Thank you so much for sharing this extremely insightful argument, Evan! I really appreciate hearing your detailed thoughts on this.
I’ve been grappling with the pros and cons of an atheoretical-empirics-based approach (in your language, “behavior”) and a theory-based approach (in your language, “understanding”) within the complex sciences, such as but not limited to AI. My current thought is that unfortunately, both of the following are true:
1) Findings based on atheoretical empirics are susceptible to being brittle, in that it is unclear whether or in precisely which settings these findings will replicate. (e.g., see “A problem in theory” by Michael Muthukrishna and Joe Henrich: https://www.nature.com/articles/s41562-018-0522-1)
2) While theoretical models enable one to meaningfully attempt predictions that extrapolate outside of the empirical sample, these models can always fail, especially in the complex sciences. “There is no such thing as a validated predictive model” (https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-023-02779-w).
A common difficulty that theory-based predictions out-of-distribution run into is the tradeoff between precision and generality. Levin (https://www.jstor.org/stable/27836590) described this idea by saying that among three desirable properties—generality, precision, and realism—a theory can only simultaneously achieve two. The following is Levin’s triangle:
Thank you so much for sharing this extremely insightful argument, Evan! I really appreciate hearing your detailed thoughts on this.
I’ve been grappling with the pros and cons of an atheoretical-empirics-based approach (in your language, “behavior”) and a theory-based approach (in your language, “understanding”) within the complex sciences, such as but not limited to AI. My current thought is that unfortunately, both of the following are true:
1) Findings based on atheoretical empirics are susceptible to being brittle, in that it is unclear whether or in precisely which settings these findings will replicate. (e.g., see “A problem in theory” by Michael Muthukrishna and Joe Henrich: https://www.nature.com/articles/s41562-018-0522-1)
2) While theoretical models enable one to meaningfully attempt predictions that extrapolate outside of the empirical sample, these models can always fail, especially in the complex sciences. “There is no such thing as a validated predictive model” (https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-023-02779-w).
A common difficulty that theory-based predictions out-of-distribution run into is the tradeoff between precision and generality. Levin (https://www.jstor.org/stable/27836590) described this idea by saying that among three desirable properties—generality, precision, and realism—a theory can only simultaneously achieve two. The following is Levin’s triangle: