How Prediction Markets Can Save Democracy From Itself
It is becoming increasingly apparent that democracy has an information problem. Voters are simply unable to sift through the deluge of information and policies to stay informed at a functional level. According to Jason Brennan, Flanagan family chair of economics, ethics and public policy at Georgetown University, “Most voters are systematically misinformed about the basic facts relevant to elections, and many advocate policies they would reject if they were better informed.” Democracy is no magical panacea to all governance woes. Without a sufficiently informed populace, democracy cannot function effectively, resulting in poor and incoherent policy choices and shifts toward strong authoritarian leaders. How can we solve Democracy’s informational inadequacies, while retaining its egalitarian and smooth leadership switching properties?
Prediction markets, which are fundamentally tools for aggregating the best and most unbiased information possible, provide several direct and indirect solutions to these problems.
One core stumbling block of the current democratic system is an inability for voters and legislature to accurately measure the effect that a given policy will have. For example, a repeal of the Affordable Care Act has garnered popular support among a large group of voters who would likely face losing or paying more for healthcare if the policy were passed. Where could these citizens have gotten the vital information necessary to avoid such self-harming decision making? Politicians are spouting hyperbolic statements and baseless predictions. Mainstream media outlets profit off of sensationalist journalism, detached from realistic outcome probabilities, and driven by group think. Meanwhile, it’s nearly impossible to sift out the truth from alternative news sources. There is simply no quantifiable, accessible, and unbiased source of truthful information pertaining to the effects of policy decisions. This is precisely where prediction markets can help. In existing models of fact dissemination, actors typically have limited negative impact from spreading false forecasts. With a prediction market, spreading falsehoods will result in a financial loss whereas providing accurate information will result in gains.
Sifting Through News
A basic prediction market follows the form, “will this event occur?” These markets can be helpful to inform of the likelihood that an event will happen, which can help readers sift through sensationalist journalism. Take for example a media outlet which is reporting a high likelihood that Iraq possesses weapons of mass destruction. This news gets clicks by rising fears, however as it turned out the likelihood of Iraq having such weapons was low. A prediction market of the form, “Does Iraq possess weapons of mass destruction?” would have been helpful to guide readers, temper irrational concerns, and decrease support of foreign policy hawkishness. By informing the public in this way, prediction markets can steer public opinion away from fear driven policies and toward quantifiable, factual, information.
Estimating Policy Effect
Prediction markets can be extended beyond their basic form into markets which are combinations of events, called conditional (or multidimensional) markets. These markets ask questions of the form, “If A happens, will B happen?” or “If A happens, what will the change in B be?” Such markets can provide an indispensable tool toward informing of the effect of policies or other decisions. Let’s go back to health care as an example. As it stands, public sentiment supporting ACA repeal is largely driven by viral falsehoods, loud figureheads, and filter bubbles. There is no clear and unbiased warning sign informing these supporters that such a policy decision could have significant negative impacts on their livelihood. In this case, we can make markets that show the public exactly that. Take a market which asks, “If the ACA is repealed, what will the change in number of American’s insured in 1 year be?” This market would aggregate the best possible estimate of this outcome in a quantifiable and unbiased fashion. Markets such as these can provide a clear and continuously updating source of truth for the public.
Governance
Futarchy is a proposed form of governance which uses prediction markets to automate decision making. With Futarchy, instead of asking legislature to make policy decisions, the market is asked. For example, a government may want to decide whether or not to spend $2 billion on an infrastructure project. The government’s imperative for funding such a project is ultimately raising GDP. Ideally, members of the legislature would become experts on this topic and vote in an informed and unbiased member. Realistically, this is unlikely to be the case. In the case of Futarchy, the government would create two markets: the first asks, “If we fund this project, what will our GDP be in 2 years?” and the other asks, “If we do not fund this project, what will our GDP be in 2 years?” Following a trading period, where participants are financially incentivized to provide the most accurate information available, the market with the greater estimated outcome automatically becomes the chosen policy. Those who had purchased shared in the losing market would have all of their funds returned. Those who participated in the winning market will receive a gain or loss, depending on the accuracy of their predictions, after the 2 year period has elapsed.
This mechanism fills the requirements outlined, both solving informational inefficiencies, and retaining democracy’s egalitarian properties. In the Futarchy case, voters can still have equal say. Independent of the Futarchy decision making, the government would hold a voting process. For example, the government could ask its citizens to rank their priorities of what they want from their government. One voter could rank GDP first, education level second, and healthcare coverage third, and another voter could rank differently. All of these rankings would be compiled into a vector which represents the priorities that the government is required to optimize for. Now, each Futarchy market will optimize for that vector. In this way, voters are still fully represented by the government in an egalitarian fashion, and informational inefficiencies are eliminated by the market. Futarchy on a federal government level seems like a moonshot. However, in the rapidly evolving blockchain ecosystem, it can be tested today. We are committed to being at the forefront of those efforts.
