Agent-based organizational cybernetics for organizational learning

Yusuke Goto, Shingo Takahashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes guidelines to evaluate agent’s internal model effectively in organizational learning. We developed an approach named Agent-based Organizational Cybernetics (AOC) to describe problems in organizational learning with an agent-based model and to design some prescriptions for them. This paper focuses on four typical evaluation problems of internal model in organizational learning: (1) environmental scanning, (2) policy making, (3) coordination, and (4) task resolution. We design and test some guidelines for each problem. The results provide some effective guidelines in organizational learning. Organizational-performance(OP)-based evaluation realizes organization’s high performance in all four problem situations. However, the OP-based evaluation cannot guarantee that an agent gets a right internal model all the time. So, it is effective to also consider some other complementary evaluations.

Original languageEnglish
Title of host publicationProceedings of the 4th Conference of the European Social Simulation Association, ESSA 2007
EditorsFrederic Amblard
PublisherThe European Social Simulation Association (ESSA)
Pages659-670
Number of pages12
ISBN (Electronic)9782952032674
Publication statusPublished - 2007
Externally publishedYes
Event4th Annual Conference of the European Social Simulation Association, ESSA 2007 - Toulouse, France
Duration: 2007 Sep 102007 Sep 14

Publication series

NameProceedings of the 4th Conference of the European Social Simulation Association, ESSA 2007

Conference

Conference4th Annual Conference of the European Social Simulation Association, ESSA 2007
Country/TerritoryFrance
CityToulouse
Period07/9/1007/9/14

Keywords

  • Agent-based organizational cybernetics
  • Computational organization theory
  • Organizational cybernetics
  • Organizational learning

ASJC Scopus subject areas

  • Modelling and Simulation

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