# Computational Social Science, the Evolution of Policy Design and Rule Making in Smart Societies
## 3 From Nature to Social Facts: Computational Social Science
_From (social) structure to function: social network analysis._ The networks, structures made of entities (nodes) and relations between entities (links) each of which is defined by specific properties, are recurring in a large amount of social phenomena. Communities, groups, political parties, criminal organizations and international alliances are common examples of networks of interest to social scientists. Social Network Analysis (SNA) is a research methodology rooted in social psychology and in the mathematical theory of graphs that exploits formal and computational methods to investigate the structural and functional properties of social networks. Thanks to a large family of metrics and methods, SNA allows to extract insightful information and to make meaningful inferences about the features of a social network simply starting from the analysis of its structural pattern of nodes and relations [41]. The investigation develops in two phases: the construction of a graphical representation (graph) of data related to the target social phenomenon and the quantitative analysis of the graph based on standard methods and metrics. Through this process, network properties, such as resilience, vulnerability and functionality, can be inferred in a way that is not even conceivable with plain observation or more traditional social science methods (surveys, questionnaires or even statistical analysis). As emerges by a growing literature, SNA has already had large applications across the social sciences, providing a deeper understanding of many different social phenomena: language diffusion; opinion dynamics; belief systems (e.g., political ideologies); treaty systems and their historical evolution; international and transnational organizations like terrorist networks [42; 43]. Moreover, SNA can be applied to the design of robust and sustainable networks in areas of primary importance for policy-makers, like infrastructures and public transport, security and health. _Spatial analysis of social phenomena._ Geographic Information Systems (GIS) are systems designed to store, integrate, analyze, share and display information placed in spatially-/geographically-referenced contexts. GIS were first introduced in social research by social geographers to visualize and analyze spatially-referenced data about the social world. Nowadays, _SocialGIS_ allows one to investigate the spatial dimension of social phenomena being applied in various fields of social sciences, from criminology [31] to the regional economy [44], often in conjunction with other quantitative techniques. In general terms, the methodology is characterized by the capacity to offer a synoptic view of various categories of social data thanks to the superposition of several levels of information (as occurs, for example, in Google Earth). Thanks to these features, _SocialGIS_ can be a valuable ally for the policy-maker, as it enables one to monitor in innovative ways the scenarios object of intervention and the impact of public choices. _Reproduce to explain (and predict): social simulation models._ Scientific explanation is strictly connected with experiments. They not only push to explicitly formulate hypotheses about the factors and the processes that can causally explain a given phenomenon, but also allow one to validate the assumption by means of the comparison between the experiments' results and the empirical predictions made by the researcher. Social simulations are computational models that provide social scientists with the opportunity to explain in experimental terms and to predict the evolution of complex social phenomena reproducing the real-world processes that generate the social reality. The two main simulation techniques used today in social research are _Systems dynamics_[45] and _Agent-Based Models_ (ABM) [46; 47]. _System dynamics models_ are based on the idea that the evolution of a social system can be represented as the result of complex cycles of action and feedback which can be described in mathematical terms.