Andreas Weigend
Stanford University
Stat 252 and MS&E 238

Data Mining and Electronic Business


Homework 5 - Designing Prediction Markets for Internet Startups

(due by 5PM on Sunday, May 13; may be done in groups of up to 3 people)
Background:
Roughly speaking, a prediction market is a system in which participants can buy and sell contracts regarding predictions of uncertain events in the future (e.g., who will win the 2008 Presidential election). Prediction markets are based on the "wisdom of the crowds;" so a successful prediction market is one in which many players actively participate. The transactions in the market should reveal the crowd's expectation of some uncertain event (e.g., probability of that event occurring). If you are still unclear about prediction markets, please read Alex Kirtland's short article "Communicating Complex Ideas".
In the //Prediction Markets// paper by Wolfers and Zitzewitz, the authors identified at least three types of prediction market contracts: (i) winner-takes-all, (ii) index, and (iii) spread. In this assignment, you will be writing at least one type of each contract, in addition to one additional contract of your choice. Therefore, you will submit at least four contracts.

The Assignment:
Identify at least four well-defined/landmark events that an internet start-up company may experience in the future. The goal of this assignment is two-fold: (1) to create a well-defined contract for each of the identified events and (2) to discuss how a prediction market for each event can be successfully implemented. In your homework submission, please address the following bullet points.
  • Identify at least four outcomes for which you are to base each prediction market. The outcome must be well-defined and the time period for which the event needs to occur must be clear.
  • How would you set up a prediction market for each outcome? [For example, you might consider using the commercial platform by Chicago-based Inkling Markets.]
    • How do individuals first learn about your prediction market?
    • How do people enter your market?
    • How can you incentivize individuals to actively participate in your market?
    • How do people play? When individuals enter your market, does everyone start off equally with the same number of points or "money"?
    • What barriers would you set up to make sure that your market is tamper resistant?
    • What types of variables should be collected? Note that variables need to be measurable.
  • For each of the outcomes identified in the first bullet point, write a detailed contract detailing the conditions of the payoff. That is, what determines the payoff? What are the time intervals involved? You will be writing at least one type of each contract, in addition to one additional contract of your choice. Therefore, you will submit at least four contracts.
    ----
    Remarks on Grading:
  • As opposed to previous assignments, there is no computer programming or compiling involved.
  • You will not actually implement your prediction markets. Rather, we want you to focus your energy on developing well thought-out submissions.
  • We are looking for concrete, clear, insightful, and intelligent submissions.

Readings on http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets

  1. References:

    1. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/AEI-Brookings2007.pdf This paper seeks to provide recommendations for changes to law in order to spur the development of prediction markets in order to assist both the private and public sector. Its recommendations focus around a safe harbor for particular groups to support the development and further understanding of prediction markets.
    2. Manifest on Prediction Markets published by the luminaires on May 7, 2007 (thanks to Gregor Hochmuth for pointing this out)
    3. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/ChenChuMullenPennock2005.pdf This paper examines the forecast accuracy of a prediction market compared to experts on the topic of the 2003 NFL games. It shows that at the same time point ahead of the game, prediction markets give identically accurate predictions as the experts.
    4. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/HahnTetlock_Brookings2005.pdf
      1. This 200-page book (available at Amazon) is a set of articles by different authors that discuss the different aspects of prediction markets, from basic principles to more advanced concepts.
      2. The first few chapters introduce information or predictive markets that you can typically get from any introduction to information markets. It talks about how prediction markets can influence public policy by letting the highest bidder actually do something for what he bid for. If the highest bidder succeeds in achieving the goal, he or she makes the price of the contract.
      3. One interesting chapter deals with methods for producing incentives to make more people participate in the market as well as incentives for people to release information underlying predictions. Typically, you would imagine that people who have inside information would be reluctant to share this information as it provides them with an edge during the trading process. This chapter discusses several methods to encourage such sharing.
    5. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Hanson2003.pdf Combinatorial Information Market Design, by Hanson, Robin (2003)
      1. Hanson describes information markets and argues why Market Scoring Rules overcome the most common problems in information markets - thin markets and opinion pooling in the thick market case. After introducing market scoring rules, the author looks at several market design issues: how to represent variables to support both conditional and unconditional estimates, how to avoid becoming a money pump from errors in calculating probabilities, and how to ensure that users can cover their bets, without preventing them from using previous bets as collateral for future bets.
    6. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/IEEE2005TechBuzzGame.pdf This article discusses how Yahoo got into the predictive markets game. This was a joint venture between O'Reilly Media and Yahoo! Research. They had as their key research objectives the ff: 1. evaluate the power of prediction markets to forecast high-tech trends 2. test their dynamic parimutuel market system for allocating and pricing shares. An interesting conclusion from this paper is that the pricing mechanism needs to be fool-proof as a couple of 17-year old traders figured out the problems with pricing and took advantage of it, thereby guaranteeing a positive net profit at all times.
    7. Kirtland, A. (2006). http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Kirtland2006.doc Desigining Prediction Markets for End Users, this is annotation of http://www.boxesandarrows.com/view/communicating_c Good current intro with relevant examples.
      1. In his online article, Alex Kirtland describes the rules necessary to create a prediction market. The main rules the author gives are:
        1. Make people want to play
        2. Provide bidding/trading examples carefully
        3. Don't make too many comparisons to the stock market but use as a learning
        4. Make it simple
        5. Provide information that keep the user informed
        6. Use contextual help to guide users through the process.
    8. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Pennock2004.pdf David M. Pennock introduces "DPM - Dynamic pari-mutuel market", a hybrid between a pari-mutuel market (example, bets at horse racing and "Tech Buzz Game", where two or more exhaustive and mutually exclusive outcomes will occur at some time in the future and those that were right distribute the gains that are result of the money lost by the people that were wrong) and Continuous Double Action or CDA (for example the stock market, where orders to buy are constantly matched with orders to sell, and where the participants can secure gains and cut losses at any time they find a party to make the transaction). DPM combines the infinite liquidity and risk-free nature of a pari-mutuel market with the dynamic nature of a CDA.
    9. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/RothColes_Harvard2007MarketDesignIdeas.doc Al Roth and Peter Coles present a list of markets, some of them Internet-based markets with the purpose allowing analysis from a market design point of view. Amongst the markets presented, we can cite:
      1. Zorpa.com a UK based internet company that matches lenders with borrowers bypassing traditional banks.
      2. Along the same line we find Prosper.com
      3. TicketReserve and , market for ticket options for future possible events and spectacles
      4. Google's internal prediction market
      5. Not online reference available, but the article "Here's an idea: Let everyone have ideas" shows a clever way to put collective intelligence into action in a corporate environment.
    10. Galebach, B., Pennock, D., Servan-Schreiber, E., & Wolfers, J. (2004). Prediction Markets: Does Money Matter? //Electronic Markets//. 14:3. The Authors try to determine if there is an informational advantage to using one type of predictive market versus another. They try to make this determination by conducting a comparative analysis of two forms of prediction markets, real money and play money. The events being predicted are NHL football outcomes the results from the two types of prediction markets are then compared to the results of individual human predictions. The predictive power of each type of market is compared using 6 assessment metrics:
      1. Mean Absolute Error
      2. Root Mean Squared Error
      3. Average Quadratic Score
      4. Average Logarithmic Score
      5. Linear Regression
      6. Randomization Test.None of the metrics were able to determine that either market is significantly superior to the other and the authors conclusion is that predictive ability of the two markets and combined probability of the human predictors are indistinguishable from one another.
    11. Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. //Journal of Economic Perspectives//. 18:2, pp 107-126. Wolfers and Zitewitz provide the foundation for understanding prediction markets. Their treatise covers, the types of contracts that can be traded on prediction markets, the knowledge gained from markets that have been trading predictive contracts, talk about the designs of the types of markets, and suggest areas that are in their minds ripe for additional research and/or market making.
      1. Types of Contracts:
        1. Winner-take-all contract - Contract pays the holder $x if a deicrete event takes place for example, World Bank President Wolfowitz resigns by June 1st. The "price on a winner-take-all market represents the market's expectation of the probablity that an event will occur".
        2. Index contract - The contract value is a derivative of a number for example the percentage of the World Bank's governing board that will vote to oust Bank President Wolfowitz. The price paid for a index contract is the "mean value that the market assigns to the outcome."
        3. Spread contract - Traders of Spread contracts bid on contracts that state a margin by which an event will occur, such as the number of World Bank board members that vote to oust Wolfowitz minus the number of World Bank board members that vote not to oust him. The price of spread contracts is fixed but the spread adjusts based on traders expectations of the outcome of the underlying event. The spread then is the traders expectations of the median value of the underlying event.
    12. Wolfers, J., & Zitzewitz, E. (2005). Prediction Markets in Theory and Practice. Forthcoming, The New Palgrave Dictionary of Economics, 2nd edition. Wolfes and Zitewitz examine the theory of prediction markets using an expected utility framework.
      1. The authors outline what they believe are the three key benefits of prediction markets:
        1. Information aggregation
        2. Truthful revelation of beliefs
        3. Information discovery
      2. Observations about prediction markets:
        1. The prices of contracts on prediction markets respond rapidly to new information.
        2. Time series of prices of contracts follow a random walk, which is a necessary and sufficient condition for weak form of market efficiency.
        3. There are few opportunities for arbitrage profits as different markets offer prices which are very similar.
        4. Attempts at manipulation of prediction markets have largely failed in the past.
        5. Forecasts made using information from prediction markets has typically outperformed forecasts using other types of information.
      3. Types of predictions that can be made using prediction market data:
        1. Election result predictions
        2. Government policy predictions
        3. War/terror engagement
        4. Contingent Markets (Hybrid types):
          1. If winner-take-all then index
          2. If index then spread.
    13. Wolfers, J., & Zitzewitz, E. (2005). Five Open Questions About Prediction Markets. Unpublished.
      1. Wolfers and Zitewitz examine the barriers/questions faceing prediction markets which will determine the efficiacy of such markets to be widely used as forecasting, decision-making and risk management tools.
        1. Applications of prediction markets:
          1. forecasting - HP printer success contracts
          2. Risk management - hedge against economic or policital events (not very liquid as of now)
        2. Five questions
          1. How to attract uninformed traders?
            1. Solutions: offering sports betting, subsidization, and exploitation of career concerns.
          2. How to trade off interest and contractability?
          3. How to limit manipulation?
          4. Are markets well calibrated on small probabilities?
          5. How to separate correlation from causation?
    14. http://www.nature.com/nature/journal/v438/n7070/full/438900a.html
      1. This article compares Wikipedia (a bottom-up approach, wisdom of the crowd model), to Encyclopedia Britannica (a top-down approach, age-old authority) - and shows very interesting (and later controversial) results about the accuracy that can be achieved with a "wisdom of the crowd" model.
      2. You may need to be on campus to view it, or work through a proxy of the Lane Library, with SuNET ID access.
      3. Enjoy!
>
>
>
  • Contributors

>
** Samitha Samaranayake ([[mailto:samitha@alum.mit.edu|samitha@alum.mit.edu)]]



    1. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/AEI-Brookings2007.pdf
    2. Manifest on Prediction Markets published by the luminaires on May 7, 2007 (thanks to Gregor Hochmuth for pointing this out)
    3. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/ChenChuMullenPennock2005.pdf
    4. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/HahnTetlock_Brookings2005.pdf
    5. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Hanson2003.pdf
    6. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/IEEE2005TechBuzzGame.pdf
    7. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Kirtland2006.doc
      Designing Prediction Markets for End-Users, this is annotation of http://www.boxesandarrows.com/view/communicating_c
      Good current intro with relevant examples
    8. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Pennock2004.pdf
    9. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/RothColes_Harvard2007MarketDesignIdeas.doc
    10. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/Servan-SchreiberWolfersPennockGalebach2004.pdf
    11. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/WolfersZitzewitz2004.pdf
    12. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/WolfersZitzewitz2005Palgrave.pdf
    13. http://www.weigend.com/Teaching/Stanford/Readings/PredictionMarkets/WolfersZitzewitz2005Questions.pdf