The Model Thinker: What You Need to Know to Make Data Work for You (pp. 13-25)

The Model Thinker: What You Need to Know to Make Data Work for You (pp. 13-25)Chapter 2: Why Model?Types of ModelsEmbodiment ApproachAnalogy ApproachAlternative Reality ApproachGood ModelsThe Seven Uses of ModelsREDCAPEReasonExplainDesignCommunicateActPredictExplore

Chapter 2: Why Model?

Knowing reality means constructing systems of transformations that correspond, more or less adequately, to reality.


Types of Models

Embodiment Approach

Analogy Approach

When a physicist assumes way friction but otherwise makes realistic assumptions, she takes the embodiment approach. When an economist represents competing firms a different species and defines produce niches, she makes an analogy. She does so using a model developed to embody a different system.

No bright line differentiates the embodiment approach from the analogy approach.

Alternative Reality Approach

Good Models


The Seven Uses of Models


ReasonTo identify conditions and deduce logical implications
ExplainTo provide (testable) explanations for empirical phenomena
DesignTo choose features of institutions, policies, and rules
CommunicateTo relate knowledge and understandings
ActTo guide policy choices and strategic actions
PredictTo make numerical and categorical predictions of future and unknown phenomena
ExploreTo investigate possibilities and hypotheticals


Two heads are better than oneToo many cooks spoil the broth
He who hesitates is lostA stitch in time saves nine
Tie yourself to the mastKeep your options open
The perfect is the enemy of the goodDo it well or not at all
Actions speak louder than wordsThe pen is mightier than the sword

(Examples above are taken from the text.)



As for the claim that models can explain anything: it is true, they can. However, a model-based explanation includes formal assumptions and explicitly causal chains. Those assumptions and causal chains can be taken to data. A model that claims that high levels of criminal behavior can be explained by low probabilities of being caught can be test.







The great end of life is not knowledge but actions.

— Francis Bacon

The general take away here is that models can be helpful for showing if it makes sense to do or not do something specific in the real world. This (to me at least) ties closely to the Design section.



In perhaps the most famous example of applying an explanatory model to predict, the French mathematician Urban Le Verrier applied the Newtonian laws created to explain planetary movements to evaluate the discrepancies in the orbit of Uranus. He discovered the orbits to be consistent with the presence of a large planet in the out region of the solar system. On September 18, 1846, he sent his prediction to the Berlin Observatory. Five days later, astronomers located the planet Neptune exactly where Le Verrier had predicted it would be.

However, prediction explanation!




Notes by Matthew R. DeVerna