The primary purpose of this monograph has been twofold. Firstly, the objective has been to provide a systematic account of some of the basic models and techniques developed by the modern theory of complex networks. In addition, a second motivation has been to illustrate the broad range of socioeconomic phenomena to which this theory can be naturally applied. In the latter pursuit, our approach is polar to that commonly espoused by classical game theory and economic analysis. It stresses the implications of complexity on the interaction structure, while downplaying the role of incentives in shaping agents' behavior. But, as repeatedly argued, neither of those one-sided perspectives can be judged satisfactory. In general, both complexity and incentive considerations should jointly play a key role in any proper understanding (and thus modeling) of most social phenomena.

To further elaborate on this point, it is useful to recapitulate what are some of the main benefits to be expected from explicitly accounting for complexity in the analysis of socioeconomic environments.

• First, of course, the theoretical approach undoubtedly becomes more realistic since, indeed, we find that so many interesting social problems in the real world are embedded in a complex and ever-changing social network (cf. Chapter 1). It is fitting, therefore, that those problems should also be modeled in ways that respect such underlying complexity.

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