While I didn’t explore the general case, which would be easier to if/when I formalize the dilemma, my intuition from the specific thought experiment is that generally when faced with an adversary (that is less than optimally adversarial, as is more common in the real world) that has an asymmetric advantage over an agent (e.g., as in the above case the adversary is a price-setter with an ability to predict agent’s decisions) if the agent has some non-zero social values, CDT agents do better than FDT agents.
From a policy perspective, it also seems more reasonable to imagine agents under CDT; policies aimed at aligning agent’s causal expectations with optimal social outcomes seem more effective at addressing e.g. freeriders. Of course, decision theory looks at problems from the agents perspective, not how we should assume agents are likely to act from a policy perspective, but part of the theoretical utility is in developing models for behavior which can serve a policy perspective. And from there, CDT which is commonly implicit in agent based models, seems to work out better in practice. Braess’s paradox which is derived from real world observations is easily explained assuming agents make decisions under CDT, but if we assumed agents acted under FDT, it wouldn’t occur.
Edit: to be clear, Braess’ paradox not occurring would be a good thing, if people made driving decisions in a way that optimized overall traffick that would be better. But in this world we live in it does occur. Also, it is noteworthy that if we imagine individual FDT agents, their utility would likely be unchanged and Braess’s paradox would still occur, since they would make their decisions based on the empirically observed behavior of others who don’t operate under FDT.
While I didn’t explore the general case, which would be easier to if/when I formalize the dilemma, my intuition from the specific thought experiment is that generally when faced with an adversary (that is less than optimally adversarial, as is more common in the real world) that has an asymmetric advantage over an agent (e.g., as in the above case the adversary is a price-setter with an ability to predict agent’s decisions) if the agent has some non-zero social values, CDT agents do better than FDT agents.
From a policy perspective, it also seems more reasonable to imagine agents under CDT; policies aimed at aligning agent’s causal expectations with optimal social outcomes seem more effective at addressing e.g. freeriders. Of course, decision theory looks at problems from the agents perspective, not how we should assume agents are likely to act from a policy perspective, but part of the theoretical utility is in developing models for behavior which can serve a policy perspective. And from there, CDT which is commonly implicit in agent based models, seems to work out better in practice. Braess’s paradox which is derived from real world observations is easily explained assuming agents make decisions under CDT, but if we assumed agents acted under FDT, it wouldn’t occur.
Edit: to be clear, Braess’ paradox not occurring would be a good thing, if people made driving decisions in a way that optimized overall traffick that would be better. But in this world we live in it does occur. Also, it is noteworthy that if we imagine individual FDT agents, their utility would likely be unchanged and Braess’s paradox would still occur, since they would make their decisions based on the empirically observed behavior of others who don’t operate under FDT.