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Playing with Half a Deck: 26 Reasons to Model

Playing with Half a Deck: 26 Reasons to Model. Scott E Page. Explain Empirical Phenomena : Models enable us to explain phenomena such as why raising prices lowers demand or moving to the ideological center wins more votes.

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Playing with Half a Deck: 26 Reasons to Model

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  1. Playing with Half a Deck:26 Reasons to Model Scott E Page

  2. Explain Empirical Phenomena: Models enable us to explain phenomena such as why raising prices lowers demand or moving to the ideological center wins more votes.

  3. Predict Points: Models enable us to predict future events such as eclipses or election returns.

  4. Predict Patterns: Models enable us to predict patterns such as S-shaped adoption patterns.

  5. Retrodict Past: Models enable us to predict past patterns that were not recognized or recorded – Big Bang.

  6. Predict Other: Models can also predict other, related phenomena. An ``earth sucks” model. It would predict that heavier objects would fall less quickly (oops). The Newtonian model predicted Pluto prior to it having been found.

  7. Produce Bounds: Even if a model cannot predict exactly, it can place bounds on what might be possible – first best results in economics.

  8. Estimate Hidden Parameters: Some data cannot be seen such as the contagiousness of a disease or connectedness of a network, but they can be estimated from a model.

  9. Calibrate: Models enable us to calibrate multiple forces – global warming models.

  10. Real Time Decision Aids: Models can be used to aid real time decision making by evaluating the impact of a decision.

  11. Institutional Design: Models enable us to design new institutions and organizations. Models of network failure help design supply chains. Models of preference aggregation can help in the design of voting rules and electoral institutions.

  12. Experimental Design: Models can help determine which experimental design would be most informative. Suppose we want to discern which of two learning rules people use. We can construct a model to help construct an optimal experiment.

  13. Help Choose Among Institutions: We have three blunt institutional solutions to any problem: democracy, markets, or hierarchy. Examples: Pollution Permits? Good idea – what about mercury? Vouchers? Good idea – what about informational problems

  14. Naming of the Parts: Models oblige us to identify the relevant actors, their information, their behaviors, and their environment.

  15. Work Through Logic: Models oblige us to work through the logic of a situation. Take for example, the decisions on the auto industry bailout.

  16. Identify Logical Boundaries: Models help us to disentangle the contexts in which one analogy (two heads are better than one) applies rather than another (too many cooks spoil the broth).

  17. Inform Data Collection: Models help us to figure out which data would be useful – education models. Initially didn’t look at partnership characteristics with HIV-AIDS.

  18. Identify and Rank Levers: Models can enable us to see the relevant impacts of choice variables. Does cutting taxes have a larger effect than reducing regulation?

  19. Comparative Statics and What Ifs: Models can enable us to discern how outcomes change as independent variables change – how negative campaigning increases with closeness of election

  20. Counterfactuals: Models enable us to run counterfactuals – suppose that we had not vaccinated people for smallpox.

  21. Humility: Models make us aware of how little we know about the how parts of the world work – voting behavior, market crashes.

  22. Equilibrium, Cycle, Complex, or Chaotic: Models can help us determine whether a system is likely to go to equilibrium (a bazaar) to cycle (predator prey), to be complex (a stock market), or to be chaotic (weather systems)

  23. Inductively Explore: Models can help us to explore implications of ideas. What if we create an evolutionary system?

  24. Acquire Tools: Markov theory was not developed for any of the applications we consider in this class. By constructing models we build tools that can spillover into other applications.

  25. Fan Out Interdisciplinarity: Models often apply across multiple disciplines. This is true of network models, coordination models, and replicator dynamics models.

  26. To Become Large: Phil Tetlock in his book Expert Political Judgment shows that foxes (people who are skeptical and hold lots of models in their heads) are much better at prediction than hedgehogs (people who have a single model). Thus, having competing models in your head makes you more effective

  27. Thanks Carl Simon Josh Epstein

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