Skip to main content
SpringerLink
Log in

Behavioral types of the dark side: identifying heterogeneous conflict strategies

  • Original Paper
  • Published:
Journal of the Economic Science Association Aims and scope Submit manuscript

Abstract

Cooperation motives are traditionally elicited in experimental games where players have misaligned interests that yield noncooperation in equilibrium. Research finds a typology of behavioral types such as free riders and conditional cooperators. However, intrinsic motives in conflict settings such as appeasement, punishment, and greed are elusive in such games where noncooperation is the equilibrium prediction. To identify types in the dark side of human interaction, we apply hierarchical cluster analysis to data from the Vendetta Game, which has a payoff structure similar to public goods games but a dynamic move structure that yields cooperation in equilibrium. It allows us to observe diverse non-equilibrium conflict strategies, and to understand how feuds perpetuate. We relate our method and typology to other social dilemmas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. Negative emotions motivate the punishment of free riders (Joffily et al. 2014), and giving subjects alternative outlets to vent reduces feuding (Bolle et al. 2014) and inefficient punishment (Dickinson and Masclet 2015).

  2. We use session averages as the unit of independent observation for these non-parametric tests to control for the non-independence of observations within each session.

  3. We do not use decisions when a player is disadvantaged but cannot steal more than the equilibrium amount (typically when the state of the game is one step away from the terminal point), or when one is equal/advantaged and the co-player has nothing left to steal (cannot play Greed). Four subjects were unclassifiable because they were either never disadvantaged or never equal/advantaged.

  4. There are many alternatives to our method, which we have chosen because of its simplicity and transparency. In addition to pure data mining methods, clustering can be carried out with respect to the parameters of a game theoretic model (e.g., Bolle et al. 2012). Our analysis distinguishes between disadvantaged or equal/advantaged states which are derived from game theoretic equilibrium analysis and are equality oriented.

  5. Comparisons between Sharks and Non-Sharks use independent session-level means, while comparisons between Sharks and other types match individual-level emotions. Table 3 shows no contradiction between the evaluation methods.

  6. In experiments with a stranger design (random matching in every round), players may adapt to average behavior in the population but cannot reply to the behavior of a specific opponent as in the VG.

  7. Analyzing decisions in the HDL and the HDH separately, only 30 and 32 of the 86 subjects can be characterized by applying a cluster analysis. Therefore, although behavior in the two games differ, we analyze them jointly.

  8. This may be implemented by a mixed Markov strategy or by an algorithm of switching after a number of rounds. Such strategies are more sophisticated than the simple Markov strategies found in the VG.

  9. We compute C-Lasso using the MATLAB code of Shi Zhentao (https://github.com/zhentaoshi/C-Lasso). Our linear probability model captures the sequence of moves with four Markov states, controls for HDH and round, and selects two groups. Group 2 estimates for S1–S4 (0.99, 0.50, 0.76, 0.48) are compatible with Stayers, and others including Meeks and TfTs are captured by Group 1 (0.92, 0.66, 0.44, 0.55); all coefficients are significant at p < 0.0001. Group 1 contains 8 of 11 Meeks and 8 of 9 TfTs while Group 2 contains 22 of 30 Stayers. Thus, 76.5% of hierarchical cluster types are consistently grouped by C-Lasso.

References

  • Blanco, M., Engelmann, D., & Normann, H. T. (2011). A within-subject analysis of other-regarding preferences. Games and Economic Behavior, 72(2), 321–338.

    Article  Google Scholar 

  • Bolle, F., Breitmoser, Y., Heimel, J., & Vogel, C. (2012). Multiple motives of pro-social behavior: Evidence from the solidarity game. Theory and Decision, 72(3), 303–321.

    Article  Google Scholar 

  • Bolle, F., Tan, J. H. W., & Zizzo, D. J. (2014). Vendettas. American Economic Journal: Microeconomics, 6, 93–130.

    Google Scholar 

  • Bordt, S., Helmut, F., & Kögel, H. 2019. Estimating grouped patterns of heterogeneity in repeated public goods experiments. Working paper.

  • Bosman, R., & Van Winden, F. (2002). Emotional hazard in a power-to-take experiment. The Economic Journal, 112(476), 147–169.

    Article  Google Scholar 

  • Bowles, S., & Gintis, H. (2004). The evolution of strong reciprocity: Cooperation in heterogeneous populations. Theoretical Population Biology, 65(1), 17–28.

    Article  Google Scholar 

  • Cappelen, A. W., Konow, J., Sørensen, E. Ø., & Tungodden, B. (2013). Just luck: An experimental study of risk-taking and fairness. American Economic Review, 103(4), 1398–1413.

    Article  Google Scholar 

  • Charness, G., & Rabin, M. (2002). Understanding social preferences with simple tests. The Quarterly Journal of Economics, 117(3), 817–869.

    Article  Google Scholar 

  • Chaudhuri, A. (2011). Sustaining cooperation in laboratory public goods experiments: A selective survey of the literature. Experimental Economics, 14(1), 47–83.

    Article  Google Scholar 

  • Chowdhury, S. M., & Gürtler, O. (2015). Sabotage in contests: A survey. Public Choice, 164(1–2), 135–155.

    Article  Google Scholar 

  • Crawford, V. P., & Iriberri, N. (2007). Fatal attraction: Salience, naivete, and sophistication in experimental “hide-and-seek” games. American Economic Review, 97(5), 1731–1750.

    Article  Google Scholar 

  • Dal Bó, P., & Fréchette, G. R. (2011). The evolution of cooperation in infinitely repeated games: Experimental evidence. American Economic Review, 101(1), 411–429.

    Article  Google Scholar 

  • Dal Bó, P., & Fréchette, G. R. (2018). On the determinants of cooperation in infinitely repeated games: A survey. Journal of Economic Literature, 56(1), 60–114.

    Article  Google Scholar 

  • Davidson, M. N., & Greenhalgh, L. (1999). The role of emotion in negotiation: The impact of anger and race. Research on Negotiation in Organizations, 7, 3–26.

    Google Scholar 

  • Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 224–227.

    Article  Google Scholar 

  • Dickinson, D. L., & Masclet, D. (2015). Emotion venting and punishment in public good experiments. Journal of Public Economics, 122, 55–67.

    Article  Google Scholar 

  • Dunn, C. J. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.

    Article  Google Scholar 

  • Elster, J. (1998). Emotions and economic theory. Journal of Economic Literature, 36(1), 47–74.

    Google Scholar 

  • Fallucchi, F., Luccasen, R. A., & Turocy, T. (2019). Identifying discrete behavioural types: A re-analysis of public goods game contributions by hierarchical clustering. Journal of the Economic Science Association, 5(2), 238–254.

    Article  Google Scholar 

  • Fallucchi, F., Mercatanti, A., & Niederreiter, J. (2020). Identifying types in contest experiments. International Journal of Game Theory., 50, 39–61.

    Article  Google Scholar 

  • Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817–868.

    Article  Google Scholar 

  • Feltovich, N. (2011). The effect of subtracting a constant from all payoffs in a Hawk-Dove game: Experimental evidence of loss aversion in strategic behavior. Southern Economic Journal, 77(4), 814–826.

    Article  Google Scholar 

  • Fischbacher, U., Gächter, S., & Fehr, E. (2001). Are people conditionally cooperative? Evidence from a public goods experiment. Economics Letters, 71(3), 397–404.

    Article  Google Scholar 

  • Gelfand, M. J., & Brett, J. M. (2004). The handbookof negotiation and culture. Stanford University Press.

    Google Scholar 

  • Hopfensitz, A., & Reuben, E. (2009). The importance of emotions for the effectiveness of social punishment. The Economic Journal, 119(540), 1534–1559.

    Article  Google Scholar 

  • Joffily, M., Masclet, D., Noussair, C. N., & Villeval, M. C. (2014). Emotions, sanctions, and cooperation. Southern Economic Journal, 80(4), 1002–1027.

    Article  Google Scholar 

  • Nikiforakis, N., Noussair, C. N., & Wilkening, T. (2012). Normative conflict and feuds: The limits of self-enforcement. Journal of Public Economics, 96(9), 797–807.

    Article  Google Scholar 

  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.

    Article  Google Scholar 

  • Su, L., Shi, Z., & Phillips, P. C. (2016). Identifying latent structures in panel data. Econometrica, 84(6), 2215–2264.

    Article  Google Scholar 

  • Thöni, C., & Volk, S. (2018). Conditional cooperation: Review and refinement. Economics Letters, 171, 37–40.

    Article  Google Scholar 

Download references

Acknowledgements

The research assistance of Hue Jun Yu is much appreciated. We thank Maria Bigoni, Dirk Engelmann, and two anonymous referees for the invaluable comments. Thanks to Yves Breitmoser, Simon Gächter, Yohanes Eko Riyanto, and Wang Wenjie for the advice and encouragement, and to Nick Feltovich for providing his experimental data. Tan gratefully acknowledges the financial support of Nanyang Technological University through the Start-Up Grant and the Ministry of Education Singapore AcRF Tier 1 Grant RG126/20.

Author information

Authors and Affiliations

  1. European University Viadrina, Frankfurt (Oder), Germany

    Friedel Bolle

  2. Nanyang Technological University, Singapore, Singapore

    Jonathan H. W. Tan

Authors
  1. Friedel Bolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan H. W. Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonathan H. W. Tan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 29 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bolle, F., Tan, J.H.W. Behavioral types of the dark side: identifying heterogeneous conflict strategies. J Econ Sci Assoc 7, 49–63 (2021). https://doi.org/10.1007/s40881-021-00101-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40881-021-00101-z

Keywords

JEL classifications

Search

Navigation