Improving the Prediction of Individual Engagement in Recommendations Using Cognitive Models
This work addresses resource allocation in public health programs, offering incremental improvements by augmenting existing computational approaches with cognitive models.
The study tackled the problem of predicting individual engagement in recommendations for public health programs by using a cognitive model based on Instance-Based Learning (IBL) Theory, showing that IBL models outperform general time-series forecasters like LSTMs in predicting dynamics and provide estimates of volatility and sensitivity to interventions.
For public health programs with limited resources, the ability to predict how behaviors change over time and in response to interventions is crucial for deciding when and to whom interventions should be allocated. Using data from a real-world maternal health program, we demonstrate how a cognitive model based on Instance-Based Learning (IBL) Theory can augment existing purely computational approaches. Our findings show that, compared to general time-series forecasters (e.g., LSTMs), IBL models, which reflect human decision-making processes, better predict the dynamics of individuals' states. Additionally, IBL provides estimates of the volatility in individuals' states and their sensitivity to interventions, which can improve the efficiency of training of other time series models.