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!
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?
And for large sums of money, you don't need prospect theory to explain loss aversion. Plain old marginal utility will do.
(I might have missed an explicit description of these "decision-making mechanisms" in the paper)
>we find that the ... most complex class ... lies outside the simple classes
Another curious statenent
Great push. We actually can't make any mechanistic claims from the data/math in this paper. From an ML prediction standpoint, we're mixing a PT and EU theory together. But to what extent that is the actual cognitive process we have to remain agnostic about. That being said, a reason this arbitration between EU and PT is intriguing is because there's a lot of work about arbitration between dual process models in psychology (System 1 and 2; model-free and model-based; labor versus leisure; etc.)
It's unsurprising that the effects are seen most when the amounts are small. With large amounts, people think harder and are more likely to follow rational choice theory.
huitzitziltzin•5mo ago