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Information, Incentives and Decentralized Decision-Making in a Bayesian Framework

G. Anandalingam, Kalyan Chatterjee and Jagdish S. Gangolly
The Journal of the Operational Research Society
Vol. 38, No. 6 (Jun., 1987), pp. 499-508
DOI: 10.2307/2582763
Stable URL: http://www.jstor.org/stable/2582763
Page Count: 10
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Information, Incentives and Decentralized Decision-Making in a Bayesian Framework
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Abstract

This paper examines the problem of choosing an organization structure for decision-making for two-level organizations under various information conditions. Decentralized decision-making (DDM), centralized decision-making with reporting by division (CDRD), and centralized decision-making (CDM) are considered. The information conditions pertain to the observability of states and actions. It is shown that if the party not making the decisions can infer the choice of action from the ex post knowledge of the state, then DDM and CDRD are essentially equivalent in that it is possible to devise optimal incentive schemes in both cases. If, however, the action choice cannot be inferred, then CDRD is at least as preferred as DDM by the centre (regulator). If ex post observation (or inference) of the state can be made only by the party making the decision, we show that the centre (regulator) prefers CDRD to DDM. For this case, we derive an incentive scheme which elicits truthful information from the division (regulated entity). Finally, the incentive schemes are applied to the problem of regulating an industry that pollutes the environment.

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