# Computational Social Science, the Evolution of Policy Design and Rule Making in Smart Societies
## 5 _In Silico_ Management of Social Complexity: Agent-Based Models and Policy Design  
Among the different CSS methodologies so far tested in policy-making, social simulation occupies a very special place. The opportunity, offered by the simulations, to better understand and, above all, to somehow predict the evolution of complex socio-economic phenomena has long attracted the attention of scholars with different scientific backgrounds for various reasons who are interested in policy issues. In an frequently cited essay published in 1998 [65], the American economist [PERSON] provides an effective representation of the reasons that should prompt policymakers to look at the scientific paradigm and the investigative methods (simulations in particular) offered by computational social science: social systems and economic dynamics show all of the features of complex phenomena [5; 66]. Generated by feedback loops, evolutionary processes and interactions between individuals who constantly change their behavior, the social and economic phenomena are complex, nonlinear and difficult to predict through the most common methods of analysis. However useful, the scientific tools usually supporting policy modeling, mainly based on predictive statistical analyses, show inherent limitations because of their inability to account for the role that the individual and the local dimension plays in determining the overall development of social dynamics. The causal role played, in the evolution of social phenomena, by the individual preferences, by psychological, cognitive and cultural factors or, again, by the reactions of different social actors to political decisions is a puzzle that is difficult to solve with traditional approaches. The circumstance is confirmed, with specific reference to economic policy, in a statement made by the former European Central Bank president [PERSON] in November 2010:  
_\"When the crisis came, macro models failed to predict the crisis and [...] to explain what was happening [...] in the face of crisis, we felt abandoned by conventional tools [...] The key lesson [...] is the danger of relying on a single tool, methodology or paradigm.\"_  
What is needed, in this scenario, is a research methodology allowing one to capture the microfoundations of macro-social outcomes, and the above-mentioned agent-based models represent the most promising solution to this end. [PERSON]'s speech contains a meaningful statement in this regard:  
_\"A large number of aspects of the observed behavior of financial markets is hard to reconcile with the efficient market hypothesis. But a determined empirical approach--Which places a premium on inductive reasoning based on the data, rather than deductive reasoning grounded in abstract premises or assumptions--lies at the heart of These methods [...] simulations will play a helpful role. Agent-based modeling [...] Allows for more complex interactions between agents.\"_  
Having been designed to illuminate and predict the micro-level processes at the root of social macro-phenomena, agent-based models were soon considered as a possible tool to support policy-makers. The focus on this research perspective has grown considerably over the last ten years [67; 68; 69], transcending the boundaries of the scientific world and, as shown by the [PERSON]'s speech, reaching the world of institutions. The issues addressed by the research conducted in this area are very heterogeneous, ranging from the management of environmental resources to the impact of land use decisions, from the effects of economic policies to that of retirements [70; 71; 72]. Following the classification proposed in [73] (see, also, for a more detailed analysis, [74]), social simulation models conceived for policy design can be divided into two categories characterized by different theoretical premises and development methods: the _prescriptive_ and the _participatory models_, two categories on which it is interesting to offer some detail also in view of the considerations that will be proposed later on.