Self-resolving Information Markets: An Experimental Case Study

Main Article Content

Kristoffer Ahlstrom-Vij
Nick Williams

Abstract

On traditional information markets (TIMs), rewards are tied to the occurrence (or non-occurrence) of events external to the market, such as some particular candidate winning an election. For that reason, they can only be used when it is possible to wait for some external event to resolve the market. In cases involving long time-horizons or counterfactual events, this is not an option. Hence, the need for a self-resolving information market (SRIM), resolved with reference to factors internal to the market itself. In the present paper, we first offer some theoretical reasons for thinking that, since the only thing that can be expected to be salient to all participants on a SRIM is the content of the question bet on, a convention will arise of taking that question at face value, and betting accordingly, in which case trading behaviour on SRIMs can be expected to be identical to that on TIMs. This is the ‘face value’ hypothesis. If this hypothesis holds, SRIMs have the potential of incorporating the accuracy of TIMs while shedding their limitations in relation to long-term predictions and the evaluation of counterfactuals. We then report on a laboratory experiment that demonstrates that trading behaviour can indeed come out highly similar across SRIMs and TIMs. As such, the study can be thought of as an experimental case study on SRIMs. Finally, we discuss some limitations of the study, and also points towards fruitful areas of future research in light of our results.

Article Details

Section
Articles
Author Biography

Kristoffer Ahlstrom-Vij, Birbeck College, University of London

Reader in Philosophy

References

Abramowicz, M. 2007. Predictocracy: Market Mechanisms for Public and Private Decision Making. New Haven, CT: Yale University Press.

Antweiler, W. 2012. ‘Long-Term Prediction Markets.’ The Journal of Prediction Markets 6(3): 43-61.

Berg, J. and Rietz, T. 2014. ‘Market Design, Manipulation and Accuracy in Political Prediction Markets: Lessons from the Iowa Electronic Markets.’ Political Science and Politics 47(2): 293–296.

Berg, J., Nelson, F., and Rietz, T. 2008. ‘Prediction Market Accuracy in the Long Run.’ International Journal of Forecasting 24: 285–300.

Buckley, P. 2017. ‘Evidencing the Forecasting Performance of Predication Markets: An Empirical Comparative Study.’ The Journal of Prediction Markets 11(2): 60-76.

Buckley, P., and O’Brien, F. 2015. ‘The Effect of Malicious Manipulations on Prediction Market Accuracy.’ Information Systems Frontiers 19(3): 611-623.

Camerer, C. 1998. ‘Can Asset Markets Be Manipulated?’ Journal of Political Economy 106: 457–482.

Chen, K.‐Y. and Plott, C. 2002. ‘Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem.’ CalTech Social Science Working Paper No. 1131.

Debnath, S., Pennock, D., Lawrence, S., and Giles, C.L. 2003. ‘Information Incorporation in Online In‐game Sports Betting Markets.’ Proceedings of the 4th Annual ACM Conference on Electronic Commerce (EC’03): 258–259.

Deschamps, B. and Gergaud, O. 2007. ‘Efficiency in Betting Markets: Evidence from English Football.’ The Journal of Prediction Markets 1: 61–73.

Espinoza, N., Erdeniz, R., and Kolk, K (ms.), ‘Risk Markets,’ unpublished manuscript.

Forsythe, R., Frank, M., Krishnamurthy, V., and Ross, T. 1998. ‘Markets as Predictors of Election Outcomes: Campaign Events and Judgment Bias in the 1993 UBC Election Stock Market.’ Canadian Public Policy 24: 329–351.

Graefe, A., and Weinhardt, C. 2008. ‘Long-term Forecasting with Prediction Markets—A Field Experiment on Applicability and Expert Confidence.’ The Journal of Prediction Markets 2(2): 71-92.

Hahn, R. and Tetlock, P. 2006. Information Markets: A New Way of Making Decisions. Washington, DC: AEI Press.

Hanson, R. 2007. ‘Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation.’ Journal of Prediction Markets 1: 3-15.

Hanson, R. 2013. ‘Shall We Vote on Values, But Bet on Beliefs?’ Journal of Political Philosophy 21(2): 151-178.

Hanson, R. and Oprea, R. 2009. ‘Manipulators Increase Information Market Accuracy.’ Economica 76(302): 304–314.

Hanson, R., Oprea, R., and Porter, D. 2006. ‘Information Aggregation and Manipulation in an Experimental Market.’ Journal of Economic Behavior and Organization 60: 449–459.

Horn, C. F., Ohneberg, M., Ivens, B. S., and Brem, A. 2014. ‘Prediction Markets—A Literature Review 2014 Following Tziralis and Tatsiopoulos.’ The Journal of Prediction Markets 8(2): 89-126.

Keynes, J. M. 2015. The General Theory of Employment, Interest and Money. In R. Skidelsky (ed.), The Essential Keynes, Penguin; originally published in 1936.

Klingert, F. M. A. 2017. ‘The Structure of Prediction Market Research: Important Publications and Research Clusters.’ The Journal of Prediction Markets 11(1): 51-65.

Lewis, D. 1969. Convention: A Philosophical Study. Cambridge, MA: Harvard University Press.

Luckner, S., Schröder, J., and Slamka, C. 2008. ‘On the Forecast Accuracy of Sports Prediction Markets.’ In Negotiation, Auctions & Market Engineering, Lecture Notes in Business Information Processing (LNBIP), edited by H. Gimpel, N.R. Jennings, G. Kersten, A. Okenfels, and C. Weinhardt, 227–234. Dordrecht: Springer.

Mattingly, K. and Ponsonby, A.-L. 2004. ‘A Consideration of Group Work Processes in Modern Epidemiology.’ Annals of Epidemiology 24(4): 319-323.

McHugh, P. and Jackson, A. 2012. ‘Prediction Market Accuracy: The Impact of Size, Incentives, Context and Interpretation.’ The Journal of Prediction Markets 6(2): 22-46.

McKenzie, J. 2013. ‘Predicting Box Office with and Without Markets: Do Internet Users Know Anything?’ Information Economics & Policy 25: 70-80.

O’Leary, D. E. 2011. ‘Prediction Markets as a Forecasting Tool.’ Advances in Business and Management Forecasting 8: 169-184.

Oprea, R., Porter, D., Hibbert, C., Hanson, R., and Tila, D. 2007. ‘Can Manipulators Mislead Market Observers?’ Chapman University, E.S.I. Working Papers 08‐01.

Pennock, D., Lawrence, S., Nielsen, F.A., and Giles, C.L. 2001. ‘Extracting Collective Probabilistic Forecasts from Web Games.’ Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 174–183.

Polgreen, P., Nelson, F., Neumann, G., and Weinstein, R. 2007. ‘Use of Prediction Markets to Forecast Infectious Disease Activity.’ Clinical Infectious Diseases 44: 272–279.

Rajakovich, D. and Vladimirov, V. 2009. ‘Prediction Markets as a Medical Forecasting Tool: Demand for Hospital Services.’ The Journal of Prediction Markets 3: 78-106.

Rosenbloom, E.S. and Notz, W. 2006. ‘Statistical Tests of Real‐Money Versus Play‐Money Prediction Markets.’ Electronic Markets 16(1): 63-69.

Schelling, T. 1960. The Strategy of Conflict. Cambridge, MA: Harvard University Press.

Servan‐Schreiber, E., Wolfers, J., Pennock, D., and Galebach, B. 2004. ‘Prediction Markets: Does Money Matter?’ Electronic Markets 14(3): 243-251.

Spann, M. and Skiera, B. 2003. ‘Internet‐Based Virtual Stock Markets for Business Forecasting.’ Management Science 49: 1310–1326.

Tziralis, G., and Tatsiopoulos, I. 2007. ‘Prediction Markets: An Extended Literature Review.’ The Journal of Prediction Markets 1: 75-91.