方法对比

	Firm Survival and Exit Analysis	Agent-Based Model of Competitive Strategy
领域	战略管理	战略管理
方法族	Survival analysis	Process / pipeline
起源年份	1995	2007
提出者	David B. Audretsch & Talat Mahmood; Paul A. Geroski	Jason P. Davis, Kathleen M. Eisenhardt & Christopher B. Bingham (simulation-methods roadmap)
类型	Hazard / duration model of firm survival and exit	Agent-based computational simulation of competitive strategy
开创性文献	Audretsch, D. B., & Mahmood, T. (1995). New Firm Survival: New Results Using a Hazard Function. The Review of Economics and Statistics, 77(1), 97-103.	Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing Theory Through Simulation Methods. Academy of Management Review, 32(2), 480-499.
别名	Firm Exit Hazard Modeling, Business Survival Duration Analysis, Post-Entry Firm Survival Analysis, Firm Mortality Hazard Models	Competitive Strategy Agent-Based Simulation, Firm-Interaction Simulation Modeling, Computational Model of Competitive Dynamics, Multi-Firm Agent-Based Strategy Model
相关	3	3
摘要	Firm survival and exit analysis applies hazard and duration models to the question of why some firms survive and others fail, treating the age at which a firm exits the market as a time-to-event outcome. Audretsch and Mahmood's 1995 study of more than twelve thousand U.S. manufacturing establishments showed that a hazard function can relate post-entry survival not only to industry and market-structure conditions but to firm-specific characteristics such as size, innovative activity, and scale economies. Geroski's 1995 survey of entry placed this within the broader dynamics of industries, where high entry rates coexist with high exit rates and most entrants fail young. The method gives strategy researchers a rigorous way to measure the instantaneous risk of failure and to identify which firm and environmental factors push it up or down.	An agent-based model of competitive strategy represents firms as autonomous, heterogeneous, adaptive agents whose decision rules and local interactions generate emergent industry-level dynamics that no single firm designs. Davis, Eisenhardt, and Bingham's 2007 roadmap for developing theory through simulation places this kind of computational modeling in the sweet spot between inductive case research and formal mathematics, well suited to longitudinal, nonlinear, and interactive strategy phenomena. Instead of solving for an equilibrium, the analyst builds firms with strategies, lets them compete over many simulated periods, and studies the market structures, survival patterns, and performance dispersions that emerge. The method gives strategy researchers a controlled laboratory for theory building about competitive dynamics that are too complex and path-dependent for closed-form analysis.