i’m trying to understand the point about the vitamin d example. if a futarchy market is set up to predict whether “increasing vitamin d consumption” will “increase average lifespan,” wouldn’t participants who believe that wealth (or another confounder) is the actual causal factor, and not vitamin d itself, be incentivized to bet against the vitamin d policy leading to increased lifespan?
the market’s incentive structure seems designed to filter for beliefs about the causal efficacy of the proposed intervention, not merely correlations. if people believe wealth is the cause, they wouldn’t expect a vitamin d policy to succeed in raising lifespans, and would bet accordingly. it appears there might be a slight confusion between correlation in observational data and the causal impact of a direct intervention as assessed by a prediction market.
interesting. altho what we really care about is social utility efficiency (voter satisfaction efficiency), and it’s famously hard to model that in multi-winner systems. SPAV is already very nice tho for being simple, and defaulting to the excellent approval voting method in the single-winner case.
i would advocate using spav with the 1/(2m+1) rule instead of 1/(m+1) because the standard formula (jefferson/d’hondt) systematically biases results in favor of larger coalitions. the webster method (1/(2m+1)) is unbiased, ensuring that a group with x% of the vote receives as close to x% of the seats as possible, regardless of whether they are a large majority or a small minority. strictly speaking, webster minimizes the total error of representation via standard rounding, whereas jefferson effectively rounds down to the detriment of smaller factions.