When I once explained a first iteration of Gnosis as a decentralized prediction market he said: “Ok, this is transparent and you don’t control the funds but from the architecture perspective it is nevertheless a monolithic centralized system.”
Indeed - we are used to say that everting that runes on Ethereum is decentralized while it is not.
So Gnosis is meant to be a marketplace for all the roles that are necessary to run a prediction market - all will be exchangeable and all should have it’s own incentive mechanism.
Lets first have a look at the different Typ of people who will place predictions/ trade on the markets.
We can identify 9 kind of motivations to make a prediction. Since betting is a 0 sum game some can only win if others lose. So we can sort those actors by whether or not they are expected to bet net payer or gainer.
Payer:
- Buy information – remove uncertainty – Lets say you need information if a future event will happen or not for a decision. Putting up bets on a prediction market could be the most cost efficient way to get this information. By creating bets on and against the outcome of the event you create an incentive for everyone who has more information about this event. To achieve this the use of a automated market maker like this is most efficient.
Example:
a) News sites that wants to support their articles with reliable forecasts.
b) …
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Creating an incentive – setting up a bounty – Bet that something will not happen to give others an incentive to make it happen. Lets say you bet 10 BTC against a new feature beeing in the next Bitcoin core version. This is basically setting up a bounty for everyone to make it happen. We have written more about this scheme here.
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Signaling, make a statement – Lets say you are starting a kickstarter campaign for a product. It probably would increase your credibility significantly if you place a big bet on the event that you will deliver in time if the funds are raised.
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to govern – Futarchy – This is similar to 3. However, with this type of bet you want to change the price of a market since the price will directly determine what decision will be made.
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Gambling – While today bookmakers pay out only about 90% of the wagers prediction markets could increase this number close to 100%. Even a weekly random bet of $100 on a sport event would sum up to average costs of less than $50 per year (assuming a payout rate of > 99%)
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Hedging, insure yourself – An insurance is basically a bet. A fire insurance for example is a bet that your house will burn down. If it does you get a lot of money, if it doesn’t you loose the money invested. Prediction markets could open the role of an insurance company to everyone a could make the margins razor
Gainer:
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Selling information – Corresponding to buy you can sell information. If you have an information that most of the public does not have (yet) you can sell it on the market.
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Trader/ Analyst. Every market with decent liquidity will attract traders and analysts. The will create models to do a better forecast and they will most likely beat the market and will be the guarantee for good forecasts.
Neutral
9. Liquidity providers: they will follow the bets (remember more or less everything is public) of successful traders or will make some “staking agreements” with them.
Beside people participating in the markets there will be:
oracles and arbitrators, proposition writers, interface designer (building new/different frontends), marketing (affiliates)
I will add a role description soon.
All of those should be net gainers if they do the role good and must be financed by the net payers.
The challenge will now be to create one or different coins with issuance and distribution mechanisms that will incentives all roles. For different topics different “net payer” roles will be more dominant. For sports for example gamblers will be the main net payer. For other topics where people are emotionally involved (presidential elections, celebrity news) this will also be the case. For topics like: where and when will be the next Devcoin other “net payers” will be needed.