ELI5: What is Futarchy?
Submitted for Superteam UK Scribe Hackathon.
I admit I came across this futarchy term a year ago when I was still finishing my study and was captivated by the idea. I didn’t have time to explore further. But the hackathon offers this as one of the topics, so LFG!!
ELI5: Explain Like I’m 5 (years old)
Futarchy is a form of governance proposed by economist Robin Hanson, which combines prediction markets with democratic decision-making. In a futarchy, policy decisions are made based on which policies would increase a specified welfare measure, as predicted by a prediction market.
In a nation’s setting, the elections are held like this:
Voters decide on what metrics to maximise. The metrics can range from GDP, health metrics, or environmental quality indicators. And then, the prediction markets are set up. These prediction markets determine which policies are expected to improve these metrics the most.
We heard about Vox populi, vox Dei, as a saying that glorifies democracy. For futarchy, collective decision-making is taken more effectively and beyond the electoral vote counts. Futarchy’s manifesto is vote on values but bet on beliefs.
Vote on values but bet on beliefs
We’ll go deep into the meaning and examples, but let’s start with pondering on the question:
What’s the problem with democracy, you may ask, and why has futarchy emerged as an alternative solution?
Note: The transcript of the interview with Robin Hanson, who initiated the idea of futarchical governance, can be read here [1].
From the interview, I can summarise the problems of democracy as follows. And one does not have to follow the transcript because these have been common phenomena in modern political settings.
- People aren’t entirely honest
- There’s a lack of accountability holding over the winning representatives
- The “herd mentality” of the elected representatives as the natural by-product of collectivism
The idea is instead of relying on human representatives with their own sets of beliefs that might not serve the best interests of the constituents or as such advertised during the electoral campaigns, people vote on the policies to achieve a certain goal.
Here’s the twist: the vote includes a stake in their asset.
Now, this is what makes things interesting, as we incorporate the loss aversion mindset into the game. Previously, in the democratic elections, there were relatively loose consequences for the swing voters or the undecided. While the error is uncorrected, this could make or break the next governance cycle. But, with futarchy, economic motivation comes into play.
Quoting Wendell Berry, an American poet, that good solutions exist only in proof and problems must be solved in work and in place by people who will suffer the consequences of their mistakes, futarchy takes that notion into practice. With the prediction market, it works like investing in the stock market. The bettors gain financially if their predictions are correct. However, a wee difference is the losing policy or proposition does not cause the supporters to lose money.
The idea is that betting markets, where people put money behind their predictions, can more accurately forecast the outcomes of different policies than traditional voting or expert opinion alone.
This approach aims to leverage three things:
- the wisdom of the masses
- the incentives of bettors to make more informed and effective policy decisions (more on to this later, with thoughts experiment of Shelling points and the prisoner’s dilemma)
- The efficient market hypothesis
It’s an innovative, albeit experimental, concept that seeks to improve governance by making it more data-driven and outcome-oriented.
1/ The wisdom of the masses
Futarchy does not nullify democracy in the sense that the collective voice is taken.
Digressing a bit here, I read The Slow Fix book [2] which elaborates the steps taken to solve or manage complex problems by adhering to the slow movement. Being slow here does not mean intentionally progressing at a snail’s pace. Rather, it’s being holistic in our approach. There’s no quick fix for everything. Any instant cure bears more problems in the later stage. Therefore, one must think from different angles, assessing the problem at hand and weighing the options to solve it.
Chapter 9 of this book talks about the wisdom of the masses but in an active participation sense. The example presented is Gudjonsson’s National Assembly meeting in Reykjavik, Iceland. Targeting the diverse population to answer big questions such as: what kind of country Iceland should aspire to be and how they could make it happen, the assembly meetings gathered citizens from different walks of life: politicians, economists, traders, employees, students, and more, to discuss and agree on the proposed actions.
Another example in another chapter is Le Laboratoire (Le Labo) by David Edwards, where a setup is dedicated as a creative think-tank for creative minds, ranging from scientists and artists like. The questions might be about how to make a vessel for transporting water, based on biological cells. There were chefs, chemists, designers, and other professionals jumping into the crowdsourcing of ideas that spanned several months.
This is similar to having an array of advisors behind a politician who attempts to support or reject a bill for a country, but having the politician is no longer required.
The first recipe done: the wisdom of the crowd. Now, let’s move on to the next ingredient: economic incentives.
2/ The incentives of bettors
Many things in the world take an interesting turn of events if money is involved. We can take two examples in the behavioural finance.
Firstly, the prisoner’s dilemma, a fundamental problem in game theory. Suppose, you and your accomplice are detained because of a crime. You and your accomplice are interrogated separately and must decide whether to betray the other or remain silent. If both of you remain silent, you face minimal punishment (say, a year each). If one betrays and the other remains silent, the betrayer goes free while the other receives maximum punishment (say, ten years for the betrayed). If both betray, they both receive moderate punishment (say, four years each). The dilemma demonstrates the conflict between individual and collective rationality. The rational option is to stay silent, so you get only a year sentence. But the temptation to achieve the maximum individual payoff, that is for you to go free, is unbearable.
In the case of you betraying your accomplice, you can get either going out free or a four-year sentence. You take the risk, rather than staying silent with the possibility of receiving only a one-year sentence, because you assume that there is a 50% chance that you will get the other party silent. Hence, the 50% chance of freedom.
Or, is it?
The prisoner’s dilemma addresses the situation where you can’t trust each other. There’s a betting psychology here where your view is skewed towards 50:50 of free or a five-year sentence. You don’t even consider staying silent, because the probability of the outcomes if you stay silent is 50% of the least sentence but also 50% of the worst.
Humans are loss-averse. In order to avoid the worst, you shape your reality that there is only one option, which is to betray. This is how a scarcity mindset has taken over one prisoner’s mind.
Schelling points are also called focal points because it’s a coordination game where the players agree on something without communication. This is based on Thomas Schelling’s book about this part of game theory. To illustrate this, imagine the collision scenarios where it depends on how the opposite drivers (or bikers, as bikers are usually on a dedicated lane), come on the road.
What about the other example of Schelling points? This is where the situation is different because it’s not about loss aversion. Rather, coordination.
This is to simulate the idea that the consensus mechanism is not clear, so people have no anchor on the best coordination scenario (assuming the driving lane is not set in stone).
If biker A takes a left and biker B takes a right, the collision will surely happen. This is why we must accustom ourselves to the driving lane law of the place we’re living in. Since everybody follows a rule (say, in the UK it’s on the left side of the road, the US is on the right), the opposite oncoming traffic does not collide.
The bikers’ problem above can be illustrated in a visual manner. A game of 2 players might have 4 outcomes, and the higher payoff is achieved if both players select the most beneficial outcome, and no payoff if they select an unfavourable outcome. In a 2x2 matrix, it can be illustrated as follows.
So, similarly, the prisoner’s dilemma can be shown as:
You can see from the payoff matrix above, that despite the most logical path being both going silent, hence, the total of 2 years, the felons prefer to betray each other, which yields a total payoff of 8 years. That’s why it’s called the dilemma.
Now, we know that understanding how others will behave or decide is a critical matter to reach a consensus that benefits the majority or achieves the goal. The incentives up the stake of the game.
3/ The efficient market hypothesis
There are three levels of market efficiency:
- Weak-form efficiency. In this form of efficiency, prices already reflect all past market information. This means that even the historical price data do not have an edge over the price.
- Semi-strong-form efficiency. In this form, prices reflect all available information. This means that even the fundamental analysis does not have an edge.
- Strong-form efficiency. Prices in this form already reflect all information.
Therefore, the random walk is not random because it is a feature, rather than a critique, of the efficient market hypothesis. After all, the randomness means the market price is unpredictable.
This is the basis for implementing the prediction market in the decision-making part of the futarchy.
Do you want to know why the prediction markets are good?
1/ Markets don’t tolerate imbalance for too long
For instance, let’s talk about arbitrage opportunities.
Suppose a basket of stocks is fairly priced at $100. Due to the supply-demand mechanism, perhaps it’s on the hype, so at one exchange, its price goes up to $120. But at another exchange, its price is $90. Now, what stops me from buying the stocks at $90 and selling them at $120, profiting $30?
With many investors thinking of the same, the first exchange will see high selling pressure, resulting in a lowering of the price, probably back to $100. Conversely, the latter exchange will see high buying pressure, ramping up the price to $100 or more.
So the portfolio is priced at $100 again in both exchanges. In financial terms, it’s called the mispricing correction.
2/ Markets incentivise better predictors
because those predictors will naturally receive incurring gains from the assets. Think about it: the better you’re at predicting the outcome, the more rewarding your investment is. So, the investors/bettors will do their best to utilise the information they have to predict better.
Therefore, incorporating the diverse knowledge from the crowd and the incentives to obtain the highest payoff, the prediction market is the pot where the ingredients are mixed together, baked in all the information necessary to decide on a matter.
How Futarchy Works in a Country
I’m using the illustration from the Ethereum blog [3].
The real implementation in a DAO will be slightly different, so let’s review the country/political process first.
In this step, the country agrees on the metric to evaluate and the maturity (after what time the metric is evaluated).
The proposal may come from someone in the government to bail out the banks. This will be published to the market.
Two prediction markets are then created, with one share or token representing Yes, bail out the banks, and the other token representing No, don’t bail out the banks. The markets are let run temporarily according to the country’s policy. Notice the difference in the average prices.
By observing the prices, we learn that the market is keen towards keeping the banks, as you can see the average price is higher. The mechanism is like this:
More people believe in keeping the banks, so more people buy the “No” tokens.
A smart question: what if people want to “rig” the market? Rich people favouring a policy to tank might do pump-and-dump. Conversely, when they want a policy to succeed they just buy-buy-buy until the price inflates.
This is where we believe in the wisdom of the market because the correction will happen.
In the first case, the bettors who want the policy to win will buy when the price crashes, effectively picking up the prices again. In the latter case, those who want the policy to fail will sell the tokens or shares to the rich people, lowering the price.
In this step, after the markets have been closed in a specified time agreed in the beginning, the winning policy starts getting implemented.
And the failing policy trades are reverted.
In the last step, after the agreed maturity date, the winning market bettors receive the welfare assets (basically the token shares of the winning policy).
How Futarchy Works in Blockchain
Why blockchain?
It’s simple, blockchain enables carrying out real governance models that are not possible or restricted in other existing landscapes. For example, prediction markets are heavily restricted, if not outright banned, in the United States.
But in blockchain, the DAO (decentralised autonomous organisation) has such no restriction to experiment with the types of governance and voting mechanisms to achieve futarchical conduct.
A succinct yet explicative video made by The Meta DAO should be your starting point in understanding futarchy in a DAO.
It works as follows:
1/ There is a proposal from a DAO member about something that impacts the DAO treasury because it affects the token price. Anything that affects the treasury is for the DAO to decide upon.
2/ There are two possible outcomes: the proposal is accepted and the proposal is rejected. The smart contract starts the prediction market of these two options.
3/ How do the trades in one prediction market happen?
Those betting on the proposal being accepted trade on how much increase in the DAO’s worth or token price if the proposal is implemented.
Since blockchains don’t allow transaction reversals, a conditional vault is created once an investor deposits some tokens.
Say, you believe that the DAO token price will increase: from 10 USDC per token to 20 USDC per token, then how you’d do it is by locking up 10 USDC in the vault.
META tokens will be minted for you, and these will be issued in 2 conditional tokens:
fMETA (failed META) and pMETA (passed META). You can use the fMETA to trade in the failed market. Conversely, the pMETA to “vote” for the passed market, indicating you vote for the proposal to pass.
As in normal trading, there are market orders and limit orders.
The trade between the members in the order book works like this:
Suppose you think the token will be worth 20 USDC if the proposal passes, but another member thinks the token will be worth 25 USDC if the proposal passes. The current market price of the token is now 22 USDC. The pMETA token trade will happen between you and them. You will be content selling the pMETA token because you believe it is overpriced, profiting 2 USDC once the market is finalised. The other member, buying from you, hopes that they will profit 3 USDC once the proposal passes.
4/ Based on the DAO’s policy discretion, the smart contract runs the prediction market for a specific duration (say, a week).
5/ The TWAP (Time-weighted Average Price) is used to calculate which market wins.
6/ Bets of the failing outcome in the failing market are reversed. Tokens are redeemed for the initial assets.
7/ Those winning the bets receive financial gain. For example, now the proposal has passed and the final price of a token is 25 USDC. Your initial stake at 10 USDC for 1 token gains 15 USDC.
How You Can Participate in Futarchical Governance
Join the MetaDAO where you can participate in trading your bets.
Digressing here, assessing the UI:
It looks self-explanatory, you can try it out. Even if you’re new to DAO and web3, your understanding of futarchy after reading my explanation above and watching the video suffices to navigate through this prediction market.
Go to https://app.themetadao.org/ to try this out.
Critique of Futarchy
Compiling from several resources [4], [5], below are critiques of futarchy.
- The efficient market hypothesis is an ideal condition, which is hard to prove because the real stock market is less than ideal, e.g. for testing the alpha (the fund manager’s performance). Moreover, the EMH comes with a set of predefined assumptions from the CAPM theory, which do not have ground to be executed in the real market. For example, CAPM assumes that individuals are price-takers (not market-makers), investors are rational beings and rely on optimising the mean-variance of their portfolio, or that information is costless and available to all traders. Behavioural finance disputes this.
- Causality and conditionality. The ideal futarchy method begs the question of whether the policy aims for the measures we’re trying to achieve.
- Temporal discounting. How far can we measure the success of the policy, to do what it claims to do? The execution could also introduce other variables and make it stray from the original outline.
- Poverty-affected cognitive biases: the wisdom of the rich (plutocracy). Unlike normal voting in presidential elections, voting in futarchy requires the voters to part with some of their assets, as it requires the act of buying or selling. This limits the participation of those under the poverty line where their main priority is to use their assets to survive, lest on voting.
- Thin markets. For the betting market to work, it requires liquid trading pairs instead of the thin market where prices sway in higher slippage.
However, there’s not a single perfect governance system. Robin Hanson claims that the prediction market works better concerning other similar info institutions, such as the racetrack market odds that improve the prediction, or how the weather forecast is influenced by the orange juice commodity futures [6]. The Meta DAO’s proposed solutions to alleviate the downsides of the early market problems (low liquidity) are to introduce liquidity mining, benefits for the early adopters to introduce the network effect, and installing human oversight, such as in the multi-sig method for veto right.
Perhaps, what is at work is the self-fulfilling prophecy of the collective consciousness, in a way that confirmation bias comes into play.
Therefore, back to the essential ingredients of futarchy: the wisdom of the crowd and the incentives of the bettors, how cooperative and committed we are towards achieving goals that benefit our society is the basis for the working futarchy system.
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Until next time,
References
[1] R. Hanania, ‘Futarchy: Robin Hanson on How Prediction Markets Can Take over the World’. Accessed: Feb. 18, 2024. [Online]. Available: https://www.richardhanania.com/p/futarchy-robin-hanson-on-how-prediction
[2] C. Honoré, The Slow Fix, 2014th ed. William Collins, 2014.
[3] ‘An Introduction to Futarchy’, Ethereum Foundation Blog. Accessed: Feb. 26, 2024. [Online]. Available: https://blog.ethereum.org/2014/08/21/introduction-futarchy
[4] ‘Issues with futarchy’, Rethink Priorities. Accessed: Feb. 18, 2024. [Online]. Available: https://rethinkpriorities.org/publications/issues-with-futarchy
[5] Lizka, ‘Summary and Takeaways: Hanson’s “Shall We Vote on Values, But Bet on Beliefs?”’, Accessed: Feb. 18, 2024. [Online]. Available: https://forum.effectivealtruism.org/posts/ijohdoDbPvdeXMpiz/summary-and-takeaways-hanson-s-shall-we-vote-on-values-but
[6] Proph3t, ‘From Corporations to Nations: How the Meta-DAO is Going to Change Everything (Part 3)’, Medium. Accessed: Feb. 18, 2024. [Online]. Available: https://blog.themetadao.org/from-corporations-to-nations-how-the-meta-dao-is-going-to-change-everything-part-3-16b3880fd86c
