Consider a conditional prediction market, e.g. “if my cool policy is implemented, then widget production will increase by at least 15%”. To my understanding, markets like this are intended as a tool for finding P(outcome|event) and the market just gets unwound or undone or refunded if event doesn’t occur.
I can work through the math and see that refunding the market indeed makes the price reflect P(outcome|event), but this exacerbates one of the biggest issues with prediction markets: no one wants to lock up $1 of capital to extract $0.10 of profit in a year, so no one will lock up $1 of capital to extract $0.10 of profit in a year and only if some extra event happens.
My question is: are there any interesting or viable alternative ways to run a counterfactual or conditional prediction market? Off the top of my head, I could imagine using markets for P(event) and P(event→outcome) to derive P(outcome|event), which would still pay out something if event didn’t occur.
[Question] How “should” counterfactual prediction markets work?
Consider a conditional prediction market, e.g. “if my cool policy is implemented, then widget production will increase by at least 15%”. To my understanding, markets like this are intended as a tool for finding P(outcome|event) and the market just gets unwound or undone or refunded if event doesn’t occur.
I can work through the math and see that refunding the market indeed makes the price reflect P(outcome|event), but this exacerbates one of the biggest issues with prediction markets: no one wants to lock up $1 of capital to extract $0.10 of profit in a year, so no one will lock up $1 of capital to extract $0.10 of profit in a year and only if some extra event happens.
My question is: are there any interesting or viable alternative ways to run a counterfactual or conditional prediction market? Off the top of my head, I could imagine using markets for P(event) and P(event→outcome) to derive P(outcome|event), which would still pay out something if event didn’t occur.