Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published October 4, 2017 | Submitted
Report Open

Incentive Compatibility in Risk Assessment Mechanisms

Page, Talbot

Abstract

This paper defines a risk assessment mechanism and compares its incentive properties with those of deterministic incentive mechanisms, particularly the Groves mechanism. Many risk assessments involve prediction for rare or unique events; in such cases there is limited opportunity for feedback and evaluation of the assessment process. To develop a feedback mechanism, the paper requires assessments to be made for indicator events, linked to the rare or unique events of ultimate interest. Assessments are made by several assessors, or assessment techniques, acting in competition. The feedback mechanism is a transfer function based on the probability assessments of all the assessors and the outcome of the indicator event. The incentive properties of risk assessment mechanisms are in some ways similar to those for deterministic mechanisms and in some ways quite different. The paper defines one risk assessment mechanism that looks like a Groves mechanism: it directly reveals probability and for risk neutral assessors has an unbiased or truthful dominant strategy which is discontinuous and which cannot solve the budget problem. The paper also defines a class of risk assessment mechanisms which do not look like a Groves mechanism; mechanisms in this class have unbiased dominant strategies which are continuous and which do solve the budget problem.

Additional Information

Revised. Original dated to December 1981. This research was supported by the National Science Foundation and by the Mellon Foundation. I would like to thank Richard McKelvey, John Ferejohn, Ed Green, Joshua Foreman, and Jim Gerard for many helpful comments, and especially to thank Gib Bogle for programming the Monte Carlo simulation.

Attached Files

Submitted - sswp409_-_revised.pdf

Files

sswp409_-_revised.pdf
Files (644.0 kB)
Name Size Download all
md5:da57b6934c7d71282259daeeb45c7baf
644.0 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
January 14, 2024