Canonical Tasks, Environments and Models for Social Simulation

The purpose of this paper is to propose and describe an alternative to an overarching theory for social simulation research. The approach is an analogy of the canonical matrix. Canonical matrices are matrices of a standard form and there are transformations that can be performed on other matrices to show that they can be made into canonical matrices. All matrices which, by means of allowable operations, can be transformed into a canonical matrix have the properties of the canonical matrix. This conception of canonicity is applied to three models in the computational organization theory literature. The models are mapped into their respective canonical forms. The canonical forms are shown to be transitively subsumptive (i.e., one of them is “nested” within a second which itself is “nested” within the third. The consequences of these subsumption relations are investigated by means of simulation experiments.

[1]  L. Vogel,et al.  Strategy and Structure , 1986 .

[2]  Peter Tyson,et al.  Artificial societies , 1997 .

[3]  Kathleen M. Carley,et al.  Radar‐soar: Towards an artificial organization composed of intelligent agents* , 1995 .

[4]  R.S.J. Tol A decision analytic treatise of the enhanced greenhouse effect , 1997 .

[5]  Ken Binmore,et al.  Evolutionary Stability in Alternating-Offers Bargaining Games , 1998 .

[6]  Bruce Edmonds,et al.  Modelling Economic Learning as Modelling , 1998 .

[7]  Bruce Edmonds,et al.  SDML: A Multi-Agent Language for Organizational Modelling , 1998, Comput. Math. Organ. Theory.

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

[9]  A. Newell Unified Theories of Cognition , 1990 .

[10]  R. Hegselmann,et al.  Simulating Social Phenomena , 1997 .

[11]  Ken Binmore,et al.  Evolutionary Stability in Alternating-Offers Bargaining Gamesl , 1996 .

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

[13]  Paul R. Cohen,et al.  Heuristic reasoning about uncertainty: an artificial intelligence approach , 1984 .

[14]  Pietro Terna,et al.  A Laboratory for Agent Based Computational Economics: The Self-development of Consistency in Agents’ Behaviour , 1997 .

[15]  K. G. Lockyer An introduction to critical path analysis , 1965 .

[16]  Scott Moss,et al.  Boundedly versus Procedurally Rational Expectations , 1999 .

[17]  Kerstin Dautenhahn,et al.  Hierarchical Organization of Robots: A Social Simulation Study , 1998, ESM.

[18]  YAN JIN,et al.  The virtual design team: A computational model of project organizations , 1996, Comput. Math. Organ. Theory.

[19]  Scott Moss,et al.  Critical Incident Management: An Empirically Derived Computational Model , 1998, J. Artif. Soc. Soc. Simul..

[20]  Abraham Kandel,et al.  Compression and Expansion of Fuzzy Rule Bases by Using Crisp-fuzzy Neural Networks , 1998, Cybern. Syst..