Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 8, Iss. 1, January, 2004, pp. 65-102
@2004 Society for Chaos Theory in Psychology & Life Sciences


Situated Learning Theory: Adding Rate and Complexity Effects via Kauffman”s NK Model

Yu Yuan, University of Southern California
Bill McKelvey, University of California, Los Angeles

Abstract: For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman’s NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

Keywords: situated learning theory, group learning, rate of learning, complexity catastrophe, agent-based models, Kauffman, NK model, rugged landscape