Skip to main content

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                     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.