1. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Distributed Prediction Markets modeled by
Weighted Bayesian Graphical Game
Janyl Jumadinova
Advisor: Raj Dasgupta
C-MANTIC Research Group
Computer Science Department
University of Nebraska at Omaha
UNO Research Fair 2013
1 / 24
2. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Outline
Problem: Distributed information aggregation - the
interaction among multiple prediction markets.
Solution: A software agent-based distributed prediction
market model where prediction markets running similar
events can influence each other.
Experimental validation: Comparison with other
models and trading approaches.
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3. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
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 different
people and,
- forecast the possible outcome of the event based on the
aggregated opinion.
Prediction markets operate similarly to financial markets.
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4. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
3 / 24
5. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
4 / 24
6. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
5 / 24
7. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
6 / 24
8. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
7 / 24
9. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
8 / 24
10. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
9 / 24
11. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Do Prediction Markets Work?
Yes, evidence from real markets, laboratory experiments, and theory
I.E.M. beat political polls 451/596 [Forsythe 1999, Berg
2001, Pennock 2002]
HP market beat sales forecast 6/8 [Plott 2000]
Sports betting markets provide accurate forecasts of
game outcomes [Debnath 2003, Schmidt 2002]
Market games work [Pennock 2001]
Laboratory experiments confirm information
aggregation [Forsythe 1990, Plott 1997, Chen 2001]
Theory of Rational Expectations [Lucas 1972, Grossman
1981]
and more...
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12. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
11 / 24
13. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
12 / 24
14. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
13 / 24
15. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Event: 2012 Presidential Election
14 / 24
16. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Research Problem
Distributed prediction markets
- Multiple prediction markets running simultaneously have
similar events.
- The expected outcomes of an event in one prediction
market will influence the outcome of a similar event in a
different prediction market.
15 / 24
17. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Research Problem
Distributed prediction markets
- Multiple prediction markets running simultaneously have
similar events.
- The expected outcomes of an event in one prediction
market will influence the outcome of a similar event in a
different prediction market.
Inter-market effects: evidence from financial markets.
Inter-market relationship has not been studied in
prediction markets.
15 / 24
18. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Our Solution
1 A software agent-based
distributed prediction market model:
- comprises of multiple, parallel running prediction
markets,
- uses a graphical structure between the market makers of
the different markets to represent inter-market influence.
2 A graphical game-based algorithm that determines the
best action for the participants in the prediction market
using our proposed model.
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19. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Model of the Distributed Prediction Market
Model using a framework from the field of game theory
in microeconomics, called graphical games.
Model interaction as a game.
Game consists of a set of
players,
actions,
and a specification of utility (monetary gain)
for each action.
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20. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Model of the Distributed Prediction Market
Weighted Bayesian Graphical Games
Weighted: use weights to model the influence of one
market maker on others.
Bayesian: used to model the uncertainty of one market
maker about the other market makers and incorporate
different types of market makers.
Graphical Games: allows to capture the interaction
between multiple market makers.18 / 24
21. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Bayes-Nash Equilibrium
Propose an algorithm to calculate the equilibrium of the
game efficiently.
Determines the best action for the market makers in
each prediction market using our proposed model.
The best action gives the maximum utility to each
market maker.
We prove that our algorithm guarantees truthful
revelation by the market makers.
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22. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Simulation Results
Comparison
For comparison we use two well-known techniques for
trading
Greedy strategy
Maximizes immediate utility.
Does not consider the types of the market makers.
Influence-less market
Conventional single, isolated markets:
- the market price is determined by the market maker
based on that market’s traders’ decisions only.
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23. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Comparison to other strategies
21 / 24
24. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Comparison to other strategies
Market makers using our Algorithm obtain 56% more
utility than the market makers following the next best
greedy strategy.
Interacting market makers in a distributed prediction
market are able to improve their utilities and predict
prices with less fluctuations.
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26. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Neighborhood.
Market makers with a small number of neighbors get
less utility than when the number of neighbors is larger,
But this relationship is not linear.
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27. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Benefits of our research
Information aggregation is pervasive...
Internet-based social networks, sensor networks, daily
lives of people, etc.
Our novel framework for distributed prediction markets
leads to several challenging and important directions
that can help to gain a better understanding of the
distributed information aggregation problem.
Shows how the related markets can affect each other.
Can be used to take some of the guesswork out of
buying/selling securities.
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28. Distributed
Prediction Markets
modeled by
Weighted Bayesian
Graphical Game
Janyl Jumadinova
Advisor: Raj
Dasgupta
Outline
Introduction
Research Problem
Our Solution
Conclusion
Thank You!
Questions?
jjumadinova@unomaha.edu
http://myweb.unomaha.edu/∼ jjumadinova
C-MANTIC Research Group
http://cmantic.unomaha.edu
This research has been sponsored as part of the
COMRADES project funded by the Office of Naval Research.
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