This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation. In particular, in this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research.
This lecture is also coupled with notes discussing the Lab 3 (Monte Carlo simulation) results and general experience. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).
Concept explorers referenced in this lecture material (particularly for Lab 3 material):
- https://tpavlic.github.io/asu-simulating-stochastic-systems/monte_carlo/mc_explorer.html
- https://tpavlic.github.io/asu-simulating-stochastic-systems/monte_carlo/mc_examples.html
- https://tpavlic.github.io/asu-simulating-stochastic-systems/input_modeling/prob_models.html
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