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on Evolutionary Economics |
| By: | Jean-Paul Carvalho; Augustin Bergeron; Joseph Henrich; Nathan Nun; Jonathan Weigel |
| Abstract: | We study the evolution of belief systems that suppress productive effort. These include concerns about the envy of others, beliefs in the importance of luck for success, disdain for competitive effort, and traditional beliefs in witchcraft. We show that such demotivating beliefs can evolve when interactions are zero-sum in nature, i.e., gains for one individual tend to come at the expense of others. Within a population, our model predicts a divergence between material and subjective payoffs, with material welfare being hump-shaped and subjective well-being being decreasing in demotivating beliefs. Across societies, our model predicts a positive relationship between zero-sum thinking and demotivating beliefs and a negative relationship between zero-sum thinking (or demotivating beliefs) and both material welfare and subjective well-being. We test the model’s predictions using data from two samples in the Democratic Republic of Congo and from the World Values Survey. In the DRC, we find a positive relationship between zero-sum thinking and the presence of demotivating beliefs, such as concerns about envy and beliefs in witchcraft. Globally, zero-sum thinking is associated with skepticism about the importance of hard work for success, lower income, less educational attainment, less financial security, and lower life satisfaction. Comparing individuals in the same zero-sum environment, we observe the divergence between material outcomes and subjective well-being predicted by our model. |
| Date: | 2023–08–23 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:1024&r=evo |
| 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=evo |
| By: | Enrico Berkes; Davide M. Coluccia; Gaia Dossi; Mara P. Squicciarini |
| Abstract: | How do societies respond to adversity? After a negative shock, separate strands of research document either an increase in religiosity or a boost in innovation efforts. In this paper, we show that both reactions can occur at the same time, driven by different individuals within society. The setting of our study is the 1918-1919 influenza pandemic in the United States. To measure religiosity, we construct a novel indicator based on naming patterns of newborns. We measure innovation through the universe of granted patents. Exploiting plausibly exogenous county-level variation in exposure to the pandemic, we provide evidence that more-affected counties become both more religious and more innovative. Looking within counties, we uncover heterogeneous responses: individuals from more religious backgrounds further embrace religion, while those from less religious backgrounds become more likely to choose a scientific occupation. Facing adversity widens the distance in religiosity between science oriented individuals and the rest of the population, and it increases the polarization of religious beliefs. |
| Keywords: | Religiosity, science, innovation, Great Influenza Pandemic |
| Date: | 2023–03–23 |
| URL: | http://d.repec.org/n?u=RePEc:cep:poidwp:068&r=evo |
| 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=evo |
| 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=evo |
| By: | Damien Nouvel |
| Abstract: | It is evident that the COVID-19 pandemic has accelerated the adoption and popularity of proptech. Remote work, social distancing, and the need for contactless transactions have driven the need for enhanced solutions that enable virtual property tours, online lease signing, and other digital services. Such global growth of the proptech sector has been mirrored by accelerated funding campaigns, increased number of proptech startups, and unprecedent interest from real estate developers as well as policy makers. A specific shift has been observed in the networks of the proptech around the world. Industry associations, accelerators, incubators, co-working spaces, and other forms of collaboration and networking have played a decisive role in taking the proptech into the next level. However, this networking intensifying efforts were not only attributed to the crisis as a trigger, but also to well-developed international-local high-tech innovation linkages, active innovation personal networks, and, in most cases, regional public and private readiness to adopt the new changes. In this study, we adopt a complexity-evolutionary perspective to illustrate the evolution of the proptech networks before and after the Covid-19 pandemic. Such approach is allowing us to assess the emergence of a cluster of proptech through studying the linkages between real estate actors, high-tech actors and startups and the funding organisations. Notions such as small events, windows of opportunities, local-international pipelines, regional readiness and path dependency are employed to draw a quasi-full picture of the proptech ecosystem in the last five years and its perspective for the near future. The study is taking the French proptech market as a case study. Our paper introduces the theoretical approach and the preliminary results of this study. |
| Keywords: | Complex adaptive systems; innovation networks; proptech; real estate digitalisation |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_233&r=evo |