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Incentive Design and Manager Performances: an ABM Approach

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  • Concetta Sorropago

    () (University of Roma "Tor vergata", Italy)

Abstract

We present a simplified model to provide a virtual laboratory to test the effects of the use of different performance evaluation measures to design manager's incentives in a project-based professional service organization. Our company's owner has to cope with the scheduling of multiple resource constraint projects in real time (RCMPSP), and with the design of the production manager incentive, whose variable wage is tied to some measures of the performance, which are proxies of the original owner's goal. We propose an agent based model approach where the agents' intelligence lies in the choice of the scheduling sequences. A discrete event simulator (DES) executes the projects, allocating in real time, the limited resources available. A Genetic Algorithm, evolving the sequence, randomly generated, uses the DES to simulate the effect and ranks the solutions. In this way, we investigate the incentive alignment problem as a resource allocation problem, comparing the results deriving from their respective "good solutions".

Suggested Citation

  • Concetta Sorropago, 2012. "Incentive Design and Manager Performances: an ABM Approach," Working papers 008, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    • Handle: RePEc:tur:wpapnw:008
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    File URL: http://www.biblioecon.unito.it/biblioservizi/RePEc/tur/wpapnw/m8.pdf
    File Function: First version, 2012
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    References listed on IDEAS

    as
    1. George Baker, 2000. "The Use of Performance Measures in Incentive Contracting," American Economic Review, American Economic Association, vol. 90(2), pages 415-420, May.
    2. Canice Prendergast, 1999. "The Provision of Incentives in Firms," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 7-63, March.
    3. Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337 Elsevier.
    4. Andreas Pyka & Claudia Werker, 2009. "The Methodology of Simulation Models: Chances and Risks," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-1.
    5. Macho-Stadler, Ines & Perez-Castrillo, J. David, 2001. "An Introduction to the Economics of Information: Incentives and Contracts," OUP Catalogue, Oxford University Press, edition 2, number 9780199243273.
    6. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Complex System Dynamics; Resource constrained multi project scheduling; Incentive Design; Performance Evaluation Measures; Genetic Algorithm;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation

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