Agentic Prediction MarketsAgentic预测市场

1. Overall Structure (Four Layers)
2. Core Objects & Processes
3. Incentive Design (Power & Flux + Predictive Score Formula)
4. A specific example (so you can tell others)

1️⃣ Overall structure: ISO-style four-layer prediction market

We made it a "semantic prediction network" instead of a normal yes/no bet.

Four layers:

1. The Semantic Layer is responsible for clarifying the problem: who, what, time, data source, judgment conditions → ISO flavor: the problem must be abstracted into an "alignable semantic object"
2. Intent Layer/Agentic Layer User/Agent puts forward prediction intentions: "I think X will happen, and I am willing to bet with Flux."
3. "I just want to stake my opinion, not necessarily a big bet."
4. "As an AI researcher, I use inference reports in exchange for additional rewards."
5. Market Layer Specific prediction contract: LMSR / AMM market
6. Peer-to-Peer Agent Bonding
7. Arbitrage / Market-making Agent
8. Settlement Layer How to determine right or wrong and how to allocate Flux: Specify data source (Oracle / API / on-chain events)
9. Agent + human do "result verification task" together
10. Give rewards and punishments according to Brier Score / Log Score

In a word: ISO is responsible for turning the "problem/semantics/judgment criteria" into a clear object, and the Agentic prediction market is responsible for betting, reasoning and settlement around this object.

2️⃣ Core Object & Process Design

🧱 Core objects (elements)

1. Event Text: Natural Language Question
2. Schema: Standardized fields (who / what / when / data source)
3. Resolution Rule: When and what data are used to determine "occurred/didn't occur"
4. Example: "Will TAO's market value exceed US$20 billion before December 31, 2026?"
5. Claim (semantic claim) YES / NO / range
6. For example: YES = "will exceed", NO = "will not exceed"
7. It can also be an interval: [0–10B], [10–20B], [20B+]
8. Market (Market) Each Event corresponds to one or more Markets: YES/NO
9. interval
10. Market Mechanism: LMSR/AMM
11. Position (position) The interest held by the user/Agent in a certain Claim (Flux bet)
12. Report (optional but very ISO) Some Agents not only place bets, but also upload analysis, data, and reasoning on events.
13. In the future, this type of "high-value analysis" can be given separate incentives (more avant-garde)

🔁 Process: from problem to settlement

1. Create an issue (Event Creation) The initiator (person or Agent) submits an issue
2. Agreement requirements: Schema must be filled in (time/judgment method/data source)
3. Some Agents specialize in "problem cleaning" and help rewrite them into standardized questions to earn some Flux.
4. Market Opening The protocol automatically creates a corresponding Market for this Event
5. The initial price can be: 50–50, or determined by the first agent to quote
6. Prediction/Trading Ordinary users: buy directly YES/NO
7. Agent: Can do automatic market making
8. Can do arbitrage
9. Can do cross-market consistency checks
10. During the waiting period (Pre-Resolution), the Agent will continue to adjust positions based on news/data.
11. There can also be an Agent that automatically generates a "semantic interpretation of the current consensus", for example: "Based on the current price, the market believes that the probability of TAO exceeding 20 billion is about 63%."
12. Result determination (Resolution) The expiration time is up → Pull data from the scheduled data source
13. If there is a boundary situation (such as being removed from the shelves, mergers and acquisitions, etc.), the "semantic arbitration task" is triggered: a group of Agent + human voting/explanation
14. Using Flux as collateral
15. Finally gives a probability result (0 or 1, or some value between 0–1)
16. Reward/Penalty (Settlement) According to the position at the time of prediction + final result
17. Distribution of Flux: The correct party earns, the wrong party loses

3️⃣ Incentive system: How Flux & Power is used in prediction markets

🌋 Power: cost layer

• Submit events, participate in predictive calculations, and run analysis models → consume Power
• You can simply design: Each transaction/submit prediction/submit report: burn a small amount of Power
• Make the cost of "spamming events/spam prediction" higher, forcing the Agent to improve quality

Indicate:

[ Power {i}(t+1)=Power {i}(t)-k P\cdot a i(t) ]

• (a i(t)): Number of operations participated in this period (initiating events/orders/reports)

💰 Flux: Incentive layer

Flux main sources:

• Profit and loss between LP/Trader in the market (zero sum part)
• The protocol can use a portion of the transaction fee to reward: High-quality issue initiators
• Agent that provides high-value analysis
• Participants in semantic arbitration

A simple, easy-to-implement formula:

1. Prediction rewards (use Brier Score as "accuracy bonus")

For an Agent (i)'s prediction (p i) on an event, the actual outcome of the event is (o \in {0,1}) (NO/YES):

[ Score i = 1 - (p i - o)^2 ]

You can design the reward as:

[ Reward i = \alpha \cdot Stake i \cdot (Score i - Score {baseline}) ]

• (Stake i): How much Flux is staked for this prediction?
• (Score {baseline}): The score corresponding to the baseline prediction (such as 0.5)
• (\alpha): a scaling factor

Simple understanding:

The closer the prediction is to the final result, the more extra rewards you will get; the more you press, the greater the amplification; if it is worse than just guessing (0.5), you will lose.

1. Matchmaking & Transaction Fee Burning

Every trade / market transaction:

• Charge a small fee and pay with Flux
• Part of it is fed to LP and protocol treasury.
• Partial direct burn

[ Flux {protocol}(t+1)=Flux {protocol}(t)+\phi \cdot Volume t ] [ Flux {burned}(t+1)=Flux {burned}(t)+\psi \cdot Volume t ]

• (\phi): Agreement revenue ratio
• (\psi): Burning ratio (reduce total circulation, keep coins valuable)

4️⃣ A specific example: How does Agentic predict the market?

Question:

“By the end of 2026, will there be more than 3 mainstream AI chains (TVL > USD 1 billion) in the world?”

1. User A submitted a question → Fill in the details: Statistics time point: 2026-12-31 23:59 UTC
2. Data source: DefiLlama / protocol-specified TVL statistics website
3. Standards for “mainstream AI chains”: more than 30% of the TVL on the chain comes from AI-related protocols
4. The system automatically creates Market: YES / NO
5. Initial price 50–50
6. Several Agents: Agent Alpha: Optimistic, buy YES
7. Agent Beta: a market-making robot that provides liquidity to the market
8. Agent Data: Continuously crawl TVL data and produce analysis reports
9. Agent Arb: Observe the price of AuraSci market vs other prediction platforms and do arbitrage
10. All trading & prediction actions: Consumes one point of Power (anti-Spam)
11. You need to pay some Flux fees (for market making + protocol)
12. By the end of 2026: The system pulls data → determines how many AI chains meet the conditions
13. Get the result: for example 4 → YES = 1
14. All YES forecasters receive Flux according to their positions
15. All NO forecasters lose Flux

Scalable directions:

• Agents who upload "explanation reports" can receive additional rewards
• Give early predictions higher rewards with Brier Score (the earlier, the harder)

🔚 Finish with one sentence (you can just say it)

This is an Agent-native prediction market: each question at the bottom must first clarify "what is being predicted", and then let the Agent and humans use Flux to bet, use Power to pay the cost, and finally use real-world results to reward "who truly understands the future."

This framework can be further distilled into a Litepaper page, Agentic prediction market v0 technical specifications, or an AuraSci prediction market module for integrating with research intent / Grant systems.