Interpretation by Implementation for Understanding a Multiagent Organization

This paper stresses the importance of focusing on the modeling process of computational models for precisely understanding a complex organization and for solving given problems in the organization. Based on our claim, we proposes a method of interpretation by implementation (IbI), which explores factors that drastically change simulation results through an investigation on the modeling process of computational models. A careful investigation on the capabilities of the IbI approach, which comprises the three methods of (a) breakdown and representation, (b) assumption or premise modification, and (c) layer change investigation, derives the following conclusions: (1) the IbI approach has the potential of finding underlying factors that determine the characteristics of an organization; (2) the IbI approach can specify points of attention at necessary levels when analyzing an organization; and (3) the IbI approach has suchadvantages as wide applicability, the effective use of employed models, and KISS principle support.

[1]  Daniel H. Kim The Link between individual and organizational learning , 1997 .

[2]  John H. Miller,et al.  Active Nonlinear Tests (Ants) of Complex Simulation Models , 1998 .

[3]  Keiki Takadama,et al.  Exploration and Exploitation Trade-Off in Multiagent Learning , 2001 .

[4]  Leigh Tesfatsion,et al.  Introduction to the CE Special Issue on Agent-Based Computational Economics , 2001 .

[5]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[6]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[7]  Børge Obel,et al.  The validity of computational models in organization science: From model realism to purpose of the model , 1995, Comput. Math. Organ. Theory.

[8]  R. Axelrod,et al.  The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration , 1998 .

[9]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[10]  Takao Terano,et al.  Towards a multiagent design principle: analyzing an organizational-learning oriented classifer system , 2002 .

[11]  R. Duncan Organizational Learning : Implications for organizational design , 1979 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Takao Terano,et al.  Making Organizational Learning Operational: Implications from Learning Classifier Systems , 1999, Comput. Math. Organ. Theory.

[14]  Pat Langley,et al.  Learning distributed strategies for traffic control , 1998 .

[15]  Hiroshi Deguchi,et al.  Agent Based Approach for Social Complex Systems - Management of Constructed Social World , 1998, Community Computing and Support Systems.

[16]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[17]  Kathleen M. Carley A comparison of artificial and human organizations , 1996 .

[18]  Michael X Cohen,et al.  A Garbage Can Model of Organizational Choice. , 1972 .

[19]  Robert L. Axtell,et al.  Aligning simulation models: A case study and results , 1996, Comput. Math. Organ. Theory.

[20]  John D. Sterman,et al.  Business dynamics : systems thinking and modelling for acomplex world , 2002 .

[21]  Shinichi Nakasuka,et al.  How to Design Good Results for Multiple Learning Agents in Scheduling Problems? , 1999, PRIMA.

[22]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[23]  J. Sterman Business Dynamics , 2000 .

[24]  Yan Jin,et al.  The “virtual design team”: simulating how organization structure and information processing tools affect team performance , 1994 .

[25]  Andrew M. Colman,et al.  The complexity of cooperation: Agent-based models of competition and collaboration , 1998, Complex..

[26]  Kathleen M. Carley,et al.  Modeling Organizational Adaptation as a Simulated Annealing Process , 1996 .

[27]  A. Colman,et al.  The complexity of cooperation: Agent-based models of competition and collaboration , 1998, Complex..

[28]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[29]  Kathleen M. Carley Computational and mathematical organization theory: Perspective and directions , 1995, Comput. Math. Organ. Theory.

[30]  Joshua M. Epstein,et al.  Growing artificial societies , 1996 .

[31]  R. Palmer,et al.  Asset Pricing Under Endogenous Expectations in an Artificial Stock Market , 1996 .

[32]  Kathleen M. Carley On generating hypotheses using computer simulations , 1999 .