  Agent-Based Models are a powerful class of computational models widely used
to simulate complex phenomena in many different application areas. However, one
of the most critical aspects, poorly investigated in the literature, regards an
important step of the model credibility assessment: solution verification. This
study overcomes this limitation by proposing a general verification framework
for Agent-Based Models that aims at evaluating the numerical errors associated
with the model. A step-by-step procedure, which consists of two main
verification studies (deterministic and stochastic model verification), is
described in detail and applied to a specific mission critical scenario: the
quantification of the numerical approximation error for UISS-TB, an ABM of the
human immune system developed to predict the progression of pulmonary
tuberculosis. Results provide indications on the possibility to use the
proposed model verification workflow to systematically identify and quantify
numerical approximation errors associated with UISS-TB and, in general, with
any other ABMs.
