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no complexity theory in economics

Roger E. Farmer has a blog post on why economists should not use complexity theory. At first, I though he was going to argue that complexity models have been dis-proven or they use unreasonable assumptions. Instead, he simply says we don’t have enough data:

The obvious question that Buzz asked was: are economic systems like this? The answer is: we have no way of knowing given current data limitations. Physicists can generate potentially infinite amounts of data by experiment. Macroeconomists have a few hundred data points at most. In finance we have daily data and potentially very large data sets, but the evidence there is disappointing. It’s been a while since I looked at that literature, but as I recall, there is no evidence of low dimensional chaos in financial data.

Where does that leave non-linear theory and chaos theory in economics? Is the economic world chaotic? Perhaps. But there is currently not enough data to tell a low dimensional chaotic system apart from a linear model hit by random shocks. Until we have better data, Occam’s razor argues for the linear stochastic model.

If someone can write down a three equation model that describes economic data as well as the Lorentz equations describe physical systems: I’m all on board. But in the absence of experimental data, lots and lots of experimental data, how would we know if the theory was correct?

On one level, this is a fair point. Macro-economics is notorious for having sparse data. We can’t re-run the US economy under different conditions a million times. We have quarterly unemployment rates and that’s it. On another level, this is an extremely lame criticism. One thing that we’ve learned is that we have access to all kinds of data. For example, could we have m-turker participate in an online market a  million times? Or, could we mine eBay sales data? In other words, Farmer’s post doesn’t undermine the case for complexity. Rather, it suggests that we might search harder and build bigger tools. And, in the end, isn’t that how science progresses?

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Written by fabiorojas

October 7, 2016 at 12:39 am

4 Responses

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  1. Fabio, I’m not sure I see any conflict between what he said and what you said.

    His final point is: “If someone can write down a three equation model that describes economic data as well as the Lorentz equations describe physical systems: I’m all on board. But in the absence of experimental data, lots and lots of experimental data, how would we know if the theory was correct?”

    And, you suggest how to get lots and lots of data to generate that three equation model (e.g., eBay) as well as how to get lots and lots of experimental data to test it (e.g., mTurks).

    Am I missing something?

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  2. Nobody fully understands what machine learning-based models mean. Why wouldn’t we study and use something that produces accurate predictions simply because we don’t fully comprehend how it works?

    Nice to see an economist come clean about their aesthetic tastes for a change, however.

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    Michael F. Martin

    October 7, 2016 at 3:41 pm

  3. One tenet of complexity is that small perturbations in systems as they scale can end up leading to dramatically different outcomes. That means that it’s not clear that the non-linearities from e-Bay will scale to entire economies or experiments with Turkers. I don’t mean to imply that useful things can’t be learned, but that there are real problems connecting complexity theory to empirical studies given with the types of data that we can generate in the social sciences.

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    mike_bader

    October 7, 2016 at 11:04 pm

  4. I have to apologize that my comment is unrelated to the post, but I’d like to hear what org-heads think about the following proposal for our journal review process:
    https://blue.cse.buffalo.edu/posts/2015-07-14-my-case-for-reverse-blind-review/

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    Jon

    October 26, 2016 at 2:13 am


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