Gnosis X launches next Monday
The range of use cases for prediction markets is huge–and we certainly won’t be able to leverage its full potential on our own. With Gnosis X, our aim is to provide a level playing field for developers, creators, and businesses building customized dApps on top of the Gnosis platform.
We’re super excited to announce that we’ll be launching Gnosis X on March 19th, 2018. Gnosis X is a recurring challenge inviting developers to build prediction market applications on top of the Gnosis platform. Starting March 19th, we’ll open program registrations and begin providing dedicated developer support via Gitter, our forum, Github, and email. The best prediction market application per category will be rewarded with GNO up to $100,000.
To be able to better compare the different prediction market applications, assign a specialized jury to properly evaluate those, and provide some general direction, we’re proposing different use case categories. Today, we’d like to introduce the three different categories your prediction market use cases should fall into during this first round of the challenge.
Science and R&D
In his article “Could Gambling Save Science”, Robin Hanson comments on the application possibilities of prediction markets in the science field. He outlines the tendency of academic institutions rewarding scientists for telling a good story rather than being right, and explains how prediction markets could change the current academic and scientific landscape. Hanson points out that prediction markets on the most disputed science questions could be treated socially as the current academic consensus. Funding agencies would be able to subsidize prediction markets on questions of interest to them, and research labs pay for much of their research with shares they’ve won from previous prediction markets.
Hanson illustrates this market mechanism using the quite funny hypothetical scenario of deadly peanut butter: Imagine a research lab called “Munchem Biolabs” found compelling evidence that peanut butter was more deadly than most pesticides — a conclusion that the food company “Lunch Industries Exclusive” (LIE) wanted to desperately suppress. With prediction markets on the question, “Munchem Biolabs” would probably move the market odds of deadly peanut butter up rather high, while LIE would be either a) using overwhelming cash to bring the market price down, or b) producing competing studies, advertising, and other material to persuade others to buy shares on their side. Not only might Munchem find allies, but LIE employees who knew they were bluffing might be tempted to pick up profits with predictions. In addition to that, if LIE’s bluff was to spread out, more participants would likely jump into the market and counteract LIE’s predictions. With this example, Hanson shows that prediction markets create financial incentives for those with superior and expert knowledge to participate.
Within the scientific landscape, prediction markets have been used to assess the reproducibility of both preclinical and scientific research. Reproducibility concerns have been raised in many fields including medicine, neuroscience, genetics, psychology, and economics, especially as there currently is no mechanism to identify what is unlikely to replicate. The lack of preclinical research reproducibility, for example, also comes with substantial economic costs: in the US, it has been estimated to be in the range of $30 billion per year. To overcome this obstacle, prediction markets have been used to evaluate the reproducibility of preclinical psychological research, and hereby outperformed conventional institutions .
As illustrated with the above mentioned examples, the Science/R&D category is extremely versatile and offers many more application possibilities for prediction markets:
2018 will be an exciting year in space exploration — you could build a prediction market around any of the upcoming space missions and forecast whether the mission to mercury (BepiColombo) will be successful or if SpaceX will land on Mars, or even whether we might eventually encounter martians. During our prediction market tournament Olympia, we’ve already included a prediction market in this category, asking “Will SpaceX launch the top-secret Zuma Satellite on January 5th, 2018, as planned after their two previous delays?” which successfully predicted the delay of the launch well before market resolution.
Your use case could also be related to technological development, asking questions like “Will the new MacBook to be released in 2018 have an LCD or OLED screen?” or “Will the ‘Moth Eye’ smartphone coating be purchasable in 2018?”, or “Will the majority of cars drive autonomously in 2020?”.
There are many more prediction market use cases in the Science/R&D category that would have never occurred to us. We’re very much looking forward to all the exciting dApps you’re going to impress us with in the coming months! We are happy to announce that, apart of the Gnosis founders Martin Köppelmann and Stefan George, Robin Hanson will be part of the jury evaluating submissions in this category.
2017 demonstrated an immense interest in and potential of token-powered platforms and networks, with sales of tokens generating $3.88 billion in total last year. Public interest is likely to continue growing in 2018, as will the attention and scrutiny paid to scammers, untrustworthy companies, and unregulated and bad token models. The Brooklyn Project aims to address these growing areas of concern by providing powerful tools in order to protect consumers and enhance token based networks. It’s an “open source” industry-wide initiative to promote token-powered economic growth and consumer protection. By encouraging well thought out and reasonable regulation, the project will help bring blockchain technology to its full power .
To accelerate the process, we dedicated the second category of Gnosis X towards token transparency, which would finally lead to consumer protection. You could build prediction market use cases that are designed to inform consumers and regulators about important aspects of token projects, including, for example, the technical feasibility of the project and its roadmap, the fairness of token prices, or even the trustworthiness and regulatory compliance of projects. Some example questions asked could be: “Will the team achieve the set milestones?”, “Will the token sale reach its market cap?”, “How fast will the tokens be sold out?”, or “Will the average price of token X drop by XX% after a specified period?”, and so on.
Google has already implemented a successful example which would fall in this category: They used prediction markets to forecast market capitalization prior to an initial public offering (IPO), and they forecasted Google’s post-IPO market capitalization relatively accurately. Google’s auction-based IPO price was 15.3% below the first-day closing market capitalization, while the final prediction market forecast was only 4% above it . Prediction markets hence proved to be successful in forecasting the value of stock prices and could thus also be used to evaluate token prices.
Corporate companies such as EA (Electronic Arts) or Microsoft have successfully used prediction markets for both quality assurance and project evaluation . Questions on topics like “Will project X meet its development roadmap?”, or “How many bugs will be found in the smart contract?”, or “How many active users will app X have by the end of the year?” could be asked to evaluate the success of any blockchain project.
We highly value transparency in the blockchain space, and thus hope for this category to contribute to the promotion of token-powered economic growth and consumer protection. Projects in this category will be evaluated by Robin Hanson and Patrick Berarducci.
Blockchain Project Integration
In our last category, we’d like to invite developers to create prediction market applications that integrate into other existing blockchain projects. We see great application possibilities for prediction markets in the existing blockchain space, and expect to see even more in the future.
One use case we’re currently working on is the incorporation of prediction markets within Propy. Propy is a global real estate store, allowing buyers, sellers, and all other parties to come together through blockchain smart contracts. With Propy integrating a Gnosis prediction market, users will be able to predict future real estate prices, and hence give investors never-before-seen insight into real estate market prices to ensure they’re entering the market at the most opportune moment.
We’re also in the process of building out a prediction market integration with Frontier, a decentralized token research platform. Frontier is part of the Brooklyn project and aims to produce premium quality fundamental analysis for the token market, while employing innovative token models to ensure transparency and accountability for fund managers and analysts. Frontier will leverage Gnosis prediction markets in order to track analysts’ calls and generate research signals around token-based projects.
Another great blockchain project integration for prediction markets is Aragon, a project that aims to disintermediate the creation and maintenance of organizational structures by using blockchain technology. Aragon is on a mission to empower people across the world to easily and securely manage their organizations. In the context of decentralized governance, prediction markets could be used for decentralized decision making processes, such as those handled by the Aragon Network Jurisdiction Decentralized Court. All issues that are not accounted for in the smart contract code are solved by Aragon’s Decentralized Court. If the applicant is ever unsatisfied with the ruling of the Decentralized Court, they have the option to elevate the issue to the next realm. This means that they would use a prediction market were all the Judges of the Network can participate, providing the applicant with a much larger audience assessing the Court’s ruling. The judges would then review the rules and materials, and vote on their judgement.
We believe that prediction markets will be an important tool for the blockchain space, and we can’t wait to see which integration examples you can come up with that connect promising blockchain projects to the power of prediction markets. The jury for this category will consist of Joseph Lubin and Alex van de Sande.
In our next post, we’ll outline the challenge procedure and introduce the software development kit to get you started as quickly as possible!
 Dreber, Anna, et al. “Using prediction markets to estimate the reproducibility of scientific research.” Proceedings of the National Academy of Sciences 112.50 (2015): 15343–15347.
 Lubin, Joseph. “Announcing ‘The Brooklyn Project’ for Token Launches.” ConsenSys Media, ConsenSys Media, 30 Nov. 2017, media.consensys.net/announcing-the-brooklyn-project-for-token-launches-22ba89279f5f.
 Berg, Joyce E., George R. Neumann, and Thomas A. Rietz. “Searching for google’s value: Using prediction markets to forecast market capitalization prior to an initial public offering.” Management Science 55.3 (2009): 348–361.
 O’Leary, Daniel E. “Prediction markets as a forecasting tool.” Advances in business and management forecasting. Emerald Group Publishing Limited, 2011. 169–184.
Thanks to Mareen Gläske.