Complex agent-based vs, simpler conditional probability models: tradeoffs in accuracy and cost

Oregon Convention Center 

Agent-based and microsimulation models can quickly become structurally and computationally complex, and require substantial efforts to build, parameterize, calibrate and validate. Simpler "back of the envelope" models can provide ballpark estimates in a much shorter time but with lower accuracy. We discuss compensations for complex networks and non-linearities in simpler models with practical applications to policy and epidemiology. We show from the examples of policy evaluation studies how simple models could provide the upper and lower boundaries of the estimates and discuss the utility of population averaged (conditional probabilities), microsimulation, and agent-based models and the tradeoffs of accuracy, cost, and complexity.

Agent-based models

Microsimulation models

Model simplification

Population averaged model

Forecasting

Epidemic model 

Section on Statistics in Epidemiology