A Particle Swarm Model of Organizational Adaptation

This study introduces the particle swarm metaphor to the domain of organizational adaptation. A simulation model (OrgSwarm) is constructed to examine the impact of strategic inertia, in the presence of errorful assessments of future payoffs to potential strategies, on the adaptation of the strategic fitness of a population of organizations. The results indicate that agent (organization) uncertainty as to the payoffs of potential strategies has the affect of lowering average payoffs obtained by a population of organizations. The results also indicate that a degree of strategic inertia, in the presence of an election mechanism, assists rather than hampers adaptive efforts in static and slowly changing strategic environments.

[1]  Daniel A. Levinthal.  Adaptation on rugged landscapes , 1997 .

[2]  Tim M. Blackwell,et al.  Swarms in Dynamic Environments , 2003, GECCO.

[3]  Daniel A. Levinthal,et al.  Looking Forward and Looking Backward: Cognitive and Experiential Search , 2000 .

[4]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[5]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[6]  M. Porter.  Competitive Advantage: Creating and Sustaining Superior Performance , 1985 .

[7]  James Kennedy,et al.  Minds and Cultures: Particle Swarm Implications For Beings in Sociocognitive Space , 1999, Adapt. Behav..

[8]  S. Kauffman,et al.  Towards a general theory of adaptive walks on rugged landscapes. , 1987, Journal of theoretical biology.

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Brendan Kitts,et al.  Intellectual capital: from intangible assets to fitness landscapes , 2001, Expert Syst. Appl..

[11]  Jan W. Rivkin.  Imitation of Complex Strategies , 2000 .

[12]  M. Porter.  What is strategy , 2000 .

[13]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.