Mantonakis et al. (2009) suggested the pairwise-competition model (PCM) of preference construction. The basic idea is that humans form preferences among choice objects (e.g., types of wines) sequentially, such that the preferred object is the ultimate winner of a sequence of pairwise competitions: The object sampled first is compared to the second, the winner of this competition is then compared to the third object, resulting in a winner that is compared to the fourth, and so on. For a set of m objects, this recursive process terminates after m-1 pairwise competitions once the final object has been compared against the previous favorite. Canic and Pachur (2014) recently proposed a formal specification and elaboration of the PCM. Moreover, using computer simulations, they showed how variations in the model's parameters affect primacy and recency effects in sequential choice and how the model can account for various patterns of serial position effects documented in the literature. We appreciate the contributions of Mantonakis and collaborators as well as Canic and Pachur. Clearly, innovative theoretical ideas and their formal clarification are key elements of progress in science. However, we argue that making use of the full spectrum of formal modeling advantages necessarily involves fitting the model to empirical data. By narrowing down model-based analyses to computer simulations—as Canic and Pachur (2014) did—important questions remain unanswered and misleading conclusions cannot be ruled out. First, simulating a model and showing that it predicts serial-position effects similar to those found in empirical data does not imply that the model fits these data. A successful model should account for the entire distribution of observed data, not just for selected qualitative aspects such as primacy and recency effects. Formal goodness-of-fit tests provide the necessary information but simulations do not. Second, if there are several nested versions of a model as in case of the PCM, simulations cannot tell us which model version provides the best account of the data taking model complexity into account (as influenced, among others, by the number of estimated parameters). The model fitting toolbox, in contrast, includes appropriate model selection measures, enabling us to identify the most parsimonious model version that is consistent with the data. Third, model fitting involves parameter estimation, that is, identification of the parameter values that best account for an existing set of data. It also provides statistical tests for parameters, enabling statistically sound evaluations of parameter differences between groups or conditions. Simulations, in contrast, do not provide such an elaborated framework. Because they ignore sampling variability in empirical data, they may mistakenly suggest parameter differences when in fact there are none. To illustrate the advantages of model fitting, we apply Canic and Pachur's (2014) formalization of the PCM to the data of Mantonakis and collaborators1.