Choices, values, and frames.

Kahneman, Daniel; Tversky, Amos

doi:10.1037/0003-066x.39.4.341

Cited by 5,913 publications

(3,568 citation statements)

References 12 publications

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“…This contradicts our finding that voters acquire less information than standard game theory predicts. "Loss aversion" (Kahneman and Tversky 1983) also fails to account for our findings. While loss averse voters would be less willing than rational voters to acquire costly information, as we observe, such voters would not be willing to vote if uninformed.…”

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confidence: 50%

“…There is also a natural alternative explanation for the under-acquisition of information in our experiment: loss aversion, as introduced by Kahneman and Tversky (1983). Loss averse voters would be less inclined to pay for information, since the cost of information is a certain loss in case the voter is not decisive, or even worse, of the imperfect signal the voter buys is incorrect and the voter is decisive.…”

Section: Final Remarksmentioning

confidence: 99%

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Ignorance and bias in collective decisions

Elbittar

Gomberg

Martinelli

et al. 2020

Journal of Economic Behavior & Organization

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ABSTRACT. We study theoretically and experimentally a committee with common interests. Committee members do not know which of two alternatives is the best, but each member can acquire privately a costly signal before casting a vote under either majority or unanimity rule. In the experiment, as predicted by Bayesian equilibrium, voters are more likely to acquire information under majority rule, and attempt to counter the bias in favor of one alternative under unanimity rule. As opposed to Bayesian equilibrium predictions, however, many committee members vote when uninformed. Moreover, uninformed voting is strongly associated with a lower propensity to acquire information. We show that an equilibrium model of subjective prior beliefs can account for both these phenomena, and provides a good overall fit to the observed patterns of behavior both in terms of rational ignorance and biases. On trouve de plus, que si la probabilité de la voix de chaque Votant est plus grande que 1 2 , c'est-à-dire, s'il est plus probable qu'il jugera conformément à la vérité, plus le nombre des Votans augmentera, plus la probabilité de la vérité de la décision sera grande : la limite de cette probabilité sera la certitude [. . . ] Une assemblée très-nombreuse ne peut pas être composée d'hommes très-éclairés; il est même vraisemblable que ceux qui la forment joindront sur bien des objets beaucoup d'ignorance à beaucoup de préjugés. Condorcet (1785) [1986, p. 29-30]

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confidence: 50%

Section: Final Remarksmentioning

confidence: 99%

Ignorance and bias in collective decisions

Elbittar

Gomberg

Martinelli

et al. 2020

Journal of Economic Behavior & Organization

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“…8 Notwithstanding the foregoing appeal to tone down claims of irrationality, framing effects do undermine the normative status of discounting models, because economic analysis is intended to apply to consequences, not descriptions. This general principle of descriptive invariance has been widely discussed as a fundamental, if often tacit, requirement for rationality (e.g., Arrow, 1982;Kahneman & Tversky, 1984;Wakker, 2010). Descriptive invariance is assumed not just in rational choice models, but also in many psychological models of intertemporal choice, including Ainslie's (1975) account of hyperbolic discounting, Loewenstein and Prelec's (1992) generalized hyperbolic discounting model, Scholten and Read's (2006) interval discounting model, Killeen's (2009) additive-utility model, and many others.…”

Section: Discussionmentioning

confidence: 99%

DRIFT: An analysis of outcome framing in intertemporal choice.

Read¹,

Frederick²,

Scholten³

2013

Journal of Experimental Psychology: Learning, Memory, and Cogni

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People prefer to receive good outcomes immediately rather than wait, and they must be compensated for waiting. But what influences their decision about how much compensation is required for a given wait? To give a partial answer to this question, we develop the DRIFT model, a heuristic description of how framing influences intertemporal choice. We describe 4 experiments showing the implications of this model. In the experiments, we vary how the difference between a smaller sooner outcome and a larger later outcome is framed-either as total interest earned, as an interest rate, or as total amount earned (the conventional frame in studies of intertemporal choice)-and whether the larger later outcome is described as resulting from the investment of the smaller sooner one. These alternate frames have several effects. First, the investment language increases patience. Second, the explicit provision of the (otherwise implicit) experimental interest rate sharply reduces the magnitude effect. Correspondingly, we find that interest frames increase patience when the rewards are small, but they decrease patience when they are large. Third, the interest-rate frame induces somewhat greater discounting for longer time periods and, thus, reverses the common finding of "hyperbolic" discounting. Thus, many of the "stylized facts" implied by studies involving choices between a smaller sooner and a larger later amount are eliminated or reverse under alternate outcome frames.Keywords: intertemporal choice, delay discounting, framing, choice modelling Any interesting choice involves tradeoffs. For intertemporal choices, the tradeoffs involve time and amount-smaller sooner rewards compared with against larger later rewards. For some, the perfect real world example may be the decision of whether to endure lower (or no) wages during college so as to permit greater earnings later.The dominant method for investigating intertemporal choices in the lab has been to elicit choices between smaller-sooner and larger-later amounts of money (see, e.g., Ainslie & Haendel, 1983;Hardisty & Weber, 2009;Kirby, 1997;Read, 2001;Sayman & Öncüler, 2009;Weber et al., 2007;Zauberman, Kim, Malkoc, & Bettmann, 2009). Most of this research has focused on how discount rates are influenced by differences in the magnitude or timing of those outcomes (e.g., Benzion, Rapoport, & Yagil, 1989;Keren & Roelofsma, 1995;Kirby & Herrnstein, 1995;Scholten & Read, 2006;Thaler, 1981). A second research stream has focused on how these choices correlate with other individual differences, such as smoking behavior (Baker, Johnson, & Bickel, 2003;Chabris, Laibson, Morris, Schuldt, & Taubinsky, 2008) or demographic characteristics (Frederick, 2005). A third focus is on the influence of ephemeral states, such as whether participants have been aroused by viewing women in bikinis ( Van den Bergh, Dewitte, & Warlop, 2008) or by the sight or scent of freshly baked cookies (Li, 2008).The present article falls into a fourth stream of research that examines whether, and how, discounting ...

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“…On the other hand, behavioural finance/descriptive theories challenge the rational behaviour assumption and assume that individuals are generally not rational and can involve 'behavioural biases or cognitive errors' in their actual decisionmaking (de Dreu & Bikker 2012, p. 2146. Behavioural finance has gained more attention with prominent theories such as Prospect theory (Kahneman & Tversky 1979, 1984 in which individuals are reported to view gains and losses differently and their risk tolerance is found to be associated with how the problem is framed (i.e., problem framing). This behavioural perspective is adopted in this study because a large portion of clients in the advice context are not sophisticated investors (i.e.…”

Section: The Risk Tolerance/asset Allocation Decision Frameworkmentioning

confidence: 99%

The Influence of Financial Risk Tolerance on Investment Decision-Making in a Financial Advice Context

Nguyen¹,

Gallery²,

Newton³

2016

AABFJ

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Client risk tolerance is universally assessed in the advisory process to help financial advisers provide suitable advice that assists clients in their investment decision-making. Although there is a well-established literature on risk tolerance and decision-making, little is known about financial risk tolerance and its influence on investor decisions in the financial advice context. Thus, the purpose of this study is to examine this influence with a focus on the key expected risk tolerance determinants: client financial literacy, trust in the financial advice service, and relationship length with the service. A new theoretical model and related hypotheses were proposed and tested using survey data from financial adviser clients in Australia (N=538). Results revealed a positive relationship between client risk tolerance and investment decision-making. Further, client trust and relationship length with the service were found to be positively associated with client financial literacy and risk tolerance. These findings, which provide a more comprehensive understanding of how risk tolerance and its antecedents influence client decisions, have the potential to improve advice in the financial services industry. KeywordsRisk tolerance, individual investment decisions, client-adviser relationship, financial advice context, financial planning AbstractClient risk tolerance is universally assessed in the advisory process to help financial advisers provide suitable advice that assists clients in their investment decision-making. Although there is a well-established literature on risk tolerance and decision-making, little is known about financial risk tolerance and its influence on investor decisions in the financial advice context. Thus, the purpose of this study is to examine this influence with a focus on the key expected risk tolerance determinants: client financial literacy, trust in the financial advice service, and relationship length with the service. A new theoretical model and related hypotheses were proposed and tested using survey data from financial adviser clients in Australia (N=538). Results revealed a positive relationship between client risk tolerance and investment decisionmaking. Further, client trust and relationship length with the service were found to be positively associated with client financial literacy and risk tolerance. These findings, which provide a more comprehensive understanding of how risk tolerance and its antecedents influence client decisions, have the potential to improve advice in the financial services industry. JEL Classification: D14

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Choices, values, and frames.

Cited by 5,913 publications

References 12 publications

Ignorance and bias in collective decisions

Ignorance and bias in collective decisions

DRIFT: An analysis of outcome framing in intertemporal choice.

The Influence of Financial Risk Tolerance on Investment Decision-Making in a Financial Advice Context

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