Participants Needed for Futarchy Signaling Markets

As part of the Futarchy Signaling Markets Nest grant, we will be launching an experimental Futarchy markets DAO on mainnet for the upcoming ANV-5

How to Participate

Join #futarchy and message me, @mike.calvanese with your Ethereum address. We’ll include you in an airdrop of collateral tokens that can be used for trading on ANV-5 Futarchy markets.

How the Markets Work

We will deploy a Futarchy DAO (using Futarchy Template). This DAO will allow participants to trade on markets (using Futarchy App). Each market will correspond to a select AGP proposed for ANV-5.

Participants will use their collateral tokens to bet on a future ANT/DAI price given 2 conditions: 1) if the corresponding proposal passes, and 2) if the corresponding proposal does not pass. This will “signal” which condition the market thinks will have the most favorable impact on ANT/DAI price.

Final ANT/DAI price will be resolved based on Uniswap price data handled by Oracle Manager App, part of the Futarchy DAO template.

Why Participate?

This is the first Futarchy related experiment on Ethereum mainnet, and a major step toward using Futarchy for DAO governance. The idea of Futarchy pre-dates Ethereum and Bitcoin read about it here.

Since we’ll be airdropping collateral tokens for this launch, the only cost to participate is gas.

You’ll be competing against the other participants. We’ll publish participant performance results after the markets resolve.

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Is the Futarchy app (or template) on Rinkeby yet?

@burrrata futarchy-app is deployed on rinkeby at futarchy.open.aragonpm.eth.

futarchy-template isn’t on open APM yet, but will be deployed on rinkeby at futarchy-template.open.aragonpm.eth. There’s a DAO instance deployed via futarchy-template here https://rinkeby.aragon.org/#/0x0c01b3465e6cd1d670bc95dfa3bc4d40c6aabdca/0xcd3861409974f14bbd4bf6493211e1231724d8ca

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This is an interesting experiment however at first glance I would say this is a crude implementation of futarchy. Perhaps you could help convince me otherwise? The main reason I say this is, if there are ten proposals, and futarchy markets for each, and the outcome being predicted for each proposal is “based on whether this proposal is approved or rejected, will the price of ANT be higher or lower in the future?”, there is no way to tell which proposal is actually having a positive or negative affect on the price of ANT. And so I’m not sure that voters really learn much from the experiment.

Let’s say traders bet on average that all of the proposals will increase the price of ANT. But then the price of ANT is lower than expected. Which proposal is to blame for the lower price? What if the bettors on some proposals would have been correct if not for one of the other proposals being approved (a proposal they might have even bet against)?

Now let’s say that traders bet on average that half of the proposals will increase the price of ANT, and half of them will not (signaling that some AGPs should be approved and others rejected). But then the price of ANT is lower than expected. What if the price is lower because of the proposals that were rejected or because of the proposals that were approved? Meaning, the futarchy market results in the opposite of what you’d hope for: good proposals were rejected, or bad proposals were approved. And because all proposals are being judged by the same metrics, it’s impossible to tell which is which. All you can say is “well, the price of ANT is down, so all the people who bet it would be up if these proposals were approved lose”.

With all that said, I am wondering: how did you come up with the methodology for this experiment and what you are hoping to learn from it?

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These are great questions. It seems that your questions point out theoretical flaws in futarchy as a governance model, rather than our implementation specifically. We’re certainly open to ideas on how we can improve the implementation for this initial launch.

First I’ll say a few things about the signaling markets launch and what we hope to learn, and then I’ll address the more general questions about futarchy.

This will be the first application launched on Ethereum related to futarchy that we know of. The market topics will only be understood only by a small community, and participation rates will probably be low (maybe 10 - 20 individuals). Our primary goal is to test the UX of futarchy-app by presenting conditional markets to users on a topic that they are familiar with. We’re hoping to learn, directly from participants, their motivations for trading long or short on the various proposal markets. This along with trade data analysis will help us improve for future launches. It will not prove or disprove the viability of futarchy governance, but having data on a real implementation of conditional prediction markets might tell us something interesting. We’re thinking about this less like a scientific experiment and more like an early stage application launch - in general, we want to see what our users and data tell us, and try to improve for future iterations.

We’ve been asked about this market isolation problem before. If there are 2 conditional markets with the same success metric (price in this case), how does market A account for the conditional outcome of market B? If these were perfectly efficient markets, then prices would reflect all available information in the world. Part of the perfectly aggregated information for market A would be the likelihood of market B’s condition passing or failing, and the impact that market B passing or failing would have on the price given the condition where market A passes and where market A fails. This is complex to think about for 2 markets, let alone 10, 100, or 1,000 markets. Since in the real world there is no such thing as a perfectly efficient market, there is always an opportunity to compete. So it’s possible that as market participation scales up, new participants start accounting for cross-conditional info that isn’t already accounted for as a way to compete.

Let’s say traders bet on average that all of the proposals will increase the price of ANT. But then the price of ANT is lower than expected. Which proposal is to blame for the lower price?

We won’t know which proposal is to blame for the lower price. It’s possible that neither proposal was to blame - maybe there was a downturn in the crypto markets caused by a high profile hack. All we know is that the traders on the ANV signaling markets, at one time in the past, believed that the ANT price would increase if a set of proposals passed, and that they were willing to bet X amount of collateral on this.

What if the bettors on some proposals would have been correct if not for one of the other proposals being approved (a proposal they might have even bet against)?

Each individual trader should have factored in the likelihood of the other proposals being approved. If they risked collateral on a bet and lost value, another trader with better information gained that value.

Now let’s say that traders bet on average that half of the proposals will increase the price of ANT, and half of them will not (signaling that some AGPs should be approved and others rejected). But then the price of ANT is lower than expected. What if the price is lower because of the proposals that were rejected or because of the proposals that were approved? Meaning, the futarchy market results in the opposite of what you’d hope for: good proposals were rejected, or bad proposals were approved. And because all proposals are being judged by the same metrics, it’s impossible to tell which is which. All you can say is “well, the price of ANT is down, so all the people who bet it would be up if these proposals were approved lose”.

This is a tough one to answer. With low participation, imperfectly efficient markets like the ones we’re about to launch, this scenario is totally possible. If we were to use these markets to actually pass/fail AGP proposals, “good” proposals could be rejected and “bad” proposals could be accepted. Of course, this could also happen with token weighted voting. An individual proposal’s impact on ANT price will always be complex to determine, regardless of whether the proposal was approved with token weighted voting, futarchy, or any governance mechanism. Running signaling markets will not tell us what the ANT price impact of a proposal pass/fail decision was or is. But it might tell us what market participants thought the proposal’s impact on ANT price would be at various times throughout the history of a given market.

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I tend to agree with you @light that futarchy may not be well suited for more than one significant vote at a time per success metric. I view this experiment as part demonstration and user testing and also part data collecting to better understand this question of how futarchy may be suitable to use in governance. Hopefully before long there will be more experiments, a better framing of the questions to be answered, and more rigorous methodologies applied.

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Another important distinction to make here is futarchy vs. conditional prediction markets. If governance is taken out of the equation (no “futarchy”), is there any reason why 2 conditional markets can’t exist at the same time if they’re resolved by the same metric?

Think about these 4 conditions as separate markets:

  • If “Proposal A” passes, what will the price of ANT be 4 weeks from now?
  • If “Proposal A” fails, what will the price of ANT be 4 weeks from now?
  • If “Proposal B” passes, what will the price of ANT be 4 weeks from now?
  • If “Proposal B” fails, what will the price of ANT be 4 weeks from now?

If these aren’t governing anything and they just exist for people to take bets on, does this present similar problems?

I think it does pose similar problems, because traders cannot consider those questions in isolation of each other. I think questions that aggregate as much relevant information as possible are more likely to yield useful information. In the case of your examples, the questions I would pose are:

  • If Proposal A and Proposal B pass, what will the price of ANT be 4 weeks from now?
  • If Proposal A and Proposal B fail, what will the price of ANT be 4 weeks from now?
  • If Proposal A passes and Proposal B fails, what will the price of ANT be 4 weeks from now?
  • If Proposal A failed and Proposal B passes, what will the price of ANT be 4 weeks from now?

Of course, we cannot account for every variable that would affect the ANT price in our questions/ markets. And because it is affected by so many variables, it’s possible that the ANT price is not even the best metric to bet on. But because with futarchy we’re trying to predict a metric based on the outcome of a slate of proposals and not just one proposal being voted on, it makes sense to me to try to account for all of the possible outcomes of the vote in each question/ market to try and see which outcome from the vote in totality the market likes best.

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