Artificial intelligence can serve as an embedded blockchain arbiter in prediction markets, according to Andrew Hall, a professor of political economy at Stanford University’s Graduate School of Business.
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He illustrated the issue of “fair” dispute resolution using the example of the Venezuelan presidential elections.
Last year, contracts worth over $6 million were placed on the event’s outcome. However, post-campaign, the market was left in confusion:
- The government declared Nicolás Maduro the winner;
- The opposition and international observers alleged fraud.
“Should the resolution of prediction market contracts follow the ‘official’ information (Maduro’s victory) or the ‘consensus of credible reports’ (opposition’s victory)?” Hall pondered.
This is not an isolated incident, the expert noted. In another case, someone allegedly manipulated the map of Ukraine regarding a territorial dispute.
Hall believes it is crucial to establish a fair contract resolution system that can be trusted. In such a scenario, prices would become meaningful signals for society.
The Problem Beyond Prediction Markets
Similar issues plague financial markets. The International Swaps and Derivatives Association has struggled for years with settlement problems in the credit default swap market—contracts that pay out in the event of a company or country default.
Decision-making committees vote on whether credit events have occurred. However, the process is criticised for its lack of transparency, potential conflicts of interest, and inconsistent outcomes.
“The fundamental problem remains the same: when large sums depend on determining what happened in an ambiguous situation, any settlement mechanism becomes a target for manipulation, and ambiguity a potential point of debate,” Hall stated.
Attributes of a Good Solution
The expert outlined several key attributes that any viable solution should possess:
- Resistance to manipulation—if a verdict can be influenced by editing Wikipedia, spreading fake news, bribing oracles, or exploiting loopholes, the market becomes a game won by the best manipulator;
- Reasonable accuracy—the mechanism must reach the correct conclusion in most cases. Perfect accuracy is unattainable, but systematic errors and obvious blunders must be avoided;
- Transparency—traders must clearly understand how the mechanism operates;
- Neutrality—participants need assurance that the system does not favour any particular user or outcome.
Human committees may satisfy some of these attributes, but they are susceptible to manipulation and cannot be neutral.
AI as the Solution
Hall proposes using large language models as arbiters, with each model and prompt recorded on the blockchain at the contract’s inception.
The basic architecture is as follows:
- When creating a contract, the market maker specifies not only the dispute resolution criteria in natural language but also the LLM and the exact prompt to be used for determining the outcome.
- The specification is recorded on the blockchain using cryptography.
- When trading begins, participants can review the full mechanism of the contract—they know exactly how the model accesses specified information sources and reaches a decision.
This method addresses several key issues:
- AI resists manipulation (though not absolutely). The results of a large LLM are not easily edited. To alter the decision, a fraudster would need to change the information sources the model relies on;
- Accuracy is ensured—neural networks can swiftly navigate the web and seek new information;
- Transparency—the entire dispute resolution mechanism is open for analysis and verification. No rule changes or subjective decisions can occur;
- Credibility—LLMs have no financial stake in the outcome and cannot be bribed.
Among the drawbacks: AI can make mistakes. It might misinterpret a news article or invent a fact.
Manipulation is not impossible, just harder to achieve. Fraudsters might commission specific information to be published in major media outlets. This is costly but feasible.
There is also the risk of an attack on the LLM’s training data. However, this would require action long before the contract is made.
Conclusion
An AI-based solution replaces one set of problems with another, more manageable one. Platforms should experiment with different LLMs to gain experience, Hall suggests.
As best practices emerge, the community needs to work on standardising combinations of AI programmes. This will help concentrate liquidity, the author believes.
In January, a16z crypto analysts predicted growth in prediction markets and ZK-proofs.