Conditional Markets

#Definition

A conditional market is a prediction market where trades are only valid if a specific precondition is met. If the condition fails to occur, the market is voided and all positions are refunded. This structure allows traders to express beliefs about outcomes contingent on specific scenarios.

The format is typically: "What will [outcome] be if [condition]?" For example, "What will the stock market return if the incumbent wins reelection?" The market only resolves on the stock return question if the incumbent actually wins; otherwise, all bets are off.

#Why It Matters in Prediction Markets

Conditional markets unlock a category of questions that unconditional markets cannot address: counterfactual comparisons and policy-dependent outcomes.

Isolating causal effects

By comparing conditional markets ("GDP growth if Policy A passes" vs. "GDP growth if Policy B passes"), observers can estimate the market's view of each policy's causal impact. This is the foundation of futarchy and decision markets.

Handling uncertainty about preconditions

Sometimes traders have strong views about an outcome given a scenario but are uncertain whether the scenario will occur. Conditional markets let them express the conditional belief without betting on the precondition itself.

Enabling complex forecasting

Questions like "Will inflation exceed 3% if the Fed cuts rates?" combine monetary policy prediction with macroeconomic forecasting. Conditional markets separate these questions, allowing specialists to contribute to the domain they understand best.

Supporting decision-making

Organizations can use conditional markets to compare strategies: "What will customer satisfaction be if we launch Feature A?" versus "...if we launch Feature B?" The conditionals allow direct comparison of outcomes under different choices.

#Unconditional vs. Conditional Markets

#How It Works

#Conditional Logic Flow

#Market Structure

A conditional market specifies:

1. The condition: An event that must occur for the market to resolve normally
2. The outcome question: What the market actually predicts, contingent on the condition
3. Void rules: What happens if the condition is not met (typically full refund)

#Resolution Logic

If condition occurs:
    → Market resolves based on outcome (Yes/No, or value)
If condition does not occur:
    → Market is voided
    → All positions refunded at original cost

/**
 * Calculates conditional probability P(A|B).
 * 
 * @param probAandB - Probability of both Outcome A AND Condition B occurring
 * @param probB - Probability of Condition B occurring
 * @returns Conditional probability P(A|B)
 */
function calculateConditionalProbability(probAandB, probB) {
  if (probB === 0) return 0; // Avoid division by zero
  
  return probAandB / probB;
}

// Example: P(Recession AND RateHike) = 0.20, P(RateHike) = 0.40
// P(Recession | RateHike) = 0.20 / 0.40 = 0.50
const conditionalProb = calculateConditionalProbability(0.20, 0.40);

#Numerical Example

Consider a conditional market: "Will Company X stock rise 10%+ if their new product launches in Q1?"

A trader believes:

• 60% chance product launches in Q1
• 80% chance stock rises 10%+ if the product launches

The trader buys Yes at $0.65 on the conditional market.

Scenario A: Product launches in Q1 (60% chance)

• Stock rises 10%+: Trader receives $1.00, profits$0.35
• Stock doesn't rise 10%+: Trader receives $0.00, loses$0.65

Scenario B: Product doesn't launch in Q1 (40% chance)

• Market voided: Trader receives $0.65 refund, breaks even

Expected value calculation:

EV = 0.60 × [(0.80 × $1.00) + (0.20 × $0.00)] + 0.40 × $0.65 - $0.65
EV = 0.60 × $0.80 + 0.40 × $0.65 - $0.65
EV = $0.48 + $0.26 - $0.65 = $0.09

The position has positive expected value of $0.09 per share.

#Paired Conditional Markets

The most powerful use of conditional markets involves comparing outcomes across different conditions:

Market A: "GDP growth if Candidate X wins" Market B: "GDP growth if Candidate Y wins"

If Market A shows higher expected GDP than Market B, the aggregate wisdom suggests Candidate X would be better for economic growth. This comparison is the basis of futarchy-style governance.

#Examples

#Example 1: Policy Analysis

Two conditional markets run simultaneously:

• "Unemployment rate in 2026 if minimum wage increases to $15"
• "Unemployment rate in 2026 if minimum wage stays at $12"

After the policy decision is made, only one market resolves; the other voids. The price difference before resolution indicates the market's estimate of the policy's employment impact.

#Example 2: Corporate Strategy

A company considering two product strategies creates internal conditional markets:

• "Revenue growth if we pursue Strategy A"
• "Revenue growth if we pursue Strategy B"

Employees trade based on their domain knowledge. Management reviews market prices to inform the decision. After choosing Strategy B, that market resolves on actual revenue; Strategy A market voids.

#Example 3: Sports Scenarios

A conditional sports market: "Will Team X win the championship if they make the playoffs?"

This separates playoff qualification (uncertain) from championship prospects given qualification. Traders who believe Team X is a strong contender once in the playoffs can express that view without betting on whether they'll qualify.

#Example 4: Counterfactual Analysis

Historical counterfactual markets (mostly theoretical): "What would the 2008 stock market return have been if Lehman Brothers had been bailed out?"

While such markets can't resolve on historical events, similar structures help analyze future decisions where multiple paths are possible but only one will be taken.

#Creating a Conditional Market

To structure a valid conditional market, you need three clear components:

1. The Condition (Precondition):

   • Must be observable and definitive.
   • Bad: "If the economy gets better..."
   • Good: "If US GDP growth > 2.0% in Q3 2025..."

2. The Outcome Question:

   • The variable you want to predict.
   • Example: "...what will the S&P 500 closing price be on Dec 31, 2025?"

3. The Void Rule:

   • Explicitly state that if the condition is NOT met (GDP <= 2.0%), the market voids.
   • Standard phrasing: "This market resolves to the value of [Outcome] if [Condition] occurs. If [Condition] does not occur by [Date], this market resolves to N/A (Void) and all positions are refunded."

#Risks, Pitfalls, and Misunderstandings

Conditional probability confusion

Traders often confuse P(outcome | condition) with P(outcome AND condition). A conditional market prices only the former. If you think both the condition is likely and the outcome is likely given the condition, you may be overpaying in a conditional market that only pays for the conditional probability.

Void probability affects expected value

The chance of voiding matters for capital allocation. A 50% void probability means your capital sits idle half the time. Calculate expected value accounting for all scenarios, including voids.

Resolution source alignment

The condition and outcome may have different resolution sources or timing. Ensure you understand when each resolves and whether edge cases (delayed condition, ambiguous outcome) are clearly specified.

Thin liquidity in paired markets

Conditional markets often have lower liquidity than unconditional markets because the question is more complex. Each side of a paired conditional market may be even thinner. Slippage can be severe.

Manipulation through condition

In some setups, actors might influence whether the condition occurs to benefit their market position. If a trader holds large positions betting against an outcome conditional on a bill passing, they might lobby against the bill to void the market and avoid losses.

#Practical Tips for Traders

• Calculate EV across all branches: Include the void scenario. A cheap-looking conditional bet may be fairly priced when accounting for high void probability

• Consider opportunity cost of voids: Capital locked in conditional markets that void generates no return. If you expect 50% void probability, effective capital at risk is halved

• Use conditionals to separate beliefs: If you're confident about an outcome given a scenario but uncertain about the scenario itself, conditional markets let you express the first belief cleanly

• Compare paired markets for policy insights: The spread between "outcome if A" and "outcome if B" markets reveals collective beliefs about causal effects, useful for decision-making beyond trading

• Check resolution timing: The condition may resolve long before or after the outcome question. Understand the timeline and how it affects liquidity and information incorporation

• Watch for strategic void-seeking: Participants with losing positions may prefer voiding to losing. Consider whether any party can influence the condition's occurrence

• Size positions for thin markets: Conditional markets are often less liquid than their unconditional counterparts. Reduce position sizes accordingly

#Futarchy: Governance via Conditional Markets

The most ambitious use of conditional markets is Futarchy, a governance system proposed by Robin Hanson.

Concept: "Vote on values, bet on beliefs."

Example:

1. Goal: Maximize National GDP.
2. Proposal: "Should we bail out the banks?"
3. Markets:
   • Market A: "GDP in 5 years IF we bail out banks."
   • Market B: "GDP in 5 years IF we DO NOT bail out banks."
4. Decision: If Market A price > Market B price, the policy is automatically enacted.

This uses market signals to determine which policy is expected to yield the best result.

#Related Terms

• Prediction Market
• Futarchy
• Decision Market
• Binary Market
• Expected Value (EV)
• Resolution Criteria
• Conditional Tokens

#FAQ

#What happens to my money if the condition doesn't occur?

In most conditional market designs, your position is refunded at the original purchase price if the condition fails. You neither gain nor lose on the outcome question; it's as if the bet never happened. Some platforms may deduct fees even on voids, so check the specific rules.

#How do conditional markets compare different policy options?

By running parallel markets like "Outcome if Policy A" and "Outcome if Policy B," observers compare the market's prediction for each scenario. If Policy A's market shows better expected outcomes, the aggregate information suggests Policy A is preferable. This is the core mechanism behind futarchy governance proposals.

#Are conditional markets harder to price than regular markets?

Yes, for two reasons. First, traders must estimate both the conditional probability P(outcome | condition) and understand how void probability affects expected returns. Second, conditional markets often have lower liquidity, making prices less reliable and execution more costly.

#Can I trade conditional markets on Polymarket or Kalshi?

Some conditional-style markets appear on major platforms, though they may not use explicit conditional mechanics. Questions like "What will X be at time T if Y happens first?" embed conditional logic. Full conditional token frameworks (where void/refund is automatic) are more common in experimental or blockchain-native platforms.

#Why would someone prefer conditional markets over regular prediction markets?

Conditional markets allow expression of specific beliefs that unconditional markets cannot capture. If you know a lot about economic outcomes given a particular election result but have no view on the election itself, conditional markets let you trade your expertise without betting on the election. They also enable comparative policy analysis and causal inference.