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Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies…
In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI…
Understanding the statistical dynamics of growth and inequality is a fundamental challenge to ecology and society. Recent analyses of wealth and income dynamics in contemporary societies show that economic inequality is very dynamic and…
The systematic differences of gender representation across occupations, gender-based occupational segregation, has been suggested as one of the most important determinants of the still existing gender wage gap. Despite some signs of a…
To increase autonomy in reinforcement learning, agents need to learn useful behaviours without reliance on manually designed reward functions. To that end, skill discovery methods have been used to learn the intrinsic options available to…
It is a widely observed phenomenon that wealth is distributed significantly more unequal than wages. In this paper we study this phenomenon using a new extension of P\'olyas urn, modelling wealth growth through wages and capital returns. We…
In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to…
We study the problem of unsupervised skill discovery, whose goal is to learn a set of diverse and useful skills with no external reward. There have been a number of skill discovery methods based on maximizing the mutual information (MI)…
The empirical literature on the relationship between income inequality and economic growth has produced highly heterogeneous and often conflicting results. This paper investigates the sources of this heterogeneity using a meta-analytic…
Ordered response scales are ubiquitous in economics, but their interpretation rests on an untested assumption: that numerical labels reflect equal psychological intervals. The contribution of this paper is to provide a systematic assessment…
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. Recent works have proposed many different…
We use a controlled laboratory experiment to study the causal impact of income decreases within a time period on redistribution decisions at the end of that period, in an environment where we keep fixed the sum of incomes over the period.…
Unsupervised Skill Discovery (USD) allows agents to autonomously learn diverse behaviors without task-specific rewards. While recent USD methods have shown promise, their application to real-world robotics remains underexplored. In this…
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament. These games are played among randomly drawn agents from the population. The agents in our…
Difference-in-differences is one of the most used identification strategies in empirical work in economics. This chapter reviews a number of important, recent developments related to difference-in-differences. First, this chapter reviews…
Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on…
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are observed over time. The information across time and space…
Gender bias in computing is a hard problem that has resisted decades of research. One obstacle has been the absence of systematic data that might indicate when gender bias emerged in computing and how it has changed. This article presents a…
Labor share, the fraction of economic output accrued as wages, is inexplicably declining in industrialized countries. Whilst numerous prior works attempt to explain the decline via economic factors, our novel approach links the decline to…
Homelessness in American cities is becoming an ever more prominent issue, but its causes remain contested, ranging from mental health and substance abuse to housing affordability and local labor markets. To shed light on this issue, I…