User Journeys Through Prediction DAO¶

This guide walks you through using ClearPath and FairWins, showing how participants interact with the platform from first visit to achieving their goals.

Choosing Your Platform¶

The landing page presents two pathways built on shared infrastructure. ClearPath serves DAOs needing formal governance with futarchy principles: the community votes on success metrics (welfare metrics), and prediction markets determine which proposals maximize those metrics. It includes treasury management, minority protections, and institutional safeguards.

FairWins enables open prediction markets where anyone can create markets on any topic, set resolution criteria, and trade without governance overhead. Both platforms use zero-knowledge proofs to protect trading positions while maintaining market transparency.

ClearPath: DAO Governance Through Markets¶

Joining and Trading¶

After connecting your wallet, the dashboard shows active proposals and welfare metrics (treasury value, network activity, security, developer momentum). Each proposal has a market with PASS and FAIL tokens. Prices reflect collective belief about whether the proposal will improve the selected metric.

When you trade, the system generates zero-knowledge proofs that encrypt your position. Other participants see aggregate prices adjust but cannot identify your holdings. This privacy prevents vote buying and front-running while enabling honest price discovery.

As proposals move through trading, oracle reporting, challenge periods, and resolution, you monitor your portfolio. When markets resolve, winning tokens become redeemable for payouts based on actual welfare metric values. Over time, you develop intuition about proposal quality and market dynamics.

Submitting Proposals¶

The proposal submission interface asks for title, description, funding amount, recipient address, welfare metric selection, and milestones. The 50 ETC bond (returned on resolution) ensures serious proposals. After the seven-day review period where community members discuss your idea, markets open for trading.

You watch as prices evolve, revealing collective judgment about your proposal's merit. Whether it passes or fails, you learn from how the market evaluates different aspects and can refine future proposals based on this feedback.

Beyond Trading¶

You can participate in welfare metric governance to shape success measures, serve as an oracle reporter by posting a 100 ETC bond to submit verified values, or challenge incorrect reports with evidence and a 150 ETC bond. The ragequit mechanism lets you exit with your proportional treasury share if you oppose a passed proposal, protecting minority rights without blocking majority decisions.

FairWins: Open Prediction Markets¶

Discovering and Trading¶

FairWins presents diverse markets created by users on any topic. Each shows the creator's resolution criteria, evidence sources, and timeline. Markets use YES/NO tokens instead of PASS/FAIL, but mechanics are identical: LMSR provides liquidity, privacy protection encrypts positions, and prices reflect aggregate beliefs.

When you find a market where your research suggests mispricing, you take a position. Privacy protection prevents others from copying your strategy. You can exit early by trading out or hold through resolution for conditional token payouts.

Creating Markets¶

The market creation interface guides you through defining your prediction precisely. Clear resolution criteria prevent disputes. You specify what counts as success, verification sources, and timing. Initial liquidity (minimum 100 USDC) seeds the LMSR market maker, and a creator bond (returned after proper resolution) aligns incentives with fair outcomes.

After creation, your market appears in the marketplace. As trading develops, prices reveal how others evaluate your question. When resolution time arrives, you submit outcome evidence. Proper resolution builds reputation, making future markets you create more attractive to participants.

Common Patterns Across Platforms¶

Information and Decision Making¶

Success requires research before trading. You evaluate proposals or questions, consider multiple perspectives, and form independent judgments. Privacy protection ensures your research generates returns without being immediately copied by others watching your trades.

The decision process continues as markets evolve. New information might strengthen or weaken your thesis. Price movements signal what others think. You balance conviction against risk, choosing position sizes appropriate for your confidence level.

While positions stay private, discussion happens in community channels. Proposal creators engage with questions, market creators clarify criteria, and traders share analysis. These interactions enrich understanding without enabling manipulation, since private positions prevent vote buying even when analysis is public.

Learning Through Cycles¶

Each completed market teaches lessons. Correct predictions build confidence in your methods. When markets move against you, you examine where reasoning failed. This feedback develops better judgment about quality, likelihood, and market efficiency over time.

The futarchy foundation means trading generates collective intelligence beyond personal returns. Accurate markets help DAOs make better decisions in ClearPath and provide valuable signals in FairWins. Your participation contributes to these broader purposes while pursuing individual goals.

Growing Into Advanced Usage¶

As you gain experience, you might hold positions across multiple markets, understanding correlations and managing overall exposure. You develop specialization in particular domains where you add most value. Reputation matters more for some roles: FairWins creators benefit from fair resolution track records, ClearPath proposers gain credibility through successes, oracle reporters build reputations for accuracy.

The key-change capability offers protection beyond basic encryption. If someone attempts vote buying, changing your key invalidates previous commitments, preventing verification of your positions. This MACI-inspired feature stops coercion while maintaining privacy.

Your path through Prediction DAO evolves from understanding basics to sophisticated engagement. Over time, the mechanics fade into background infrastructure, letting you focus on substantive questions: which proposals deserve support, what events seem likely, how welfare metrics respond to actions. The platforms succeed when they enable this focus on collective intelligence rather than technical complexity.