Effects of feedback and complexity on repeated decisions from description

Lejarraga, Tomás; González, Cleotilde

doi:10.1016/j.obhdp.2011.05.001

Cited by 120 publications

(132 citation statements)

References 33 publications

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“…An experiment that evaluates (and rejects) this "expected repetitions" hypothesis is presented in the section Control Conditions and Robustness Checks below. Figure 3 also shows that the emergence of underweighting of rare events in decisions with feedback is robust (Barron & Erev, 2003;Lejarraga & Gonzalez, 2011;and see Row 11 in Table 1). Experience reduces sensitivity to the rare event in all five problems.…”

Section: The Reflection and Reversed Reflection Effects A Comparisonmentioning

confidence: 82%

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

Erev¹,

Ert

Plonsky³

et al. 2017

Psychological Review

208

258

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Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization. For example, people tend to overweight rare events in oneshot decisions under risk, and to exhibit the opposite bias when they rely on past experience.The common explanations of these results assume that the contradicting anomalies reflect situation-specific processes that involve the weighting of subjective values and the use of simple heuristics. The current paper analyzes 14 choice anomalies that have been described by different models, including the Allais, St. Petersburg, and Ellsberg paradoxes, and the reflection effect. Next, it uses a choice prediction competition methodology to clarify the interaction between the different anomalies. It focuses on decisions under risk (known payoff distributions) and under ambiguity (unknown probabilities), with and without feedback concerning the outcomes of past choices. The results demonstrate that it is not necessary to assume situation-specific processes. The distinct anomalies can be captured by assuming high sensitivity to the expected return and four additional tendencies: pessimism, bias toward equal weighting, sensitivity to payoff sign, and an effort to minimize the probability of immediate regret. Importantly, feedback increases sensitivity to probability of regret. Simple abstractions of these assumptions, variants of the model Best Estimate And Sampling Tools (BEAST), allow surprisingly accurate ex ante predictions of behavior. Unlike the popular models, BEAST does not assume subjective weighting functions or cognitive shortcuts.Rather, it assumes the use of sampling tools and reliance on small samples, in addition to the estimation of the expected values.

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Section: The Reflection and Reversed Reflection Effects A Comparisonmentioning

confidence: 82%

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

Erev¹,

Ert

Plonsky³

et al. 2017

Psychological Review

208

258

View full text Add to dashboard Cite

show abstract

“…For example, recent studies in experiencebased decision-making provided participants with descriptions about the underlying distributions that generate rewards (e.g., Lejarraga & Gonzalez, 2011;Weiss-Cohen, Konstantinidis, Speekenbrink, & Harvey, 2016). Just as in the CMAB, this presents a naturalistic decision environment in which different sources of information (e.g., descriptions and participants' own experience) need to be integrated in order to choose between alternatives or courses of action.…”

Section: Contextual Multi-armed Banditsmentioning

confidence: 99%

Putting bandits into context: How function learning supports decision making.

Schulz¹,

Konstantinidis

Speekenbrink³

2018

Journal of Experimental Psychology: Learning, Memory, and Cogni

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We introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximise their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, we explore participants' behaviour in such learning environments. We predict participants' behaviour by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behaviour is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a "probability of improvement" decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favourite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is non-linear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies.

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“…This task made decisions from description procedurally more comparable to decisions from experience. There is evidence, however, that choices in repeated decisions from description can differ from choices where only a single choice is made (Barron & Erev, 2003;Keren & Wagenaar, 1987;Lejarraga & Gonzalez, 2011;Lopes, 1981). To determine whether the first described choice was consistent with subsequent described choices, the data were re-analyzed using only the first choices that participants made for the gain and loss description decisions.…”

Section: Appendixmentioning

confidence: 99%

The Role of Memory in Distinguishing Risky Decisions from Experience and Description

Madan

Ludvig

2017

Quarterly Journal of Experimental Psychology

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Described and experienced risky choices 2 AbstractPeople's risk preferences differ for choices based on described probabilities versus those based on information learned through experience. For decisions from description, people are typically more risk averse for gains than losses. In contrast, for decisions from experience, people are sometimes more risk seeking for gains than losses, especially for choices with the possibility of extreme outcomes (big wins or big losses), which are systematically overweighed in memory.Using a within-subject design, this study evaluated whether this memory bias plays a role in the differences in risky choice between description and experience. As in previous studies, people were more risk seeking for losses than for gains in description but showed the opposite pattern in experience. People also more readily remembered the extreme outcomes and judged them as having occurred more frequently. These memory biases correlated with risk preferences in decisions from experience but not in decisions from description. These results suggest that systematic memory biases may be responsible for some of the differences in risk preference across description and experience.

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Effects of feedback and complexity on repeated decisions from description

Cited by 120 publications

References 33 publications

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

Putting bandits into context: How function learning supports decision making.

The Role of Memory in Distinguishing Risky Decisions from Experience and Description

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