informs ® doi 10.1287/mnsc.1050.0379

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.

Security critical systems must perform at the required security level, make effective use of available resources, and meet end-users expectations. Balancing these needs, and at the same time fulfilling budget and time-to-market 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 Aspect-Oriented Modeling (AOM) for a cost-benefit trade-off analysis of treatment strategies. AOM allows developers to model pervasive security treatments separately from other system functionality. This ease the trade-off by making it possible to swap treatment strategies in and out when computing Return on Security Investments (RoSI). The trade-off 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.

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 subjectmatter-expert colleagues. This paper reviews the state-of-the-art, 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.

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.

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 ...

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

Quantitative estimation of security attributes makes it possible to do cost-effective development of security critical systems. By predicting the impact and cost of potential misuses, as well as the cost and effect of security treatment strategies, one can treat security risks at the right time for the correct cost. The Aspect-Oriented Risk-Driven Development (AORDD) framework supports cost-effective development through its Bayesian Belief Network (BBN) based cost-benefit trade-off analysis and estimation repositories. Estimation of misuse and treatment strategy attributes is done using disparate information sources. The AORDD framework supports two types of information sources; empirical or observable data and expert opinions.

Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a data-sparse 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 risk-analytic cases the calculated expected value of information will be biased downward. The well-documented 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.

Geographic modeling of individual exposures using air pollution modeling techniques can help in both the design of environmental epidemiologic studies and in the assignment of measures that delineate regions that receive the highest exposure in space and time. Geographic modeling can help in the interpretation of environmental sampling data associated with airborne concentration or deposition, and can act as a sophisticated interpolator for such data, allowing values to be assigned to locations between points where the data have actually been collected. Recent advances allow for quantification of the uncertainty in a geographic model and the resulting impact on estimates of association, variability, and study power. In this paper we present the terminology and methodology of geographic modeling, describe applications to date in the field of epidemiology, and evaluate the potential of this relatively new tool.- Environ Health Perspect 1 07(Suppl 1):181-190 (1999).