# (Sub) Optimality and (non) optimal satisficing in risky decision experiments

- 204 Downloads
- 1 Citations

## Abstract

We implement a risky choice experiment based on one-dimensional choice variables and risk neutrality induced via binary lottery incentives. Each participant confronts many parameter constellations with varying optimal payoffs. We assess (sub)optimality, as well as (non)optimal satisficing by eliciting aspirations in addition to choices. Treatments differ in the probability that a binary random event, which are payoff—but not optimal choice—relevant is experimentally induced and whether participants choose portfolios directly or via satisficing, i.e., by forming aspirations and checking for satisficing before making their choice. By incentivizing aspiration formation, we can test satisficing, and in cases of satisficing, determine whether it is optimal.

## Keywords

(un)Bounded rationality Satisficing Risk Uncertainty Experiments## JEL Classification

D03 D81 C91## References

- Buchanan, J., & Kock, N. (2001). Information overload: A decision making perspective. In
*Multiple Criteria Decision Making in the New Millennium*, 49–58 (Springer Berlin Heidelberg).Google Scholar - Camerer, C. F. (1991). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In K. A. Ericsson & J. Smith (Eds.),
*Towards a general theory of expertise: Prospects and limits*. New York: Cambridge University Press.Google Scholar - Cho, I. K., & Kreps, D. M. (1987). Signaling games and stable equilibria.
*The Quarterly Journal of Economics*,*102*, 179–221.CrossRefGoogle Scholar - Fechner, G. T. (1876)
*Vorschule der aesthetik*(Vol. 1). Germany: Breitkopf and Härtel.Google Scholar - Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments.
*Experimental Economics*,*10*, 171–178.CrossRefGoogle Scholar - Gary, M. S., & Wood, R. E. (2011). Mental models, decision rules, and performance heterogeneity.
*Strategic Management Journal*,*32*, 569–594.CrossRefGoogle Scholar - Gilboa, I., & Schmeidler, D. (2001).
*A theory of case-based decisions*. Cambridge: Cambridge University Press.CrossRefGoogle Scholar - Güth, W., Levati, M. V., & Ploner, M. (2009). An experimental analysis of satisficing in saving decisions.
*Journal of Mathematical Psychology*,*53*, 265–272.CrossRefGoogle Scholar - Güth, W., & Ploner, M. (2016).
*Mentally perceiving how means achieve ends*. Rationality and Society. doi: 10.1177/1043463116678114 - Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with ORSEE.
*Journal of the Economic Science Association*,*1*, 114–125.CrossRefGoogle Scholar - Harless, D. W., & Camerer, C. F. (1994). The predictive utility of generalized expected utility theories.
*Econometrica*,*62*, 1251–1289.CrossRefGoogle Scholar - Hey, J. D. (1995). Experimental investigations of errors in decision making under risk.
*European Economic Review*,*39*, 633–640.CrossRefGoogle Scholar - Hey, J. D., & Orme, C. (1994). Investigating generalizations of expected utility theory using experimental data.
*Econometrica*,*62*, 1291–1326.CrossRefGoogle Scholar - Krantz, D. H., & Kunreuther, H. C. (2007). Goals and plans in decision making.
*Judgment and Decision Making*,*2*, 137–168.Google Scholar - Kruglanski, A. W. (1996). Goals as knowledge structures. In P. M. Gollwitzer & J. A. Bargh (Eds.),
*The psychology of action: Linking cognition and motivation to behavior*(pp. 599–619). New York: Guilford Press.Google Scholar - Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., & Chun, W. Y. (2002). A theory of goal systems. In M. P. Zanna (Ed.),
*Advances in experimental social psychology*(pp. 331–378). San Diego: Academic Press.Google Scholar - Loomes, G., & Sugden, R. (1995). Incorporating a stochastic element into decision theories.
*European Economic Review*,*39*, 641–648.CrossRefGoogle Scholar - McKelvey, R. D., & Palfrey, T. R. (1995). Quantal response equilibria in normal form games.
*Games and Economic Behaviour*,*7*, 6–38.CrossRefGoogle Scholar - Marley, A. A. J. (1997). Probabilistic choice as a consequence of nonlinear (sub) optimization.
*Journal of Mathematical Psychology*,*41*, 382–391.Google Scholar - Myerson, R. (1978). Refinements of the Nash equilibrium concept.
*International Journal of Game Theory*,*7*, 73–80.CrossRefGoogle Scholar - Pearl, J. (2003). Causality: models, reasoning and inference.
*Econometric Theory*,*19*, 675–685.CrossRefGoogle Scholar - Sauermann, H., & Selten, R. (1962). Anspruchsanpassungstheorie der Unternehmung.
*Zeitschrift fü die Gesamte Staatswissenschaft*,*118*, 577–597.Google Scholar - Savikhin, A.C. (2013). The Application of Visual Analytics to Financial Decision-Making and Risk Management: Notes from Behavioural Economics. In
*Financial Analysis and Risk Management*, 99-114 ( Springer Berlin Heidelberg).Google Scholar - 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.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Simon, H. A. (1955). A behavioral model of rational choice.
*The Quarterly Journal of Economics*,*69*, 99–118.CrossRefGoogle Scholar