How manipulation-resistant are Prediction Markets?

Our Undertaking in Empirical Cryptoeconomics

It often is after-the-fact that we realize how much harm results from a decision made by key organizations such as nations, firms, cities, or clubs. Before such a decision was made, there were certainly people who had a deep understanding about its consequences and therefore good reasons to disapprove it. However, these relevant experts were not enticed enough to share their knowledge with properly-motivated decision makers, nor were non-experts induced to learn that these decisions are inferior. Most importantly, decision-makers ultimately are not held accountable if the decision turns out to not have the consequences they promised.

The Failure of our Information Institutions

An important contribution to the implementation of inefficient decisions is that our information institutions, i.e. public relations teams, organized interest groups, news media, conversation forums, think tanks, universities, journals, elite committees, or state agencies fail to induce people to acquire and share relevant information.



Robin Hanson, the inventor of Futarchy, father of modern prediction markets, and currently Professor of Economics at George Mason University, therefore argues that we should consider augmenting our (political) information institutions with speculative market institutions. Since speculative markets excel at inducing people to acquire information, share it via trades, and aggregate that information into consensus prices that convince wider audiences, they seem to be ideal information institutions.

Futarchy as the new Form of Decision-making

Inspired by the information-aggregation power of speculative markets, Hanson developed the concept of Futarchy — a form of governance where only those policies become law for which speculative markets have clearly estimated that they would increase national welfare.



The prices of speculative markets could estimate a national welfare metric (such as GDP) conditional on a proposed policy being adopted, and on that policy not being adopted. This would be possible through called-off trades (i.e. which are made null and void if the proposed policy is being adopted (or not)) in assets that pay in proportion to the measured national welfare. Finally, the policy would only be adopted if the market expects that policy to increase national welfare relative to the status quo.



This also means that with Futarchy, decision-makers (i.e. the market participants) are held accountable if the predicted consequence (the national welfare metric estimate f.ex.) would not come true: they would lose their money. To impact a speculative market, you literally have to put your money where your mouth is.



Futarchy can be applied as a form of decision-making to many kinds of organizations. The organization would simply need to define a publicly verifiable metric, and their decision-making would then depend on the outcome of a pair of prediction markets on that metric.



As Vitalik Buterin described in a blog post back in 2014, the first market would be denominated in a currency which pays out $1 if a company makes decision A and $0 otherwise. The second market would be denominated in a currency which pays out $1 if a company takes decision B and $0 otherwise.



Therefore, the first market only has a value if its condition is met, i.e. if decision A is made. Similarly, the second market only has a value if decision B is made. If governed by Futarchy, only the decision for which the market has estimated a higher price will be made.



To illustrate the application of Futarchy in a corporate environment, let’s assume that a company sets up two prediction markets on its revenues for the next 5 years conditional on hiring a given CEO A or a given CEO B by the end of the month.

Two prediction markets before the hiring decision is made

At decision time (f.ex. at the end of the month), the expected value of the second market B (if CEO B is hired) is clearly higher than the expected value of the first market A (if CEO A is hired). Thus, CEO B is hired.



Whether the winning outcome is chosen by the integral over the entire market length, the integral over the past day, or a weighted integral over the past week doesn’t matter as long as the decision metric is specified in the smart contract beforehand.

Prediction market after the hiring decision has been made

CEO B is the winner of the conditional prediction market. Market participants who bet on CEO A get their investment refunded: Since CEO A has not been hired, CEO A tokens do not contain any value.



Those who bet on CEO B, however, are still participating in the prediction market which is just no longer a conditional market — the hiring decision has been made.