Key cruxes

Pragmatic

• Despite being around as an idea for 20 years, futarchy hasn't happened. Prediction markets aren't clearly more accurate than Metaculus and those markets that exist generally aren't useful for decision makers.
  • Michael Story puts it well (emphasis his). It's easy to think that prediction markets tell us a lot about the world. But maybe instead they tell us who is a good bettor and who isn't. And perhaps we should hire those people to be grant funders or decision makers, but it's not clear we should use the process to make decisions.

"Most of the useful information you produce is about the people, not the world outside. Forecasting tournaments and markets are very good at telling you all kinds of things about your participants: are they well calibrated, do they understand the world, do they understand things better working alone or in a team, do they update their beliefs in a sensible measured way or swing about all over the place? If you want to get a rough epistemic ranking out of a group of people then a forecasting tournament or market is ideal. A project like GJP (which I am very proud of) was, contrary to what people often say, not an exercise primarily focused on producing data about the future. It was a study of people! 

• Often mechanistic ideas reduce friction and so allow things to happen at scale. It is unclear if grantmaking is at a scale that this is necessary. Prediction markets themselves are only starting to move to a scale of $10ks for things other than top level political decisions or actual financial markets. Maybe this is one better left to expert consensus for now
• Incentives are short term

Theoretical

• Causality might diverge from conditionality in the case of advisory/indirect markets. Traders are sometimes rewarded for guessing at hidden info about the world—information that is revealed by the fact that a policy decision was made—instead of causal relationships between the policy and outcomes.^[11]
  • For instance, suppose a company is running a market to decide whether to keep an unpopular CEO, and they ask if, say, stocks conditional on the CEO leaving would be lower than stocks conditional on the CEO staying. But traders might correctly think that most cases where the CEO were to get fired, there would have been a recent disaster in the company, so they would trade the CEO-leaves stocks at a low price unrelated to their competence. 
• Maintaining a careful and aligned measure of welfare is likely to be extremely difficult. It is hard to capture everything we value as a society (especially on different levels, like cities and states), and it would also be very difficult to avoid manipulations. Hanson notes this issue (in objections 13-15, 22-23),

Prerequisites

In his paper, Hanson:

• claims that poor information institutions are a major failing of democracy
• claims that prediction markets are a better kind of information institution than most
• outlines several proposals for using prediction markets (or “decision markets”) for policy decisions

What is a decision market?

Hanson views decision markets—a variation on prediction markets—as an excellent source of information, and builds his entire paper around the concept, so it is worth understanding the basic mechanism. A basic decision market is a pair of prediction (or “speculative”) markets in which each prediction market is conditional on an event, like a policy being accepted or rejected. It works as follows.

If an entity (e.g. a company) needs to make a big decision (e.g. choosing a new store location), it has different means of collecting information to inform this decision. The entity could consult experts, it could run trials, it could poll its own local employees, etc. It could also run a decision market or a prediction market to aggregate a group’s collective knowledge on the topic in a way that seems to outperform polling. To do this, the entity would host bets on whatever outcomes interest it (e.g. profits), and make these bets conditional on the different options that are available (e.g. a shortlist of locations). This involves setting up contracts that are rendered void if an option is not picked in the end.^[4] The entity can then extract information from the prices that naturally emerge for contracts that are conditional on different options, and use that information to come to a decision on the topic.^[5]

A hypothetical example of a decision market

Suppose a company is opening a new store, and wants to open it either in Arcadia or in Boston. The company can set up the market by declaring some incentives (like a fake currency) and encourage bets on the outcome of opening the store in Arcadia or in Boston (bets that the company will enforce). The company might announce that “shares of Arcadia” or “shares of Boston” are contracts that will eventually be worth N of the currency unit, where the value of N depends on the future net revenue from the corresponding store. The company will pay out Arcadia contracts at a specified time if Arcadia is chosen (in which case all trades about Boston will be reversed), and it will pay out Boston contracts if Boston is chosen (in which case Arcadia contracts will be reversed).

Now suppose two employees disagree about how much a store in Arcadia would bring in profits, and therefore about what one should pay for a share of Arcadia. Xander thinks that a share of Arcadia will be worth 80 units (ie. he thinks N will be 80), and Zoe thinks...

Futarchy is a proposed government system in which decisions are made based on betting markets. It was originally proposed by Robin Hanson, who gave the motto "Vote on Values, But Bet on Beliefs".

Related Pages:Prediction Markets, Government, Law and Legal systems, Voting Theory, Politics