Agent-based modeling of knowledge dynamics

Abstract

Knowledge is distributed unevenly through most enterprises. Hence, flows of knowledge (e.g., across time, people, locations, organizations) are critical to organizational efficacy and performance under a knowledge-based view of the firm. However, supported principally by narrative textual theory in the emerging knowledge management (KM) field, the researcher has difficulty describing how different kinds of knowledge will flow through various parts of an organization. This causes difficulty also for predicting the effects of alternate approaches to dispersing knowledge that ‘clumps’ in various areas. This problem is also manifest for the KM professional, who lacks clear theory or tools to anticipate how any particular information technology or other managerial intervention may enhance or impede specific knowledge flows in the enterprise. In this expository article, we build upon a steady stream of research in computational organization theory to develop agent-based models of knowledge dynamics. This work draws from emerging theory for multidimensional representation of the knowledge-flow phenomenon, which enables the dynamics of enterprise knowledge flows to be formalized and emulated through computational models. This approach provides the means for knowledge-flow processes to be visualized and analyzed in new ways. Computational experimentation enables the performance of many alternate process designs and technological interventions to be compared through examination of dynamic models, before committing to a specific approach in practice. We illustrate this research method and modeling environment through semi-formal representation and agent-based emulation of several knowledge-flow processes from the domain of software development. We also outline key directions for the new kinds of KM research and practice elucidated by this work.

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