Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference

• URL: http://arxiv.org/abs/2410.08362v2
• Date: Sat, 19 Oct 2024 19:47:42 GMT
• Title: Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference
• Authors: Raphael C. Kim, Falco J. Bargagli-Stoffi, Kevin L. Chen, Rachel C. Nethery,
• Abstract summary: Emissions-reducing interventions on coal-fired power plants have proven to be an effective, but costly, strategy for reducing pollution-related health burdens. We introduce novel learning methods to determine the optimal policy under arbitrary network interference (BNI) We find that annual IHD hospitalization rates could be reduced in a range from 20.66-44.51 per 10,000 person-years through optimal policies under different cost constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The substantial effect of air pollution on cardiovascular disease and mortality burdens is well-established. Emissions-reducing interventions on coal-fired power plants -- a major source of hazardous air pollution -- have proven to be an effective, but costly, strategy for reducing pollution-related health burdens. Targeting the power plants that achieve maximum health benefits while satisfying realistic cost constraints is challenging. The primary difficulty lies in quantifying the health benefits of intervening at particular plants. This is further complicated because interventions are applied on power plants, while health impacts occur in potentially distant communities, a setting known as bipartite network interference (BNI). In this paper, we introduce novel policy learning methods based on Q- and A-Learning to determine the optimal policy under arbitrary BNI. We derive asymptotic properties and demonstrate finite sample efficacy in simulations. We apply our novel methods to a comprehensive dataset of Medicare claims, power plant data, and pollution transport networks. Our goal is to determine the optimal strategy for installing power plant scrubbers to minimize ischemic heart disease (IHD) hospitalizations under various cost constraints. We find that annual IHD hospitalization rates could be reduced in a range from 20.66-44.51 per 10,000 person-years through optimal policies under different cost constraints.

Related papers

• Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources [47.57108369791273]
  Scarcity of health care resources could result in the unavoidable consequence of rationing. There is no universally accepted standard for health care resource allocation protocols. We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients.
  arXiv  Detail & Related papers  (2023-09-15T17:28:06Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration [53.122045119395594]
  We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies.
  arXiv  Detail & Related papers  (2023-01-30T12:22:30Z)
• Estimating Geographic Spillover Effects of COVID-19 Policies From Large-Scale Mobility Networks [54.90772000796717]
  County-level policies provide flexibility between regions, but may become less effective in the presence of geographic spillovers. We estimate spillovers using a mobility network with billions of timestamped edges. We find that county-level restrictions are only 54% as effective as statewide restrictions at reducing mobility.
  arXiv  Detail & Related papers  (2022-12-12T20:16:54Z)
• Discrete Stochastic Optimization for Public Health Interventions with Constraints [1.8275108630751844]
  This paper addresses aspects of the 2009 H1N1 and the COVID-19 pandemics with the spread of disease modeled by the open source Monte Carlo simulations. The objective of the optimization is to determine the best combination of intervention strategies so as to result in minimal economic loss to society.
  arXiv  Detail & Related papers  (2022-06-27T21:21:25Z)
• An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest [5.406112598028401]
  We use the concept of net-monetary-benefit (NMB) to assess the trade-off between health benefits and related costs. We identify the optimal CE-ITR using NMB-based classification algorithms. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial.
  arXiv  Detail & Related papers  (2022-04-23T01:36:24Z)
• Reinforcement Learning for Optimization of COVID-19 Mitigation policies [29.4529156655747]
  The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. Governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies.
  arXiv  Detail & Related papers  (2020-10-20T18:40:15Z)
• Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning [1.0765359420035392]
  This study used a novel technique, multi-agent deep reinforcement learning, to optimize dissolved oxygen and chemical dosage in a WWTP. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy.
  arXiv  Detail & Related papers  (2020-08-19T05:34:47Z)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)
• Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation [49.69139684065241]
  Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
  arXiv  Detail & Related papers  (2020-04-02T17:04:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.