Abstract
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.
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Axtell, R., R. Axelrod, J.M. Epstein and M.D. Cohen (1996), “Aligning Simulation Models: A Case Study and Results,” Computational and Mathematical Organization Theory, 1(2), 123-141.
Binmore, K., M. Piccione and L. Samuelson (1998), “Evolutionary Stability in Alternating-Offers Bargaining Games,” Journal of Economic Theory, 80(2), 257-291.
Carley, K.M. and D. Svoboda (1996), “Modeling Organizational Adaptation as a Simulated Annealing Process,” Sociological Methods and Research, 25(1), 138-168.
Chandler, A.D. (1962), Strategy and Structure, MIT Press, Cambridge, MA.
Cohen, P.R. (1985), Heuristic Reasoning: An Artificial Intelligence Approach. Pitman Advanced Publishing Program, Boston.
Conte, R., R. Hegselmann and P. Terna (1997), Simulating Social Phenomena. Springer-Verlag, Berlin. Lecture Notes in Economics and Mathematical Systems.
Edmonds, B. and S. Moss (1998), “Modelling Economic Learning as Modelling,” Cybernetics and Systems, 29(1), 5-37.
Gilbert, G.N. and R. Conte (1995), Artificial Societies. UCL Press, London.
Jin, Y. and R. Levitt (1996), “The Virtual Design Team: A Computational Model of Project Organizations,” Computational and Mathematical Organization Theory, 2, 171-195.
Lockyer, K.G. (1969), An Introduction to Critical Path Analysis, 3rd ed. Pitman, London.
Moss, S. (1998), “Critical Incident Management: An Empirically Derived Computational Model,” Journal of Artificial Societies and Social Simulation, 1(4). http://www.soc.surrey.ac.uk/JASSS/1/4/1.html.
Moss, S. and K. Dautenhahn (1998), “Hierarchical Organization of Robots: A Social Simulation Study,” (Manchester: Centre for Policy Modelling Technical Report 98-36). http://www.cpm.mmu.ac.uk/cpmrep36.html.
Moss, S. and E.-M. Sent (1999), “Boundedly versus Procedurally Rational Expectations,” in A.H. Hallett and P. McAdam (Eds.) Analyses in Macro Modelling. Kluwer Academic Publishers, Amsterdam, pp. 115-146.
Moss, S., H. Gaylard, S. Wallis and B. Edmonds (1998), SDML: A Multi-Agent Language for Organizational Modelling, Computational and Mathematical Organization Theory, 4(1), 43-70.
Newell, A. (1990), Unified Theories of Cognition. Harvard University Press, Cambridge, MA.
Terna, (1997), “A Laboratory for Agent Based Computational Economics: The Self-development of Consistency in Agents' Behaviour,” in Conte et al. (1997), pp. 73-88.
Tol, R.S.J. (1996), “A Decision-Analytic Treatise of the Enhanced Greenhouse Effect,” Vrije University, Amsterdam.
Ye, M. and K.E. Carley (1995), “Radar Soar: Towards an Artificial Organization Composed of Intelligent Agents,” Journal of Mathematical Sociology, 20, 219-246.
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Moss, S. Canonical Tasks, Environments and Models for Social Simulation. Computational & Mathematical Organization Theory 6, 249–275 (2000). https://doi.org/10.1023/A:1009629602618
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DOI: https://doi.org/10.1023/A:1009629602618