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Motivation System on Prediction Market

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9876))

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Abstract

Prediction Market serves as an alternative tool mainly applied to gather the information widespread among the numerous experts. This tool can be used as a supplementary teaching aid in the financial engineering courses. The outcomes of selected markets give also the useful continuous feedback to the teachers. The contribution is the focus on motivational and incentive system. The prediction market inflation is introduced as motivation tool. The participants’ activity is analyzed by the influence of inflation engagement. Two groups of market participants are compared with respect to participants’ activity and inflation administration in the experiment. The comparison is maintained on the same market in the same conditions for all participants. The implemented system of signals allows to apply inflation only to the selected group and in the selected periods during the experiments. Finally, the increased number of the active shares on the counts of the participants’ activity is considered.

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Authors and Affiliations

  1. Faculty of Economics, University of West Bohemia, Hradební 22, 350 02, Cheb, Czech Republic

    Mikuláš Gangur

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  1. Mikuláš Gangur
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Correspondence to Mikuláš Gangur .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Aristotle University of Thessaloniki , Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Manageme, Democritus University of Thrace Department of Forestry and Manageme, Orestiada Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology , Wrocław, Poland

    Bogdan Trawiński

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© 2016 Springer International Publishing Switzerland

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Gangur, M. (2016). Motivation System on Prediction Market. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_34

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  • DOI: https://doi.org/10.1007/978-3-319-45246-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45245-6

  • Online ISBN: 978-3-319-45246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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