Yet the flexibility of ABMs does not come for free: results are often hard to interpret (Rahmandad and Sterman 2008). This makes ABMs extremely flexible, as it is relatively easy to vary the building blocks of an ABM. We can broadly define ABMs as computational models in which aggregate outcomes emerge from agents’ properties, behaviors and interactions, without the imposition of any top-down constraint. As Anderson ( 1999) emphasizes, ABMs allow researchers to examine open systems–common in management situations–whose behavior cannot be described analytically by equations derived from energy conservation principles or decision-theoretical axioms. 1972), researchers have increasingly used agent-based simulation techniques to address relevant organizational, strategic and operational questions (Prietula et al. Since early classics such as the Garbage Can Model (Cohen et al. Among the several simulation approaches available, agent-based models (ABMs) play a special role (Prietula and Carley 1994). Simulation modeling has become increasingly important in studying organizational behavior (Carley 2002 Harrison et al. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. Agent-based models (ABMs) are increasingly used in the management sciences.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |