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Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI., Comment: PNAS Nexus, in press

We experimentally study a game in which success requires a sufficient total contribution by members of a group. There are significant uncertainties surrounding the chance and the total effort required for success. A theoretical model with max-min preferences towards ambiguity predicts higher contributions under ambiguity than under risk. However, in a large representative sample of the Spanish population (1,500 participants) we find that the ATE of ambiguity on contributions is zero. The main significant interaction with the personal characteristics of the participants is with risk attitudes, and it increases contributions. This suggests that policymakers concerned with ambiguous problems (like climate change) do not need to worry excessively about ambiguity.

Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relations, including tests for personal traits of all the students. We introduce a metric -- the `triadic influence' -- that measures the influence of nearest-neighbors in the relationships of their contacts. We use neural networks to predict the relationships and to extract the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, the triadic influence (a simple one-dimensional metric) achieves the highest accuracy at predicting the relationship between two students. We postulate that the probabilities extracted from the neural networks -- functions of the triadic influence and the personalities of the students -- control the evolution of real social networks, opening a new avenue for the quantitative study of these systems.

Inbreeding homophily is a prevalent feature of human social networks with important individual and group-level social, economic, and health consequences. The literature has proposed an overwhelming number of dimensions along which human relationships might sort, without proposing a unified empirically-grounded framework for their categorization. We exploit rich data on a sample of University freshmen with very similar characteristic - age, race and education- and contrast the relative importance of observable vs. unobservables characteristics in their friendship formation. We employ Bayesian Model Averaging, a methodology explicitly designed to target model uncertainty and to assess the robustness of each candidate attribute while predicting friendships. We show that, while observable features such as assignment of students to sections, gender, and smoking are robust key determinants of whether two individuals befriend each other, unobservable attributes, such as personality, cognitive abilities, economic preferences, or socio-economic aspects, are largely sensible to the model specification, and are not important predictors of friendships.

The use of hypothetical instead of real decision-making incentives remains under debate after decades of economic experiments. Standard incentivized experiments involve substantial monetary costs due to participants' earnings and often logistic costs as well. In time preferences experiments, which involve future payments, real payments are particularly problematic. Since immediate rewards frequently have lower transaction costs than delayed rewards in experimental tasks, among other issues, (quasi)hyperbolic functional forms cannot be accurately estimated. What if hypothetical payments provide accurate data which, moreover, avoid transaction cost problems? In this paper, we test whether the use of hypothetical - versus real - payments affects the elicitation of short-term and long-term discounting in a standard multiple price list task. One-out-of-ten participants probabilistic payment schemes are also considered. We analyze data from three studies: a lab experiment in Spain, a well-powered field experiment in Nigeria, and an online extension focused on probabilistic payments. Our results indicate that paid and hypothetical time preferences are mostly the same and, therefore, that hypothetical rewards are a good alternative to real rewards. However, our data suggest that probabilistic payments are not.

Previous research shows that women are more altruist than men in dictator game experiments. Yet, little is known whether women are expected to be more altruist than men. Here we elicit third-parties' beliefs about dictators' donations conditional on knowing the gender of the dictator. Our data provide evidence of three main findings: (i) women are expected to be more altruist than men; (ii) both men and women have correct beliefs about the level of altruism among men; and (iii) both men and women overestimate the level of altruism among women. In doing so, our results uncover a perception gap according to which, although women are more altruist than men, they are expected to be even more altruist than they actually are.