5 Cryptoeconomic Experiments
We would like to empirically prove that adding manipulators (or noisy traders) to a prediction market built on the Ethereum infrastructure does not reduce average price accuracy, and that prediction markets will therefore be among the most manipulation-resistant.
We’ll be running the five experiments proposed by Vitalik Buterin to test the viability of Futarchy:
1. Experiment: Information is publicly available - no risk
We will launch a prediction market on an output of a smart contract on September 30th, 2017. The contract will be simply and verifiably coded to output 5.
We will then launch a separate prediction market where a specific set of users is rewarded if the average market price exceeds 5. The more it exceeds 5, the higher will be their reward. Therefore, manipulators are encouraged to try to manipulate the market upwards.
From an honest trader’s perspective, you can make a trade to bring the price back to 5 with a guaranteed return if the forecast is above 5. Therefore, our hypothesis is that the market will manage to keep the average market price at 5.
2. Experiment: Information is publicly available - added risk
Same as 1), except that instead of the simple output of 5, the smart contract will return either 0 or 10 this time, depending on the value of a random bit. We assume that the expected value will still be 5, despite the added risk.
From an honest trader’s perspective, you can make a trade to bring the price back to 5 with a positive expected value if the forecast is above 5. However, there still is a chance to lose your investment.
3. Experiment: Information about relative prices is publicly available - but not about absolute prices
We’ll be launching two markets: one where the smart contract pays ethereum_blockchain_difficulty / 10^15 and the other pays just ethereum_blockchain_difficulty / 10^15 + 5. The manipulators who will manage to push the difference in prices above 5 will be rewarded.
Let’s take the example of a company deciding whether to hire a given CEO A or a CEO B, and making a prediction market on its revenues for the next 3 years in the cases of
a) hiring CEO A
b) hiring CEO B.
Assume that you know for a fact that CEO A will increase a company’s revenue by $5 million. However, you do not know what exactly the company’s revenue forecast will be, and therefore, how big of a part of the revenue can be attributed to CEO B.
The difference of the two markets (CEO A and CEO B) should be 5, but if the price of market A turns out to be 17 and the price of market B turns out to be 10, you can not be sure whether 17 is the accurate price (this would mean that the overall company’s revenue is forecasted to be at $12m since the CEO A attributes for $5m), or whether 10 is the accurate price since you do not have any information about the company’s revenue.
*From an honest trader’s perspective, you **know that a trade exists with a positive expected value **if the price difference of the two markets is above 5. However, it is not clear whether the price for market A is too low or whether the price for market B is too high. *
4. Experiment: A few experts are 100% sure about something but can’t prove their knowledge to the world (I know it but I can’t prove it)
Same as 3), except that the difference of 5 in revenue that CEO A would add to the company is this time replaced with X that is only revealed to a select team. We will use an interactive protocol to commit to the constant so that the team members are convinced that we will input a value into the smart contract that can only be X. However, the protocol will be set up in a way that they cannot prove the constant X to others.
From the insider’s perspective, you **know that a trade exists with a positive expected value **if the price difference of the two markets is above X. However, it is not clear whether the price for market A is too low or whether the price for market B is too high.
5. Experiment: “Unknown Bias”
Same as 3) or 4), but we will randomize and privately reveal the direction in which the manipulators are being incentivized to manipulate.
While in experiment 3) and 4), market participants are aware that manipulators had an incentive to manipulate the market upwards, i.e. to increase the price difference between the two markets, the direction for manipulation is not publicly known in this experiment. Here, only a select group will be informed about the direction in which the manipulators are being incentivized to manipulate.
-From the insider’s perspective, you **know that a trade exists with a positive expected value **if the price difference of the two markets is above or below X. However, it is not clear whether the price for market A is too low or whether the price for market B is too high. -
Through these experiments and general tools we plan to build at Gnosis, we aim to forge a solid platform for DAOs (and other types of organizations) to use Futarchy to inform and automate their decision-making.
We hope to conduct the first cryptoeconomic experiment at the end of September and are looking forward to see the manipulation strategies of our community. Will you be able to beat the market?