Skip to main content
SpringerLink
Log in

Risk behavior for gain, loss, and mixed prospects

  • Published:
Theory and Decision Aims and scope Submit manuscript

Abstract

This study extends experimental tests of (cumulative) prospect theory (PT) over prospects with more than three outcomes and tests second-order stochastic dominance principles (Levy and Levy, Management Science 48:1334–1349, 2002; Baucells and Heukamp, Management Science 52:1409–1423, 2006). It considers choice behavior of people facing prospects of three different types: gain prospects (losing is not possible), loss prospects (gaining is not possible), and mixed prospects (both gaining and losing are possible). The data supports the distinction of risk behavior into these three categories of prospects, Further, probability weighting and diminishing sensitivity of utility as predicted by PT are observed. Loss aversion is, however, less pronounced, except for choices where one prospect is degenerate. The data suggests that the probability of losing may be relevant for loss aversion.

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

Similar content being viewed by others

Notes

  1. Experimental analyses for dominance criteria of higher order are discussed elsewhere (e.g., Deck and Schlesinger 2010). The independence axiom of expected utility entails itself a dominance principle that is appealing: first-order stochastic dominance (FSD) requires that the prospect should be preferred that has higher decumulative probability among all outcomes than a second prospect. FSD is a simple criterion, people agree with this principle and apply it in simple situations. However, in nontransparent choice situations many violate this dominance criterion; see Birnbaum and Navarrete (1998) and Birnbaum (2005) for experimental evidence. The aforementioned biases have little influence on FSD.

  2. See also Hogarth and Einhorn (1990), Tversky and Kahneman (1992), Wu and Gonzalez (1996), Gonzalez and Wu (1999), Abdellaoui (2000), Bleichrodt and Pinto (2000), Bleichrodt et al. (2001), Etchart-Vincent (2004), Abdellaoui et al. (2005), and Abdellaoui et al. (2011).

  3. As degenerate lotteries are identified with the corresponding outcome, the prospect \((p_{1:}0,\ldots ,p_{n}:0)\), that has no gains and no losses, is identified with the reference outcome zero.

  4. See Tversky and Kahneman (1992), Goldstein and Einhorn (1987), Prelec (1998), Diecidue et al. (2009), or Abdellaoui et al. (2010) for parametric specifications incorporating optimism, insensitivity, and pessimism.

  5. In the experiment we use prospects in which each outcome has probability \( 1/5 \); for prospects with equal outcomes the latter are displayed with the coalesced probabilities, e.g., two 1/5 chances of obtaining £5 are presented as a single 2/5 chance for £5.

  6. As we restrict attention to prospects that have the same mean this definition makes sense. SSD is, in general, also defined for prospects without equal means. The more general definition implies our definition used here but the reverse implication does not hold.

  7. The case \(c=d\) occurs naturally in the parametric probability weighting functions of Goldstein and Einhorn (1987), Tversky and Kahneman (1992), Lattimore et al. (1992), Prelec (1998), and Diecidue et al. (2009).

  8. Based on the aggregate data of Abdellaoui (2000) the decision weights of 0.33 (0.25) are approximately 0.33 for gains and 0.35 for losses (0.29 for gains and 0.29 for losses) for the one-parameter Tversky and Kahneman (1992) probability weighting function. For the two-parameter Goldstein and Einhorn (1987) probability weighting function we have \( w^{+}(0.33)\approx 0.30\) and \(w^{-}(0.33)\approx 0.35\) (\(w^{+}(0.25)\approx 0.3\) and \(w^{-}(0.25)\approx 0.29\))

  9. See Appendix 1.

  10. See Cohen et al. (1985), Camerer (1989), Battalio et al. (1990), Harless (1992), Harless and Camerer (1994), Myagkov and Plott (1997), Di Mauro and Maffioletti (2002), Smith et al. (2002), Mason et al. (2005), and Brooks and Zank (2005).

  11. The descriptions “safer” or “riskier” are short for “safer in the SSD-sense” or “riskier in the SSD-sense,” respectively.

  12. We have also used the parameters estimates of Abdellaoui (2000) for the two-parameter probability weighting function of Goldstein and Einhorn (1987). These give similar predictions.

  13. Adding the data for the mixed condition and a parameter, \(\lambda \), for LA gives similar estimates. \(\lambda =0.93\) (\(\mathrm{SE}=0.059\)), is found insignificantly different from one.

  14. The estimated value for the slope of the regression line, \(\hat{\beta }=1.3\), has a standard error of 0.1732. This results in a test statistic value of 7.506 while the upper \(5\,\%\) critical value of the \(t_{1}\) distribution is 6.314. The intercept for this regression line takes the value of 37 with a standard error of 1.8707.

  15. Because very few subjects were MWSD for gains and for losses we skip the corresponding table.

  16. We observe that this test is equivalent to verifying if common outcomes being gains or being losses matters for choice behavior

  17. Another explanation could be that the high number tasks involving mixed prospects and loss prospects may have generated pessimism about gaining any amount of money out of this experiment. This would explain why most subjects were not willing to pay a large proportion of their earnings from the experiment to participate again in the same study (a finding which can be interpretted as a form of loss aversion). 82 subjects provided us with such information: on average, those who lost from their fixed payment (31 subjects earned £6.64 on average) were willing to pay \(44, 66\,\%\) of their ernings; those who gained (27 subjects earned £24.22 on average) were willing to pay \(23.85\,\%\) of their earnings, and those who neither gained nor lost (24 subjects received £17) were willing to pay \(24.5\,\%\) of their earnings to repeat the experiment.

    Recall that the expected pay from the experiment was £17, while the minimum one can ensure is £2. Thus, it may well be, that the frequent reoccurence of tasks with potential losses has induced many subjects to exhibit more risk neutral behavior in the SSD-sense.

    However, this result is obtained using a valuation task (i.e., assessing the value of an experiment where losing is likely) which may trigger additional LA.

References

  • Abdellaoui, M. (2000). Parameter-free elicitation of utility and probability weighting functions. Management Science, 46, 1497–1512.

    Article  Google Scholar 

  • Abdellaoui, M., Baillon, A., Placido, L., & Wakker, P. P. (2011). The rich domain of uncertainty: Source functions and their experimental implementation. American Economic Review, 101, 695–723.

    Google Scholar 

  • Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Measuring loss aversion under prospect theory: A parameter-free approach. Management Science, 53, 1659–1674.

    Google Scholar 

  • Abdellaoui, M., Bleichrodt, H., & l’Haridon, O. (2008). A tractable method to measure utility and loss aversion under prospect theory. Journal of Risk and Uncertainty, 36, 245–266.

    Google Scholar 

  • Abdellaoui, M., & l’Haridon, O., & Zank, H. (2010). Separating curvature and elevation: A parametric probability weighting function. Journal of Risk and Uncertainty, 41, 39–65.

    Google Scholar 

  • Abdellaoui, M., Vossmann, F., & Weber, M. (2005). Choice-based elicitation and decomposition of decision weights for gains and losses under uncertainty. Management Science (forthcoming).

  • Baltussen, G., Post, T., & van Vliet, P. (2006). Violations of Cpt in mixed gambles. Management Science, 52, 1288–1290.

    Article  Google Scholar 

  • Battalio, R. C., Kagel, J. H., & Jiranyakul, K. (1990). Testing between alternative models of choice under uncertainty: Some initial results. Journal of Risk and Uncertainty, 3, 25–50.

    Article  Google Scholar 

  • Baucells, M., & Heukamp, F. H. (2004). Reevaluation of the results by Levy and Levy (2002a). Organizational Behavior and Human Decision Processes, 94, 15–21.

    Article  Google Scholar 

  • Baucells, M., & Heukamp, F. H. (2006). Stochastic dominance and cumulative prospect theory. Management Science, 52, 1409–1423.

    Article  Google Scholar 

  • Bawa, V. S. (1982). Stochastic dominance: A research bibliography. Management Science, 28, 698–712.

    Article  Google Scholar 

  • Beattie, J., & Loomes, G. (1997). The impact of incentives upon risky choice experiments. Journal of Risk and Uncertainty, 14, 155–168.

    Article  Google Scholar 

  • Benartzi, S., & Thaler, R. H. (1999). Risk aversion or myopia? Choices in Repeated Gambles and Retirement Investments, Management Science, 45, 364–381.

    Google Scholar 

  • Birnbaum, M. H. (2005). Three new tests of independence that differentiate models of risky decision making. Management Science, 51, 1346–1358.

    Article  Google Scholar 

  • Birnbaum, M. H., & Navarrete, J. B. (1998). Testing descriptive utility theories: Violations of stochastic dominance and cumulative independence. Journal of Risk and Uncertainty, 17, 49–78.

    Article  Google Scholar 

  • Bleichrodt, H., & Pinto, J. L. (2000). A parameter-free elicitation of the probability weighting function in medical decision analysis. Management Science, 46, 1485–1496.

    Article  Google Scholar 

  • Bleichrodt, H., Pinto, J. L., & Wakker, P. P. (2001). Making descriptive use of prospect theory to improve the prescriptive use of expected utility. Management Science, 47, 1498–1514.

    Article  Google Scholar 

  • Brooks, P., & Zank, H. (2005). Loss averse behavior. Journal of Risk and Uncertainty, 31, 301–325.

    Article  Google Scholar 

  • Camerer, C. F. (1989). An experimental test of several generalized utility theories. Journal of Risk and Uncertainty, 2, 61–104.

    Article  Google Scholar 

  • Camerer, C. F. (1995). Individual decision making. In J. H. Kagel & A. E. Roth (Eds.), Handbook of experimental economics (pp. 587–703). Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Chateauneuf, A., & Wakker, P. P. (1999). An axiomatization of cumulative prospect theory for decision under risk. Journal of Risk and Uncertainty, 18, 137–145.

    Article  Google Scholar 

  • Chew, S. H., Karni, E., & Safra, Z. (1987). Risk aversion iChn the theory of expected utility with rank dependent probabilities. Journal of Economic Theory, 42, 370–381.

    Article  Google Scholar 

  • Cohen, M., Jaffray, J.-Y., & Said, T. (1985). Individual behavior under risk and under uncertainty: An experimental study. Theory and Decision, 18, 203–228.

    Article  Google Scholar 

  • Cubitt, R. P., Starmer, C., & Sugden, R. (1998). On the validity of the random lottery incentive system. Experimental Economics, 1, 115–131.

    Google Scholar 

  • Deck, C., & Schlesinger, H. (2010). Exploring higher order risk effects. Review of Economic Studies, 77, 1403–1420.

    Article  Google Scholar 

  • Di Mauro, C., & Maffioletti, A. (2002). The valuation of insurance under uncertainty: Does information matter? Geneva Papers on Risk and Insurance Theory, 26, 195–224.

    Article  Google Scholar 

  • Diecidue, E., Schmidt, U., & Zank, H. (2009). Parametric weighting functions. Journal of Economic Theory, 144, 1102–1118.

    Article  Google Scholar 

  • Etchart-Vincent, N. (2004). Is probability weighting sensitive to the magnitude of consequences? An Experimental Investigation on Losses, Journal of Risk and Uncertainty, 28, 217–235.

    Article  Google Scholar 

  • Fennema, H., & Wakker, P. P. (1997). Original and cumulative prospect theory: A discussion of empirical differences. Journal of Behavioral Decision Making, 10, 53–64.

    Article  Google Scholar 

  • Goldstein, W. M., & Einhorn, H. J. (1987). Expression theory and the preference reversal phenomena. Psychological Review, 94, 236–254.

    Article  Google Scholar 

  • Gonzalez, R., & Wu, G. (1999). On the Shape of the Probability weighting function. Cognitive Psychology, 38, 129–166.

    Article  Google Scholar 

  • Hadar, J., & Russell, W. R. (1969). Rules for ordering uncertain prospects. American Economic Review, 59, 25–34.

    Google Scholar 

  • Harbaugh, W. T., Krause, K., & Vesterlund, L. (2009). The fourfold pattern of risk attitudes in choice and pricing tasks. The Economic Journal, 120, 595–611.

    Article  Google Scholar 

  • Harless, D. W. (1992). Predictions about indifference curves inside the unit triangle: A test of variants of expected utility theory. Journal of Economic Behavior and Organization, 18, 391–414.

    Article  Google Scholar 

  • Harless, D. W., & Camerer, C. (1994). The predictive utility of generalized expected utility theories. Econometrica, 62, 1251–1289.

    Article  Google Scholar 

  • Hey, J. D., & Orme, C. (1994). Investigating generalizations of expected utility theory using experimental data. Econometrica, 62, 1291–1326.

    Article  Google Scholar 

  • Hogarth, R. M., & Einhorn, H. J. (1990). Venture theory: A model of decision weights. Management Science, 36, 780–803.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.

    Article  Google Scholar 

  • Köbberling, V., & Wakker, P. P. (2003). Preference foundations for nonexpected utility: A generalized and simplified technique. Mathematics of Operations Research, 28, 395–423.

    Article  Google Scholar 

  • Kothiyal, A., Spinu, V., & Wakker, P. P. (2011). Prospect theory for continuous distributions: A preference foundation. Journal of Risk and Uncertainty, 42, 195–210.

    Article  Google Scholar 

  • Lattimore, P. M., Baker, J. R., & Witte, A. D. (1992). The influence of probability on risky choice. Journal of Economic Behavior and Organization, 17, 377–400.

    Article  Google Scholar 

  • Laury, S. K., & Holt, C. A. (2000). Further reflections on prospect theory. Working Paper: The Andrew Young School of Political Studies, Georgia State University, Atlanta, USA.

    Google Scholar 

  • Levy, H. (1992). Stochastic dominance and expected utility: Survey and analysis. Management Science, 38, 555–593.

    Article  Google Scholar 

  • Levy, M., & Levy, H. (2002). Prospect theory: Much ado about nothing. Management Science, 48, 1334–1349.

    Article  Google Scholar 

  • Loomes, G., Moffatt, P. G., & Sugden, R. (2002). A microeconometric test of alternative stochastic theories of risky choice. Journal of Risk and Uncertainty, 24, 103–130.

    Article  Google Scholar 

  • Lopes, L. L. (1984). Risk and distributional inequality. Journal of Experimental Psychology: Human Perception and Performance, 10, 465–485.

    Google Scholar 

  • Markowitz, H. (1952). The utility of wealth. The Journal of Political Economy, 60, 151–158.

    Article  Google Scholar 

  • Mason, C. F., Shogren, J. F., Settle, C., & List, J. A. (2005). Investigating risky choices over losses using experimental data. Journal of Risk and Uncertainty, 31, 187–215.

    Google Scholar 

  • Myagkov, M., & Plott, C. R. (1997). Exchange economies and loss exposure: Experiments exploring prospect theory and competitive equilibria in market environments. American Economic Review, 87, 801–828.

    Google Scholar 

  • Payne, J. W. (2005). It is whether you win or lose: The importance of the overall probabilities of winning or losing in risky choice. Journal of Risk and Uncertainty, 30, 5–19.

    Article  Google Scholar 

  • Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–527.

    Article  Google Scholar 

  • Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behaviour and Organization, 3, 323–343.

    Article  Google Scholar 

  • Rothschild, M., & Stiglitz, J. E. (1970). Increasing risk: I. A Definition, Journal of Economic Theory, 2, 225–243.

    Article  Google Scholar 

  • Ryan, M. J. (2006). Risk aversion in RDEU. Journal of Mathematical Economics, 42, 675–697.

    Google Scholar 

  • Schmidt, U., & Zank, H. (2005). What is loss aversion? Journal of Risk and Uncertainty, 30, 157–167.

    Google Scholar 

  • Schmidt, U., & Zank, H. (2008). Risk aversion in cumulative prospect theory. Management Science, 54, 208–216.

    Google Scholar 

  • Schmidt, U., & Zank, H. (2012). A genuine foundation for prospect theory. Journal of Risk and Uncertainty, 45, 97–113.

    Google Scholar 

  • Smith, K., Dickhaut, J. W., McCabe, K., & Pardo, J. V. (2002). Neuronal substrates for choice under ambiguity. Risk Certainty, Gains and Losses, Management Science, 48, 711–718.

    Google Scholar 

  • Starmer, C. (2000). Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature, 38, 332–382.

    Article  Google Scholar 

  • Starmer, C., & Sugden, R. (1989). Violations of the independence axiom in common ratio problems: An experimental test of some competing hypotheses. Annals of Operations Research, 19, 79–102.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.

    Article  Google Scholar 

  • Wakker, P. P. (2001). Testing and characterizing properties of nonadditive measures through violations of the sure-thing principle. Econometrica, 69, 1039–1059.

    Article  Google Scholar 

  • Wakker, P. P. (2003). The data of Levy and Levy (2002) prospect theory: Much ado about nothing? Actually Support Prospect Theory, Management Science, 49, 979–981.

    Article  Google Scholar 

  • Wakker, P. P. (2010). Prospect theory: For risk and ambiguity. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Wakker, P. P., Erev, I., & Weber, E. U. (1994). Comonotonic independence: The critical test between classical and rank-dependent utility theories. Journal of Risk and Uncertainty, 9, 195–230.

    Article  Google Scholar 

  • Wakker, P. P., & Tversky, A. (1993). An axiomatization of cumulative prospect theory. Journal of Risk and Uncertainty, 7, 147–176.

    Article  Google Scholar 

  • Weber, E. U., & Kirsner, B. (1997). Reasons for rank-dependent utility evaluation. Journal of Risk and Uncertainty, 14, 41–61.

    Article  Google Scholar 

  • Werner, K. M., & Zank, H. (2012). Foundations for prospect theory through probability midpoint consistency. Economics Discussion Paper 1210, University of Manchester, Manchester.

  • Wu, G., & Gonzalez, R. (1996). Curvature of the probability weighting function. Management Science, 42, 1676–1690.

    Article  Google Scholar 

  • Wu, G., & Markle, A. B. (2008). An empirical test of gain-loss separability in prospect theory. Management Science, 54, 1322–1335.

    Article  Google Scholar 

  • Zeisberger, S., Vrecko, D., & Langer, T. (2012). Measuring the time stability of prospect theory preferences. Theory and Decision, 72, 359–386.

    Article  Google Scholar 

Download references

Acknowledgments

Horst Zank acknowledges the financial support from the British Academy for the Research Grant SG-36804

Author information

Authors and Affiliations

  1. Wealth and Investment Management, Barclays, 1 Churchill Place, London E14 5HP, UK

    Peter Brooks

  2. Department of Economics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Simon Peters & Horst Zank

Authors
  1. Peter Brooks
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Horst Zank
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Horst Zank.

Additional information

Disclaimer: The views expressed in this article are those of the authors and should not be taken to represent the views of Barclays Bank PLC or any member of the Barclays Group. Whilst every care has been taken in the compilation of this information and every attempt made to present up-to-date information, we cannot guarantee that inaccuracies will not occur. The authors will not be held responsible for any claim, loss, damage or inconvenience caused as a result of any information within this article.

Appendices

Appendix 1:“Experiments on Individual Choice” instructions

(This experiment has been approved by the Senate Committee on the Ethics of Research on Human Beings of the University of Manchester)

Welcome to this session. The aim of this experiment is to investigate how people make decisions. We will ask you to make several decisions, and will record your choice. The records will be used for scientific purposes only. Our published results will not identify any individuals. Our general interest is to observe and analyse how people make decisions. We expect that 100 or more people will participate in this experiment.

This experiment is not a test. There is no way for us to tell whether your decisions are good or bad. That is for you to judge. People are different, and faced with the same situation they will prefer to take different courses of action. What you need to consider is the fact that the amount of money that you receive by participating in this experiment depends partly on your decisions, and partly on luck.

We will ask you to perform 105 tasks. Each task consists of choosing one of two gambles. An example of a task is described below:

figure a

After deciding which gamble to play by marking “left” or “right” and pressing the “Submit” button the next task appears.

Now we explain what a gamble is and how it is played. A complete gamble is visualised on the screen as 15 balls numbered consecutively from 1 to 15 with amounts of money underneath balls of the same colour. An example is the following gamble:

figure b

This is how a gamble is played: A bag contains all 15 balls. One ball is drawn at random. Each ball in the bag is equally likely to be drawn. The outcome of a gamble is the sum of money indicated underneath the drawn ball.

In the example above, the red balls indicate that £15.00 will be gained for a ball with the number 1, 2, 3, 4, 5 or 6 on it. The green balls indicate that £3.00 will be lost for a ball with the number 7, 8, or 9 on it. The blue balls indicate that £5.00 will be lost for a ball with the number 10, 11, or 12 on it. Finally, the orange balls indicate that £3.00 will be gained for a ball with the number 13, 14, or 15 on it.

In this experiment there will be several types of gambles. Three examples of gambles are explained below.

Gamble type 1 A bag contains 15 balls numbered from 1 to 15. One ball is drawn at random. Each ball in the bag is equally likely to be drawn. If the ball is numbered 1, 2, 3, 4, 5, or 6 the outcome is the amount of money indicated underneath those balls (therefore, there is a 40 % chance of getting that amount). If the ball is numbered 7, 8, 9, 10, 11, or 12, the outcome is the amount of money indicated underneath those balls (therefore, there is a 40 % chance of getting that amount). If the ball is numbered 13, 14, or 15, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). We represent this gamble with balls in the respective colours, as follows:

figure c

Gamble type 2 A bag contains 15 balls numbered from 1 to 15. One ball is drawn at random. Each ball in the bag is equally likely to be drawn. If the ball is numbered 1, 2, or 3, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). If the ball is numbered 4, 5, or 6, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). If the ball is numbered 7, 8, or 9, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). If the ball is numbered 10, 11, or 12, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). If the ball is numbered 13, 14, or 15, the outcome is the amount of money indicated underneath those balls (therefore, there is a 20 % chance of getting that amount). We represent this gamble with balls in the respective colours, as follows:

figure d

Gamble type 3 A bag contains 15 balls numbered from 1 to 15. One ball is drawn at random. Each ball in the bag is equally likely to be drawn. If the ball is numbered 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 the outcome is the amount of money indicated underneath those balls (therefore, there is a 100 % chance of getting that amount). We represent this gamble with balls in the respective colour, as follows:

figure e

Most gambles involve negative and positive amounts of money. In such a gamble one may lose some amount of money from the fixed payment (£17.00) that you receive if you complete all tasks. For example the gamble below indicates that you can either lose £15.00 with 20 % chance, or gain £2.00 with 20 % chance, or gain £10.00 with 40 % chance, or gain 15 with a 20 % chance.

figure f

If you complete all tasks, then you receive a participation fee of £17.00 plus an additional amount of money determined by your decision in one randomly selected task. The computer will select this gamble after all tasks have been completed, and we will play that gamble for real. The additional amount of money ranges from £\(-\) 15.00 to £15.00. Therefore the final sum of money that you receive will be a positive amount in the range of £2.00 and £32.00; it will never be negative.

There is enough time allocated for completing all tasks. You may withdraw from the experiment at any time. If you withdraw from the experiment, we will not be able to compensate you for your effort.

Take your time to make sure that you have understood everything. The window with these instructions will be accessible at all times. You may also ask the experimenters for help. Please do not use the “Back” button of your internet browser unless you are asked on the computer screen. Also, please do not distract (or talk to) other people taking part in the experiment. If you completed all tasks please remain seated and indicate to the experimenter that you have finished.

When you are ready to start with the tasks, press the “Proceed with the Experiment” link below, and then follow the instructions set by the computer.

Proceed with the experiment.

Appendix 2: “Experiments on Individual Choice” payment form

figure g

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brooks, P., Peters, S. & Zank, H. Risk behavior for gain, loss, and mixed prospects. Theory Decis 77, 153–182 (2014). https://doi.org/10.1007/s11238-013-9396-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11238-013-9396-x

Keywords

JEL Classification

Search

Navigation