When Black Swans Become the Norm: How Should the Prediction Market Balance Rules and Community Consensus?

Byline: Yue Xiaoyu

For a prediction platform, creating a new market isn’t hard. The real challenge lies in where the liquidity comes from, and how things are settled in the end.

Liquidity determines whether this market can actually be played. Without depth, users can’t participate smoothly.

Settlement determines whether the platform can be trusted, directly affecting community consensus and long-term trust.

1. In prediction markets, there will always be things that happen outside the rules and outside anyone’s expectations.

This is both its biggest challenge and its most unique appeal.

In other words, black swans are the norm for prediction markets.

Once a black swan appears, it will directly impact users’ funds, and then the problem will erupt in a concentrated way:

When the outcome goes against common sense, against intuition, or even completely beyond the preset rules, what exactly should ordinary users do?

At present, there’s a core first principle in the industry: use the settlement rules agreed upon in advance as the highest authority.

When the settlement rules conflict with community consensus, the rules take priority.

Because settlement rules are, in essence, a contract that everyone agrees to in advance—like law. Once a platform changes them arbitrarily, the platform’s entire trust foundation collapses instantly.

Therefore, settlement rules can’t be modified casually.

2. But this also creates a new problem: the rules are dead, while the world is alive, and many things are constantly changing.

There’s always something unexpected that happens. The community often gets stuck in disputes over the rules, obsessing over minor details;

And some people deliberately exploit loopholes in the rules, using word games to overturn common sense and consensus.

Countless cases have already proven that endless nitpicking over rule wording only leads the community into arguments—and breeds distrust toward the platform.

The existing solution is to introduce community arbitration.

Community arbitration is actually very similar to the jury system in the common-law legal tradition of the U.S. and Europe.

Even the most rigorous law can’t cover every real-world scenario, and that’s exactly why juries exist in the common-law world:

They bring in common sense, consensus, and human judgment to make up for the rigidity and lag of legal text, so that decisions are closer to the real world—not blindly chained to words.

Now, the optimistic oracle UMA that is widely used in prediction markets today works like this: someone proposes a market outcome, assumes that’s correct, and then others challenge it. In the end, the final result is determined by a vote from UMA token holders.

But UMA also has its own problems, such as large holders dominating and governance attacks, which makes it unable to fully reflect community consensus.

3. So, how can community consensus be truly reflected?

A better solution is to make decisions based on intent.

Prediction markets should proactively define the answers when creating the market, including the reason the market exists.

This is the Intent-First principle.

When creating each market, you must fill in three parts—none of them can be modified:

WHAT: a precise description of the event (for example, “Any part of the U.S. government being shut down before X month X day in 2026”).

WHY: the real purpose for the market’s existence (for example, “So that federal employees/citizens can know in advance whether employment and services are affected”).

LITERAL RULES: detailed textual rules (as a fallback).

During arbitration, AI can be introduced to gather broader information, automatically capturing real-time consensus from mainstream media, government official websites, and content communities.

That way, you can first anchor to externally verifiable reality, rather than purely textual rules.

Only then can it truly reflect real, broad community consensus.

4. If future prediction markets want to go mainstream and become widely adopted, they must face one reality:

Most ordinary users won’t read and study the rules word by word like they’re studying legal documents.

They participate in predictions based on common sense, intuition, and general consensus.

Only by maximizing the avoidance of rule loopholes and disputes over rules beyond the rules, and avoiding meaningless rule battles, can prediction markets truly go further.

Creating markets in an intent-first way, using rules as the foundation and relying on community consensus as the backstop—this may be the feasible path for the long-term healthy development of prediction markets.