This is basically the same problem as Gears vs Behavior, specialized to the context of prediction markets. To a large extent, we can use prediction markets to pull out insights into system gears using tricks similar to those discussed in that piece. In particular, causal models are easily adapted to prediction markets: just use conditional bets, which only activate when certain conditions are satisfied. Robin Hanson talks about these fairly often; they’re central to a lot of his ideas about prediction-market-driven decision-making systems (see e.g. here).
Very good and helpful! These strategies can make prediction markets *super effective*, however getting a working prediction market on conditional statements increases the difficulty of creating a sufficiently liquid market. There exists a difficult to resolve tension between optimizing for market efficiency and optimizing for “gear discovery.”
People who want to use markets do need to be wary of this problem.
“of all the hidden factors which caused the market consensus to reach this point, which, if any of them, do we have any power to affect?” A prediction market can only answer the question you ask it. You can use a conditional market to ask if a particular factor has an effect on an outcome. Yes of course it will cost more to ask more questions. If there were a lot of possible factors, you might offer a prize to whomever proposes a factor that turns out to have a big effect. Yes it would cost to offer such a prize, because it could be work to find such factors.
Good point. But it is not just a cost problem. My conjecture in the above comment is that conditional markets are more prone to market failure because the structure of conditional questions decreases the pool of people who can participate.
I need more examples of conditional markets in action to figure out what the greatest causes of market failure are for conditional markets.
This is basically the same problem as Gears vs Behavior, specialized to the context of prediction markets. To a large extent, we can use prediction markets to pull out insights into system gears using tricks similar to those discussed in that piece. In particular, causal models are easily adapted to prediction markets: just use conditional bets, which only activate when certain conditions are satisfied. Robin Hanson talks about these fairly often; they’re central to a lot of his ideas about prediction-market-driven decision-making systems (see e.g. here).
Very good and helpful! These strategies can make prediction markets *super effective*, however getting a working prediction market on conditional statements increases the difficulty of creating a sufficiently liquid market. There exists a difficult to resolve tension between optimizing for market efficiency and optimizing for “gear discovery.”
People who want to use markets do need to be wary of this problem.
“of all the hidden factors which caused the market consensus to reach this point, which, if any of them, do we have any power to affect?” A prediction market can only answer the question you ask it. You can use a conditional market to ask if a particular factor has an effect on an outcome. Yes of course it will cost more to ask more questions. If there were a lot of possible factors, you might offer a prize to whomever proposes a factor that turns out to have a big effect. Yes it would cost to offer such a prize, because it could be work to find such factors.
Good point. But it is not just a cost problem. My conjecture in the above comment is that conditional markets are more prone to market failure because the structure of conditional questions decreases the pool of people who can participate.
I need more examples of conditional markets in action to figure out what the greatest causes of market failure are for conditional markets.
Markets can work fine with only a few participants. But they do need sufficient incentives to participate.