In Futarchy, markets are used to decide on and implement policies. These markets follow a general form of “What will a future welfare metric be if a policy is implemented?” For example, a corporation could ask, “What will our Q4 revenue be if we fire our CEO?” and conversely, “What will our Q4 revenue be if we don’t fire our CEO?” Following this, speculators who believe they hold unique insights into the outcome of firing or keeping the CEO are incentivized to participate in these markets. If they think that revenue will be maximized by firing the CEO, then they will buy long shares in the expected revenue if the CEO is fired and short shares in the expected revenue if the CEO is not fired. Upon market closure, a decision is made corresponding to the greater expected outcome. In our CEO example, if the market value for expected Q4 revenue if the CEO is fired is greater than the revenue if CEO is not fired market, then the organization fires the CEO. Market participants are then rewarded depending on their accuracy in predicting future revenue.



In this model governance is both marketized and automated. Policies are determined by values found on an open market and implemented through bound delegates or an automated process. Prediction markets have shown to be the most efficient information aggregation tool leading to the prediction that Futarchy can more accurately identify policies that will optimize outcomes while also lowering bureaucratic overhead.