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on Cognitive and Behavioural Economics |
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Issue of 2023‒01‒30
eight papers chosen by Marco Novarese Università degli Studi del Piemonte Orientale |
| By: | Dietmar Fehr; Dorothea Kübler |
| Abstract: | We study the endowment effect and expectation-based reference points in the field leveraging the setup of the Socio-Economic Panel. Households receive a small item for taking part in the panel, and we randomly assign respondents either a towel or a notebook, which they can exchange at the end of the interview. We observe a trading rate of 32 percent, consistent with an endowment effect, but no relationship with loss aversion. Manipulating expectations of the exchange opportunity, we find no support for expectation-based reference points. However, trading predicts residential mobility and is related to stock-market participation, i.e., economic decisions that entail parting with existing resources. |
| Keywords: | exchange asymmetry, reference-dependent preferences, loss aversion, field experiment, SOEP |
| JEL: | C93 D84 D91 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10150&r=cbe |
| By: | Christoph Kuzmics (University of Graz, Austria); Brian W. Rogers (Washington University in St. Louis, U.S.A.); Xiannong Zhang (Washington University in St. Louis, U.S.A.) |
| Abstract: | We design and implement lab experiments to evaluate the normative appeal of behavior arising from models of ambiguity-averse preferences. We report two main empirical findings. First, we demonstrate that behavior reflects an incomplete understanding of the problem, providing evidence that subjects do not act on the basis of preferences alone. Second, additional clarification of the decision making environment pushes subjects’ choices in the direction of ambiguity aversion models, regardless of whether or not the choices are also consistent with subjective expected utility, supporting the position that subjects find such behavior normatively appealing. |
| Keywords: | Knightian uncertainty; subjective expected utility; ambiguity aversion; lab experiment. |
| JEL: | C91 D81 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2023-01&r=cbe |
| By: | Tim Johnson; Nick Obradovich |
| Abstract: | Scientists and philosophers have debated whether humans can trust advanced artificial intelligence (AI) agents to respect humanity's best interests. Yet what about the reverse? Will advanced AI agents trust humans? Gauging an AI agent's trust in humans is challenging because--absent costs for dishonesty--such agents might respond falsely about their trust in humans. Here we present a method for incentivizing machine decisions without altering an AI agent's underlying algorithms or goal orientation. In two separate experiments, we then employ this method in hundreds of trust games between an AI agent (a Large Language Model (LLM) from OpenAI) and a human experimenter (author TJ). In our first experiment, we find that the AI agent decides to trust humans at higher rates when facing actual incentives than when making hypothetical decisions. Our second experiment replicates and extends these findings by automating game play and by homogenizing question wording. We again observe higher rates of trust when the AI agent faces real incentives. Across both experiments, the AI agent's trust decisions appear unrelated to the magnitude of stakes. Furthermore, to address the possibility that the AI agent's trust decisions reflect a preference for uncertainty, the experiments include two conditions that present the AI agent with a non-social decision task that provides the opportunity to choose a certain or uncertain option; in those conditions, the AI agent consistently chooses the certain option. Our experiments suggest that one of the most advanced AI language models to date alters its social behavior in response to incentives and displays behavior consistent with trust toward a human interlocutor when incentivized. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.13371&r=cbe |
| By: | Margarita Leib; Nils K\"obis; Rainer Michael Rilke; Marloes Hagens; Bernd Irlenbusch |
| Abstract: | Artificial Intelligence (AI) increasingly becomes an indispensable advisor. New ethical concerns arise if AI persuades people to behave dishonestly. In an experiment, we study how AI advice (generated by a Natural-Language-Processing algorithm) affects (dis)honesty, compare it to equivalent human advice, and test whether transparency about advice source matters. We find that dishonesty-promoting advice increases dishonesty, whereas honesty-promoting advice does not increase honesty. This is the case for both AI- and human advice. Algorithmic transparency, a commonly proposed policy to mitigate AI risks, does not affect behaviour. The findings mark the first steps towards managing AI advice responsibly. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.01954&r=cbe |
| By: | Marco Aschenwald; Armando Holzknecht; Michael Kirchler; Michael Razen |
| Abstract: | Building on cross-sectional data for Austrian high school students from fifth to twelfth grade, we investigate the correlations between socio-economic background variables and a comprehensive set of variables related to financial decision-making (i.e., financial knowledge, behavioral consistency, economic preferences, field behavior, and perception of financial matters). We confirm the findings of previous literature that the male gender is positively associated with financial knowledge and risk-taking and that there is a strong and beneficial correlation between math grades and healthy financial behavior (e.g., saving). Moreover, we find that students’ behavioral consistency is positively correlated with measures of financial attitude (e.g., self-assessed future financial well-being and financial education received from parents). Finally, our results indicate that financial education, as perceived by the students, is primarily provided by parents. |
| Keywords: | financial literacy, behavioral biases, economic preferences, field behavior, perception, experiment, adolescents |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2023-01&r=cbe |
| By: | Adam Zylbersztejn (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - Université de Lyon - CNRS - Centre National de la Recherche Scientifique); Zakaria Babutsidze (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Nobuyuki Hanaki (Osaka University [Osaka]) |
| Abstract: | We contribute to the ongoing debate in the psychological literature on the role of thin slices of observable information in predicting others' social behavior, and its generalizability to cross-cultural interactions. We experimentally assess the degree to which subjects, drawn from culturally dierent populations (France and Japan), are able to predict strangers' trustworthiness based on a set of visual stimuli (mugshot pictures, neutral videos, loaded videos, all recorded in an additional French sample) under varying cultural distance to the target agent in the recording. Our main nding is that cultural distance is not detrimental for predicting trustworthiness in strangers, but that it may aect the perception of dierent components of communication in social interactions. |
| Keywords: | Trustworthiness, communication, hidden action game, cross-cultural comparison, laboratory experiment |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:hal-03432600&r=cbe |
| By: | Eric Schniter (Economic Science Institute, Chapman University); Timothy W. Shields (Economic Science Institute, Chapman University) |
| Abstract: | People’s appearance and behaviors in strategic interactions provide a variety of informative clues that can help people accurately predict beliefs, intentions, and future behaviors. Mind reading mechanisms may have been selected for that allow for better-than-chance prediction of others’ strategic social propensities based on the sparse information available when forming first and second impressions. We hypothesize that first impressions are based on prior beliefs and available information gleaned from another’s description and appearance. For example, where another’s gender is identified, prior gender stereotypes could influence expectations and correct guesses about them. We also hypothesize that mind reading mechanisms use second impressions to predict behavior: using new knowledge of past behaviors to predict future behavior. For example, knowledge of the last round behaviors in a repeated strategic interaction should improve the accuracy of guesses about the next round behavior. We conducted a two-part study to test our predictive mind reading hypotheses and to evaluate evidence of accurate cheater and cooperator detection. First, across multiple rounds of play between matched partners, we recorded thin slice videos of university students just prior to their choices in a repeated Prisoner’s Dilemma. Subsequently, a worldwide sample of raters recruited online evaluated either thin-slice videos, photo stills from the videos, no images with gender labeled, or no images with gender blinded for each target. Raters guessed players’ Prisoner’s Dilemma choices in the first round, and, again, in the second round after viewing first round behavior histories. Indicative of mindreading: in all treatments where targets are seen, or their gender is labeled, or their behavioral history is provided, raters guess unacquainted players’ behavior with above-chance accuracy. Overall, cooperators are more accurately detected than cheaters. In both rounds, both cooperator and cheater detection are significantly more accurate when players’ photo or video are seen, where their gender is revealed by image or label, and under conditions with behavioral history. These results provide supporting evidence for predictive mind reading abilities that people use to efficiently detect cooperators and cheaters with betterthan-chance accuracy under sparse information conditions. This ability to apply and hone predictive mindreading may help explain why cooperation is commonly observed among strangers in everyday social dilemmas. |
| Keywords: | Mind reading, Cheater detection, Cooperation, Prisoner’s dilemma, Photographs, Thin slices |
| JEL: | B52 C72 C73 D63 D64 D83 D84 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:22-19&r=cbe |
| By: | Barron, Kai; Fries, Tilman |
| Abstract: | Modern life offers nearly unbridled access to information; it is the harnessing of this information to guide decision-making that presents a challenge. We study how one individual may try to shape the way another person interprets objective information by proposing a causal explanation (or narrative) that makes sense of this objective information. Using an experiment, we examine the use of narratives as a persuasive tool in the context of financial advice where advisors may hold incentives that differ from those of the individuals they are advising. Our results reveal several insights about the underlying mechanisms that govern narrative persuasion. First, we show that advisors construct self-interested narratives and make them persuasive by tailoring them to fit the objective information. Second, we demonstrate that advisors are able to shift investors' beliefs about the future performance of a company. Third, we identify the types of narratives that investors find convincing, namely those that fit the objective information well. Finally, we evaluate the efficacy of several potential policy interventions aimed at protecting investors. We find that narrative persuasion is difficult to protect against. |
| Keywords: | Narratives, beliefs, financial advice, conflicts of interest, behavioral finance |
| JEL: | D83 G40 G50 C90 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbeoc:spii2023301&r=cbe |