Journal of Risk and Uncertainty

, Volume 51, Issue 2, pp 147–170 | Cite as

Heterogeneity in preferences towards complexity

  • Peter G. Moffatt
  • Stefania Sitzia
  • Daniel John Zizzo


We analyze lottery-choice data in a way that separately estimates the effects of risk aversion and complexity aversion. Complexity is represented by the number of different outcomes in the lottery. A finite mixture random effects model is estimated which assumes that a proportion of the population are complexity-neutral. We find that around 33% of the population are complexity-neutral, around 50% complexity-averse, and the remaining 17% are complexity-loving. Subjects who do react to complexity appear to have a bias towards complexity aversion at the start of the experiment, but complexity aversion reduces with experience, to the extent that the average subject is (almost) complexity-neutral by the end of the experiment. Complexity aversion is found to increase with age and to be higher for non-UK students than for UK students. We also find some evidence that, when evaluating complex lotteries, subjects perceive probabilities in accordance with Prospective Reference Theory.


Complexity aversion Complexity preferences Risk preferences Mixture models Learning 

JEL Classifications

C91 D03 D81 



Funding from the University of East Anglia, advice from Bob Sugden and from participants at the Asia Pacific meeting of the Economic Science Association in Auckland in February 2014, and research assistance from Axel Sonntag are gratefully acknowledged. The usual disclaimer applies. The experimental instructions can be found in the appendix.


  1. Bruce, C., & Johnson, J. E. V. (1996). Decision-making under risk: The effect of complexity on performance. Psychological Reports, 79, 67–76.CrossRefGoogle Scholar
  2. Chavas, J. P., & Pope, R. (1982). Hedging and production decisions under a linear mean-variance preference function. Western Journal of Agricultural Economics, 7(1), 99–110.Google Scholar
  3. Ellison, G., & Ellison, S. F. (2004). Search, obfuscation, and price elasticities on the internet. Discussion Paper, MIT and NBER.Google Scholar
  4. Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10, 171–178.CrossRefGoogle Scholar
  5. Gale, D., & Sabourian, H. (2005). Complexity and competition. Econometrica, 73(3), 739–769.CrossRefGoogle Scholar
  6. Garrod, L., Hviid, M., Loomes, G., & Waddams Price, C. (2009). Competition remedies in consumer markets. Loyola Consumer Law Review, 21(4), 439–495.Google Scholar
  7. Huck, S., & Weizsäcker, G. (1999). Risk, complexity, and deviations from expected-value maximization: Results of a lottery choice experiment. Journal of Economic Psychology, 20(6), 699–715.CrossRefGoogle Scholar
  8. Humphrey, S. J. (1995). Regret aversion or event-splitting effects? More evidence under risk and uncertainty. Journal of Risk and Uncertainty, 11(3), 263–274.CrossRefGoogle Scholar
  9. Humphrey, S. J. (2001). Are event-splitting effects actually boundary effects? Journal of Risk and Uncertainty, 22(1), 79–93.CrossRefGoogle Scholar
  10. Iyengar, S. S., & Lepper, M. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79, 995–1006.CrossRefGoogle Scholar
  11. Mador, G., Sonsino, D., & Benzion, U. (2000). On complexity and lotteries evaluation—Three experimental observations. Journal of Economic Psychology, 21(6), 625–637.CrossRefGoogle Scholar
  12. Siegel, S., & Castellan, N. J. (1988). Non-parametric statistics for the behavioral sciences. 2nd edition, McGraw Hill.Google Scholar
  13. Sitzia, S., & Zizzo, D. J. (2011). Does product complexity matter for competition in experimental retail markets? Theory and Decision, 70(1), 65–82.CrossRefGoogle Scholar
  14. Sitzia, S., Zheng, J., & Zizzo, D. J. (2012). Complexity and smart nudges with inattentive consumers. Social Science Research Network Discussion Paper.Google Scholar
  15. Sonsino, D., Benzion, U., & Mador, G. (2002). The complexity effects on choice with uncertainty—Experimental evidence. The Economic Journal, 112(482), 936–965.CrossRefGoogle Scholar
  16. Starmer, C., & Sugden, R. (1993). Testing for juxtaposition and event-splitting effects. Journal of Risk and Uncertainty, 6(3), 235–254.CrossRefGoogle Scholar
  17. Stodder, J. (1997). Complexity aversion: Simplification in the Herrnstein and Allais behaviors. Eastern Economic Journal, 23(1), 1–31.Google Scholar
  18. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.Google Scholar
  19. Train, K. E. (2003). Discrete choice methods with simulation. Cambridge University Press.Google Scholar
  20. Viscusi, W. K. (1989). Prospective reference theory: Toward an explanation of the paradoxes. Journal of Risk and Uncertainty, 2(3), 235–263.CrossRefGoogle Scholar
  21. Weber, B. J. (2007). The effects of losses and event splitting on the Allais paradox. Judgment in Decision Making, 2(2), 115–125.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Peter G. Moffatt
    • 1
  • Stefania Sitzia
    • 2
  • Daniel John Zizzo
    • 3
  1. 1.School of Economics, Norwich Research ParkUniversity of East AngliaNorwichUK
  2. 2.School of Economics and CBESS, Norwich Research ParkUniversity of East AngliaNorwichUK
  3. 3.Newcastle University Business School and BENCNewcastle UniversityNewcastle upon TyneUK

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