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Conducting real-time multiplayer experiments on the web

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Abstract

Group behavior experiments require potentially large numbers of participants to interact in real time with perfect information about one another. In this paper, we address the methodological challenge of developing and conducting such experiments on the web, thereby broadening access to online labor markets as well as allowing for participation through mobile devices. In particular, we combine a set of recent web development technologies, including Node.js with the Socket.io module, HTML5 canvas, and jQuery, to provide a secure platform for pedagogical demonstrations and scalable, unsupervised experiment administration. Template code is provided for an example real-time behavioral game theory experiment which automatically pairs participants into dyads and places them into a virtual world. In total, this treatment is intended to allow those with a background in non-web-based programming to modify the template, which handles the technical server–client networking details, for their own experiments.

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Notes

  1. https://github.com/hawkrobe/MWERT

  2. https://github.com/felixge/node-mysql

References

  • Bishop, M. (2002). Computer security: Art and science. Addison-Wesley Professional.

  • Boer, K., Kaymak, U., & Spiering, J. (2007). From discrete-time models to continuous-time, asynchronous modeling of financial markets. Computational Intelligence, 23(2), 142–161.

    Article  Google Scholar 

  • Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.

    Article  PubMed  Google Scholar 

  • Cao, M., Morse, A. S., & Anderson, B. D. (2008). Agreeing asynchronously. IEEE Transactions on Automatic Control, 53(8), 1826–1838.

    Article  Google Scholar 

  • Charness, G., Oprea, R., & Friedman, D. (2012, April). Continuous time and communication in a public-goods experiment (University of California at Santa Barbara, Economics Working Paper Series No. qt5404914p). Department of Economics, UC Santa Barbara. Retrieved from http://ideas.repec.org/p/cdl/ucsbec/qt5404914p.html

  • Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621.

    Article  PubMed  Google Scholar 

  • Dale, R., Fusaroli, R., Duran, N., & Richardson, D. C. (2014). The self-organization of human interaction. Psychology of Learning and Motivation, 59, 43–96.

    Article  Google Scholar 

  • Deck, C., & Nikiforakis, N. (2012). Perfect and imperfect real-time monitoring in a minimum-effort game. Experimental Economics, 15(1), 71–88.

    Article  Google Scholar 

  • Friedman, D., & Oprea, R. (2012). A continuous dilemma. The American Economic Review, 102(1), 337–363.

    Article  Google Scholar 

  • Goldin, G., & Darlow, A. (2013). TurkGate (version 0.4.0) [Computer software manual]. Providence, RI.

  • Goldstone, R. L., Ashpole, B. C., & Roberts, M. E. (2005). Knowledge of resources and competitors in human foraging. Psychonomic Bulletin & Review, 12(1), 81–87.

    Article  Google Scholar 

  • Helbing, D., Schönhof, M., Stark, H.-U., & Hołyst, J. A. (2005). How individuals learn to take turns: Emergence of alternating cooperation in a congestion game and the prisoner’s dilemma. Advances in Complex Systems, 8(1), 87–116.

    Article  Google Scholar 

  • Hughes-Croucher, T., & Wilson, M. (2012). Node: Up and running: Scalable server-side code with JavaScript. O’Reilly Media, Incorporated.

  • Lau, S.-H. P., & Mui, V.-L. (2008). Using turn taking to mitigate coordination and conflict problems in the repeated battle of the sexes game. Theory and Decision, 65(2), 153–183.

    Article  Google Scholar 

  • Majumder, S. R., Diermeier, D., Rietz, T. A., & Amaral, L. A. N. (2009). Price dynamics in political prediction markets. Proceedings of the National Academy of Sciences, 106(3), 679–684.

    Article  Google Scholar 

  • Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44(1), 1–23.

    Article  PubMed  Google Scholar 

  • Mason, W., & Watts, D. J. (2010). Financial incentives and the performance of crowds. ACM SigKDD Explorations Newsletter, 11(2), 100–108.

    Article  Google Scholar 

  • McDonnell, J., Martin, J., Markant, D., Coenen, A., Rich, A., & Gureckis, T. (2012). psiturk (version 1.02) [Computer software manual]. New York, NY. Retrieved from https://github.com/NYUCCL/psiTurk

  • Michinov, N., & Primois, C. (2005). Improving productivity and creativity in online groups through social comparison process: New evidence for asynchronous electronic brainstorming. Computers in Human Behavior, 21(1), 11–28.

    Article  Google Scholar 

  • Miller, N., Garnier, S., Hartnett, A. T., & Couzin, I. D. (2013). Both information and social cohesion determine collective decisions in animal groups. Proceedings of the National Academy of Sciences, 110(13), 5263–5268.

    Article  Google Scholar 

  • Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., & Theraulaz, G. (2009). Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society B: Biological Sciences, 276(1668), 2755–2762.

    Article  PubMed Central  PubMed  Google Scholar 

  • Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.

    Article  Google Scholar 

  • Paolacci, G., Chandler, J., & Ipeirotis, P. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411–419.

    Google Scholar 

  • Pettit, J., Friedman, D., Kephart, C., & Oprea, R. (2014). Software for continuous game experiments. Experimental Economics. doi:10.1007/s10683-013-9387-3

    Google Scholar 

  • Reips, U.-D. (2002). Standards for internet-based experimenting. Experimental Psychology, 49(4), 243–256.

    Article  PubMed  Google Scholar 

  • Spivey, M., & Dale, R. (2006). Continuous dynamics in real-time cognition. Current Directions in Psychological Science, 15(5), 207–211.

    Article  Google Scholar 

  • Suri, S., & Watts, D. J. (2011). Cooperation and contagion in web-based, networked public goods experiments. PLoS One, 6(3), e16836.

    Article  PubMed Central  PubMed  Google Scholar 

  • Takada, M. (2012). Mixu’s Node book: A book about using Node.js. Available at mixu.net.

  • Tilkov, S., & Vinoski, S. (2010). Node. js: Using JavaScript to build high-performance network programs. IEEE Internet Computing, 14(6), 80–83.

    Article  Google Scholar 

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Author Note

This research was in part supported by National Science Foundation REESE grant 0910218 to the Percepts and Concepts Laboratory. We thank Shoshana Berleant and the members of the Santa Fe Institute mailing list for participating in usability tests of the framework and also Rob Goldstone and Johan Bollen for comments on early drafts.

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Correspondence to Robert X. D. Hawkins.

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Hawkins, R.X.D. Conducting real-time multiplayer experiments on the web. Behav Res 47, 966–976 (2015). https://doi.org/10.3758/s13428-014-0515-6

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  • DOI: https://doi.org/10.3758/s13428-014-0515-6

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