The impact of rewarding medium effort and the role of sample size

Erev, Ido; Freedman, Gail Gilboa; Roth, Yefim

doi:10.1002/bdm.2125

Cited by 8 publications

(9 citation statements)

References 29 publications

(50 reference statements)

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“…In addition, our results demonstrate that enforcement can be effective even if it does not use harsh punishments and does not reduce the expected return from reckless choices. When responsible behavior implies an efficient Nash equilibrium (the environment examined here), it is enough to ensure that the enforcement increases the probability that responsible behavior leads to the best possible payoffs toward 1 (to 0.95 in the current study, see similar observation in Erev et al, 2019).…”

Section: Discussionsupporting

confidence: 53%

On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

et al. 2020

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The COVID-19 pandemic poses a major challenge to policy makers on how to encourage compliance to social distancing and personal protection rules. This paper compares the effectiveness of two policies that aim to increase the frequency of responsible health behavior using smartphone-tracking applications. The first involves enhanced alert capabilities, which remove social externalities and protect the users from others’ reckless behavior. The second adds a rule enforcement mechanism that reduces the users’ benefit from reckless behavior. Both strategies should be effective if agents are expected-value maximizers, risk averse, and behave in accordance with cumulative prospect theory (Tversky and Kahneman, 1992) or in accordance with the Cognitive Hierarchy model (Camerer et al., 2004). A multi-player trust-game experiment was designed to compare the effectiveness of the two policies. The results reveal a substantial advantage to the enforcement application, even one with occasional misses. The enhanced-alert strategy was completely ineffective. The findings align with the small samples hypothesis, suggesting that decision makers tend to select the options that lead to the best payoff in a small sample of similar past experiences. In the current context, the tendency to rely on a small sample appears to be more consequential than other deviations from rational choice.

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Section: Discussionsupporting

confidence: 53%

On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

et al. 2020

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“…The current research also makes a theoretical contribution—extending the two‐sample model developed in Roth et al (see also Erev, Gilboa Freedman, & Roth, 2019) to multiple alternatives. Roth et al proposed the two‐sample model as a generalization of the basic one‐sample “reliance on small samples” model.…”

Section: Introductionmentioning

confidence: 75%

“…Interestingly, the best fit of the data is obtained with a much smaller sample size ( k = 1 or 2) than in other decisions‐from‐experience studies ( k = 9; Erev & Haruvy, 2016; Erev & Roth, 2014; Yakobi, Cohen, Naveh, & Erev, 2020). The smaller sample size was also found to best capture learning from the experience of others (Erev et al, 2019). Why this is so demands further investigation.…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

The decision to check in multialternative choices and limited sensitivity to default

Roth

2020

Behavioral Decision Making

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Given the rapid proliferation of smartphone applications and data aggregation websites, in many situations people can use decision aids to guide their choices. For example, they may consider whether to use a navigation device to check the fastest route or whether to use a price comparison website to find the cheapest deal. In what circumstances will subjects use a costly comparison decision aid (which I refer to as "checking") to choose for them? In six studies, I investigate the impact of the number of available alternatives and checking's attractiveness on the decision to check. While at first increasing the attractiveness of checking led to higher checking rates, a further increase in the number of available alternatives (and thus checking's attractiveness) did not increase the checking rate. Surprisingly, even when checking had a 40% higher expected value compared with not checking, the observed checking rate was below 45%, contrary to risk and ambiguity aversion predictions. Furthermore, labeling the checking alternative as the default had no impact on its choice rate. I find large individual differences in decisions to check. Surprisingly, subjects' initial decisions had high predictive power over their subsequent checking rates, even after 100 trials with full feedback. I propose two simple learning models that capture well the aggregated results. K E Y W O R D S checking decisions, decisions from experience, inertia, information search, two-sample 1 | INTRODUCTION "Comparison is the death of joy." Mark Twain Imagine that you want to book accommodation in a foreign city. Even a small city may have scores of different hotels, not to mention hundreds of other alternatives, such as apartments for rent through Airbnb. These alternatives may differ in many dimensions-price, location, cleanliness, etc. Today's consumers constantly face choice decisions between a varying number of alternatives: which road to drive to work, where to book a room for their next vacation, or which breakfast cereal to choose. To help them resolve these decisions, consumers also have access to an exploding number of decision aids, such as price and quality comparison websites and apps. The present paper asks how consumers make decisions in this environment, and in particular, under what circumstances they use available decision aids. An abundant literature examines how consumers choose between products in multidimensional (multiattribute) settings, going back to the famous MouseLab studies (e.g., Payne, 1976; Payne, Bettman, & Johnson, 1988). The general findings of these studies show that people often make suboptimal decisions, whether because of confusion, required cognitive effort or task complexity (Stark & Choplin, 2009). More recent studies propose additional factors behind consumers' suboptimal decisions, such as time constraints (Dhar & Nowlis, 1999),

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“…The attentive participants had similar checking rates as the inattentive participants (0.31 (SD = 0.32) and 0.29 (SD = 0.31), respectively). The relatively high checking rate indicated that participants were more sensitive to the typical outcome than to the high magnitude loss (e.g., Erev, Gilboa Freedman, & Roth, 2019). The learning curves (Figure 6, right panel) showed a similar pattern to Study 4A.…”

Section: Resultsmentioning

confidence: 62%

Attention Checks in Decisions From Experience: The Case of Checking Experiments

Roth¹,

Yakobi²

2022

Preprint

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Can online participants be compared to laboratory subjects. In the five studies (Ntotal = 1519) reported in this article, we comprehensively compared the behavior of online attentive and inattentive participants; i.e., those who passed or failed a simple attention check. The findings show that in a decisions from experience paradigm (i.e., a multi-trial repeated choice task), even one simple attention check is sufficient to differentiate between attentive and inattentive participants. The validity of this separation is further evidenced in the significant difference in the reported conscientiousness measure.The results fully replicated three previously run lab studies for the attentive participants, but not for the inattentive participants. This highlights the importance of using attention checks to avoid spurious conclusions. There are three explanations for the behavioral differences between the attentive and inattentive participants. Attentive participants exhibited a pre-disposition towards checking. They also had a greater likelihood of noticing distinct outcomes. Finally, the inattentive participants tended to behave more randomly. When checking was beneficial, these three effects pointed in the same direction, leading to a major difference between participants. When checking was detrimental, these effects cancelled each other out, resulting in similar checking rates and indistinguishable behavior.

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The impact of rewarding medium effort and the role of sample size

Cited by 8 publications

References 29 publications

On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics

The decision to check in multialternative choices and limited sensitivity to default

Attention Checks in Decisions From Experience: The Case of Checking Experiments

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