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Is fairness intuitive? An experiment accounting for subjective utility differences under time pressure

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Abstract

Evidence from response time studies and time pressure experiments has led several authors to conclude that “fairness is intuitive”. In light of conflicting findings, we provide theoretical arguments showing under which conditions an increase in “fairness” due to time pressure indeed provides unambiguous evidence in favor of the “fairness is intuitive” hypothesis. Drawing on recent applications of the Drift Diffusion Model (Krajbich et al. in Nat Commun 6:7455, 2015a), we demonstrate how the subjective difficulty of making a choice affects decisions under time pressure and time delay, thereby making an unambiguous interpretation of time pressure effects contingent on the choice situation. To explore our theoretical considerations and to retest the “fairness is intuitive” hypothesis, we analyze choices in two-person binary dictator and prisoner’s dilemma games under time pressure or time delay. In addition, we manipulate the subjective difficulty of choosing the fair relative to the selfish option. Our main finding is that time pressure does not consistently promote fairness in situations where this would be predicted after accounting for choice difficulty. Hence, our results cast doubt on the hypothesis that “fairness is intuitive”.

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Fig. 1
Fig. 2

Note: This figure displays the propensity to switch between first and second choice in a given game across five different classifications of consistent decision making. The left panel displays switching behaviour when first choices have been made under time delay and the right panel displays switching behaviour when first choices have been made under time pressure. The percentages indicate how common a classification is within the population

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Notes

  1. Obviously, the economics literature has come up with various notions of what constitutes a “fair” choice (Rabin 1993; Engelmann and Strobel 2004; Fehr and Schmidt 2006). In Sect. 2, we will describe in more detail what we refer to as a “fair” choice in the context of our paper.

  2. We will refer to versions of the DDM that have recently been applied to value-based choices and social dilemma situations (Polanía et al. 2014; Krajbich et al. 2014, 2015b). For a more extensive review of the behavioral foundations and the application of DDM in psychology refer to the descriptions in Ratcliff (1988), Ratcliff and Rouder (1998) and a recent summary of this topic aimed at economists by Clithero (2016).

  3. A more detailed description of the DDM as well as proofs and derivations of all predictions are contained in Online Appendix A.

  4. We are grateful to an anonymous referee for pointing out this crucial distinction and helping us to refine our model.

  5. Online Appendix A contains a more formal discussion of the possible results.

  6. Note that observing no effect is not necessarily evidence against the DDM in these situations. This is because the true model might be that “selfishness is intuitive”. Hence, a negative effect of time pressure attributable to the “selfishness is intuitive” model might be cancelled out by a positive effect attributable to the DDM. For this reason, we cannot jointly reject the FII hypothesis and the DDM.

  7. Labeling the equal outcome as fair in the binary dictator game also aligns our FII predictions with recent findings in Capraro et al. (2017) who show that equal outcomes are preferred by intuitive decision makers whereas deliberation allows for a variety of motives to affect decisions.

  8. We randomized the order in which the prisoner’s dilemma games were displayed across sessions. The filler games were presented in the same order in all sessions. Subjects were not informed that they would make the same choices in both blocks.

  9. To our knowledge there is no common method according to which time pressure was defined in previous studies. For instance, subjects in Rand et al. (2012) were constrained to decide within 10 s which corresponds to the median response time in their response time correlation study. Buckert et al. (2017) define time pressure as 2/3 of the median response time in a Cournot game. Our analysis of response times in the Unconstrained treatment revealed that the response time distribution of the 25% fastest decision makers was independent of the order in which the games were presented. Thus, the time limit in our study avoids heterogeneous effects across different order conditions.

  10. For instance, Myrseth and Wollbrant (2017) argue that any time limit above 4 s could allow decision makers to engage in some level of deliberation. Spiliopoulos and Ortmann (2017) report that mean response times fall by up to 30% when subjects face the exact same game multiple times.

  11. One important limitation of previous studies has been that a large fraction of subjects violate the time constraints set by the experimenter which potentially reduces their explanatory power (Tinghög et al. 2013; Bouwmeester et al. 2017). In contrast, we observed few violations of the time limit: Averaged over all decisions and treatments, the time pressure conditions were violated in 2.5% of the BD and 1.7% of the PD games. There is no significant difference in violations between the TP and STP condition.

  12. In the Time Pressure treatment, subjects were constrained to indicate their belief within 12 s. In the Time Delay treatment, subjects could indicate their belief only after 12 s had passed.

  13. Despite being unincentivized and thus noisy, this survey approach can provide some insights into the modal fairness perceptions of subjects (Faravelli 2007; Cubitt et al. 2011; Reuben and Riedl 2013).

  14. In the Unconstrained condition the games were separated by additional distribution tasks, which were replaced by different filler games in the subsequent Time Pressure and Time Delay conditions. A full analysis of all 12 BD games is available upon request.

  15. We later added the VERY LOW game in the subsequent Time Pressure and Time Delay sessions because each of these games represent a type 1 situation.

  16. Since we do not observe response time correlations or choice frequencies for this game, all time pressure results can only be interpreted under the assumption that it indeed represents a type 2 situation in which there is a majority of fair decision makers. The second assumption is unlikely to hold, given that in the LOW game already only 34% of subjects cooperate despite stronger incentives to cooperate.

  17. Note, that the mean decision time in the TD condition is significantly higher than 6 s and only a minority of decisions (13.8%) is made within the range of 6–7 s. This indicates that there are only few subjects in the TD condition, who have already completed their decision process when reaching the delay cutoff.

  18. More detailed statistics on response time distributions for each game are available in Online Appendix C.

  19. We do not find evidence that there are differences in switching behavior for the VERY LOW game, while there is significantly more switching for the HIGH game in the TP but not in the STP condition. Since both of these game can only provide ambiguous evidence in favor or against the FII hypothesis, we are not discussing these results in more detail.

  20. This method rests on the assumption that subjects have transitive preferences over own-other allocations that are only violated by mistake (Andreoni and Miller 2002).

  21. The five choice patterns are (F, F, F, F), (S, F, F, F), (S, S, F, F), (S, S, S, F) and (S, S, S, S). For any other pattern [e.g. (S, F, S, F)] there is no clear indication in which decision a subject might have made an error that violates transitivity and hence where the indifference point for this subject might be located. Note that a consistent pattern does not necessarily imply that subjects have not made any error since a (S, F, F, F) subject could have made an error in either the VERY LOW game implying that her actual preferences are (F, F, F, F) or in the LOW game implying that her actual preferences are (S, S, F, F) or could have made more than one error.

  22. Due to the smaller group size for some classifications in the STP condition, we pooled data from both time pressure conditions for this analysis.

  23. All response time statistics are available in Online Appendix C.

  24. The full analysis of the remaining games and switching patterns can be found in Online Appendix C.

  25. For a discussion of the last paper see Myrseth and Wollbrant (2016) and Jagau and van Veelen (2017).

  26. This is in line with Fiedler et al. (2013) and Capraro and Cococcioni (2016), but differs from Rand et al. (2012). We, however, believe that giving subjects a possibility to fully deliberate on a task before entering the decision screen will affect the chances of isolating intuitive tendencies via time pressure.

References

  • Achtziger, A., Alós-Ferrer, C., & Wagner, A. K. (2015). Money, depletion, and prosociality in the dictator game. Journal of Neuroscience, Psychology, and Economics, 8(1), 1.

    Article  Google Scholar 

  • Alós-Ferrer, C. (2016). A dual-process diffusion model. Journal of Behavioral Decision Making,. https://doi.org/10.1002/bdm.1960.

    Article  Google Scholar 

  • Alós-Ferrer, C., & Strack, F. (2014). From dual processes to multiple selves: Implications for economic behavior. Journal of Economic Psychology, 41, 1–11.

    Article  Google Scholar 

  • Andreoni, J., & Miller, J. (2002). Giving according to garp: An experimental test of the consistency of preferences for altruism. Econometrica, 70(2), 737–753.

    Article  Google Scholar 

  • Bear, A., & Rand, D. G. (2016). Intuition, deliberation, and the evolution of cooperation. Proceedings of the National Academy of Sciences, 113(4), 936–941.

    Article  Google Scholar 

  • Bock, O., Baetge, I., & Nicklisch, A. (2014). hroot: Hamburg registration and organization online tool. European Economic Review, 71, 117–120.

    Article  Google Scholar 

  • Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700.

    Article  Google Scholar 

  • Bouwmeester, S., Verkoeijen, P. P., Aczel, B., Barbosa, F., Bègue, L., Brañas-Garza, P., et al. (2017). Registered replication report: Rand, greene, and nowak (2012). Perspectives on Psychological Science, 12(3), 527–542.

    Article  Google Scholar 

  • Buckert, M., Oechssler, J., & Schwieren, C. (2017). Imitation under stress. Journal of Economic Behavior and Organization, 139, 252–266.

    Article  Google Scholar 

  • Caplin, A., & Martin, D. (2015). The dual-process drift diffusion model: Evidence from response times. Economic Inquiry, 54(2), 1274–1282.

    Article  Google Scholar 

  • Cappelen, A. W., Nielsen, U. H., Tungodden, B., Tyran, J.-R., & Wengström, E. (2016). Fairness is intuitive. Experimental Economics, 19, 727–740.

    Article  Google Scholar 

  • Capraro, V., & Cococcioni, G. (2016). Rethinking spontaneous giving: Extreme time pressure and ego-depletion favor self-regarding reactions. Scientific Reports, 6, 27219.

    Article  Google Scholar 

  • Capraro, V., Corgnet, B., Espín, A. M., & Hernán-González, R. (2017). Deliberation favours social efficiency by making people disregard their relative shares: Evidence from USA and India. Royal Society Open Science, 4(2), 160605.

    Article  Google Scholar 

  • Chen, F., & Fischbacher, U. (2015). Cognitive processes of distributional preferences: A response time study. Research Paper Series Thurgauer Wirtschaftsinstitut.

  • Clithero, J. A. (2016). Response times in economics: Looking through the lens of sequential sampling models. Available at SSRN. https://doi.org/10.2139/ssrn.2795871.

  • Cubitt, R. P., Drouvelis, M., Gächter, S., & Kabalin, R. (2011). Moral judgments in social dilemmas: How bad is free riding? Journal of Public Economics, 95(3), 253–264.

    Article  Google Scholar 

  • Dreber, A., Fudenberg, D., Levine, D. K., & Rand, D. G. (2014). Self-control, social preferences and the effect of delayed payments. Available at SSRN. https://doi.org/10.2139/ssrn.1752366.

  • Drouvelis, M., & Grosskopf, B. (2016). The effects of induced emotions on pro-social behaviour. Journal of Public Economics, 134, 1–8.

    Article  Google Scholar 

  • Duffy, S., & Smith, J. (2014). Cognitive load in the multi-player prisoner’s dilemma game: Are there brains in games? Journal of Behavioral and Experimental Economics, 51, 47–56.

    Article  Google Scholar 

  • Engelmann, D., & Strobel, M. (2004). Inequality aversion, efficiency, and maximin preferences in simple distribution experiments. The American Economic Review, 94(4), 857–869.

    Article  Google Scholar 

  • Faravelli, M. (2007). How context matters: A survey based experiment on distributive justice. Journal of Public Economics, 91(7), 1399–1422.

    Article  Google Scholar 

  • Fehr, E., & Schmidt, K. M. (2006). The economics of fairness, reciprocity and altruism experimental evidence and new theories. Handbook of the Economics of Giving, Altruism and Reciprocity, 1, 615–691.

    Article  Google Scholar 

  • Fiedler, S., Glöckner, A., Nicklisch, A., & Dickert, S. (2013). Social value orientation and information search in social dilemmas: An eye-tracking analysis. Organizational Behavior and Human Decision Processes, 120(2), 272–284.

    Article  Google Scholar 

  • Fischbacher, U. (2007). z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178.

    Article  Google Scholar 

  • Frederick, S. (2005). Cognitive reflection and decision making. The Journal of Economic Perspectives, 19(4), 25–42.

    Article  Google Scholar 

  • Goeschl, T., & Lohse, J. (2016). Cooperation in public good games. Calculated or confused? AWI Discussion Paper Series No 626.

  • Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. Journal of Neuroscience, 35(6), 2476–2484.

    Article  Google Scholar 

  • Hieber, P., & Scherer, M. (2012). A note on first-passage times of continuously time-changed brownian motion. Statistics & Probability Letters, 82(1), 165–172.

    Article  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 

  • Jagau, S., & van Veelen, M. (2017). A general evolutionary framework for the role of intuition and deliberation in cooperation. Nature Human Behavior, 1, 0152.

    Article  Google Scholar 

  • Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58(9), 697.

    Article  Google Scholar 

  • Kocher, M. G., Martinsson, P., Myrseth, K. O. R., & Wollbrant, C. E. (2016). Strong, bold, and kind: Self-control and cooperation in social dilemmas. Experimental Economics, 20(1), 44–69.

    Article  Google Scholar 

  • Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015a). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications, 6, 7455.

    Article  Google Scholar 

  • Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015b). A common mechanism underlying food choice and social decisions. PLoS Computational Biology, 11(10), e1004371.

    Article  Google Scholar 

  • Krajbich, I., Oud, B., & Fehr, E. (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. The American Economic Review, 104(5), 501–506.

    Article  Google Scholar 

  • Ledyard, J. (Unpublished). Public goods: A survey of experimental research. Social Science Working Paper, 861. California Institute of Technology, Pasadena, CA.

  • Loewenstein, G. (2000). Emotions in economic theory and economic behavior. The American Economic Review, 90(2), 426–432.

    Article  Google Scholar 

  • Lohse, J. (2016). Smart or selfish—When smart guys finish nice. Journal of Behavioral and Experimental Economics, 64(10), 28–40.

    Article  Google Scholar 

  • Martinsson, P., Myrseth, K. O. R., & Wollbrant, C. (2014). Social dilemmas: When self-control benefits cooperation. Journal of Economic Psychology, 45, 213–236.

    Article  Google Scholar 

  • Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The drift diffusion model can account for value-based choice response times under high and low time pressure. Judgment and Decision Making, 5(6), 437–449.

    Google Scholar 

  • Mischkowski, D., & Glöckner, A. (2016). Spontaneous cooperation for prosocials, but not for proselfs: Social value orientation moderates spontaneous cooperation behavior. Scientific Reports, 6, 21555.

    Article  Google Scholar 

  • Mrkva, K. (2017). Giving, fast and slow: Reflection increases costly (but not uncostly) charitable giving. Journal of Behavioral Decision Making, 30(5), 1052–1065.

    Article  Google Scholar 

  • Myrseth, K. O. R., & Wollbrant, C. E. (2016). Models inconsistent with altruism cannot explain the evolution of human cooperation. Proceedings of the National Academy of Sciences, 113(18), E2472.

    Article  Google Scholar 

  • Myrseth, K. O. R., & Wollbrant, C. E. (2017). Cognitive foundations of cooperation revisited: Commentary on rand et al. (2012, 2014). Journal of Behavioral and Experimental Economics, 69, 133–138.

    Article  Google Scholar 

  • Nishi, A., Christakis, N. A., & Rand, D. G. (2017). Cooperation, decision time, and culture: Online experiments with american and indian participants. PLoS ONE, 12(2), e0171252.

    Article  Google Scholar 

  • Palmer, J., Huk, A. C., & Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision, 5(5), 1–1.

    Article  Google Scholar 

  • Polanía, R., Krajbich, I., Grueschow, M., & Ruff, C. C. (2014). Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision making. Neuron, 82(3), 709–720.

    Article  Google Scholar 

  • Rabin, M. (1993). Incorporating fairness into game theory and economics. The American Economic Review, 83(5), 1281–1302.

    Google Scholar 

  • Rand, D. G., Brescoll, V. L., Everett, J. A., Capraro, V., & Barcelo, H. (2016). Social heuristics and social roles: Intuition favors altruism for women but not for men. Journal of Experimental Psychology: General, 145(4), 389.

    Article  Google Scholar 

  • Rand, D. G., Greene, J. D., & Nowak, M. A. (2012). Spontaneous giving and calculated greed. Nature, 489(7416), 427–430.

    Article  Google Scholar 

  • Rand, D. G., Peysakhovich, A., Kraft-Todd, G. T., Newman, G. E., Wurzbacher, O., Nowak, M. A., et al. (2014). Social heuristics shape intuitive cooperation. Nature Communications, 5, 3677.

    Article  Google Scholar 

  • Ratcliff, R. (1988). Continuous versus discrete information processing: Modeling accumulation of partial information. American Psychological Association, 95(2), 238–255.

    Google Scholar 

  • Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356.

    Article  Google Scholar 

  • Recalde, M. P., Riedl, A., & Vesterlund, L. (2014). Error prone inference from response time: The case of intuitive generosity. CESifo Working Paper Series No. 4987.

  • Reuben, E., & Riedl, A. (2013). Enforcement of contribution norms in public good games with heterogeneous populations. Games and Economic Behavior, 77(1), 122–137.

    Article  Google Scholar 

  • Rubinstein, A. (2007). Instinctive and cognitive reasoning: A study of response times. The Economic Journal, 117(523), 1243–1259.

    Article  Google Scholar 

  • Smith, P. L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44(3), 408–463.

    Article  Google Scholar 

  • Spiliopoulos, L., & Ortmann, A. (2017). The BCD of response time analysis in experimental economics. Experimental Economics. https://doi.org/10.1007/s10683-017-9528-1.

    Article  Google Scholar 

  • Stromland, E., Tjotta, S., & Torsvik, G. (2016). Cooperating, fast and slow: Testing the social heuristics hypothesis. CESifo Working Paper Series No. 5875.

  • Tinghög, G., Andersson, D., Bonn, C., Böttiger, H., Josephson, C., Lundgren, G., et al. (2013). Intuition and cooperation reconsidered. Nature, 498(7452), 427–430.

    Article  Google Scholar 

  • Tinghög, G., Andersson, D., Bonn, C., Johannesson, M., Kirchler, M., Koppel, L., et al. (2016). Intuition and moral decision-making-the effect of time pressure and cognitive load on moral judgment and altruistic behavior. PLoS ONE, 11(10), e0164012.

    Article  Google Scholar 

  • Verkoeijen, P. P., & Bouwmeester, S. (2014). Does intuition cause cooperation? PLoS ONE, 9(5), e96654.

    Article  Google Scholar 

  • Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory & Cognition, 32(7), 1206–1220.

    Article  Google Scholar 

  • Wright, P. (1974). The harassed decision maker: Time pressures, distractions, and the use of evidence. Journal of Applied Psychology, 59(5), 555–561.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Christoph Vanberg, Gustav Tinghög, Ariel Rubinstein, Ernst Fehr, Tobias Pfrommer, Daniel Heyen, Gert Pönitzsch, as well as seminar participants at the University of Heidelberg, ZEW Mannheim, IfW Kiel, Thurgau Experimental Economics Meeting, IMEBESS Rome, Kings College London and ESA Bergen for their helpful comments. In addition, we would like to thank the editors as well as two anonymous referees for their tremendously helpful feedback on an earlier version of this paper. 

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Correspondence to Anna Louisa Merkel.

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Merkel, A.L., Lohse, J. Is fairness intuitive? An experiment accounting for subjective utility differences under time pressure. Exp Econ 22, 24–50 (2019). https://doi.org/10.1007/s10683-018-9566-3

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