Abstract
This paper attempts to empirically assess how advice may reduce suboptimality in a portfolio choice experiment with risk-neutral participants induced via binary-lottery incentives. Previous studies (Theory Decis 83:195–243, 2017 and Theory Decis 85:151–177, 2018) with a larger set of choice tasks report overwhelming evidence of suboptimality and how it is slightly reduced by learning and experience. Participants confront 15 randomly ordered portfolio choices, which they experience again in 2 successive phases. Intermediate advice between phases alerts participants that less-risky investments can improve the outcome for at least one chance event without harming their success chances in the other random event. Compared to the pure choice treatment, another cognitively more demanding treatment additionally asks participants to form event-specific success aspirations that allow us to test satisficing and its optimality. The results show that intermediate advice increases the share of satisficing but not of optimal behavior beyond learning through experience. However, it significantly lowers the average distance from optimality.
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Notes
When viewing rational choice theory as a philosophical exercise, intrapersonal payoff aggregation is hardly avoidable. In behavioral economics, however, it seems only a possibility that one might often want to avoid by applying a multiple-selves approach.
The fact that binary-lottery incentives do not trigger optimality (see Selten et al. 1999) has often been misunderstood. If they do not result in optimality, this questions not only risk-neutral optimality induced via binary-lottery incentives but more generally expected utility maximization.
Administering advice immediately together with the instructions would have cognitively overburdened participants and could have rendered immediate advice ineffective.
Di Cagno et al. (2017), only ran two phases; however, three \(c=0\) control tasks were included in each phase, which we excluded in this experiment.
References
Agarwal, S., Chomsisengphet, S., Mahoney, N., & Stroebel, J. (2015). Regulating consumer financial products: evidence from credit cards. The Quarterly Journal of Economics,130, 111–164.
Agarwal, S., Driscoll, J.C., Gabaix, X., Laibson, D. (2009). The Age of Reason: Financial Decisions over the Life Cycle and Implications for Regulation. Brookings Papers on Economic Activity 2009, 51–101.
Aperteguia, J., Huck, S., & Oechssler, J. (2007). Imitation- theory and experimental evidence. Journal of Economic Theory,136(1), 217–235.
Baicker, K., Mullainathan, S., & Schwartzstein, J. (2015). Behavioural hazard in health insurance. The Quarterly Journal of Economics,130, 1623–1667.
Benartzi, S. (2017). How digital tools and behavioral economics will save retirement. Harvard Business Review. Retrieved Dec 7, 2017 from https://hbr.org.
Camerer, C. F. (2003). Strategizing in the brain. (Psycology and Economics). Science, 300(5626), 1673.
Choi, J. J. (2015). Contributions to defined contribution pension plans. Annual Review of Financial Economics,7, 161–178.
Conlisk, J. (1996). Why bounded rationality? Journal of Economic Literature,34(2), 669–700.
Di Cagno, D., Galliera, A., Güth, W., Marzo, F., Pace, N. (2017). (Sub) Optimality and (non) optimal satisficing in risky decision experiments. Theory and Decision, 83(2), 195–243.
Di Cagno, D., Galliera, A., Güth, W., Pace, N. (2018). Behavioral patterns and reduction of sub-optimality: an experimental choice analysis. Theory and Decision, 85(2), 151–177.
Embrey, I. (2020). States of nature and states of mind: a generalized theory of decision-making. Theory and Decision,88, 5–35.
Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics,10(2), 171–178.
Gigerenzer, G. (2000). Adaptive thinking: rationality in the real world. New York: Oxford University Press.
Gigerenzer, G. (2006). Bounded and rational. In R. J. Stainton (Ed.), Contemporary debates in cognitive science. Oxford: Blackwell.
Gómez, Y., Martınez-Moles, V., Vila, J. (2016). Spanish regulation for labeling of financial products: a behavioral-experimental analysis. Economia Politica 33, 355–378.
Greiner, B. (2015). Subject pool recruitment procedures: organizing experiments with ORSEE. Journal of the Economic Science Association,1(1), 114–125.
Güth, W., & Ploner, M. (2017). Mentally perceiving how means achieve ends. Rationality and Society,29(2), 203–225.
Hey, J., Permana, Y., & Rochanahastin, N. (2017). When and how to satisfice: an experimental investigation. Theory and Decision,83(3), 337–353.
Kosters, M., & van der Heijden, J. (2015). From mechanism to virtue: evaluating nudge-theory. Evaluation, 21(3), 276–291.
Manski, C. (2017). Optimize, satisfice, or choose without deliberation? A simple minimax-regret assessment. Theory and Decision,83(3), 155–173.
Payzan-LeNestour, E., & Bossaerts, P. (2015). Learning about unstable, publicly unobservable payoffs. The Review of Financial Studies,28, 1874–1913.
Robson, A., & Vega-Redondo, (1996). Efficient equilibrium selection in evolutionary games with random matching. Journal of Economic Theory,70, 65–92.
Sauermann, H., & Selten, R. (1962). Anspruchsanpassungstheorie der unternehmung. Zeitschrift für die gesamte Staatswissenschaft/Journal of Institutional and Theoretical Economics,4, 577–597.
Selten, R. (1998). Features of experimentally observed bounded rationality. European Economic Review,42, 413–436.
Selten, R. (2001). What is bounded rationality. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: the adaptive toolbox. Cambridge: The MIT Press.
Selten, R., Sadrieh, A., & Abbink, K. (1999). Money does not induce risk neutral behavior, but binary lotteries do even worse. Theory and Decision,46, 213–252.
Selten, R., Pittnauer, S., & Hohnisch, M. (2012). Dealing with dynamic decision problems when knowledge of the environment is limited: an approach based on goal systems. Journal of Behavioral Decision Making,25, 443–457.
Siegel, S. (1957). Level of aspiration and decision making. Psychological Review,64, 253–262.
Simon, H. (1955). A behavioral model of rational choice. Quaterly Journal of Economics,69, 99–118.
Tereszkiewicz, P. (2016). Neutral third-party counselling as nudge toward safer financial products? The case of risky mortgage loan contracts. In K. Mathis & A. Tor (Eds.), Nudging - possibilities, limitations, and applications in European law and economics (pp. 169–196). Cham: Springer.
Thaler, R. H., & Benartzi, D. (2004). Save More TomorrowTM: using behavioral economics to increase employee saving. Journal of Political Economy,112, 164–187.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Vega-Redondo, F. (1997). The evolution of Walrasian behavior. Econometrica, 65, 375–384.
Weiyi Cai, C. (2019). Nudging the financial market? A review of the nudge theory. Accounting and Finance, 60(4), 3341–3365.
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Di Cagno, D., Güth, W. & Pace, N. Experimental evidence of behavioral improvement by learning and intermediate advice. Theory Decis 91, 173–187 (2021). https://doi.org/10.1007/s11238-020-09799-5
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DOI: https://doi.org/10.1007/s11238-020-09799-5