Dissecting Ambiguity: Predicting Resolution Risk in Prediction Markets

Executive Summary

In the dynamic world of prediction markets, ambiguity in wording can lead to significant resolution risks. This article delves into how SettleRisk's API analyzes these ambiguities and predicts resolution risk, providing practical code examples and insights into the underlying methodology.

Core Concept

Ambiguity in prediction market questions often stems from linguistic nuances that can lead to varied interpretations. SettleRisk's scoring system is designed to quantify this ambiguity and assess the associated resolution risk. The risk score is calculated using a deterministic, closed-form scoring model with a 15-driver taxonomy, where each driver includes explainable text-span attribution.

Resolution Risk Scoring Formula:

Risk Score = f(Driver1, Driver2, ..., Driver15)

Where f is a weighted function of 15 drivers that contribute to the overall resolution risk.

Worked Example

Consider a market question: "Will there be a significant cyber attack on a major US bank before the end of 2026?" The term "significant" is ambiguous and can lead to different interpretations post-resolution.

Python SDK Example:

from settlerisk import SettleRiskClient

client = SettleRiskClient("your_api_key")
risk_score = client.get_risk_score("market_question_id")
print(f"The risk score for the market is: {risk_score}")

TypeScript SDK Example:

import { SettleRiskClient } from "settlerisk";

const client = new SettleRiskClient("your_api_key");
client.getRiskScore("market_question_id").then(riskScore => {
  console.log(`The risk score for the market is: ${riskScore}`);
});

Implementation Notes

When implementing the SettleRisk API, consider the following:

1. API Key Security: Keep your API key confidential to prevent unauthorized access.
2. Driver Analysis: Understand the drivers contributing to the risk score to make informed decisions.
3. SDK Integration: Both Python and TypeScript SDKs offer straightforward integration with your existing systems.

Failure Modes

| Ambiguity Type | Risk Impact | Common Examples | |---------------|------------|----------------| | Temporal | High | "Before the end of 2026" | | Causal | Medium | "Due to heavy rain" | | Quantitative | Low | "More than 10% increase" |

Checklist

• Assess Ambiguity: Regularly review market questions for potential ambiguity.
• Monitor Risk Scores: Utilize SettleRisk's API to track changes in resolution risk over time.
• SDK Updates: Keep your SDKs up to date to benefit from the latest features and security patches.
• Compliance: Ensure that your use of market data complies with all relevant regulations.

Sources + Further Reading

For a deeper understanding of SettleRisk's methodology and how it applies to resolution risk scoring, explore the following internal resources:

• Methodology: Learn more about the deterministic, closed-form scoring model.
• Features: Discover the full range of SettleRisk's capabilities.
• Pricing: Understand how different resolution risks can impact pricing strategies.

By leveraging SettleRisk's API, you can effectively predict and manage resolution risk in prediction markets, navigating the complexities of ambiguous wording with confidence and precision.