combination of subjective probability estimates for categorical events. We argue that the appropriate criterion for evaluating individual and combined estimates depends on the type of uncertainty the decision maker seeks to represent, which in turn depends on h ..."
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Cited by 18 (4 self)
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This paper concerns the evaluation and combination of subjective probability estimates for categorical events. We argue that the appropriate criterion for evaluating individual and combined estimates depends on the type of uncertainty the decision maker seeks to represent, which in turn depends on his or her model of the event space. Decision makers require accurate estimates in the presence of aleatory uncertainty about exchangeable events, diagnostic estimates given epistemic uncertainty about unique events, and some combination of the two when the events are not necessarily unique, but the best equivalence class de®nition for exchangeable events is not apparent. Following a brief reveiw of the mathematical and empirical literature on combining judgments, we present an approach to the topic that derives from (1) a weak cognitive model of the individual that assumes subjective estimates are a function of underlying judgment perturbed by random error and (2) a classi®cation of judgment contexts in terms of the underlying information structure. In support of our developments, we present new analyses of two sets of subjective probability estimates, one of exchangeable and the other of unique events. As predicted, mean estimates were more accurate than the individual values in the ®rst case and more diagnostic in
Reliability in information fusion: literature survey
 IN THE PROC. OF THE 7TH INTL. CONFERENCE ON INFORMATION FUSION
, 2004
"... Abstract The success of information fusion is defined by the quality of knowledge produced by fusion processes, with the latter in turn depending on how well data are represented, how reliable and adequate the model of data uncertainty used, and how accurate and appropriate or applicable prior know ..."
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Cited by 16 (0 self)
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Abstract The success of information fusion is defined by the quality of knowledge produced by fusion processes, with the latter in turn depending on how well data are represented, how reliable and adequate the model of data uncertainty used, and how accurate and appropriate or applicable prior knowledge is. The majority of fusion operators is based on optimistic assumptions about reliability of sources and presumes that they are all reliable. At the same time, different sources may have different reliability and it is necessary to account for this fact to avoid decreasing in performance of fusion results. The objective of this paper is to discuss the principal concepts and strategies of incorporating reliability into classical fusion operators and to provide an overview of the main approaches used in the fusion literature.
Image Database Exploration: Progress and Challenges
 In Proc. 1993 Knowledge Discovery in Databases Workshop
, 1993
"... In areas aa diverse as remote sensing, astronomy, and medical imaging, image acquisit~ion technology has undergone tremendous improvements in recent years in terms of imaging resolution, hardware miniaturization, and computational speed. For example, current and future nearearth and planetary obser ..."
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Cited by 15 (0 self)
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In areas aa diverse as remote sensing, astronomy, and medical imaging, image acquisit~ion technology has undergone tremendous improvements in recent years in terms of imaging resolution, hardware miniaturization, and computational speed. For example, current and future nearearth and planetary observation systems will return vast amounts of scientific data, a potential treasuretrove for scientific investigation and analysis. Unfortunately, advances in our ability to deal with this volume of data in an effective manner have not paralleled the hardware gains. While specialpurpose tools for particular applications exist, there is a dearth of useful generalpurpose software tools and algorithms which can assist a scientist in exploring large scientific image databases. At JPL we are currently developing interactive semiautomated image database exploration tools based on pattern recognition and machine learning technology. In this paper we discuss the general problem of automated image databasc exploration, the particular aspects,of image databases which distinguish them from other databases, and how this impacts the application of offtheshelf learning algorithms to problems of this nature. Current progress will be illustrated using two largescale image exploration projects at JPL. The paper concludes with a discussion of current and future challenges.
The expected value of information and the probability of surprise
 Risk Anal
, 1999
"... Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a datasparse situation: available data are only partially relevant to the parameter of interest, so one needs to adjust empirical distributions, use explicit judgmental distributions, or colle ..."
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Cited by 12 (1 self)
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Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a datasparse situation: available data are only partially relevant to the parameter of interest, so one needs to adjust empirical distributions, use explicit judgmental distributions, or collect new data. In determining whether or not to collect additional data, whether by measurement or by elicitation of experts, it is useful to consider the expected value of the additional information. The expected value of information depends on the prior distribution used to represent current information; if the prior distribution is too narrow, in many riskanalytic cases the calculated expected value of information will be biased downward. The welldocumented tendency toward overconfidence, including the neglect of potential surprise, suggests this bias may be substantial. We examine the expected value of information, including the role of surprise, test for bias in estimating the expected value of information, and suggest procedures to guard against overconfidence and underestimation of the expected value of information when developing prior distributions and when combining distributions obtained from multiple experts. The methods are illustrated with applications to potential carcinogens in food, commercial energy demand, and global climate change. KEY WORDS: Probability; uncertainty; data; risk assessment. 1.