Prediction markets deal in the outcome of events — political, sporting, or financial — that have yet to occur. During #D1Conf at Cancun, Mexico, Stefan George of Gnosis explained how to create prediction markets for decentralized insurance applications.

What makes a prediction market?

Simply put, prediction markets make the outcomes of future events tradable. According to Stefan, prediction markets require:

Events to be unambiguous and publicly known

Outcomes to include all the possibilities resolutions

Outcomes to be publicly verifiable

Currency to be used for monetization

“Prediction markets are like betting markets. You bet on the outcomes, and the price for these reflects the probability for the outcome happening.” — Stefan George, Gnosis

A prediction market can cater to two types of events. Categorical events are when the winning outcome is just one out of many possibilities. On the other hand, scalar events are when the winning outcome is in a predefined range.

To monetize the system, a currency token, such as Ether, must be used. In both categorical and scalar events, currency tokens are used to purchase outcome tokens. Payouts are earned by selling the outcome tokens for the outcomes that are unlikely to occur.

How can this work for insurance?

To get prediction markets to work for insurance, an insurer would have to create baskets of “No” outcome tokens for events as collateral. “You combine the ‘No’ outcomes and use those as collateral for other markets,” said Stefan.

For instance, an insurer can use the “No” outcome tokens for the earthquake in Bangalore and flood in Shanghai events as collateral for another event predicting an earthquake in San Francisco.

Using this method, the “No” outcome tokens of an event can be used in a collateral basket for any other event so long as the outcomes are not dependent.

“We have to make the collateral baskets, where the outcome tokens represent events that are not related to the event that should be funded.” — Stefan George, Gnosis

Ensuring the independency of events

According to Stefan, conditional markets, where outcome tokens for market A are used as collateral for market B, can be used to validate the independence of events. In his example, market A is predicting an earthquake in San Francisco while market B is predicting a tsunami in San Francisco.

“We can express this using the Bayesian conditional probability,” explained Stefan. “What is the change in market B under the condition of market A?”

While the chance for each of the events happening is unlikely, should market A’s prediction come true, it greatly increases the likelihood for market B’s prediction to also happen. In this case, the events are strongly related and “market A tokens should not be used in a collateral basket for funding market B.”

Getting started with Gnosis

During his presentation, Stefan encouraged users to try out the Gnosis, a decentralized platform built on Ethereum for creating prediction markets.

Aside from the Ethereum blockchain, the Gnosis stack also includes layers for the system’s core, services, and applications. “Gnosis Core is basically the smart contracts required to create a prediction market,” explained Stefan. “On top of this are services that allow for easy integration of interfaces for the core level and application level.”

“Based on these services, anyone can start making apps for insurance or anything else.” — Stefan George, Gnosis

The primary challenge in a prediction market is usually the lack of initial liquidity. “If there is no one else trading, then no one will start trading,” said Stefan.

Gnosis resolves this with automated market makers based on the logarithmic market scoring rule. Users can fund the market upon creation, and it will automatically offer trades using smart contracts.

How will this affect insurance?

While the concept of prediction markets aren’t new, the idea to use collateral baskets as Stefan refers to them to fund unrelated events has the potential to increase an insurance company’s claims coverage.

Though there is always the chance that some outcome tokens in the collateral basket fall through due to their respective events occurring, a large enough basket may be enough to make up any deficit.

In the video below, Stefan demonstrates how Gnosis can be used to create a prediction market for insurance.

What is a prediction market? (0:10) What are the types of prediction markets? (2:05) How are event tokens and payouts handled? (2:56) How can prediction markets work for insurance? (5:52) What are collateral baskets? (7:56) How is event independency validated? (9:55) How can Gnosis help? (11:54) Prediction market demo (17:53)

You can also check Stefan’s slides.