Yala’s ‘Fair-Value’ AI Agent Wants To Enhance Accuracy In Prediction Markets

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The Bitcoin-native liquidity protocol Yala is turning its attention away from stablecoins and yield farming to try and conquer the exciting world of prediction markets with the help of autonomous artificial intelligence agents. 

In a blog post last week, Yala announced it’s evolving its platform with the launch of its first AI agent, known as Yala 2.0, to try and help participants spot “fair value” opportunities in prediction markets. The goal is to improve predictive accuracy and bring more structured and robust pricing systems guided by intelligent probabilistic tools to prediction markets. 

Yala explained that prediction markets have attracted a huge audience in the wake of their impressive accuracy during the 2024 U.S. Presidential election. One year ago, most pollsters and bookmakers thought that the election would be a pretty close-run thing, with Donald Trump and Kamala Harris locked in a statistical dead heat. But those forecasts could barely have been more inaccurate, for Trump ended up crushing his Democratic rival. 

The outcome of the election was in-line with the odds given by leading prediction markets Polymarket and Kalsi, which strongly favored Trump throughout the last few months of the campaign. Analysts hailed the result as evidence that the “collective intelligence” of the crowd is more accurate than traditional prediction systems.   

What are prediction markets? 

Prediction markets work by pricing uncertainty through orderbook matching, with the prices of an outcome representing probabilities, and they have proven to be extremely efficient at surfacing collective intelligence. Their accuracy even impressed the Commodity Futures Trading Commission, which approved Kalshi as a Designated Contract Market – giving it the same status of a regulated futures and options contract market. 

Despite this stamp of approval, Yala believes prediction markets are still somewhat immature because they lack the traditional fair-value models found in traditional options and derivatives, such as the Black-Scholes method for pricing and risk management. Because of this, participants are primarily guided by speculation rather than statistically favorable odds, with a great deal of uncertainty around what truly constitutes “fair value”. 

Yala believes fair value is key to establishing prediction markets as serious financial products and so it has taken it upon itself to provide this, but doing so isn’t easy. Calculating probabilities involves weighing up hundreds of interconnected variables, making it all but impossible for humans. 

The Yala 2.0 agent tackles this complexity by crunching hundreds of dynamic, evolving signals to try and generate a more precise and accurate probability of an event outcome. Once the probability has been established, bettors can then determine a fair price for a “yes” or “no” outcome. Should the agent determine that the fair value of an outcome is higher than the current market price, that would indicate bettors are statistically more likely to profit by buying “yes”, but if it’s lower, then they would be better off going with “no”.

It’s similar in some ways to what sports bettors do. When betting on soccer games, professional gamblers always look for “value” in the odds, rather than simply choosing who they think will win the game. If they estimate a team’s chances of winning a game is one-in-ten but the bookie is offering offs of 15/1, they take the bet every time because it offers value. After all, soccer is unpredictable and surprises happen frequently. 

Evolving intelligence

Yala 2.0’s roadmap describes how the agent will build up its intelligence step-by-step. The initial phase is focused on closed, internal testing and rapid iteration, and will initially use a model that’s based on historical data, news analysis, smart-money tagging and social media sentiments. The initial focus will be on digital asset prices and sports outcomes, with Yala refining the model over time, based on the accuracy of its predictions. Its early probability estimates will be shared via X, as part of its transparent approach to calibrating the underlying model. 

The second stage will see the public launch of Yala 2.0 and its adoption of a modular architecture. During this period, Yala 2.0’s performance will be publicly verified and continuously measured in live markets with a controlled risk limit. Users will be invited to interact with the agent by entering basic prompts, such as the market type, target conduction and time horizon to generate a probability estimate. 

At this stage, Yala 2.0’s architecture will evolve to a multi-agent design that’s coordinated by a central orchestrator agent, with separate modules for date ingestion and processing, predictions and safety and governance. 

Finally, stage three will mark full maturity, where the agent expands to become a comprehensive “swarm framework” made up of a supervisor agent that governs a suite of specialist worker agents to determine probabilities across every domain. It’s at this stage that Yala 2.0 will go beyond risk-neutral pricing to generate more subjective fair-value estimates, incorporating additional micro-factors into its calculations. 

Yala 2.0 will enhance the utility of Yala’s native $YALA cryptocurrency, which is destined to become the agent’s governance token and a “value-alignment asset”. Holders will be able to stake $YALA to participate in votes and token reward distributions tied to the agent’s expansion. Meanwhile, $YALA’s tokenomics structure will evolve, with platform revenue from performance and usage fees allocated to token buybacks. 

Ultimately, Yala wants to become the “fair-value operating system” for markets like Polymarket and Kalshi and foster deeper liquidity and more accurate pricing across the fast-growing prediction economy. 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.