We first calibrate and then analyze our ABM using suites of Monte Carlo simulations, applied to a representative set of training cases of government formation in European parliamentary democracies. For each to the twenty training cases, we execute 1,000 model runs, randomizing model parameters for each run as follows. For each observable parameter, for each model run for each training case, we take the empirically observed value and perturb this with parameterized random noise. For unobservable model parameters, we randomly sample from the full range of possible values. The 1,000 runs for each case thus yield a distribution of model-predicted outcome for that case. We calibrate unobservable model parameters by selecting ranges of these associated with empirically accurate model predictions. We analyze the (calibrated and uncalibrated) model by summarizing the mapping of model inputs into model outputs in the artificial data generated by the set of Monte Carlos, using theoretically informed logistic regressions. This is the computational analogue of analyses based on deductive “comparative statics” generated by traditional formal theorists.