Michael J. North and Charles M. Macal
- Published in print:
- 2007
- Published Online:
- September 2007
- ISBN:
- 9780195172119
- eISBN:
- 9780199789894
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195172119.003.0005
- Subject:
- Business and Management, Strategy
This chapter uses a supply chain example to compare and contrast agent-based modeling and simulation with other modeling techniques, including systems dynamics, discrete-event simulation, ...
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This chapter uses a supply chain example to compare and contrast agent-based modeling and simulation with other modeling techniques, including systems dynamics, discrete-event simulation, participatory simulation, statistical modeling, risk analysis, and optimization. It also discusses why businesses and government agencies do modeling and simulation.Less
This chapter uses a supply chain example to compare and contrast agent-based modeling and simulation with other modeling techniques, including systems dynamics, discrete-event simulation, participatory simulation, statistical modeling, risk analysis, and optimization. It also discusses why businesses and government agencies do modeling and simulation.
Nathaniel Osgood
- Published in print:
- 2020
- Published Online:
- July 2020
- ISBN:
- 9780190880743
- eISBN:
- 9780190880774
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190880743.003.0012
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Dynamic modeling provides a powerful tool for enabling faster learning in a complex and uncertain world. Within this contribution, we briefly survey three prominent dynamic modeling ...
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Dynamic modeling provides a powerful tool for enabling faster learning in a complex and uncertain world. Within this contribution, we briefly survey three prominent dynamic modeling traditions—agent-based modeling, system dynamics, and discrete event simulation. Each such tradition offers unique combinations of strengths and limitations and is further distinguished by emphasis of different sets of modeling goals and norms. This chapter discusses such trade-offs between such methods, with a particular emphasis on the key distinction between aggregate and individual-based approaches, which has widespread practical ramifications. The authors further note the advent of hybrid dynamic modeling approaches, which provide unique levels of flexibility in addressing diverse intervention strategies and generative pathways at multiple scales and the capacity for the model representation to adapt with the learning and evolving understanding of key elements of model dynamics that constitute a key outcome of the modeling process.Less
Dynamic modeling provides a powerful tool for enabling faster learning in a complex and uncertain world. Within this contribution, we briefly survey three prominent dynamic modeling traditions—agent-based modeling, system dynamics, and discrete event simulation. Each such tradition offers unique combinations of strengths and limitations and is further distinguished by emphasis of different sets of modeling goals and norms. This chapter discusses such trade-offs between such methods, with a particular emphasis on the key distinction between aggregate and individual-based approaches, which has widespread practical ramifications. The authors further note the advent of hybrid dynamic modeling approaches, which provide unique levels of flexibility in addressing diverse intervention strategies and generative pathways at multiple scales and the capacity for the model representation to adapt with the learning and evolving understanding of key elements of model dynamics that constitute a key outcome of the modeling process.
