Forecasts from biased experts: a “meta‐credibility” problem

Michael R. Powers

Journal of Risk Finance, 2005, vol. 6, issue 1, 47-59

Abstract: Purpose - In forecasting unknown quantities, risk and finance decision makers often rely on one or more biased experts, statistical specialists representing parties with an interest in the decision maker's final forecast. This problem arises in a variety of contexts, and the decision maker may represent a corporate enterprise, rating agency, government regulator, etc. The purpose of the paper is to assist decision makers, experts, and others to have a better understanding of the dynamics of the problem, and to adopt strategies and practices that enhance efficiency. Design/methodology/approach - The problem is formulated as a two‐person, non‐cooperative Bayesian game with the decision maker and one expert as players, and perfect Bayesian equilibrium solutions are identified. Then the analysis is extended to variations of the game in which the expert's loss function is not common knowledge, and in which there are multiple experts. Findings - In the struggle for information between the decision maker and the experts, the experts generally benefit from greater uncertainty about the parameters of the model. Thus, in attempting to elicit as much information as possible from the experts, the decision maker must strive to minimize all sources of uncertainty. Research limitations/implications - As in most Bayesian games, the analysis requires that a variety of process assumptions and model parameters be common knowledge. These conditions may be difficult to satisfy in real‐world applications. Practical implications - The principal finding of the study is that there is truly a struggle for information between the decision maker and the experts. This generally encourages the experts to inject as much uncertainty as possible into the process. To counter this effect, the decision maker might: provide incentives for the experts to increase their sampling information; try to mitigate specific uncertainties regarding the model parameters; and try to increase the number of experts. Originality/value - This is the first paper to apply the framework of signaling games to the problem of eliciting information from biased experts. It is of value to decision makers, experts, and economic researchers.

Keywords: Financial forecasting; Bias; Bayesian statistical decision theory (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:15265940510581260

DOI: 10.1108/15265940510581260

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