From root: How do I automate taking actions with simulations?

How well does a policy generalise?

Which action-taking policy logic finds the best actions?

How sensitive is this choice to changes in the data? How sensitive is this choice to changes in the outcome model?

Answers to both of these questions tell us how the action-taking policy generalises within the problem domain to alternative scenarios and underlying system mechanisms.

How sensitive is this choice to changes in the action parameters?

In much the same way as it does in learning simulations of the real world answering this question tells us how sloppy the action-taking policy is, and as a result how generalisable it could be to other problem domains.

References and further reading

Hardwick, Robert J. 2026a. “How do I automate taking actions with simulations?” umbralcalculations, September. https://umbralcalc.github.io/posts/how_do_i_automate_taking_actions_with_simulations.html.
Hardwick, Robert J. 2026b. “Is a sloppy simulation a bad one?” umbralcalculations, November. https://umbralcalc.github.io/posts/is_a_sloppy_simulation_a_bad_one.html.
Hardwick, Robert J. 2026c. “What is a policy?” umbralcalculations, September. https://umbralcalc.github.io/posts/what_is_a_policy.html.
Transtrum, Mark K., Benjamin B. Machta, Kevin S. Brown, Bryan C. Daniels, Christopher R. Myers, and James P. Sethna. 2015. “Perspective: Sloppiness and Emergent Theories in Physics, Biology, and Beyond.” The Journal of Chemical Physics 143 (1): 010901. https://doi.org/10.1063/1.4923066.