Reinforcement Learning and Food Insecurity
If you want to make good decisions from data, you need good data.
All the links you could want
A novel ABM-RL-based modelling approach for learning and analyzing pandemic mitigation strategies using
Ontario, Canada epidemiological, socioeconomic, health, and social data. This book chapter provides a
unqiue approach to modelling pandemic outcomes as by providing agents the freedom to select their
actions and learn from their experiences, RL allows agents to learn behavioural policies that
reduce the spread of COVID-19.
This study investigates the extent to which stressful life events may increase the likelihood of food insecurity among the Canadian adult population.
Those who experienced work and health-related stressful life events were significantly more likely to be exposed to food insecurity.
This technical report is intended to validate the Longitudinal and International Study of Adults (LISA) Wave 4 (2018) Food Security (FSC) module and provide recommendations for analytical use.
Hit me up at any of the links below.
Here is a copy of my CV.