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AKX – An Exchange for Predicting Water Dam Levels in Australia

  • Stephan Stathel
  • Stefan Luckner
  • Florian Teschner
  • Christof Weinhardt
  • Andrew Reeson
  • Stuart Whitten
Conference paper
Part of the Environmental Science and Engineering book series (ESE)

Abstract

The Australian population in rural and urban areas is heavily influenced and affected by such water shortages, either economically or in their life style. Managing water resources is therefore seen as a critical environmental, social and economic issue. Good forecasts can provide better understanding for the current situation (e.g. drought severity) and consequently improve decision making.

Prediction markets have long proved to successfully forecast events in a wide range of applications. They seem to be a promising tool for aggregating and at the same time publishing information about water availability.

With the Australian Knowledge Exchange (AKX) we launched a prediction market, in which people were invited to trade their expectations about future dam levels. Our results show that traders are able to forecast water dam levels quite accurately. Nevertheless, a simple self-developed model based on historic data beats the market forecast in half of the cases. Experts seem reluctant – due to various reasons – to join and participate in (water related) prediction markets.

In summary, our first experiment results show that markets are a promising approach to forecast Natural Resource Management related figures. Further improvements are discussed which may help to increase prediction accuracy in future applications.

Keywords:

water availability prediction markets forecast experts Natural Resource Management 

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References

  1. 1.
    Australian Government (2006) Rural Water Use and the Environment: The Role of Market Mechanisms, Productivity Commission, Research Report, SSRN, 2006, 1, 369Google Scholar
  2. 2.
    Chen KY, Plott CR (2002) Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem. Social Science Working Paper No.1131. Pasadena, California Institute of TechnologyGoogle Scholar
  3. 3.
    Cowgill B, Wolfers J, Zitzewitz E (2008) Using Prediction Markets to Track Information Flows: Evidence from Google, Online available under http://www.bocowgill.com/GooglePredictionMarketPaper.pdf, downloaded 01.09.2009
  4. 4.
    Fama E (1970) Efficient Capital Market: A Review of Theorie and Empirical Work. Journal of Finance, 25, 383-417CrossRefGoogle Scholar
  5. 5.
    Forsythe R, Nelson F, Neumann GR, Wright J (1992) Anatomy of an Experimental Political Stock Market. American Economic Review, 82, pp. 1142-1161Google Scholar
  6. 6.
    Grafton Q (2008) Bungling a Bingle: Urban water policy and the big dry, Drought - Past and Future - ANUWI ConferenceGoogle Scholar
  7. 7.
    Hayek FAV (1945), The Use of Knowledge in Society. American Economic Review, 35, 519-530Google Scholar
  8. 8.
    Luckner S, Schröder K, Slamka C (2008) On the Forecast Accuracy of Sports Prediction Markets. Negotiation and Market Engineering (Eds. Gimpel, H. et al.), LNBIP 2, Springer, pp. 227-234Google Scholar
  9. 9.
    Ortner G (1997) Forecasting Markets - An Industrial Application: Part I. Working Paper, TU ViennaGoogle Scholar
  10. 10.
    Padhy P, Dash RK, Martinez K, Jennings NR (2006) A utility-based sensing and Communication model for a glacial sensor network. Proceedings of the Fifth international Joint Conference on Autonomous Agents and Multiagent Systems – AAMAS, Hakodate, JapanGoogle Scholar
  11. 11.
    Pennock DM, Lawrence S, Giles CL, Nielsen FA (2000) The power of play: Eciency and forecast accuracy in web market games, NEC Research InstituteGoogle Scholar
  12. 12.
    Roll R (1984) Orange Juice and Weather. American Economic Review, 74-5, pp. 861-880Google Scholar
  13. 13.
    Servan-Schreiber E, Wolfers J, Pennock D, Galebach B (2004) Prediction Markets: Does Money Matter?. Electronic Markets - The International Journal, 14-3Google Scholar
  14. 14.
    Spann M (2002) Virtuelle Börsen als Instrument zur Marktforschung. Wiesbaden: Deutscher UniversitätsverlagCrossRefGoogle Scholar
  15. 15.
    Spann M, Skiera B (2004) Einsatzmöglichkeiten virtueller Börsen in der Marktforschung. Zeitschrift für Betriebswirtschaftslehre (ZfB), 74 (EH2)Google Scholar
  16. 16.
    Surowiecki J (2005) The wisdom of crowds. Anchor BooksGoogle Scholar
  17. 17.
    Wolfers J, Zitzewitz E (2006) Interpreting Prediction Market Prices as Probabilities. National Bureau of Economic Research -  NBER Working Paper No. 12200Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stephan Stathel
    • 1
  • Stefan Luckner
    • 2
  • Florian Teschner
    • 2
  • Christof Weinhardt
    • 2
  • Andrew Reeson
    • 3
  • Stuart Whitten
    • 3
  1. 1.Research Center for Information TechnologyGermany
  2. 2.Karlsruhe Service Research Institute (KSRI)Germany
  3. 3.CSIRO, GPO Box 284CanberraAustralia

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