Jumadinova distributed pm_slides

DistributedPrediction Markets

modeled byWeighted BayesianGraphical Game

Janyl JumadinovaAdvisor: RajDasgupta

Outline

Introduction

Research Problem

Our Solution

Conclusion

Distributed Prediction Markets modeled byWeighted Bayesian Graphical Game

Janyl JumadinovaAdvisor: Raj Dasgupta

C-MANTIC Research GroupComputer Science Department

University of Nebraska at Omaha

UNO Research Fair 2013

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Outline

Introduction

Research Problem

Our Solution

Conclusion

Outline

Problem: Distributed information aggregation - theinteraction among multiple prediction markets.

Solution: A software agent-based distributed predictionmarket model where prediction markets running similarevents can influence each other.

Experimental validation: Comparison with othermodels and trading approaches.

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Introduction

Research Problem

Our Solution

Conclusion

Prediction Market

A Prediction market is

a market-based mechanism used to

- combine the opinions on a future event from differentpeople and,

- forecast the possible outcome of the event based on theaggregated opinion.

Prediction markets operate similarly to financial markets.

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Conclusion

Event: 2012 Presidential Election

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Conclusion

Do Prediction Markets Work?Yes, evidence from real markets, laboratory experiments, and theory

I.E.M. beat political polls 451/596 [Forsythe 1999, Berg2001, Pennock 2002]

HP market beat sales forecast 6/8 [Plott 2000]

Sports betting markets provide accurate forecasts ofgame outcomes [Debnath 2003, Schmidt 2002]

Market games work [Pennock 2001]

Laboratory experiments confirm informationaggregation [Forsythe 1990, Plott 1997, Chen 2001]

Theory of Rational Expectations [Lucas 1972, Grossman1981]

and more...

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Our Solution

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Introduction

Research Problem

Our Solution

Conclusion

Research Problem

Distributed prediction markets

- Multiple prediction markets running simultaneously havesimilar events.

- The expected outcomes of an event in one predictionmarket will influence the outcome of a similar event in adifferent prediction market.

Inter-market effects: evidence from financial markets.

Inter-market relationship has not been studied inprediction markets.

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Introduction

Research Problem

Our Solution

Conclusion

Our Solution

1 A software agent-baseddistributed prediction market model:

- comprises of multiple, parallel running predictionmarkets,

- uses a graphical structure between the market makers ofthe different markets to represent inter-market influence.

2 A graphical game-based algorithm that determines thebest action for the participants in the prediction marketusing our proposed model.

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Introduction

Research Problem

Our Solution

Conclusion

Model of the Distributed Prediction Market

Model using a framework from the field of game theoryin microeconomics, called graphical games.

Model interaction as a game.

Game consists of a set ofplayers,actions,and a specification of utility (monetary gain)for each action.

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Introduction

Research Problem

Our Solution

Conclusion

Model of the Distributed Prediction Market

Weighted Bayesian Graphical Games

Weighted: use weights to model the influence of onemarket maker on others.

Bayesian: used to model the uncertainty of one marketmaker about the other market makers and incorporatedifferent types of market makers.

Graphical Games: allows to capture the interactionbetween multiple market makers.18 / 24




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Introduction

Research Problem

Our Solution

Conclusion

Bayes-Nash Equilibrium

Propose an algorithm to calculate the equilibrium of thegame efficiently.

Determines the best action for the market makers ineach prediction market using our proposed model.

The best action gives the maximum utility to eachmarket maker.

We prove that our algorithm guarantees truthfulrevelation by the market makers.

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Introduction

Research Problem

Our Solution

Conclusion

Simulation ResultsComparison

For comparison we use two well-known techniques fortrading

Greedy strategy

Maximizes immediate utility.Does not consider the types of the market makers.

Influence-less marketConventional single, isolated markets:

- the market price is determined by the market makerbased on that market’s traders’ decisions only.

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Introduction

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Comparison to other strategies

Market makers using our Algorithm obtain 56% moreutility than the market makers following the next bestgreedy strategy.

Interacting market makers in a distributed predictionmarket are able to improve their utilities and predictprices with less fluctuations.

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Introduction

Research Problem

Our Solution

Conclusion

Neighborhood.

Market makers with a small number of neighbors getless utility than when the number of neighbors is larger,

But this relationship is not linear.

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Introduction

Research Problem

Our Solution

Conclusion

Benefits of our research

Information aggregation is pervasive...Internet-based social networks, sensor networks, dailylives of people, etc.

Our novel framework for distributed prediction marketsleads to several challenging and important directionsthat can help to gain a better understanding of thedistributed information aggregation problem.

Shows how the related markets can affect each other.Can be used to take some of the guesswork out ofbuying/selling securities.

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Introduction

Research Problem

Our Solution

Conclusion

Thank You!

Questions?

[email protected]

http://myweb.unomaha.edu/∼ jjumadinova

C-MANTIC Research Group

http://cmantic.unomaha.edu

This research has been sponsored as part of theCOMRADES project funded by the Office of Naval Research.

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mailto:[email protected]

http://myweb.unomaha.edu/$\sim $ jjumadinova/

http://cmantic.unomaha.edu/

http://cmantic.unomaha.edu/

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