Conditional Tokens

#Definition

Conditional tokens are a token standard (developed by Gnosis) that represents positions in prediction markets with potentially complex, interdependent outcomes. They allow traders to express beliefs about combinations of events, not just "Will X happen?" but "Will Y happen given that X happened?"

The standard enables markets where outcomes depend on conditions, creating rich structures for expressing nuanced predictions. A conditional token might represent "Democrats win the Senate given that Democrats win the Presidency", a position that only has value in the specific scenario where the presidency condition is met.

#Why It Matters in Prediction Markets

Conditional tokens expand what prediction markets can express:

Conditional probability trading

Real-world questions often involve dependencies. "What's the probability of recession if the Fed raises rates?" requires conditional reasoning. Conditional tokens make these probabilities tradeable.

Capital efficiency

Rather than fragmenting liquidity across separate markets for every outcome combination, conditional tokens allow shared liquidity pools that serve multiple related questions.

#Real-World Usage

The most prominent implementation of the Conditional Tokens Framework (CTF) is by Gnosis.

Polymarket uses Gnosis CTF for all its markets. Even simple binary markets on Polymarket are technically conditional tokens under the hood (conditioned on the market resolving). This architecture allows Polymarket to easily spin up complex nested markets (e.g., "Will Trump win AND Bitcoin > $100k?").

#Technical Implementation: ERC-1155

Conditional tokens use the ERC-1155 multi-token standard, which offers several advantages over simpler token standards:

Each outcome in a market receives a unique token ID within the CTF contract. For a binary market:

• tokenId_YES: Represents the YES outcome
• tokenId_NO: Represents the NO outcome

Both tokens exist within the same smart contract, enabling atomic operations like splitting collateral into YES + NO tokens or merging them back.

Complex scenario analysis

Traders can construct positions representing specific world-states. "Tech stocks rise AND Democrats win AND no recession" becomes a single tradeable position.

Combinatorial markets

Markets can offer contracts on all possible combinations of multiple binary events, enabling fine-grained probability estimation across scenarios.

#How It Works

#The Condition-Outcome Structure

Conditional tokens use a hierarchical structure:

Condition: A question with defined outcomes (e.g., "Which party wins the election?" with outcomes Democrat/Republican)

Position: A specific outcome or combination of outcomes a trader holds

Collateral: The underlying asset (usually a stablecoin) that backs all positions

#Splitting and Merging

The key operations with conditional tokens are:

Splitting collateral

$1 collateral → 1 Yes token + 1 No token

You can always split collateral into a complete set of outcome tokens.

Merging tokens

1 Yes token + 1 No token → $1 collateral

A complete set of outcome tokens can always be redeemed for collateral.

#Nested Conditions

Conditional tokens support nested conditions:

Level 1: "Will Democrats win presidency?"
  - Yes_Dem
  - No_Dem

Level 2 (nested under Yes_Dem): "Will Democrats win Senate given Dem presidency?"
  - Yes_Senate|Yes_Dem
  - No_Senate|Yes_Dem

Level 2 (nested under No_Dem): "Will Democrats win Senate given Rep presidency?"
  - Yes_Senate|No_Dem
  - No_Senate|No_Dem

This creates four distinct positions representing all combinations.

#Token Split Visual

#Numerical Example

Consider a market on two events:

• Event A: "Will the bill pass?" (Yes_A / No_A)
• Event B: "Will the President sign it?" (Yes_B / No_B, conditional on Yes_A)

Initial state: Trader has $100 collateral

Step 1: Split on Event A

$100 → 100 Yes_A tokens + 100 No_A tokens

Step 2: Split Yes_A on Event B

100 Yes_A → 100 (Yes_A AND Yes_B) + 100 (Yes_A AND No_B)

Resulting positions:

• 100 tokens: "Bill passes AND President signs"
• 100 tokens: "Bill passes AND President vetoes"
• 100 tokens: "Bill fails"

Each position pays $1 if its specific scenario occurs.

Trade: Trader believes signing is likely if bill passes. They sell their (Yes_A AND No_B) tokens for $0.20 each, receiving$20.

Final portfolio:

• 100 tokens: "Bill passes AND signs" (worth $1 if this happens)
• 100 tokens: "Bill fails" (worth $1 if this happens)
• $20 cash

#Examples

#Example 1: Election Conditional

A trader wants to bet on Senate control conditional on presidential outcome:

Available tokens:

• Dem_Senate | Dem_President (Democrat Senate given Democrat President)
• Rep_Senate | Dem_President
• Dem_Senate | Rep_President
• Rep_Senate | Rep_President

Trade: Trader believes Democratic coattails are strong. They buy "Dem_Senate | Dem_President" at $0.70, implying 70% chance Democrats hold Senate if they win the presidency.

#Example 2: Economic Scenario

Markets exist on Fed rate decisions and recession:

Positions available:

• Recession | Rate_Hike
• No_Recession | Rate_Hike
• Recession | Rate_Hold
• No_Recession | Rate_Hold

A hedge fund with recession exposure buys "Recession | Rate_Hike" tokens as a targeted hedge against their specific risk scenario.

#Example 3: Multi-Outcome Sports

A futures market on a tournament uses conditional tokens:

Round 1: Team A vs. Team B Round 2 (conditional): Winner vs. Team C

Conditional tokens allow trading "Team A wins tournament" where that token only has value if A first beats B, then beats C.

#Example 4: Product Launch Conditional

A market asks about product success conditional on launch timing:

• Success | Q1_Launch
• Failure | Q1_Launch
• Success | Q2_Launch
• Failure | Q2_Launch

Employees with knowledge of launch readiness can trade based on how timing affects success probability.

#Risks, Pitfalls, and Misunderstandings

Complexity overhead

Conditional tokens are more complex than simple binary markets. Traders may misunderstand which conditions their position depends on.

Liquidity fragmentation

While designed to share liquidity, conditional markets can still fragment volume across many outcome combinations, making some positions illiquid.

Conditional resolution

Positions conditional on events that don't occur may be voided rather than resolved. Understanding void conditions is crucial.

Pricing difficulty

Conditional probabilities are harder to estimate than marginal probabilities. Markets may be less efficient because fewer traders can accurately price complex conditionals.

Smart contract complexity

More complex token structures mean more complex smart contracts with more potential vulnerabilities.

Basis risk

Your conditional position may not perfectly match your actual risk exposure due to the discrete scenarios available.

#Practical Tips for Traders

• Understand the condition hierarchy: Before trading, map out exactly which conditions must be met for your position to pay out

• Check void conditions: Know what happens to your position if the conditioning event doesn't occur

• Verify liquidity in your specific position: Aggregate liquidity doesn't mean your particular conditional token is liquid

• Use conditionals for targeted exposure: If you have specific views on conditional probabilities, these markets let you express them precisely

• Consider constructing synthetic positions: By combining conditional tokens, you can create exposures not directly available in simpler markets

• Watch for pricing inefficiencies: Conditional markets are often less efficient due to complexity, creating opportunities for informed traders

#Related Terms

• Conditional Market
• Binary Market
• Outcome Token
• Polymarket
• Liquidity Pool

#FAQ

#How are conditional tokens different from regular prediction market shares?

Regular shares represent simple outcomes ("X happens"). Conditional tokens represent outcomes contingent on conditions ("X happens given Y happened"). This allows expressing views on how events relate to each other, not just whether they occur.

#What happens to my conditional tokens if the condition isn't met?

Typically, positions conditional on unmet conditions are voided and collateral returned. For example, "Senate outcome given Democrat President" is voided if a Republican wins. Check the specific market rules; some platforms handle this differently.

#Who uses conditional tokens?

Sophisticated traders who want to express complex views, researchers studying conditional probabilities, and protocols building advanced prediction market products. The complexity limits retail participation but enables nuanced institutional use cases.

#Can I create my own conditional token market?

On platforms using the Gnosis Conditional Token Framework (CTF), anyone can create conditions and markets. You'll need to specify the condition, outcomes, and resolution mechanism. Creating useful markets requires understanding both the technical standard and market design principles.

#How do prices of conditional tokens relate to unconditional probabilities?

If you know prices for all conditional tokens, you can calculate unconditional probabilities. For example:

P(A and B) = P(B|A) × P(A)

The conditional token price for "B given A" times the price for "A" gives the joint probability. Arbitrageurs ensure these relationships hold across related markets.