These slides and abstract: https://urban-analytics.github.io/dust/presentations.html
The reality is dynamic.
The reality is stochastic
There are unobserved variables
There is no systematic mechanism to incorporate new data into agent-based models
Try to improve estimates of the true system state by combining:
Noisy, real-world observations
Model estimates of the system state
MatSim Singapore takes 2 days to implement each scenario, even when using a cluster of 4 supercomputers (Anda, 2017)
Data Assimilation for Agent-Based Models (dust)
Main aim: create new methods for dynamically assimilating data into agent-based models.
Uncertainty in agent-based models for smart city forecasts
Developing methods that can be used to better understand uncertainty in individual-level models of cities
Bringing the Social City to the Smart City