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In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about how these experiences influence the agent's performance is valuable for various purposes,…
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact same policy to perform differently, i.e. yield different…
In model-based reinforcement learning, the transition matrix and reward vector are often estimated from random samples subject to noise. Even if the estimated model is an unbiased estimate of the true underlying model, the value function…
In the age of Artificial Intelligence and automation, machines have taken over many key managerial tasks. Replacing managers with AI systems may have a negative impact on workers outcomes. It is unclear if workers receive the same benefits…
In recent years, it has been debated whether a reduction in working hours would be a viable solution to tackle the unemployment caused by technological change. The improvement of existing production technology is gradually being seen to…
Occupational mobility is an emergent strategy to cope with technological unemployment by facilitating efficient labor redeployment. However, previous studies analyzing networks show that the boundaries to smooth mobility are constrained by…
Training a reinforcement learning agent on-policy means collecting fresh experience at every update, and that experience comes with a hidden problem. Each state in a rollout is the direct output of the previous one, causally chained…
Evidence shows that the labor participation rate of retirement age cohorts is non-negligible, and it is a widespread phenomenon globally. In the United States, the labor force participation rate for workers age 75 and older is projected to…
On April 16th, The White House launched "Opening up America Again" (OuAA) campaign while many U.S. counties had stay-at-home orders in place. We created a panel data set of 1,563 U.S. counties to study the impact of U.S. counties'…
Practitioners have reported a directional pattern in AI-assisted code generation: AI-generated code tends to fail quietly, preserving the appearance of functionality while degrading or concealing guarantees. This paper introduces the…
Generative artificial intelligence (AI) is expected to transform work, but less is known about how firms reorganize labor demand as the technology diffuses. Existing research has largely focused on which occupations are exposed to AI or…
The Paycheck Protection Program (PPP) was the largest targeted business support program in the United States, yet its firm-level effects remain contested. I link administrative PPP and SBA 7(a) records to a near-universe panel of U.S.…
We investigate whether and why people might adjust compensation for workers who use AI tools. Across 13 studies (N = 4,956), participants consistently lowered compensation for workers who used AI compared to those who did not. This "AI…
This study investigates the reaction of workers to employer-sponsored general training that provides skills useful not only in the incumbent employer but also in other firms in the industry. While previous research has focused primarily on…
We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment. Through linking Google's…
Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challenge is a distributional shift between the states in the…
Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research relying on stable dynamical systems guarantees…
Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high…
Design optimizations in human-AI collaboration often focus on cognitive aspects like attention and task load. Drawing on work design literature, we propose that effective human-AI collaboration requires broader consideration of human needs…
In recent years, Win-Stay-Lose-Learn rule has attracted wide attention as an effective strategy updating rule, and voluntary participation is proposed by introducing a third strategy in Prisoner's dilemma game. Some researches show that…