Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Steady-state analysis of a human-social behavior model: A neural-cognition perspective
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Resources, Energy and Infrastructure.
Show others and affiliations
2019 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 199-204, article id 8814786Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 199-204, article id 8814786
Series
Proceedings of the American Control Conference, ISSN 0743-1619
Keywords [en]
Decision making, Markovian jump system, Neural cognition, Social networks, Stochastic process
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262590Scopus ID: 2-s2.0-85072299741ISBN: 9781538679265 (print)OAI: oai:DiVA.org:kth-262590DiVA, id: diva2:1362985
Conference
2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Wei, JieqiangNekouei, EhsanJohansson, Karl H.

Search in DiVA

By author/editor
Wei, JieqiangNekouei, EhsanCvetkovic, Vladimir D.Johansson, Karl H.
By organisation
Automatic ControlResources, Energy and Infrastructure
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf