11 – Models and Simulations
11 – Models and Simulations
Why Should I Care?
Sometimes, you cannot operationalize. It would be too risky, not feasible, or unethical. This is when you build a model and run simulations.
Definitions
Model an artificial – often simple version – environment designed to run simulations, which are used to verify hypotheses about variables.
Simulation:
Simulation The imitation of the process of a real-world system over time, or across cases. You need a model to run a simulation.
Examples
Examples
Economic models
The list is very long. Supply and Demand. AD-AS. AE/45 degree. IS-LM. etc.
Political Science
Re-calculating election results according to different types of electoral systems
(proportional, 50% + 1, highest score, etc.).
Advantages
May lead to counter-intuitive results which help to build understanding on the importance of certain factors, parameters.
Allows some kind of verification of the hypothesis, when a real-life experiment is not possible.
Disadvantages
The results are hypothetical. This is not as strong as empirical evidence.
The results are based on deductive logic. In real-life, the situation may be much more complex.
It’s stronger than descriptive, but it’s not as strong as explanatory/causal.
The choices you made in setting the parameters may actually set the results.