Abstract: Which variables does an agent have an incentive to control with its decision, and which variables does it have an incentive to respond to? We formalise these incentives, and demonstrate unique graphical criteria for detecting them in any single decision causal influence diagram. To this end, we introduce structural causal influence models, a hybrid of the influence diagram and structural causal model frameworks. Finally, we illustrate how these incentives predict agent incentives in both fairness and AI safety applications.
Mod note: I edited the abstract into the post, since that makes the paper more easily searchable in the site-search, and also seems like it would help people get a sense of whether they want to click through to the link. Let me know if you want me to revert that.
I quite liked this paper, and read through it this morning. It also seems good to link to the accompanying Medium post, which I found a good introduction into the ideas:
Abstract: Which variables does an agent have an incentive to control with its decision, and which variables does it have an incentive to respond to? We formalise these incentives, and demonstrate unique graphical criteria for detecting them in any single decision causal influence diagram. To this end, we introduce structural causal influence models, a hybrid of the influence diagram and structural causal model frameworks. Finally, we illustrate how these incentives predict agent incentives in both fairness and AI safety applications.