Skip to main content
SpringerLink
Log in

Human Decision-Making in Multi-agent Systems

  • Living reference work entry
  • First Online:
Book cover Encyclopedia of Systems and Control

  • 295 Accesses

Abstract

In order to avoid suboptimal collective behaviors and resolve social dilemmas, researchers have tried to understand how humans make decisions when interacting with other humans or smart machines and carried out theoretical and experimental studies aimed at influencing decision-making dynamics in large populations. We identify the key challenges and open issues in the related research, list a few popular models with the corresponding results, and point out future research directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Bibliography

  • Baillieul J, Leonard NE, Morgansen KA (2012) Interaction dynamics: the interface of humans and smart machines. Proc IEEE 100:567–570

    Article  Google Scholar 

  • Bernardo JM (1994) Bayesian theory. Wiley, Chichester

    Book  Google Scholar 

  • Bitzer S, Park H, Blankenburg F, Kiebel SJ (2014) Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model. Front Hum Neurosci (102) 8:1–17

    Google Scholar 

  • Broder A, Schiffer S (2004) Bayesian strategy assessment in multi-attribute decision-making. J Behav Decis Mak 16:193–213

    Article  Google Scholar 

  • Cao M, Stewart A, Leonard NE (2010) Convergence in human decision-making dynamics. Syst Control Lett 59:87–97

    Article  MathSciNet  Google Scholar 

  • Centola D, Willer R, Macy M (2016) The emperor’s dilemma: a computational model of self-enforcing norms. Am J Sociol 110(4):1009–1040

    Article  Google Scholar 

  • Evans NJ, Holmes WR, Trueblood JS (2019) Response-time data provide critical constraints on dynamic models of multi-alternative, multi-attribute choice. Psychon Bull Rev 26:193–213

    Google Scholar 

  • Gisches EJ, Rapoport A (2012) Degrading network capacity may improve performance: private versus public monitoring in the Braess paradox. Theor Decis 73: 901–933

    Article  MathSciNet  Google Scholar 

  • Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83:1420–1443

    Article  Google Scholar 

  • Mas M, Opp K-D (2016) When is ignorance bliss? Disclosing true information and cascades of norm violation in networks. Soc. Networks 47:116–129

    Google Scholar 

  • Myerson RB (1991) Game theory: analysis of conflict. Harvard University Press, Cambridge/London

    MATH  Google Scholar 

  • Ostrom E (2008) Tragedy of the commons. The new palgrave dictionary of economics. Palgrave Macmillan, UK, pp 3573–3576

    Google Scholar 

  • Ramazi P, Riehl J, Cao M (2016) Networks of conforming or nonconforming individuals tend to reach satisfactory decisions. Proc Natl Acad Sci 113(46):12985–12990

    Article  Google Scholar 

  • Ratcliff R (1978) A theory of memory retrieval. Psychol Rev 85:59–108

    Article  Google Scholar 

  • Ratcliff R, Smith PL, Brown SD, McKoon G (2016) Diffusion decision model: current issues and history. Trends Cogn Sci 20(4):260–281

    Article  Google Scholar 

  • Reverdy P, Llhan BD, Koditschek DE (2015) A drift-diffusion model for robotic obstacle avoidance. In: Proceedings of the 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), number 15666827. IEEE

    Google Scholar 

  • Riehl J, Ramazi P, Cao M (2018) A survey on the analysis and control of evolutionary matrix games. Ann Rev Control 45:87–106

    Article  MathSciNet  Google Scholar 

  • Rossi W, Como G, Fagnai F (2019) Threshold models of cascades in large-scale networks. IEEE Trans Netw Sci Eng 6:158–172

    Article  MathSciNet  Google Scholar 

  • Sandholm WH (2010) Population games and evolutionary dynamics. MIT Press, Cambridge

    MATH  Google Scholar 

  • Steinberg R, Zangwill WI (1983) The prevalence of Braess’ paradox. Transp Sci 17:301–318

    Article  Google Scholar 

  • Stewart A, Cao M, Nedic A, Tomlin D, Leonard AE (2012) Towards human-robot teams: model-based analysis of human decision making in two-alternative choice tasks with social feedback. Proc IEEE 100(3):751–775

    Article  Google Scholar 

  • Woodruff C, Vu L, Morgansen KA, Tomlin D (2012) Deterministic modeling and evaluation of decision-making dynamics in sequential two-alternative forced choice tasks. Proc IEEE 100(3):734–750

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands

    Ming Cao

Authors
  1. Ming Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Cao .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, MA, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, MN, USA

    Tariq Samad

Section Editor information

  1. Technological Leadership Institute, University of Minnesota, Minneapolis, Minnesota, USA

    Tariq Samad

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag London Ltd., part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Cao, M. (2020). Human Decision-Making in Multi-agent Systems. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100124-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100124-1

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation