A multi agent prediction market based on Boolean Network Evolution

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

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

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

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

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

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

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

Tradition PM BN-based PM 122 4 3 4

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

Boolean Network-BasedPrediction Market (cont.)

A B C (5 D ) E F

Boolean Network-BasedPrediction Market (cont.) A F D C

Experimental results

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

Experimental results(cont.)

Experimental results(cont.) A F D C

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

Experimental results(cont.)

Experimental results(cont.) stable

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

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

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A multi agent prediction market based on Boolean Network Evolution

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A multi agent prediction market based on Boolean Network Evolution — Presentation Transcript