Abstract
Public policies are concerned with the definition of guidelines that promote the achievement of specific goals by a society/community of citizens. Citizens share a common geographic space under a set of political governance rules and, at the same time, are subject to common cultural influences. On the other hand, citizens have differentiated behaviours due to social, economical, and educational aspects as well as due to their individual personalities. Public interest relates with individual interests held in common—the result of joining the individual goals for the community. However, community goals may conflict with individuals’ self-interests. The outcome of public policies is emergent from this very complex set of rules and social and individual behaviours. Decision support in such a context is a hard endeavour that should be founded in comprehensive exploration of the set of available designs for the individual actors and the collective mechanisms of the society. Social simulation is a field that can be useful in such a complex problem, since it draws from heterogeneous rationality theory into sociology, economics, and politics, having computational tools as aid to perform analysis of the conjectures and hypotheses put forward, allowing the direct observation of the consequences of the design options made. Through social simulation it is possible to gain insights about the constraints and rules that effectively allow for the design and deployment of policies. The exploration of this set of possible models for individual actors, their relationships, and collective outcome of their individual actions is crucial for effective and efficient decision support. Ever since the work of Simon (1955), it has been known that perfect rationality is not attainable in a useful and timely fashion. Social simulation provides an alternative approach to limited rationality, since it encompasses both observer and phenomenon in the experimentation cycle. Decision support systems can be enhanced with these exploratory components, which allow for the rehearsal of alternative scenarios, and to observe in silica the outcomes of different policy designs before deploying them in real settings.
Key Terms in this Chapter
Rationality: The mental apparatus with which agents make decisions. Rationality in this context should be viewed as individual and situated, so that agents form heterogeneous communities.
Complex Social Phenomena: Puzzling and defying phenomena occurring in a social context, which are characterised by unpredictability, high-dimensionality, nonlinearity, and many times computational intractability.
Emergent Processes: Processes that display an outcome that cannot be predicted from its specification and can only be observed by analysis of the dynamics of the process.
Multi-Agent System: A computational system composed of several agents, collectively capable of reaching goals that are difficult to achieve by an individual agent or a monolithic system.
Social Simulation: A simulation experiment that focuses on complex social phenomena. Many social simulations are built from the bottom up by using multi-agent-based simulation.
Micro-Macro Link: The mutual influence between individual actions and overall societal behaviour. Individual actions are a consequence of both personal rationality and a perception of the social environment. At the same time, the overall behaviour of the society is the complex addition of all the individual actions.
Policy Decisions Support: A set of computational tools, projected by means of theoretical analysis, especially designed to evaluate the efficacy and consequences of policies and allowing for the simulation of scenarios and analysis of outcomes.
Multi-Agent-Based Simulation: A simulation experiment carried out through the paradigm of multi-agent design. Society is thus built from the bottom up, by founding overall social behaviour on the rationalities and decisions of individual agents and their organisation in the multi-agent system.