Information, incentives and decentralized decision-making in a bayesian framework

G. Anandalingam, Kalyan Chatterjee, Jagdish S. Gangolly

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)499-508
Number of pages10
JournalJournal of the Operational Research Society
Volume38
Issue number6
DOIs
StatePublished - Jun 1987

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

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