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on Neuroeconomics |
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Issue of 2019‒04‒22
three papers chosen by |
| By: | Bose, Neha (University of Warwick); Sgroi, Daniel (University of Warwick) |
| Abstract: | In a laboratory experiment, 338 participants were asked to communicate in pairs and then play two games with their partners: the 11-20 money request game (a tool for assessing level-k reasoning) and a public goods game. The communication occurred prior to any knowledge of what was to follow but played an important rolein allowing them to develop theories or mental models of their partners (“theory of mind”) which proved to be crucial explanatory factors for decision-making. We examine the players’ beliefs about the personality and intelligence of their partner, how they play in the games and analysed the language used during communication. The results indicate that beliefs about partner’s type is biased by own-type. In particular, extraverts, characterised by positive affect, projected their positivity onto their partners. The level-k strategy chosen in the 11-20 game increased with the perceived similarity between players and in the public goods game, players cooperated more when they believed their partners to be extraverted. An analysis of the text used during communication explains how it was possible for participants to draw inferences about other’s type: for instance, use of more words and more dominant words were associated with being an extravert. |
| Keywords: | theory of mind, cheap talk, communication, level-k reasoning, public goods game, cooperation, extraversion, perceived similarity, self-projection bias, laboratory experiment, text analysis. JEL Classification: D91, D83, C92. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:409&r=all |
| By: | Catherine Haeck (Department of Economics, University of Quebec in Montreal); Samuel Pare; Pierre Lefebvre (Department of Economics, University of Quebec in Montreal); Philip Merrigan (Department of Economics, University of Quebec in Montreal) |
| Abstract: | This article provides an analysis of the impact of the Quebec Parental Insurance Plan (QPIP). Using a quasi-experimental design with survey data, we find that mothers spent on average 10 additional days with their newborn following the implementation of the insurance plan, and that both mothers and fathers received higher benefits. For children, using both survey data and administrative data, we find that the QPIP had limited positive effects on their health, cognitive and behavioural development. Effects are concentrated among families of mothers with a post-secondary education. These results suggest that while paid benefits increased dramatically, the impacts on maternal time investment and child well-being are modest. |
| Keywords: | maternity leave, parental leave, child development, family well-being, natural experiment |
| JEL: | J13 J22 J24 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:grc:wpaper:19-01&r=all |
| By: | Fügener, A.; Grahl, J.; Gupta, A.; Ketter, W. |
| Abstract: | A defining question of our age is how AI will influence the workplace of the future and, thereby, the human condition. The dominant perspective is that the competition between AI and humans will be won by either humans or machines. We argue that the future workplace may not belong exclusively to humans or machines. Instead, it is better to use AI together with humans by combining their unique characteristics and abilities. In three experimental studies, we let humans and a state of the art AI classify images alone and together. As expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was inversion, where the AI delegated to a human when it was uncertain. Humans could in theory outperform all other configurations if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well; the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI. This result is novel and important for human-AI collaboration at the workplace. We believe it has broad implications for the future of work, the design of decision support systems, and management education in the age of AI. |
| Keywords: | Future of Work, Artificial Intelligence, Augmented Decision Environment, Deep Learning, Human-AI Collaboration, Machine Learning, Intelligent Software Agents |
| Date: | 2019–04–08 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:115830&r=all |