[timshell]

I'm one of the co-authors of this article.

The TLDR of this paper:

You can generalize theories of decision-making into broad functional forms and then apply gradient descent to find the best parameters for that functional form. For example, prospect theory is multiply a utility weighting function U(x) with a probability weighting function p(x). Kahneman and Tversky proposed one specific set of U(x) and p(x), but we can use autodiff to generate all.

We can apply this method to any functional form.

Happy to answer any questions!

[slinkypinky]

Can you explain what a “differentiable” decision theory is? I understand, for instance, maximizing expected value (and taking a derivative to get a maximum), but I don’t understand how the concept of maximizing expected value could itself be made into a derivative.

Edit: Seems like a “differentiable theory” is just one that can be framed in terms of an optimization problem that can be solved by gradient descent. Is that right?