What Is Fair Value in a Prediction Market? A First-Principles Explanation

Prediction markets are frequently framed as the closest thing we have to a real-time consensus engine. People with different information, incentives, and confidence levels trade against one another, and a price emerges that appears to summarize “what the world thinks.”

The story is directionally true, and still incomplete.

A price can be informative without being interpretable. A market can move quickly without offering a disciplined way to judge whether it is moving toward truth or simply drifting with sentiment.

Prediction markets often lack a standardized concept of fair value.

Defining Fair Value in Probabilistic Systems

Markets are good at telling us where the crowd is trading. They are less reliable at telling us what an outcome is worth.

At the simplest level, a prediction market contract is a probabilistic claim. A standard “Yes” contract pays out $1 if an event occurs and $0 if it does not. From a purely mathematical perspective, such a contract has an expected value equal to the probability of the event.

If there is a 60% chance of an event occurring, the fair value is $0.60.

If the market is trading at $0.55, the market is "undervaluing" the outcome.

‘Fair value’ is the price at which a rational participant is indifferent between buying and selling under a specific belief model and risk framework.

Risk-Neutral vs. Subjective Fair Value

In theory, most fair-value reasoning assumes a risk-neutral participant.

Risk-neutral fair value answers a narrow but foundational question:

At what price does this contract have zero expected profit under a no-arbitrage assumption?

Under complete markets, no arbitrage, and risk neutrality, fair value equals expected value. It’s the baseline you expect to break even in the long run.

But real participants are not abstract expected-value calculators. They face constraints:

• Drawdown and tail-risk sensitivity
• Fees, slippage and liquidity frictions
• Settlement ambiguity and rule interpretation risk
• Asymmetric downside around resolution

These constraints introduce a second, equally important concept: 

Subjective fair value is the value of a contract after adjusting probability estimates using contextual, sentiment, macro, and domain-specific signals, as well as uncertainty, risk tolerance, and market frictions.

Both are rational. They compute fair value under different real constraints.

This distinction matters because it clarifies what fair value actually is:

It is probability filtered through a specific risk and information framework, but not probability itself.

Why Market Price Is Not the Same as True Probability 

A common mistake in prediction markets is to treat price as probability without questioning the assumptions behind that equivalence.

If a contract is trading at $0.70, many assume there is a 70% chance of it happening. 

Sometimes this approximation is reasonable. Often, it is not.

Market prices reflect more than just probability. They are shaped by:

• Sentiment: narratives, attention and hype
• Liquidity: thin markets amplify individual trades
• Capital constraints: mispricing can persist if correction is expensive
• Risk preferences: traders may overpay for upside or safety
• Market microstructure: timing, order flow, and short-term imbalances

As a result, the same price can emerge under very different underlying conditions.

Prediction markets tell us where the market is trading. They do not, by themselves, tell us if the market is right.

Without a reference point, participants are left interpreting motion instead of value.

Fair Value as a North Star for Decision Making

In the stock market, investors use "fair value" models to decide if a stock is overpriced. 

Prediction markets generally lack this. Without a standardized reference point, participants are forced to guess based on "vibes" or price action.

Fair value reframes the decision-making process. It moves the conversation away from "Who will win?" toward "Is the current price a statistically reasonable estimate?"

• If Fair Value > Market Price: Buy Signal.
• If Fair Value < Market Price: Sell Signal.

Fair value does not promise correctness on any single outcome. It does not eliminate variance. What it provides is discipline.

It allows participants to evaluate price relative to expectation rather than motion, and functions as a stable reference for navigating uncertainty. 

Why AI Agents Are the Solution

The challenge is that computing fair value is cognitively intractable for humans.

Outcomes in prediction markets depend on many interacting variables: news, sentiment, macro shocks, behavioral feedback loops, and market microstructure. 

Unlike options markets, there is no single closed-form equation that captures these dynamics.

This is where AI agents offer a structural advantage as calibration and signal-integration systems.

Unlike humans, AI agents can:

• Process vast amounts of data instantly to update a probability in real time
• Integrate heterogeneous signals across markets and narratives
• Learn calibration errors over time
• Systematically maintain probabilistic representations where humans are often influenced by narrative reasoning

The goal is to make probabilistic assumptions explicit, measurable and continuously testable.

Yala: Operationalizing Fair Value in Agent Design

Yala aims to build AI-native fair value agents that generate explicit probability signals for prediction markets, signals that serve as shared reference points rather than authoritative forecasts.

Instead of asking users to infer probability from price action, Yala makes probabilistic assumptions explicit, measurable, and continuously evaluated in live markets.

Key design principles include:

• Risk-neutral and subjective fair value separation
• Modular, multi-agent architectures for signal integration
• Continuous calibration through real-market feedback

As Yala evolves from single-agent fair value estimation to multi-agent systems, fair value becomes more than a pricing tool. It becomes a shared language for reasoning under uncertainty.