Reinforcement learning for sequential decision making in population research

Dados Bibliográficos

ID21377607
AutoresNina Deliu (0000-0003-2501-8795, University of Cambridge, autor correspondente)
Ano2024
Volume58
Fascículo6
Páginas5057-5080
Data de publicação2024-12-01
Open AccessSim
TipoARTICLE
PeriódicoQuality & Quantity (JOURNAL)
Identificadores do periódicoISSN: 0033-5177 • E-ISSN: 1573-7845
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11135-023-01755-z
OpenAlexW4388214510
IdiomaEN
Citações recebidas1
Referências citadas61

Reinforcement learning (RL) algorithms have been long recognized as powerful tools for optimal sequential decision making. The framework is concerned with a decision maker, the agent, that learns how to behave in an unknown environment by making decisions and seeing their associated outcome. The goal of the RL agent is to infer, through repeated experience, an optimal decision-making policy, i.e., a sequence of action rules that would lead to the highest, typically long-term, expected utility. Today, a wide range of domains, from economics to education and healthcare, have embraced the use of RL to address specific problems. To illustrate, we used an RL-based algorithm to design a text-messaging system that delivers personalized real-time behavioural recommendations to promote physical activity and manage depression. Motivated by the recent call of the UNECE for government-wide actions to adapt to population ageing, in this work, we argue that the RL framework may provide a set of compelling strategies for supporting population research and informing population policies. After introducing the RL framework, we discuss its potential in three population-study applications: international migration, public health, and fertility

Action (physics) · Economics · Government (linguistics) · Machine learning · Management science · Microeconomics · Optimal decision · Outcome (game theory) · Population · Reinforcement learning · Set (abstract data type) · Sociology · Artificial Intelligence · Computer Science · demographic modeling and climate adaptation · Health disparities and outcomes · Health, Environment, Cognitive Aging

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