# Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security

arXiv: https://arxiv.org/abs/2504.07719 · HTML: https://mlsanity.com/paper/2504.07719/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 108 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [The Disparate Effects of Strategic Manipulation](https://mlsanity.com/paper/1808.08646/) (2018)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Equalizing Recourse across Groups](https://mlsanity.com/paper/1909.03166/) (2019)
- [Learning Off-Policy with Online Planning](https://mlsanity.com/paper/2008.10066/) (2020)
- [Fairness Under Composition](https://mlsanity.com/paper/1806.06122/) (2018)
- [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [Fair Algorithms for Learning in Allocation Problems](https://mlsanity.com/paper/1808.10549/) (2018)
- [Beyond the One Step Greedy Approach in Reinforcement Learning](https://mlsanity.com/paper/1802.03654/) (2018)
- [Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction](https://mlsanity.com/paper/2107.01715/) (2021)
- [Reinforcement Learning with Lookahead Information](https://mlsanity.com/paper/2406.02258/) (2024)
- [The Value of Reward Lookahead in Reinforcement Learning](https://mlsanity.com/paper/2403.11637/) (2024)
- [Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning](https://mlsanity.com/paper/2005.07404/) (2020)
- [Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability](https://mlsanity.com/paper/2104.12037/) (2021)
- [Fairness and Decision-making in Collaborative Shift Scheduling Systems](https://mlsanity.com/paper/2001.09755/) (2020)

## Built on by (most cited first, 2 of 2)

- [Rethinking Optimization: A Systems-Based Approach to Social Externalities](https://mlsanity.com/paper/2506.12825/) (2025)
- [Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms](https://mlsanity.com/paper/2508.13440/) (2025)

## Abstract (arXiv)

Financial instability has become a significant issue in today's society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers' income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals' long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals' financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers' consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker's capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers' instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness.