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: The imitation of the process of a real-world system over time, or across cases. You need a model to run a simulation.

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.