Non-Equilibrium Industry Dynamics with Knowledge-Based Competition: An Agent-Based Computational Model

An agent-based computational model is developed to explore the evolutionary dynamics of an industry which is subject to knowledge-based competition with entry and exit. It views the production process as a system of inter-dependent activities and the firm as an adaptive entity whose survival depends on its ability to perform various activities with greater efficiency than its rivals. The model is capable of generating many empirical regularities, including those on firm turnovers, evolving market structure, and the intra-industry technological diversity. Comparative dynamics analyses are performed to investigate how these regularities are affected by various industry-specific factors such as the attributes of the market environment, search propensities and the nature of the technology space in which individual firm’s learning takes place. Of particular interest are the findings on firm turnover and the number of surviving incumbents in the long run. The turnover of firms is found to be greater when: 1) the market demand is larger; 2) the pool of potential entrants is larger; 3) the start-up fund for a firm is smaller; and 4) the firms in the industry have a lower propensity to search. The number of surviving incumbents is higher in the long run when: 1) market demand is larger; 2) the fixed cost is lower; 3) the pool of potential entrants is larger; and 4) the production process entails a greater number of component activities.