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on Cognitive and Behavioural Economics |
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Issue of 2023‒08‒21
seven papers chosen by Marco Novarese Università degli Studi del Piemonte Orientale |
| By: | Marta Serra-Garcia; Uri Gneezy |
| Abstract: | Can algorithms help people predict behavior in high-stakes prisoner’s dilemmas? Participants watching the pre-play communication of contestants in the TV show Golden Balls display a limited ability to predict contestants’ behavior, while algorithms do significantly better. We provide participants algorithmic advice by flagging videos for which an algorithm predicts a high likelihood of cooperation or defection. We find that the effectiveness of flags depends on their timing: participants rely significantly more on flags shown before they watch the videos than flags shown after they watch them. These findings show that the timing of algorithmic feedback is key for its adoption. |
| Keywords: | detecting lies, machine learning, cooperation, experiment |
| JEL: | D83 D91 C72 C91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10518&r=cbe |
| By: | Koukoumelis, Anastasios; Levati, Maria Vittoria Prof. (University of Verona); Nardi, Chiara |
| Abstract: | Many socially desirable actions are subject to risk and occur in situations where the others are not anonymous. Assessing whether lower subject-subject anonymity affects behavior when outcomes are risky is likely important but has not been studied in depth so far. Herein, we provide evidence on this issue. In a series of allocation tasks, all of them variations of the dictator game, we systematically vary the party who is exposed to risk and manipulate recipient anonymity by reducing the social and/or moral distance between the two parties. We propose a model that extends previous work by allowing not only for ex ante and ex post fairness but also for altruism. The model is consistent with observed behavior. In particular, a reduction in social and moral distance significantly increases the likelihood of equal split and more than equal split choices. |
| Date: | 2023–07–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:d8y4r&r=cbe |
| By: | Johannes Abeler; David Huffman; Collin Raymond; David B. Huffman |
| Abstract: | Using field and laboratory experiments, we demonstrate that the complexity of incentive schemes and worker bounded rationality can affect effort provision, by shrouding attributes of the incentives. In our setting, complexity leads workers to over-provide effort relative to a fully rational benchmark, and improves efficiency. We identify contract features, and facets of worker cognitive ability, that matter for shrouding. We find that even relatively small degrees of shrouding can cause large shifts in behavior. Our results illustrate important implications of complexity for designing and regulating workplace incentive contracts. |
| Keywords: | complexity, bounded rationality, shrouded attribute, Ratchet effect, dynamic incentives, field experiments |
| JEL: | D80 D90 J20 J30 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10541&r=cbe |
| By: | Roland Bénabou; Armin Falk; Luca Henkel; Jean Tirole |
| Abstract: | We analyze how the impact of image motives on behavior varies with two key features of the choice mechanism: single versus multiple decisions, and certainty versus uncertainty of consequences. Using direct elicitation (DE) versus multiple-price-list (MPL) or equivalently Becker-DeGroot-Marschak (BDM) schemes as exemplars, we characterize how image-seeking inflates prosocial giving. The signaling bias (relative to true preferences) is shown to depend on the interaction between elicitation method and visibility level: it is greater under DE for low image concerns, and greater under MPL/BDM for high ones. We experimentally test the model’s predictions and find the predicted crossing effect. |
| Keywords: | Moral behavior, deontology, utilitarianism, consequentialism, social image, self-image, norms, preference elicitation, multiple price list, experiments |
| JEL: | C91 D01 D62 D64 D78 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2023_441&r=cbe |
| By: | Yoram Halevy; David Walker-Jones; Lanny Zrill |
| Abstract: | We investigate the problem of identifying incomplete preferences in the domain of uncertainty by proposing an incentive-compatible mechanism that bounds the behavior that can be rationalized by very general classes of complete preferences. Hence, choices that do not abide by the bounds indicate that the decision maker cannot rank the alternatives. Data collected from an experiment that implements the proposed mechanism indicates that when choices cannot be rationalized by Subjective Expected Utility they are usually incompatible with general models of complete preferences. Moreover, behavior that is indicative of incomplete preferences is empirically associated with deliberate randomization. |
| Keywords: | Incomplete Preferences, Identification, Elicitation, Choice Under Uncertainty, Deliberate Randomization, Experiment |
| JEL: | C91 D01 D81 D9 |
| Date: | 2023–07–25 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-753&r=cbe |
| By: | Bilancini, Ennio; Boncinelli, Leonardo; Nardi, Chiara; Pizziol, Veronica |
| Abstract: | We study how cooperation in one-shot Public Goods Games with large group sizes is affected by the presence of a slight chance of severe adverse events. We find that cooperation is substantial, notwithstanding a low marginal return of contributions. The cooperation level is comparable to what is found in similar settings for small-sized groups. Furthermore, we find no appreciable effect of the threat of severe adverse events, whether their realization is independent across individuals, perfectly positively or negatively correlated. We conclude that cooperation in the Public Goods Game is unlikely to be affected by rare adverse events, independently of how risk is correlated across individuals. |
| Date: | 2023–07–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:yrt63&r=cbe |
| By: | Jacob K Goeree; Bernardo Garcia-Pola |
| Abstract: | $S$ equilibrium synthesizes a century of game-theoretic modeling. $S$-beliefs determine choices as in the refinement literature and level-$k$, without anchoring on Nash equilibrium or imposing ad hoc belief formation. $S$-choices allow for mistakes as in QRE, without imposing rational expectations. $S$ equilibrium is explicitly set-valued to avoid the common practice of selecting the best prediction from an implicitly defined set of unknown, and unaccounted for, size. $S$-equilibrium sets vary with a complexity parameter, offering a trade-off between accuracy and precision unlike in $M$ equilibrium. Simple "areametrics" determine the model's parameter and show that choice sets with a relative size of 5 percent capture 58 percent percent of the data. Goodness-of-fit tests applied to data from a broad array of experimental games confirm $S$ equilibrium's ability to predict behavior in and out of sample. In contrast, choice (belief) predictions of level-$k$ and QRE are rejected in most (all) games. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.06309&r=cbe |