The study of tax behaviour is a research field which attracts increasing interest in social and behavioural sciences. Rational choice models have been traditionally used to account for that behaviour, but they face the puzzle of explaining levels of observed tax compliance which are much higher than expected. Several social influence mechanisms have been proposed in order to tackle this problem. In this article we discuss the interdisciplinary literature on this topic, and we claim that agent-based models are a promising tool in order to test theories and hypothesis in this field. To illustrate that claim, we present SIMULFIS, an agent-based model for the simulation of tax compliance that allows to combine rational choice with social influence mechanisms in order to generate aggregated patterns of tax behaviour. We present and discuss the results of a simple virtual experiment in order to show the potentialities of the model.

In this paper, we present a new agent-based model for the simulation of tax compliance and tax evasion behavior (SIMULFIS). The main novelties of the model are the introduction of a "behavioral filter approach" to model tax decisions, the combination of a set of different mechanisms to produce tax compliance (namely rational choice, normative commitments and social influence), and the use of the concept of "fraud opportunity use rate" (FOUR) as the main behavioral outcome. After describing the model in detail, we display the main behavioral and economic results of 1,920 simulations calibrated for the Spanish case and designed to test for the internal validity of SIMULFIS. The behavioral outcomes show that scenarios with strict rational agents strongly overestimate tax evasion, while the introduction of social influence and normative commitments allows to generate more plausible compliance levels under certain deterrence conditions. Interestingly, the relative effect of social influence is shown to be ambivalent: it optimizes * Corresponding author. 1350007-1 Advs. Complex Syst. 2013.16. Downloaded from www.worldscientific.com by AUSTRALIAN NATIONAL UNIVERSITY on 03/15/15. For personal use only.

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Some have argued it is possible to infer different groups’ contributions to ethnic residential segregation from their individual neighborhood preferences. From this perspective, natives tend to be more segregation-promoting than non-natives, since they prefer neighborhoods where they are the majority. It remains unclear, however, whether this holds when one evaluates their contributions to segregation within a dynamic perspective. Using register data from Statistics Sweden, I define and model ten different groups’ residential behavior based on their ethnicity and family composition. I thereby simulate the residential mobility of the full population of Stockholm municipality residents from 1998 to 2012. Even though my results at the micro-level are consistent with previous studies, the simulation results show that foreign singles’ mobility patterns are more segregation-promoting than any other groups, since this group shows a greater in-group feedback effect regarding choice of new neighborhoods, an effect that increases their flow from low-to-high segregated neighborhoods progressively. My results suggest that (1) integration initiatives would be more efficient if focused on this particular group and (2) a proper evaluation of micro-behaviors’ implications for macro-patterns of segregation requires a dynamic approach accounting for groups’ heterogeneous behaviors and their main interdependencies on shaping segregation over time.

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