Steady-state analysis of a human-social behavior model : a neural-cognition perspective

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Jieqiang Wei
  • Ehsan Nekouei
  • Junfeng Wu
  • Vladimir Cvetkovic
  • Karl H. Johansson

Detail(s)

Original languageEnglish
Title of host publication2019 American Control Conference, ACC 2019
PublisherIEEE
Pages199-204
ISBN (Electronic)978-1-5386-7926-5
Publication statusPublished - Jul 2019
Externally publishedYes

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Title2019 American Control Conference, ACC 2019
PlaceUnited States
CityPhiladelphia
Period10 - 12 July 2019

Abstract

We consider an extension of the Rescorla-Wagner model which bridges the gap between conditioning and learning on a neural-cognitive, individual psychological level, and the social population level. In this model, the interaction among individuals is captured by a Markov process. The resulting human-social behavior model is a recurrent iterated function system which behaves differently from the classical Rescorla-Wagner model due to randomness. A sufficient condition for the convergence of the forward process starting with arbitrary initial distribution is provided. Furthermore, the ergodicity properties of the internal states of agents in the proposed model are studied.

Research Area(s)

  • Decision making, Markovian jump system, Neural cognition, Social networks, Stochastic process

Citation Format(s)

Steady-state analysis of a human-social behavior model : a neural-cognition perspective. / Wei, Jieqiang; Nekouei, Ehsan; Wu, Junfeng; Cvetkovic, Vladimir; Johansson, Karl H.

2019 American Control Conference, ACC 2019. IEEE, 2019. p. 199-204 8814786 (Proceedings of the American Control Conference; Vol. 2019-July).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review