Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

Trott, Alexander; Srinivasa, Sunil; Wal, Douwe van der; Haneuse, Sebastien; Zheng, Stephan

doi:10.2139/ssrn.3900237

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…Since our goal is to evolve general policies for minimizing the spread of the pandemic and, at the same time, reducing the economic losses, we employ the simulator proposed in [8]. In fact, this simulator allows us to test rules that are applicable in every country (i.e., not only tailored to U.S. as in [17]) and that do not make use of economic subsidies.…”

Section: Methods 31 Simulatormentioning

confidence: 99%

“…In [17], the authors propose a simulator to estimate the effects on the management of closing economic activities on both the spreading of the pandemic and the economic losses. The proposed simulator is tailored on the U.S. economy, simulating all the 51 states and a central entity that manages subsidies.…”

Section: Related Work 21 Policy-making For Pandemicsmentioning

confidence: 99%

“…The main difference between the approaches proposed above is that in [8] the simulator acts at a very low scale, but with a high level of detail, being able to simulate aspects of everyday life, while in [17] the simulator aims to perform at a very large scale with a lower level of detail.…”

Section: Related Work 21 Policy-making For Pandemicsmentioning

confidence: 99%

“…To this end, some previous works focused on the use of simulators to estimate the virus propagation and economic losses given a policy [8,17]. In this setting, a policy decides the safety measures to adopt in a given scenario.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable AI for policy-making in pandemics

Custode

Iacca

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially because the government needs to trade-off the spreading of the pandemic with the economic losses. For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments. While the performance of such approaches are promising, they suffer from a fundamental issue: since such approaches are based on black-box machine learning, their real-world applicability is limited, because these policies cannot be analyzed, nor tested, and thus they are not trustable. In this work, we employ a recently developed hybrid approach, which combines reinforcement learning with evolutionary computation, for the generation of interpretable policies for containing the pandemic. These policies, trained on an existing simulator, aim to reduce the spreading of the pandemic while minimizing the economic losses. Our results show that our approach is able to find solutions that are extremely simple, yet very powerful. In fact, our approach has significantly better performance (in simulated scenarios) than both previous work and government policies.

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Section: Methods 31 Simulatormentioning

confidence: 99%

Section: Related Work 21 Policy-making For Pandemicsmentioning

confidence: 99%

Section: Related Work 21 Policy-making For Pandemicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpretable AI for policy-making in pandemics

Custode

Iacca

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…Colas et al (2021) propose a toolbox for optimizing control policies in epidemiological models. Trott et al (2021) propose to optimize economic and public policy, in particular, for COVID-19, with AI Economist (Zheng et al, 2021). Gottesman et al (2019) present guidelines for reinforcement learning in healthcare.…”

Section: Healthcarementioning

confidence: 99%

Reinforcement Learning in Practice: Opportunities and Challenges

Li¹

2022

Preprint

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This article is a gentle discussion about the field of reinforcement learning for real life, about opportunities and challenges, with perspectives and without technical details, touching a broad range of topics. The article is based on both historical and recent research papers, surveys, tutorials, talks, blogs, and books. Various groups of readers, like researchers, engineers, students, managers, investors, officers, and people wanting to know more about the field, may find the article interesting.In this article, we first give a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI. Then we discuss opportunities of RL, in particular, applications in products and services, games, recommender systems, robotics, transportation, economics and finance, healthcare, education, combinatorial optimization, computer systems, and science and engineering. The we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) model, simulation, planning, and benchmarks, 5) learning to learn a.k.a. meta-learning, 6) off-policy/offline learning, 7) software development and deployment, 8) business perspectives, and 9) more challenges. We conclude with a discussion, attempting to answer: "Why has RL not been widely adopted in practice yet?" and "When is RL helpful?".

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