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on Cognitive and Behavioural Economics |
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Issue of 2023‒09‒11
three papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Caliari, Daniele |
| Abstract: | We challenge the standard definition of economic rationality as consistency by making use of a novel distinction between axioms of decision theory: consistency and preference axioms. We argue that this distinction has been overlooked by the literature and, as a result, evidence that consistency is a proxy of decision-making ability is often based on incorrect identification strategies. We conduct an experiment to investigate the factors that drive violations of consistency alone. While we find no evidence that consistency axioms are a proxy of decisionmaking ability, we provide suggestive evidence that some preference axioms are, confirming their potential role as confounding factors. Overall, our experimental evidence raises doubts about the choice of language that equates consistency with rationality in economics. |
| Keywords: | Decision Theory, Experimental Design, Consistency, Rationality |
| JEL: | D00 D90 D91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbeoc:spii2023304&r=cbe |
| By: | Margarita Leib; Nils Köbis; 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 AIand 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. |
| Keywords: | Artificial Intelligence, Machine Behaviour, Behavioural Ethics, Advice |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:251&r=cbe |
| By: | Eva Raiber (Aix-Marseille School of Economics and Center for Economic Policy Research); Daniela Horta Saenz (Aix-Marseille School of Economics); Timothée Demont (Aix Marseille Université Économiques) |
| Abstract: | Worrisome topics, such as climate change, economic crises, or the COVID-19 pandemic are increasingly present and pervasive because of digital media and social networks. Do worries triggered by such topics affect the cognitive capacities of the youth? In an online experiment during the COVID-19 pandemic (N=1503), we test how the cognitive performance of university students responds when exposed to topics discussing current mental health issues related to social restrictions or future labor market uncertainties linked to the economic contraction. Moreover, we study how such response is affected by a performance goal. We find that the labor market topic increases cognitive performance when the latter is motivated by a goal. The positive reaction is mainly concentrated among students with larger financial and social resources, which points to an inequality-widening mechanism. Conversely, we find no effect after the mental health topic. We even find a weak negative response among those mentally vulnerable when payout is not conditioned on reaching a goal. |
| Date: | 2023–08–11 |
| URL: | http://d.repec.org/n?u=RePEc:boc:fsug23:08&r=cbe |