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Incentive Mechanisms for Multi-agent Organizational Systems

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New Frontiers in Information and Production Systems Modelling and Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 98))

Abstract

This work considers game-theoretic models of incentive mechanisms for multi-agent organizational systems. Three main principles of optimal incentive scheme design for interacting agents are derived, namely, the principle of compensation, the principle of decomposition and the principle of aggregation. Models of agents’ self-coordination are explored in terms of side-payoff games. And finally, we study identification problems for agents’ preferences.

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Notes

  1. 1.

     In short, these three scientific schools differ in the following. TC analyzes incentives under stochastic uncertainty when an agent is risk-averse (i.e., has a concave utility function). In TAS, agents are typically considered risk-neutral, but much attention belongs to incentives’ compatibility and applications. And finally, THG focuses mostly on mathematical aspects and dynamic models.

  2. 2.

     Recall that a DSE is an action vector such that each agent benefits from choosing a corresponding component regardless of the actions chosen by the rest agents.

  3. 3.

     A Nash equilibrium is an action vector such that each agent benefits from choosing a corresponding component provided that the rest agents choose equilibrium actions.

  4. 4.

     No doubt, it may happen that the answers of respondents mismatch the reality: in real-life conditions, respondents can choose other actions than they report during questioning. A separate issue concerns the truthfulness of their answers. Being active, respondents can demonstrate strategic behavior and manipulate information. For instance, if agents know that managerial decisions affecting their interests will be made based on their answers, they can report untrue information to guarantee most beneficial decisions. Analysis of deliberate and purposeful manipulation of procedures makes the subject of separate (perhaps, extremely promising) research, but goes beyond the scope of this work.

  5. 5.

     This simplifying assumption eliminates from further consideration the problems of agent’s decision-making on hiring, firing, job hopping and hunting, etc. Moreover, in most real situations an employee is unable to choose working time independently or selects it from a short range.

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Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Profsojuznaya st., 65, Moscow, 117997, Russia

    D. A. Novikov

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  1. D. A. Novikov
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Correspondence to D. A. Novikov .

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Editors and Affiliations

  1. Faculty of Computer Science and Information Systems, West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Przemysław Różewski

  2. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    Dmitry Novikov

  3. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    Natalia Bakhtadze

  4. Warsaw School of Computer Science, Warszawa, Poland

    Oleg Zaikin

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Novikov, D.A. (2016). Incentive Mechanisms for Multi-agent Organizational Systems. In: Różewski, P., Novikov, D., Bakhtadze, N., Zaikin, O. (eds) New Frontiers in Information and Production Systems Modelling and Analysis. Intelligent Systems Reference Library, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-319-23338-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-23338-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23337-6

  • Online ISBN: 978-3-319-23338-3

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