Skip to main content
SpringerLink
Log in
Book cover

International Conference on Practical Applications of Agents and Multi-Agent Systems

PAAMS 2023: Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection pp 40–51Cite as

When Extrinsic Payoffs Meet Intrinsic Expectations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 13955)

Abstract

Rational interactions between agents are often confounded due to disparity in their latent, intrinsic motivations. We address this problem by modelling interactions between agents with disparate intrinsic motivations in different kinds of social networks. Agents are modelled with a variegated profile over the following kinds of intrinsic motivations: power, achievement, and affiliation. These agents interact with their one-hop neighbours in the network through the game of Iterated Prisoners’ Dilemma and evolve their intrinsic profiles. A network is considered settled or stable, when each agent’s extrinsic payoff matches its intrinsic expectation. We then address how different network-level parameters affect the network stability. We observe that the distribution of intrinsic profiles in a stable network remains invariant to changes in network-level parameters over networks with the same average degree. Further, a high proportion of affiliation agents, who tend to cooperate, are required for various networks to reach a stable state.

Keywords

  • multi-agent systems
  • intrinsic motivation
  • game theory

J. Chhabra and K. Sama—These authors contributed equally to this work.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211(4489), 1390–1396 (1981)

    CrossRef  MathSciNet  MATH  Google Scholar 

  2. Baldassarre, G.: What are intrinsic motivations? A biological perspective. In: 2011 IEEE ICDL, vol. 2, pp. 1–8. IEEE (2011)

    Google Scholar 

  3. Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R.M., Barto, A.: Intrinsic motivations and open-ended development in animals, humans, and robots: an overview. Front. Psychol. 5, 985 (2014)

    CrossRef  Google Scholar 

  4. Barto, A.G.: Intrinsic motivation and reinforcement learning. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems, pp. 17–47. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32375-1_2

    CrossRef  Google Scholar 

  5. Frey, B.S.: How intrinsic motivation is crowded out and in. Ration. Soc. 6(3), 334–352 (1994)

    CrossRef  Google Scholar 

  6. Heath, C.: On the social psychology of agency relationships: lay theories of motivation overemphasize extrinsic incentives. Organ. Behav. Hum. Decis. Process. 78(1), 25–62 (1999)

    CrossRef  Google Scholar 

  7. Heckhausen, J.E., Heckhausen, H.E.: Motivation and Action. Cambridge University Press, Cambridge (2008)

    CrossRef  Google Scholar 

  8. Hester, T., Stone, P.: Intrinsically motivated model learning for developing curious robots. Artif. Intell. 247, 170–186 (2017)

    CrossRef  MathSciNet  MATH  Google Scholar 

  9. Hull, C.L.: Principles of behavior: an introduction to behavior theory (1943)

    Google Scholar 

  10. James, H.S., Jr.: Why did you do that? An economic examination of the effect of extrinsic compensation on intrinsic motivation and performance. J. Econ. Psychol. 26(4), 549–566 (2005)

    CrossRef  Google Scholar 

  11. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–291 (1979). http://www.jstor.org/stable/1914185

  12. Khan, M.M., Kasmarik, K., Barlow, M.: Toward computational motivation for multi-agent systems and swarms. Front. Robot. AI 5, 134 (2018)

    CrossRef  Google Scholar 

  13. Merrick, K., Shafi, K.: A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems. Front. Psychol. 4 (2013)

    Google Scholar 

  14. Merrick, K.E., Shafi, K.: Achievement, affiliation, and power: motive profiles for artificial agents. Adapt. Behav. 19(1), 40–62 (2011)

    CrossRef  Google Scholar 

  15. Morse, G.: Why we misread motives. Harv. Bus. Rev. 81(1), 18 (2003)

    Google Scholar 

  16. Nowak, M.A.: Five rules for the evolution of cooperation. Science 314(5805), 1560–1563 (2006)

    CrossRef  Google Scholar 

  17. Oudeyer, P.Y., Kaplan, F.: What is intrinsic motivation? A typology of computational approaches. Front. Neurorobot. 6 (2009)

    Google Scholar 

  18. Riolo, R.L., Cohen, M.D., Axelrod, R.: Evolution of cooperation without reciprocity. Nature 414(6862), 441–443 (2001)

    CrossRef  Google Scholar 

  19. Ryan, R.M., Deci, E.L.: Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 25(1), 54–67 (2000)

    CrossRef  Google Scholar 

  20. Santos, F.C., Pacheco, J.M.: A new route to the evolution of cooperation. J. Evol. Biol. 19(3), 726–733 (2006)

    CrossRef  Google Scholar 

  21. Schembri, M., Mirolli, M., Baldassarre, G.: Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot. In: 2007 IEEE 6th International Conference on Development and Learning, pp. 282–287. IEEE (2007)

    Google Scholar 

  22. Sen, A.K.: Rational fools: a critique of the behavioral foundations of economic theory. Philos. Public Affairs 6(4), 317–344 (1977)

    Google Scholar 

  23. Shafi, K., Merrick, K.E., Debie, E.: Evolution of intrinsic motives in multi-agent simulations. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL 2012. LNCS, vol. 7673, pp. 198–207. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34859-4_20

    CrossRef  Google Scholar 

  24. Srivastava, N., Kapoor, K., Schrater, P.R.: A cognitive basis for theories of intrinsic motivation. In: 2011 IEEE ICDL, vol. 2, pp. 1–6. IEEE (2011)

    Google Scholar 

  25. Stout, A., Konidaris, G.D., Barto, A.G.: Intrinsically motivated reinforcement learning: a promising framework for developmental robot learning. Technical report, Massachusetts University, Amherst Department of Computer Science (2005)

    Google Scholar 

  26. Sun, R.: Intrinsic motivation for truly autonomous agents. In: Abbass, H.A., Scholz, J., Reid, D.J. (eds.) Foundations of Trusted Autonomy. SSDC, vol. 117, pp. 273–292. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64816-3_15

    CrossRef  Google Scholar 

  27. Xianyu, B.: Prisoner’s dilemma game on complex networks with agents’ adaptive expectations. J. Artif. Soc. Soc. Simul. 15(3), 3 (2012)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International Institute of Information Technology, Bangalore, Bangalore, India

    Janvi Chhabra, Karthik Sama, Jayati Deshmukh & Srinath Srinivasa

Authors
  1. Janvi Chhabra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karthik Sama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jayati Deshmukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Srinath Srinivasa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Janvi Chhabra .

Editor information

Editors and Affiliations

  1. University of Lille, Lille, France

    Philippe Mathieu

  2. Umeå University, Umeå, Sweden

    Frank Dignum

  3. Universidade do Minho, Braga, Portugal

    Paulo Novais

  4. University of Salamanca, Salamanca, Spain

    Fernando De la Prieta

Rights and permissions

Reprints and Permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chhabra, J., Sama, K., Deshmukh, J., Srinivasa, S. (2023). When Extrinsic Payoffs Meet Intrinsic Expectations. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37616-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37615-3

  • Online ISBN: 978-3-031-37616-0

  • eBook Packages: Computer ScienceComputer Science (R0)

search

Navigation