Validation and Verification of Computational Models with Multiple Cognitive Agents

Wider issues of the validation of computational models - ascertaining that they are sound and consistent relative to some logical formalism and/or substantive theory - have not been a subject of the management science literature. In this paper, we demonstrate that computational models can be sound and consistent relative both to a fragment of strongly grounded autoepistemic logic (FOSGAL) and to theories of cognition without losing the expressiveness found in the informally oriented literature on organizational learning and business strategy. Validation is achieved by implementing models and their theoretical components in a programming language which corresponds to a known formal logic. The language used in this paper is SDML. The correspondence of SDML to autoepistemic logic is explained and justified. Issues associated with the verification of models - how well they correspond to observation - are also considered and extended. Benefits of explicit validation and verification of computational models are demonstrated by the implementation in SDML of a computational model of the critical-incident management organization of one of the largest public utilities in Europe. On the basis of the reported simulation results with the model, several research issues are identified both for the development of validation practices in the management sciences and for the analysis of crisis management.

[1]  P. Samuelson,et al.  Foundations of Economic Analysis. , 1948 .

[2]  Kenneth F. Wallis,et al.  Comparing Macroeconometric Models: A Review Article , 1993 .

[3]  Stephen Hill,et al.  The theory of the growth of the firm , 1981 .

[4]  Peter Gyngell,et al.  Process Innovation: Reengineering Work through Information Technology , 1994 .

[5]  Milind Tambe,et al.  Architectures for Agents that Track Other Agents in Multi-Agent Worlds , 1995, ATAL.

[6]  J. Armstrong Forecasting with Econometric Methods: Folklore Versus Fact , 1978 .

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

[8]  Fred Collopy,et al.  Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations , 1992 .

[9]  K. Weick FROM SENSEMAKING IN ORGANIZATIONS , 2021, The New Economic Sociology.

[10]  S. Moss,et al.  A smart automated macroeconometric forecasting system , 1994 .

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

[12]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[13]  Michael Hammer,et al.  Reengineering Work: Don’t Automate, Obliterate , 1990 .

[14]  Kurt Konolige,et al.  On the Relation Between Default and Autoepistemic Logic , 1987, Artif. Intell..

[15]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[16]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[17]  C. Prahalad,et al.  The dominant logic: A new linkage between diversity and performance , 1986 .

[18]  Stephen K. McNees The role of judgment in macroeconomic forecasting accuracy , 1990 .

[19]  J. R. Moore,et al.  The theory of the growth of the firm twenty-five years after , 1960 .

[20]  Richard Reviewer-Granger Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[21]  D. Turner The role of judgement in macroeconomic forecasting , 1990 .

[22]  G. Huber Organizational Learning: The Contributing Processes and the Literatures , 1991 .

[23]  D. Bunn,et al.  Interaction of judgemental and statistical forecasting methods: issues & , 1991 .

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

[25]  Richard P. Cooper,et al.  A Systematic Methodology for Cognitive Modelling , 1996, Artif. Intell..

[26]  Bruce Edmonds,et al.  Logic, Reasoning and A Programming Language for Simulating Economic and Business Processes with Artificially Intelligent Agents , 1996 .