1) A multi-agent prediction market model based on Boolean network evolution is proposed, where trading agents update their binary beliefs (0 or 1) about event outcomes using Boolean functions. 2) The model aggregates individual agent beliefs into a market price through a mean-field approach, stabilizing price fluctuations compared to conventional markets. 3) Experiments show the Boolean network model accurately predicts outcomes and scales well to large agent numbers, outperforming a standard logarithmic market scoring rule approach.

1. A Multi-Agent Prediction Market
Based on
Boolean Network Evolution
Janyl Jumadinova., Mihaela T. Matache and Prithviraj Dasgupta
WI-IAT.2011
Presenter: Yu Hsiang Huang
Date: 2011-12-09

2. Outline
• Introduction
• Related work
• Boolean Network based prediction market
• Experiment result
• Conclusion

3. Introduction
• Prediction market
• Multi-Agent system
• Boolean Network (BN)
– Boolean rule(0/1)
• BN vs. LMSR
– Eliminate the frequently fluctuating price

4. Related work
• Prediction Markets
• Outcome geopolitical events – US presidential elections
• Outcome of sporting events
• Predicting the box office performance of Hollywood movies
– Belief
• Opinion of individual trader about the outcome of a future event
• Market price

5. Boolean Network-Based
Prediction Market
• A. Prediction Market Preliminaries
– Major participants in agent-based prediction market
• A set of trading agents
• A market maker agent – central entity
– Outcome of an event is binary (will happen/won’t happen)
– Trading agent
• At time t
• Bet security (bought/sold/held)  discrete quantities
– Market maker agent
• Aggregate the price at which securities of event traded by agents
• Market price – probability of the outcome of the event
• Compare market price with actual decision in real world  cost

6. Trading period t Quantity=10
Security
bet
(bought)
Trading agents
Quantity=7
Security
bet
(sold)
Quantity=5
Security
bet
(bought)
Market maker agent
cost
Quantity=8 Quantity=10
Market Security Security
bet
(held) bet
(bought)
price cost
vs.
Actual
decision

7. Boolean Network-Based
Prediction Market(cont.)
• B. BN-based Prediction Market
– Major participants in BN-based prediction market
• Trading agent  buy and sell securities on behalf of human traders
• Market maker agent
• Information sources
– Based on traditional prediction market’s operation
• Belief
– Outcome of security corresponding to the event
– Used to calculate the market price
• State
– In BN, used to represent belief
– Updated using Boolean function
– 1 or ON : trading agent believes the event will happen
– 0 or OFF : trading agent believes the event won’t happen

8. Tradition PM
BN-based PM
1
2
2
4
3
4

10. Boolean Network-Based
Prediction Market (cont.)
• C. Trading Agents’ Boolean Belief Update
Market price State Information signal
Threshold
trust

11. Boolean Network-Based
Prediction Market (cont.)

12. (1
)

13. A
B
C
(5
D )
E
F

15. Boolean Network-Based
Prediction Market (cont.)
A F
D
C

16. Experimental results

18. Experimental results(cont.)
• B. Patterns and validation of the mean-field based
price aggregation mechanism
– Pattern formation plot
– Arranging nodes representing the trading agents in one dimensional array left to right
– State : 1 black plot
– State : 0 neutral plot

19. Experimental results(cont.)

20. Experimental results(cont.)
A F D C

21. Experimental results(cont.)
• C. Comparison to Conventional Prediction Markets
– LMSR vs. BN

22. Experimental results(cont.)

23. Experimental results(cont.)
stable

24. Experimental results(cont.)
• E. Scalability
– The number of trading agents v.s. model ‘s accuracy

25. Conclusion
• Boolean network
– Behavior of trading agent
• Aggregated market price
• Past beliefs
• Information flow
• BN v.s. LMSR
– Less fluctuation of the market price
– Analyze and predict the dynamics of prediction market
– Simpler
• Future
• Variation of weight and threshold parameters
• More possible state
• Limit untruthful belief