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on Cognitive and Behavioural Economics |
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Issue of 2025–03–24
five papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Chakraborty, Anujit (University of California, Davis); Henkel, Luca (Erasmus University Rotterdam) |
| Abstract: | In prosocial decisions, decision-makers face interpersonal uncertainty–uncertainty about how their choices impact others' utility. We use three approaches to show how it shapes classic patterns of prosocial behavior like ingroup favoritism, merit-based fairness, and self-favoring behavior. First, we compare standard allocation decisions with decisions where we remove social consequences but retain uncertainty, revealing strikingly similar patterns across both. Second, we exogenously vary interpersonal uncertainty to estimate the aversion to interpersonal uncertainty and quantify how it combines with preferences to determine prosocial decisions. Finally, we show that self-reported interpersonal uncertainty systematic ally predicts behavior across individuals, choice patterns, and behavioral interventions. |
| Keywords: | prosocial behavior, decision-making under uncertainty, interpersonal uncertainty, ingroup favoritism, merit-based fairness, self-favoring behavior |
| JEL: | C91 D01 D91 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17708 |
| By: | Benjamin Pitt (IAST - Institute for Advanced Study in Toulouse) |
| Abstract: | Spatial cognition is fundamental to human behavior, but people differ in how they remember spatial relations, variably using body-based (egocentric) and environment-based (allocentric) spatial reference frames. Despite decades of study, the causes of this variation and flexibility in spatial memory remain unclear. Here we show that people spontaneously use different reference frames on different spatial axes at the same time. When remembering the placement of a target object in a 2-dimensional array, Indigenous Tsimane' adults preferentially used allocentric space to determine lateral placement and egocentric space to determine sagittal placement in the same action. This effect of axis was also significant among US university students, whose overall preference for egocentric space was stronger on the sagittal than lateral axis. These findings support a novel account of spatial cognitive diversity and suggest that people across cultures habitually integrate egocentric and allocentric spatial reference frames into the same action. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04939902 |
| By: | Kevin He; Ran Shorrer; Mengjia Xia |
| Abstract: | We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI's choices and human choices. In every problem, human subjects' average prediction about GenAI's choice is substantially closer to the average human-subject choice than it is to the GenAI choice. At the individual level, different subjects' predictions about GenAI's choice in a given problem are highly correlated with their own choices in the same problem. We explore the implications of people overestimating GenAI alignment in a simple theoretical model. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14708 |
| By: | Hein, Ilka; Cecil, Julia (Ludwig-Maximilians-Universität München); Lermer, Eva (LMU Munich) |
| Abstract: | Artificial intelligence (AI) is increasingly taking over leadership tasks in companies, including the provision of feedback. However, the effect of AI-driven feedback on employees and its theoretical foundations are poorly understood. We aimed to close this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. Using these theories, our objective was also to investigate the moderating role of feedback valence and the mediating effect of social distance. A 2 × 2 between-subjects design was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive) via vignettes. In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from NS1 = 263 and NS2 = 449 participants were studied who completed a German online questionnaire asking for feedback acceptance, performance motivation, social distance, acceptance of the feedback source itself, and intention to seek further feedback. Regression analyses showed that AI feedback was rated as less accurate and led to lower performance motivation, acceptance of the feedback provider, and intention to seek further feedback. These effects were mediated by perceived social distance. Moreover, for feedback acceptance and performance motivation, the differences were only found for positive but not for negative feedback in the first study. This implies that AI feedback may not inherently be perceived as more negatively than human feedback as it depends on the feedback's valence. Furthermore, the mediation effects indicate that the shown negative evaluations of the AI can be explained by higher social distance and that increased social closeness to feedback providers may improve appraisals of them and of their feedback. Theoretical contributions of the studies and implications for the use of AI for providing feedback in the workplace are discussed, emphasizing the influence of effects related to construal level theory. |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:uczaw_v1 |
| By: | Davide Carpentiere; Angelo Petralia |
| Abstract: | We show that many models of choice can be alternatively represented as special cases of choice with limited attention (Masatlioglu, Nakajima, and Ozbay, 2012), and the properties of the unobserved attention filters that explain the observed choices are singled out. Moreover, for each specification, we infer information about the DM's attention and preference from irrational features of choice data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14879 |