Confronta i metodi

	System Dynamics Business Strategy Modeling	Agent-Based Model of Competitive Strategy
Campo	Gestione strategica	Gestione strategica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1961	2007
Ideatore	Jay W. Forrester; John D. Sterman	Jason P. Davis, Kathleen M. Eisenhardt & Christopher B. Bingham (simulation-methods roadmap)
Tipo	Feedback-loop (stock-and-flow) simulation of business strategy	Agent-based computational simulation of competitive strategy
Fonte seminale	Forrester, J. W. (1961). Industrial Dynamics. Cambridge, MA: MIT Press. ISBN: 9780262060035	Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing Theory Through Simulation Methods. Academy of Management Review, 32(2), 480-499.
Alias	Business Strategy System Dynamics, Feedback-Loop Strategy Simulation, Stock-and-Flow Business Modeling, Strategy Dynamics Simulation	Competitive Strategy Agent-Based Simulation, Firm-Interaction Simulation Modeling, Computational Model of Competitive Dynamics, Multi-Firm Agent-Based Strategy Model
Correlati	3	3
Sintesi	System dynamics business strategy modeling represents a firm's strategy as a system of stocks, flows, and feedback loops with delays, then simulates the resulting nonlinear behavior to understand why strategies succeed, fail, or backfire over time. Jay Forrester's 1961 Industrial Dynamics founded the field, showing that the feedback structure of a business — orders, inventories, capacity, and the information links that govern them — generates dynamics like amplification and oscillation that no single decision creates. John Sterman's 2000 Business Dynamics turned this into a comprehensive modeling discipline for managers, complete with a structured process for building, testing, and using simulation models. The method gives strategists a way to see how policies ripple through reinforcing and balancing loops, often producing counterintuitive long-run consequences.	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.