Overconfidence due to preference for control
Notice bibliographique
Résumé
A large body of research suggests that people tend to be overconfident in their own abilities in a variety of domains. At the same time, people are reluctant to delegate decisions even when they understand that it would monetarily benefit them (Botti et al., 2004; Botti and McGill, 2006; Botti et al., 2009) and they take over tasks from others (Verrey, 2019) that then lead to suboptimal group outcomes. We propose that beliefs about own and others’ performance are motivated by intrinsic preferences for control (Owens et al., 2014). Specifically, in contexts where one can decide whether to rely on own performance or the performance of someone else, people want to choose the better performance and self-rely at the same time. When learning about potential performances, one could engage in motivated reasoning to get to the desired conclusion, with high certainty, that self-reliance is indeed the money maximizing choice. In an online experiment we test whether the described belief mechanism is indeed present. We let people work on a real-effort task with a piece rate scheme. To cleanly identify the relationship between delegation and beliefs about one’s own and others’ performance, agents don’t work directly for principals. Instead, we use their piece rate performance for later sessions. Additionally, principals only learn about the option to delegate after completing the task themselves. Therefore, delegation simply translates to being paid after another participant’s prior performance. Thus, the only instrumental information for the delegation decision is principals’ beliefs about their own and the agent’s prior performance. The treatment exogenously varies whether the principals know about the opportunity to delegate before or only after receiving noisy information about the agents’ prior performance. The rationale behind this manipulation is that when the principal is aware of the opportunity to delegate, he can engage in motivated reasoning (Kunda, 1990) when processing the noisy information about the agents’ performance. If his desired outcome is not to delegate, he potentially inflates his beliefs about his own and deflates his beliefs about the agent’s performance. That is, we predict that principals are relatively more optimistic about their own performance — compared to the agent’s performance — when they know about the opportunity to delegate ahead of time, at the same time, they are more reluctant to delegate. Primary research questions: • Are people more likely to end up with a posterior, believing they are better than their counterpart, when there is more opportunity to engage in motivated reasoning? • Are people more reluctant to delegate when there is more opportunity to engage in motivated reasoning? Auxiliary questions: • When learning about the performance of others, does the opportunity to engage in motivated reasoning make people revise their beliefs about their own performance more frequently? • Can payoff considerations fully explain delegation decisions?
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,017 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,068 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».