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Expert Elicitation Method Selection Process and Method Comparison

Listed author(s):
  • Angela Dalton


  • Alan Brothers


  • Stephen Walsh


  • Paul Whitney


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    Research on integrative modeling has gained considerable attention in recent years and expert opinion has been increasingly recognized as an important data source and modeling contributor. However, little research has systematically compared and evaluated expert elicitation methods in terms of their ability to link with computational models that capture human behavior and social phenomena. In this paper, we describe a decision-making process we used for evaluating and selecting a task specific elicitation method within the framework of integrative computational social-behavioral modeling. From the existing literature, we identified the characteristics of problems that each candidate method is well suited to address. A small scale expert elicitation was also conducted to evaluate the comparative strength and weaknesses of the methods against a number of consensus-based decision criteria. By developing a set of explicit method evaluation criteria and a description characterizing decision problems for the candidate methods, we seek to gain a better understanding of the feasibility and costeffectiveness of integrating elicitation methods with computational modeling techniques. This serves an important first step toward expanding our research effort and trajectory toward greater interdisciplinary modeling research of human behavior.

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    Paper provided by University of Siena in its series Labsi Experimental Economics Laboratory University of Siena with number 030.

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    Date of creation: Sep 2010
    Handle: RePEc:usi:labsit:030
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    1. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    2. Dewispelare, Aaron R. & Herren, L. Tandy & Clemen, Robert T., 1995. "The use of probability elicitation in the high-level nuclear waste regulation program," International Journal of Forecasting, Elsevier, vol. 11(1), pages 5-24, March.
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