Promotion of Selfish Agents in Hierarchical Organisations

  • Suzanne Sadedin
  • Christian Guttmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6069)


In hierarchical organisations, a preferred outcome is to promote a more productive worker to a more influential status. However, productivity is rarely directly measurable, so an individual worker often has both motive and opportunity to misrepresent his productivity. This leads to an alternative possibility: the promotion of selfish individuals. We use an agent-based model to study how selfishness and competency of agents influence their promotion in hierarchical organisations. We consider the case where selfish agents can overstate their productivity and thus obtain undeserved promotions. Our results suggest that more productive agents reach positions of power most of the time. However, even under ideal conditions, selfish agents occasionally dominate the higher levels of a hierarchical organisation, which in turn has a dramatic effect on all lower levels. For organisations of around 100-10,000 employees with 3-4 hierarchy levels, on average, the promotion of selfish agents is minimized and the promotion of competent agents is maximized. Finally, we show that judging the productivity of an individual agent has a greater impact on promoting selfish behaviour than judging the productivity of an individual’s team. These results illustrate that agent-based models provide a powerful framework for examining how local interactions contribute to the large-scale properties of multi-layered organisations.


Human Resource Management Multiagent System Hierarchical Organisation Boolean Network Human Resource Management Practice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderegg, L., Eidenbenz, S.: Ad hoc-VCG: A truthful and cost-efficient routing protocol for mobile ad hoc networks with selfish agents. In: Proceedings of the nineth annual international conference on Mobile computing and networking, pp. 245–259 (2003)Google Scholar
  2. Anshelevich, E., Dasgupta, A., Tardos, E., Wexler, T.: Near-optimal network design with selfish agents. In: Proceedings of the thirty-fifth annual ACM symposium on Theory of computing, pp. 511–520 (2003)Google Scholar
  3. Axelrod: The Evolution of Cooperation. Basic Books, New York (1988)Google Scholar
  4. Bond, A.H., Gasser, L.: Distributed Artificial Intelligence. Morgan Kaufmann publishers Inc., San Francisco (1988)Google Scholar
  5. Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K.: Designing, implementing and updating performance measurement systems. International Journal of Operations and Production Management, 754–771 (2000)Google Scholar
  6. Castelfranchi, C.: Trust and Deception in Virtual Societies. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  7. Castelfranchi, C.: The Role of Trust and Deception in Virtual Societies. International Journal of Electronic Commerce 6(3), 55–70 (2002)Google Scholar
  8. Dastani, M., Dignum, V., Dignum, F.: Organizations and normative agents. LNCS, pp. 982–989. Springer, Heidelberg (2002)Google Scholar
  9. Dess, G.G., Robinson, R.B.J.: Measuring Organizational Performance in the Absence of Objective Measures: The Case of the Privately-Held Firm and Conglomerate Business Unit. Strategic Management Journal, 265–273 (1984)Google Scholar
  10. Dignum, V., Meyer, J., Weigand, H., Dignum, F.: An organization-oriented model for agent societies. In: Proceedings of RASTA, at AAMAS02 (2002)Google Scholar
  11. Garrido, L., Sycara, K., Brena, R.: Quantifying the utility of building agents models: An experimental study. In: Agents-00/ECML-00 Workshop on Learning Agents, Barcelona, Spain (2000)Google Scholar
  12. Gmytrasiewicz, P.J., Durfee, E.H.: Rational communication in multi-agent environments. Autonomous Agents and Multi-Agent Systems 4(3), 233–272 (2001)CrossRefGoogle Scholar
  13. Green, D., Leishman, T., Sadedin, S.: The emergence of social consensus in simulation studies with boolean networks. In: PAAA World Congress on Social Simulation, Kyoto, Japan (2006)Google Scholar
  14. Green, D., Sadedin, S., Leishman, T.: Systems theory - self-organization. Encyclopedia of Ecology 4, 3195–3203 (2008)CrossRefGoogle Scholar
  15. Guest, D.E.: Human resource management and performance: a review and research agenda. The International Journal of Human Resource Management 8, 263–276 (1997)CrossRefGoogle Scholar
  16. Guttmann, C.: Collective Iterative Allocation. PhD thesis, Monash University (2008)Google Scholar
  17. Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. The Knowledge Engineering Review 19(04), 281–316 (2005)CrossRefGoogle Scholar
  18. Jansen, V.A.A., van Baalen, M.: Altruism through beard chromodynamics. Nature 440, 663–666 (2006)CrossRefGoogle Scholar
  19. Jensen, M., Murphy, K.: Performance pay and top-management incentives. J. Political Economy 98(1), 225 (1990)Google Scholar
  20. Kacperski, K., Holyst, J.: Phase transitions as a persistent feature of groups with leaders in models of opinion formation. Physica A 287, 631 (2000)CrossRefGoogle Scholar
  21. Kanter, R.M., Summers, D.: Doing Well While Doing Good: Dilemmas of Performance Measurement in Nonprofit Organizations and the Need for a Multipleconstituency Approach. In: McKevitt, D., Lawton, A. (eds.) Public Sector Management: Theory, Critique and Practice, New York State, United States of America, pp. 261–262. Open University Press, Stony Stratford (1987)Google Scholar
  22. Kok, J.R., Vlassis, N.: Mutual modeling of teammate behavior. Technical Report UVA-02-04, Computer Science Institute, University of Amsterdam, Netherland (2001)Google Scholar
  23. Matson, E., DeLoach, S.: Formal transition in agent organizations. In: Integration of Knowledge Intensive Multi-Agent Systems, 2005, pp. 235–240 (2005)Google Scholar
  24. Nisan, N.: Algorithms for Selfish Agents Mechanism Design for Distributed Computation. In: Meinel, C., Tison, S. (eds.) STACS 1999. LNCS, vol. 1563, p. 1. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  25. Plewczynski, D.: Landau theory of social clustering. Physica A 261, 608 (1998)CrossRefGoogle Scholar
  26. Ramamoorthy, N., Carroll, S.: Individualism/Collectivism Orientations and Reactions Toward Alternative Human Resource Management Practices. Human Relations 51, 571–588 (1998)Google Scholar
  27. Ronen, A.: Solving Optimization Problems among Selfish Agents. PhD thesis, Hebrew University in Jerusalem, Israel (2000)Google Scholar
  28. Sergot: Modelling unreliable and untrustworthy agent behaviour. In: Keplicz, B.D., Jankowski, A., Skowron, A., Szczuka, M. (eds.) International workshop on monitoring, security, and rescue technique in multiagent systems, Plock, Poland, pp. 161–177. Springer, Berlin (2005)CrossRefGoogle Scholar
  29. Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers 29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  30. Stone, P., Riley, P., Veloso, M.M.: Defining and using ideal teammate and opponent agent models. In: Proceedings of the Innovative Applications of Artificial Intelligence Conference (IAAI), pp. 1040–1045 (2000)Google Scholar
  31. Stumpf, S., London, M.: Management promotions: Individual and organizational factors influencing the decision process. The Academy of Management Review 6(4), 539–549 (1981)CrossRefGoogle Scholar
  32. Vassileva, J., McCalla, G.I., Greer, J.E.: Multi-agent multi-user modeling in I-Help. User Modeling and User-Adapted Interaction 13(1-2), 179–210 (2003)CrossRefGoogle Scholar
  33. Zambonelli, F., Jennings, N., Wooldridge, M.: Organizational abstractions for the analysis and design of multi-agent systems. In: Ciancarini, P., Wooldridge, M.J. (eds.) AOSE 2000. LNCS, vol. 1957, pp. 235–251. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Suzanne Sadedin
    • 1
  • Christian Guttmann
    • 2
  1. 1.Clayton School of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Department of General Practice Faculty of MedicineNursing and Health Sciences Monash UniversityMelbourneAustralia

Personalised recommendations