Decentralized prediction markets offer accessible platforms for efficient speculation on future events
Prediction markets are markets in which binary options representing event probabilities are traded. These options range from 0.00 to 1.00, representing the probability of a specific event occurring. Traders in a prediction market can buy or sell these options, based on how their personal beliefs relate to the overall market belief. A trader is incentivized to buy the option if the market price is under the trader’s personal belief of the probability of the event occurring, and is incentivized to sell the option if the market price is above the trader’s personal estimate of the probability. The current market price of the option is therefore an aggregation of the beliefs of many individual traders.
Fundamentally, this system works because of the Efficient Market Hypothesis, which theorizes that asset prices fully reflect all available information. This therefore means that the market is an efficient integrator of diverse data sources. Another perspective of looking at this is from the perspective of the wisdom of the crowd, which refers to the collective decision-making power of groups of individuals. Academic literature on the topic has shown that while each individual in a crowd has his/her own unique bias, gathering many individuals in a crowd nullifies individual biases, thus providing very robust, accurate predictions. As long as the crowd is diverse and individuals are not influenced by the decisions of others, the crowd’s collective decisions will, on average, outperform that of any individual. Combining these two concepts together — we see that a prediction market’s equilibrium probability of an event occurring is a function of all available data, which is then synthesized by the collective cognitive ability of the crowd. As new information becomes available or new people join the crowd, the prediction market will naturally adjust the equilibrium price of the option as the crowd buys and sells it in response to this change.
Current prediction market platforms have been limited due to legal issues and a lack of decentralization. These two problems limit their user bases, which decreases prediction quality as their crowds are not diverse enough. New prediction market platforms such as Augur and Gnosis aim to fix these issues. By using a blockchain’s distributed ledger for transactions, users can make transactions on the platform without risking significant chance of local legal risks. Additionally, trust is removed from the platform, so users are not at risk of being cheated by the platform. Augur, for example, uses a decentralized network of oracles to provide the results of each prediction, which eliminates the risk of a centralized party withholding information or providing false information.
Decentralized platforms extend prediction markets beyond the most basic scenario of human users manually making predictions. For example, easy-to-use programmatic interfaces will allow for sensors to directly make transactions on the platform, which allows sensor data to be directly integrated into the market price of the option. Generally, these platforms will allow for new vehicles of investment through the use of information that is private to individual agents. Specific sensors, or specific people, may privately hold critical information, and through the potential of profit from the prediction market, will be incentivized to make transactions reflecting this knowledge, indirectly making it public as the market price changes to reflect its arrival.
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