Decentralized Anticipation Markets for Probabilistic Futures

1. Introduction

Traditional prediction markets suffer from liquidity fragmentation, opaque pricing, and limited use cases beyond binary outcomes. Try Expect reimagines this space by combining:

• Distribution Markets (as outlined in Paradigm’s research ), enabling trading over probability distributions rather than fixed outcomes.

• Decentralized Oracles, ensuring tamper-proof resolution of real-world events.

• Composable Liquidity Pools, allowing dynamic participation across correlated events.

This litepaper outlines why this approach unlocks new possibilities and how it works technically.

2. Why Try Expect?

2.1 The Problem with Existing Systems

• Binary Limitations: Most prediction markets (e.g., Polymarket) only support yes/no questions, failing to capture nuanced probabilistic realities .

• Inefficient Liquidity: Liquidity is siloed per market, leading to high slippage for tail outcomes or low-volume events .

• Opaque Pricing: Centralized platforms often lack transparency in how odds are calculated, creating mistrust .

2.2 The Opportunity

Distribution markets allow participants to trade entire probability curves, not just single points. This enables:

• Rich Derivatives: Hedging against ranges (e.g., "BTC will trade between $50K–$60K in Q3") .

• Cross-Market Efficiency: Liquidity pools can be shared across correlated events (e.g., election results and policy impacts) .

• Data-Driven Insights: Aggregate beliefs form a public good for forecasting, akin to decentralized Schelling points .

3. How Try Expect Works

3.1 Core Mechanism

1. Distribution Trading:

   • Users mint and trade "bucket tokens" representing slices of a probability distribution (e.g., 0–10%, 10–20%, etc.) .

   • Example: For "ETH price in December 2025," buckets could span $0–$1K, $1K–$2K, etc.

2. Automated Market Making (AMM):

   • A modified AMM (e.g., based on Constant Product or Log-Normal formulas) prices buckets dynamically based on demand .

   • Liquidity providers earn fees from trades and arbitrage opportunities.

3. Decentralized Resolution:

   • Oracles (e.g., Chainlink, UMA) resolve events and distribute payouts to bucket holders .

   • Disputes are handled via decentralized governance or fallback oracles.

3.2 Technical Innovations

• Composable Buckets: Buckets can be bundled into indices (e.g., "Tech Sector Outcomes") for portfolio-level trading .

• Gas Optimization: Batch settlements and EIP-712 signatures reduce on-chain costs for high-frequency events .

• Privacy-Preserving Queries: Zero-knowledge proofs (ZKPs) allow users to verify outcomes without revealing positions .

4. Use Cases

4.1 Financial Markets

• Volatility Trading: Bet on BTC price ranges instead of exact values.

• Macro Hedging: Hedge against GDP growth brackets or inflation ranges.

4.2 Societal Forecasting

• Climate Risks: Trade on probability distributions for global temperature rises.

• Election Impacts: Correlate political outcomes with policy changes (e.g., "Chance of Fed rate cut if Candidate X wins").

4.3 DeFi Integration

• Insurance Pools: DAOs can underwrite risk by selling protection in bucket formats.

• Options Pricing: Derive Black-Scholes-like models from crowd-sourced distributions .

Try Expect transforms static prediction markets into dynamic, multi-dimensional platforms. By leveraging distribution markets and decentralized infrastructure, it creates a universal layer for probabilistic thinking—applicable to finance, governance, and beyond.