The Volokh Conspiracy

So far, my posts have implicitly assumed independence between forecasts and decisions. Now, let’s consider some ways in which we might structure prediction markets to forecast the decisions themselves and their consequences, so that the forecasts might influence the decisions.

(1) Markets predicting decisions. A market that predicts a decision might end up affecting the decision. Suppose that Eugene is elected dictator, but because of his blogging responsibilities, His Tremendousness must make many decisions. So, he establishes prediction markets forecasting what decisions he will make.

Now, Eugene is presented with a decision to make, and he quickly analyzes the problem and leans toward Decision A. But then he checks the market and sees that it forecasts that he will make Decision B. He wonders, why is that? He looks more carefully and realizes that he has missed some aspects of the problem.

Some of the dynamics of the deliberative market are present here. A trader predicting a decision can profit by developing arguments that will persuade the decision maker. For example, the trader can write an argument for Decision A and bet on Decision A just before releasing the argument. Eugene might thus create a market predicting his decisions as a way of generating research and arguments relevant to those decisions.

(2) Conditional markets. A conditional market predicts some variable contingent on a condition. A simple way to run such a market is to stipulate that all money spent on the prediction market will be refunded if the condition does not occur. For example, one market could predict a corporation’s stock price if a corporation decides to build a factory, and a separate market could predict the stock price if it doesn’t build the factory. The corporation can compare the forecasts to assess the market’s perception of the effect of building the factory on stock price.

These are a useful tool, but there are important caveats. First, small deviations between two markets can’t be taken too seriously. If Market A predicts a stock price of $30.00 and Market B predicts a stock price of $30.01, the difference could just be noise. Relatedly, if the condition will have little effect on the stock price, even subsidized prediction markets will give people little incentive to study the effect of the condition. Instead, the subsidy will just give general incentives to study all factors that might affect future stock price.

Second, traders will recognize that information unknown to them may affect the decision. For example, last May, Hillary Clinton’s chance of winning the Presidency conditional on being nominated was estimated based on prediction markets at over 70%. That could indicate that Clinton was a strong candidate. It also could mean that the Democrats would stick with a weak candidate like Clinton only if other factors, like the economy, were pointing so strongly in the Democrats’ direction that Democratic primary voters did not care about electability.

In our next installment, I’ll show that “normative markets” combine the two market approaches considered above.