what-when-how
In Depth Tutorials and Information
Table 2.2
MathematicalProgrammingandSimulationModelingMethods
forSociotechnicalSystems
Solution Evaluation
Solution Generation
Certainty
−
Deterministic Simulation
−
Linear Programming
−
Econometric Models
−
Network Models
−
System of ODEs
−
Integer and mixed-integer
programming
−
Input-Output Models
−
Nonlinear programming
−
Control Theory
Uncertainty
−
Monte Carlo Simulation
−
Decision Theory
−
Econometric Models
−
Dynamic Programming
−
Stochastic Processes
−
Inventory Theory
−
Queuing Theory
−
Stochastic Programming
−
Reliability Theory
−
Stochastic Control Theory
Source:
Applied Mathematical Programming
. Bradley, Hax, and Magnanti. Addison-
Wesley, 1977.
2.2.1.3 Game Theory
Game theory is a branch of mathematics first developed by John von Neumann and
Oskar Morgenstern in the 1940s, and advanced by John Nash in the 1950s. It uses
models to predict interactions between decision-making agents in a given set of
conditions. Game theory has been applied to a variety of fields such as economics,
market analysis, and military strategy. It can be used in a complex system where
multiple agents (conscious decision-making entities) interact noncooperatively to
maximize their own beneit. he underlying assumption for game theory is that
agents know and understand the benefits they can derive from a course of action,
and that they are rational.
2.2.1.4 Agent-Based Modeling
Agent-based modeling is a bottom-up system modeling approach for predicting
and understanding the behavior of nonlinear, multiagent systems. An agent is a
conscious decision-making element of the system that tries to maximize its local
beneit. he interaction of agents in a system is a key feature of agent-based systems.
It assumes that agents communicate with each other and learn from each other.
he proponents of this approach argue that human behavior in swarms (or soci-
ety) within a CLIOS can only be predicted if individual behavior is considered a
