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Measuring Behavioural Change of Players in Public Goods Game

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Intelligent Systems and Applications (IntelliSys 2016)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 751))

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Abstract

In the public goods game, players can be classified into different types according to their participation in the game. It is an important issue for economists to be able to measure players’ strategy changes over time which can be considered as concept drift. In this study, we present a method for measuring changes in items’ cluster membership in temporal data. The method consists of three steps in the first step, the temporal data will be transformed into a discrete series of time points then each time point will be clustered separately. In the last step, the items’ membership in the clusters is compared with a reference of behaviour to determine the amount of behavioural change in each time point. Different external cluster validity indices and area under the curve are used to measure these changes. Instead of different cluster label comparison, we use these indices a new way to compare between clusters and reference points. In this study, three categories of reference of behaviours are used 1- first time point, 2- previous time pint and 3- the general overall behaviour of the items. For the public goods game, our results indicate that the players are changing over time but the change is smooth and relatively constant between any two time points.

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Notes

  1. 1.

    Available at http://infolab.cs.unipi.gr/people/ntoutsi/monic.html.

References

  1. Aggarwal, C.C.: On change diagnosis in evolving data streams. IEEE Trans. Knowl. Data Eng. 17(5), 587–600 (2005)

    Article  Google Scholar 

  2. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pé rez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46, 243–256 (2012)

    Article  Google Scholar 

  3. Baena-Garcia, M., del Campo-Avila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R.: Early drift detection method. In: 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, pp. 77–86 (2006)

    Google Scholar 

  4. Böttcher, M., Höppner, F., Spiliopoulou, M.: On exploiting the power of time in data mining. ACM SIGKDD Explor. Newsl. 10(2), 3–11 (2008). Dec

    Article  Google Scholar 

  5. Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  6. Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)

    Article  Google Scholar 

  7. Fattah, P., Aickelin, U., Wagner, C.: Optimising rule-based classification in temporal data. Zanco J. Pure Appl. Sci. 28(2), 135–146 (2016)

    Google Scholar 

  8. Figuières, C., Masclet, D., Willinger, M.: Weak moral motivation leads to the decline of voluntary contributions. J. Public Econ. Theory 15(5), 745–772 (2013)

    Article  Google Scholar 

  9. Fischbacher, U., Gächter, S.: Social preferences, beliefs, and the dynamics of free riding in public goods experiments. Am. Econ. Rev. 100(1), 541–556 (2010)

    Article  Google Scholar 

  10. Fischbacher, U., Gachter, S., Quercia, S., Gächter, S.: The behavioral validity of the strategy method in public good experiments. J. Econ. Psychol. 33(4), 897–913 (2012). Aug

    Article  Google Scholar 

  11. Fischbacher, U., Gächter, S., Whitehead, K.: Heterogeneous social preferences and the dynamics of free riding in public good experiments about the centre or contact. Am. Econ. Rev. 100(1), 541–556 (2010)

    Article  Google Scholar 

  12. Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)

    Google Scholar 

  13. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, Abdelhamid: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)

    Article  MATH  Google Scholar 

  14. Garnett, Roman., Roberts, S.J.: Learning from Data Streams with Concept Drift. Technical Report PARG-08-01, Department of Engineering Science, University of Oxford (2008)

    Google Scholar 

  15. Günnemann, S., Kremer, H., Laufkotter, C., Seidl, T.: Tracing evolving clusters by subspace and value similarity. Adv. Knowl. Discov. Data Min. 6635, 444–456 (2011)

    Google Scholar 

  16. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods: part I. ACM Sigmod Rec. 31(2), 40–45 (2002)

    Article  Google Scholar 

  17. Harel, M., Mannor, S., El-Yaniv, R., Crammer, K.: Concept drift detection through resampling. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp. 1009–1017 (2014)

    Google Scholar 

  18. Hawwash, B., Nasraoui, O.O.: Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 109–117 (2012)

    Google Scholar 

  19. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Article  Google Scholar 

  20. Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. Adv. Spat. Temporal Databases 3633, 364–381 (2005)

    Article  Google Scholar 

  21. Kaul, I., Grungberg, I., Stern, M.A.: Global public goods. In: Global Public Goods (1999)

    Google Scholar 

  22. Keser, C., van Winden, F.: Conditional cooperation and voluntary contributions to public goods. Scand. J. Econ. 102, 23–39 (2000)

    Article  Google Scholar 

  23. Meil, M.: Comparing clusterings an information based distance. J. Multivar. Anal. 98(5), 873–895 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Meila, M.: Comparing clusterings by the variation of information. In: Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, 24–27 Aug 2003, proceedings, p. 173 (2003)

    Google Scholar 

  25. Ntoutsi, I., Spiliopoulou, M., Theodoridis, Y.: Tracing cluster transitions for different cluster types. Control Cybern. 38(1), 239–259 (2009)

    MATH  Google Scholar 

  26. Ntoutsi, I., Spiliopoulou, M., Theodoridis, Y.: Summarizing cluster evolution in dynamic environments. In: Computational Science and Its Applications—ICCSA 2011, vol. 6783, pp. 562–577. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  27. Rendón, E., Abundez, I.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27–34 (2011)

    Google Scholar 

  28. Rezaei, M., Franti, P.: Set matching measures for external cluster validity. IEEE Trans. Knowl. Data Eng. 28(8), 2173–2186 (2016)

    Article  Google Scholar 

  29. Spiliopoulou, M., Ntoutsi, E., Theodoridis, Y., Schult, R.: MONIC and followups on modeling and monitoring cluster transitions. Mach. Learn. Knowl. Discov. Databases 8190(2013), 622–626 (2013)

    Article  Google Scholar 

  30. Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: Monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 706–711 (2006)

    Google Scholar 

  31. Tsymbal, A.: The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin (2004)

    Google Scholar 

  32. Vendramin, L., Campello, R.J.G.B., Hruschka, E.R.: Relative clustering validity criteria: a comparative overview. Stat. Anal. Data Min. 4(3), 209–235 (2010)

    Google Scholar 

  33. Xiaofeng, L., Weiwei, G.: Study on a classification model of data stream based on concept drift. Int. J. Multimedia Ubiquitous Eng. 9(5), 363–372 (2014)

    Article  Google Scholar 

  34. Yang, D., Guo, Z., Rundensteiner, E.A., Ward, M.O.: CLUES: a unified framework supporting interactive exploration of density-based clusters in streams. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 815–824 (2011)

    Google Scholar 

  35. Zaki, M.J., Meira Jr., M.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, New York (2014)

    Google Scholar 

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Acknowledgements

The authors record their thanks to Simon Gaechter and Felix Kolle in the School of Economics at the University of Nottingham for providing us with data from the public goods experiment and taking time to explain it to us.

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Authors and Affiliations

  1. School of Computer Science, The University of Nottingham, Nottingham, UK

    Polla Fattah & Christian Wagner

  2. School of Computer Science, The University of Nottingham Ningbo, Ningbo, China

    Uwe Aickelin

Authors
  1. Polla Fattah
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  2. Uwe Aickelin
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  3. Christian Wagner
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Corresponding author

Correspondence to Polla Fattah .

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Editors and Affiliations

  1. School of Computing, Ulster University at Jordanstown, Newtownabbey, County Antrim, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, United Kingdom

    Rahul Bhatia

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Fattah, P., Aickelin, U., Wagner, C. (2018). Measuring Behavioural Change of Players in Public Goods Game. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2016. Studies in Computational Intelligence, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-69266-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-69266-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69265-4

  • Online ISBN: 978-3-319-69266-1

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