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[Computational Complexity] Finding the Right Model

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  • Lance
    Last fall I wrote about the different focus on models and proofs in the Econ and CS theory communities. Today I ll focus on the purpose of a model and what
    Message 1 of 1 , Apr 6 6:18 AM
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      Last fall I wrote about the different focus on models and proofs in the Econ and CS theory communities. Today I'll focus on the purpose of a model and what makes a good one.

      An economist once explained the difficulty in coming up with a good model. A good example is how people prefer one choice to another. In standard utility theory, people assign a value (or utility) to each state of the world and try to maximize their expected expected. But then one notices people tend to give too much weight to low probability events like in the Allais Paradox. So one can look at more general models of preferences such as prospect theory. But these general models might be too general, and thus have little explanatory value and in particular may not allow us to predict future behavior and outcomes, the ultimate purpose of an economic model.

      In computational complexity, we don't use our models for prediction of future events. Our models of computation are meant more for comparing different types of resources: Time versus space, quantum versus classical, parallel versus serial and of course, nondeterministic versus deterministic. What makes a good model for us is robustness (small changes to the definition doesn't change the problems it can solve), nice closure properties and some connection to reality. Sure the class P, Polynomial-time, is not equal to efficient computation if the constants or exponents of the running time are large but it's a reasonable approximation.

      Part of my research agenda over the past few years is trying to find computation models for agents in economic theory. Many of the ideas of our community, such as connections between randomness and unpredictability, can play an important role in economic theory. But finding and determining what is the right models are the biggest challenge, as even the purpose of models differ in our communities. Communication is the key, working directly with people in the other community. One of the great advantages of Northwestern is having a large strong micro-economics group, many of whom understand that computation is an important resource and are willing to at least listen to us computer scientists.

      We also need to keep an open mind--the goal should be making the best connections between communities and not worrying so much about results to impress our direct peers. Having tenure definitely helps.



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      Posted By Lance to Computational Complexity at 4/06/2010 08:18:00 AM
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