((( DRIUSHA )))

Macro Equation models are the oldest type, generating predictions based on epidemiological dynamics equations that represent interactions between the infected and the uninfected,

with one key drawback being that the model uses idealizations that do not neatly map onto the ways that real smallpox infected and uninfected people interact.

Massive Simulation Models seek to overcome the limitations of Macro Equation Models by modeling the behavior of individual agents by incorporating more and variables, stretching model complexity to the limits of existing computing technologies.

Abstract Simulation Models take a similar strategy as Massive Simulation Models, but are intentionally restrained for pragmatic reasons, such as to make them more transparent to observers who wish to examine the components of the model.

Grüne-Yanoff counterintuitively argues that Macro-Simulation models’ complexity creates the illusion of being epistemically superior, while actually being more opaque and more prone to leading policymakers to make mistaken inferences from the model.

Macro-equation models can be very useful for some policy purposes, despite the presence of idealizations. Meanwhile, Abstract Simulation Models can be pragmatically superior to the more complex Massive Simulation Models by virtue of being relatively easier to assess and use."