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on Cognitive and Behavioural Economics |
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Issue of 2023‒11‒27
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Konstantinos Georgalos; Nathan Nabil |
| Abstract: | In an attempt to elucidate the classic violations of expected utility theory, the behavioural economics literature heavily relies on the influential work of Tversky and Kahneman (1992), specifically the Cumulative Prospect Theory (CPT) model and the Heuristics-and-Biases program. While both approaches have significantly contributed to our understanding of decision-making under uncertainty, empirical evidence remains inconclusive. In this study, we investigate the performance of each approach across a wide range of choice environments and increasing cognitive load, encompassing gains, losses, time pressure, and complexity. Utilising data from various studies and employing Bayesian inference, we assess the performance of CPT in comparison to an adaptive cognitive toolbox model of heuristics. For subjects classified as toolbox decision makers, we examine the content (i.e., which heuristics) and the size of the toolbox (i.e., how many heuristics). Our findings reveal that as the choice environment objectively increases in complexity, individuals transition from using sophisticated expectation-based utility models to relying on a set of simplification heuristics for decision-making. We quantify the relationship between toolbox usage and complexity, showing a significant and positive correlation between the two. Furthermore, our results indicate that as task complexity rises, individuals tend to employ smaller toolboxes with fewer heuristics for decision-making. |
| Keywords: | Complexity, Toolbox models, Heuristics, Risky choice, Bayesian modelling |
| JEL: | C91 D81 D91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:400814162&r=cbe |
| By: | Konstantinos Georgalos; Nathan Nabil |
| Abstract: | In this paper we aim to investigate how the complexity of a decision-task may change an agents strategic behaviour as a result of increased cognitive fatigue. In this framework, complexity is defined as a function of the number of outcomes in a lottery. Using Bayesian inference techniques, we quantitatively specify and estimate adaptive toolbox models of cognition, which we rigorously test against popular expectation based models; modified to account for complexity aversion. We find that for the majority of the subjects, a toolbox model performs best both in-sample, and with regards to its predictive capacity out-of-sample, suggesting that individuals result to heuristics when the complexity of a task overwhelms their cognitive load. |
| Keywords: | Complexity aversion, Toolbox models, Heuristics, Risky choice, Bayesian modelling |
| JEL: | C91 D91 D81 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:400814269&r=cbe |
| By: | Fehr, Ernst (University of Zurich); Epper, Thomas (CNRS); Senn, Julien (University of Zurich) |
| Abstract: | Parsimony is a desirable feature of economic models but almost all human behaviors are characterized by vast individual variation that appears to defy parsimony. How much parsimony do we need to give up to capture the fundamental aspects of a population's distributional preferences and to maintain high predictive ability? Using a Bayesian nonparametric clustering method that makes the trade-off between parsimony and descriptive accuracy explicit, we show that three preference types - an inequality averse, an altruistic and a predominantly selfish type - capture the essence of behavioral heterogeneity. These types independently emerge in four different data sets and are strikingly stable over time. They predict out-of-sample behavior equally well as a model that permits all individuals to differ and substantially better than a representative agent model and a state-of-the-art machine learning algorithm. Thus, a parsimonious model with three stable types captures key characteristics of distributional preferences and has excellent predictive power. |
| Keywords: | distributional preferences, altruism, inequality aversion, preference heterogeneity, stability, out-of-sample prediction, parsimony, Bayesian nonparametrics |
| JEL: | D31 D63 C49 C90 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16535&r=cbe |
| By: | Bruhin, Adrian; Petros, Fidel; Santos-Pinto, Luís |
| Abstract: | Teamwork has become increasingly important in modern organizations and the labor market. Yet little is known about the role of self-confidence in teamwork. In this paper, we present evidence from a laboratory experiment using a team effort task. Effort and ability are complements and there are synergies between teammates' efforts. We exogenously manipulate subjects' self-confidence in their ability using easy and hard general knowledge quizzes. We find that overconfidence leads to more effort, less free riding, and higher team revenue. These findings suggest that organizations could improve team performance by hiring overconfident workers. |
| Keywords: | Teamwork, Self-Confidence, Effort, Free Riding |
| JEL: | C71 C92 D91 D83 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbmbh:279569&r=cbe |