Enter: Futarchy

Futarchy in three Paragraphs:

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Democracy, while surpassing previous governance systems, exhibits significant flaws when it comes to accountability and the influence of special interest groups. Once elected, representatives face minimal accountability beyond the prospect of future elections; often leading to a discrepancy between the promises they made and their actions in office. This gap is exacerbated by the influence of lobbyists and special interests, which often swap politicians away from public interest. Moreover, electoral success often hinges on candidates’ charisma and rhetorical skills rather than their expertise in relevant policy domains. This results in key decision makers who lack the necessary knowledge to address complex or technological issues effectively.

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To address Democracy’s shortcomings, economist Robin Hanson proposed ‘Futarchy,’ a governance model where democracy sets societal values — and markets decide on how to achieve them. In this system, the public votes on their values such as increased lifespan or decreased unemployment. Decision markets are then employed for specific policy proposals, allowing individuals to ‘vote’ through market participation: they make bets on which policy they believe will be most effective in achieving the stated values. For instance, if reducing CO2 emissions is a prioritized value, and a policy to plant 10 million trees is proposed, two markets would be created: one for and one against the policy. The market’s preference, indicated by the bet with the higher price, determines the policy’s implementation — combining values with market efficiency. 

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Futarchy is not without its challenges, including the need for clear, measurable goals and the risk of market manipulation. However, its aim is not to achieve utopia but to enhance decision-making by aligning it more closely with the expertise and the collective intelligence of the populace rather than solely on persuasion or appearance. While not infallible, Futarchy introduces a layer of accountability absent in current systems. It suggests a path where choices are informed by a broader base of knowledge, potentially leading to more optimal outcomes and a governance model that better reflects the complexities of modern societies.

Introduction

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Our electoral systems have functioned similarly since the inception of democracy. Despite the industrial revolution, and the advent of the internet, many of our systems have remained stagnant for over a century. Banks still function largely the same as they did post FDR, and we still vote on pieces of paper; counted by pollsters and voting machines from the early 2000s. We still find ourselves holding onto archaic and manipulatable institutions . 

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Today, many voters hold a greater understanding of social and economic conditions than their elected representatives — and like the system they run, they are often operating with little understanding for how most people move through the world. However this is just one of the many flaws of our current democratic system of governance. 

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Democracy often fails to incentivize meaningful and transparent behavior, and in fact incentivizes the opposite:

• Representatives aren’t held accountable — election cycle politics dominate their stances but there is no enforcement of those stances.
• Minority representation: the masses are only able to choose their representatives — their knowledge, expertise, and opinions are not taken into account when it comes to matters of policy; they are only able to express a preference for who is most closely aligned, which may not be close at all.
• Expertise is useless: subject matter experts are sidelined for cults of personality — policy making prioritizes traits such as charisma, likeability, and appearances over knowledge. 

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Enter: Futarchy

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Futarchy is a relatively new mechanism of governance, coined in an essay in 2008 by Robin Hanson in his paper “Shall We Vote on Values, But Bet on Beliefs?” (https://mason.gmu.edu/~rhanson/futarchy2013.pdf). Hanson posits Futarchy as a solution to the problems of democracy as it increases accountability, empowers the masses, increases predictive policy accuracy, and eliminates echo-chambers. 

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At its core, it is an optimistic perspective on market-based governance: believing that crowd-sourcing opinions in the form of bets is a much more accurate means of measuring real voter preference. True democracy has been proven to be somewhat incompatible with DAOs — as debate and opinion triumph over action and co-creation. Current systems are mired in “what should we do?” types of discussions, leading to inactivity.

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Futarchy is the “nut up or shut up” of governance models. It still uses democratic elements to define an organization’s goals or priorities, but then it employs decision markets to find the most efficient path to achieve them. More simply, Futarchy uses:

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1. Democracy for “What do we want?”
2. Decision markets for “How do we get it?”

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What is a Decision Market?

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To understand a decision market, one first must understand how prediction markets function. A prediction market is a speculative market where participants buy and sell bets based on their beliefs about the outcomes of future events. They can involve a variety of domains, from sports betting to the outcomes of elections — and remarkably they tend to be more accurate compared to other ways of guessing.

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Decision markets are a subgenre of prediction market — and play a role in decision making, often about policy or business. In the context of Futarchy, decision markets are used to evaluate potential outcomes of different actions or policies based on consensus goals or values.

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Thought Experiment: What if the COVID-19 Stimulus Was Decided via Futarchy?

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To best understand how Futarchy might affect policy in the future, we’ll model an event in the past — as if the action had been decided by a decision market. 

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Part 1: Setting up the Decision Market

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Desired Value Goal: Assume that, during COVID-19, the value goal was “maximizing national welfare,” with specific metrics including employment rates, economic growth, and inflation. 

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Policy Proposal: The policy in question is whether to implement economic stimulus payments, considering metrics like the ones listed above.

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Decision Markets:

Prediction A: Implementing stimulus payments will be good for national welfare.

Prediction B: Implementing stimulus payment will be bad for national welfare. 

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Participants in these markets would bet on which prediction they believe to be most accurate, with the market prices reflecting the collective belief in stimulus payment versus doing nothing. 

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Part 2: Measuring the Outcome

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Fast forward 4 years, it is now 2024 and the direct impacts of this decision are measurable. Prediction A (pro-stimulus) showed a higher confidence in improving national welfare than Prediction B (no stimulus) — as this was the consensus at the time. However, today inflation rates have soared to levels that are detrimental to national welfare.

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In other words: the prediction market was wrong.

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Initially, it would appear that the Futarchy model has failed, because it led to a decision that contributed to high inflation. Critics of futarchy may argue that the prediction market did not accurately predict the long-term consequences of the stimulus payments. 

However, upon deeper reflection, some key insights emerge:

• Complex predictions: There is a challenge in predicting complex economic outcomes, as the inflation may not have arisen from the actual stimulus payments but, rather, other metrics such as global supply chain issues. Decision markets incentivize accurate measurements to get to the root cause of it making the “wrong” bet — so that it may be more accurate in the future.
• Information Incorporation: The market’s prediction would have been based only on opinions and information available at the time (Unfortunately, Futarchy didn’t build a time machine). If new, unforeseen factors contributed to inflation, then these could not be factored into the market’s decisions. 
• Most importantly, adaptive learning: The primary benefit of Futarchy is its ability to adapt and learn from past outcomes. The “wrong” bet on stimulus payments and subsequent inflation would provide valuable data to all participants that can now be used to inform future prediction markets. 

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Futarchy as a Universal Decision Maker

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Futarchy as governance model may apply broadly: from companies, to DAOs, to nation states. Private organizations could use a Futarchic model to decide on their next CEO, project deadlines, or even startup pivot directions. This process filters out people who are not already heavily opinionated or experts on the topic — and those with no knowledge of the subject, due to the potential financial loss of being wrong, would likely either:

1. Defer to experts
2. Not participate
3. Take a gamble

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It could also fix the age-old thorn in Democracy’s side: Tragedy of the Commons. Voters in a Democracy often feel scorned — as they are not incentivized to learn about the potentially harmful policies because they feel that their vote has no power. Voting is largely motivated by negative emotions, “the lesser of two evils,” so voters rarely actually vote for what they want, rather, they vote against what they don’t want. Futarchy incentivises educated bets, which require a certain amount of diligence in order to place. 

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There are pseudo-Futarchic experiments already occurring in some of the most well-known DAOs in the Solana ecosystem, namely, Jupiter DAO. The DAO’s first official vote, while not by-the-book Futarchy, utilizes a competitive vote between potential token-launch candidates for their launchpad. This will function simply as a token-weighted, democratic vote — however, because of the DAO’s reward mechanism from launched projects, it is reminiscent of a Futarchic vote. The Jupiter launchpad awards 0.75% of each launched token supply to active participants in votes, with a special caveat: These tokens will be locked for 3-6 months. Voting participants are incentivized to now place an educated 3-6 month bet on which token will benefit them the most over that time horizon. This means that users are incentivized to not simply view each project at its current hype level, but make a calculated decision on which project will be the most beneficial for them, and the DAO, in the future.

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Setting Up a Futarchy

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‍Part 1: The Bet

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1. Choose Metrics & Duration:
   A clear, measurable goal must be selected. In our earlier example we chose national welfare, which was the combination of economic growth (GDP), employment rates, and inflation levels. The duration for assessing the policy’s impact was 4 years(up to 2024), to allow for enough time to evaluate the long-term effects of stimulus payments. 

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2. Define a Decision Point:
   This is the moment when the prediction market’s outcome is used to make a final decision about the proposed policy. In our example we used a decision point early in the COVID-19 pandemic, assuming that it was the same time at which the stimulus bill passed. The decision point must include some considerations, including:

• Urgency: Urgent matters will need to be sooner, complex matters may need more time.
• Action Timeline: The expected timeframe of implementing the action should influence the decision point. 
• Data Timeline: The decision point should be set at a time where there is enough data to analyze,
• Define the Losing Action: What happens to the losers in the betting market? This can vary and depends on what specific actions are being bet upon, but, it typically falls into a few categories:

1. Revert Trades in the Losing Market: Trades are reverted at the decision point, this means losers will not experience financial risk and is beneficial for markets where participation cannot be punished. Their bets will be refunded — therefore incentivizing participation. 
2. Financial Risk for Participants: Trades are not reverted, meaning those who bet wrongly experience financial risk. This approach may be useful for incentivizing careful consideration for more complex predictions as participants have more at stake. 

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3. Create and Publish a Proposal:
   In a DAO this may be proposed on a DAO tool or forum. Within an entity like a corporation it could be proposed to the board. The proposal under consideration in our example is direct stimulus payments sent to individuals to counteract the economic impact of the COVID-19 pandemic. 

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4. Set up prediction markets for Yes and No:
   Create a market in which people can bet yes or no on the aforementioned proposal, in our example it was pro or against stimulus payments, with the criteria of “will it benefit national welfare” immediately and in 4 years.  

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Part 2: The Decision Point

1. Close the Markets, Implement the Policy with Higher Price:
   After allowing time for market participants to digest meaningful information and place their bets, both betting markets should be closed at the decision point. The policy associated with the higher-priced market (and more collective confidence) is chosen for implementation. In our COVID example, it was a “Yes” for stimulus payments. 
2. Implement Losing-Side Action:
   At the decision point, the losing-side action is implemented. In our COVID-19 example, as is common in most instances of Futarchy, the losing side trades were reverted (We called up Jerome Powell and he printed money to refund them).

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Part 3: Measuring the Outcome: Did it Work?

1. Policy Implementation:
   With the decision made, the policy is put into action. This is a critical phase of waiting — in our example it was 4 years as stimulus payments were disbursed and their effects measured.
2. Maturity and Success Metrics:
   Pre-defined data sources should be used to contextualize the outcome. In our example it was an assessment of GDP growth, employment rates, and inflation. This assessment compares desired outcomes with the real-world results. 
3. Market Rewards:
   In some Futarchic designs, there may be a rewards mechanism in place for participants. This could be exclusively for those who predicted the desired outcome or it could be a reward for all participants — this largely depends upon the Losing-Side Action approach. In our example, we asked Jerome Powell to airdrop $BONK to the Solana ecosystem in 2022 (everyone here predicted accurately). 

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Conclusion:

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While Futarchy is not a cure-all, it does provide a compelling thesis for more data and expert-driven governance. Robin Hanson outlined his response to 25 primary concerns, which you can read here. The model applies broadly to a wide range of organizations, so, implementations may vary in success — for example, it could be a revolutionary model for small groups of highly aligned people, but face challenges in economies of scale, or vice versa. 

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However, it is the most exciting experiment in governance in decades. 

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