From Agent-based models to network analysis (and return): the policy-making perspective

ort decision systems in policy-making, in the complex systems framework. Such models can be usefully employed at two different levels: to help in deciding (policy-maker level) and to empower the capabilities of people in evaluating the effectiveness of policies (citizen level). Consequently, the class of ABMs for policymaking needs to be both quite simple in its structure and highly sophisticated in its outcomes. The pursuing of simplicity and sophistication can be made more effective by applying network analysis to the emergent results. Actually, in today’s world the consequences of choices and decisions and their effects on society, and on its organization, are equally relevant. Considering the agent-based and network techniques together, we have a further important possibility. Since it is easier to have network data (i.e. social network data) than detailed behavioral individual information, we can try to understand the relationships between the dynamic changes of the networks emerging from agent-based models and the behavior of the agents. As we understand these connections, we can apply them to actual networks, to try to understand what the behavioral black boxes of real-world agents contain. We propose a simple basic structure where events, scheduled upon time, call upon agents to behave, to modify their context, and to create new structures of links among them. Events are organized as collections of small acts and steps. The metaphor is that of a recipe, i.e. a set of directions with a list of ingredients for making or preparing something, especially food (as defined in the American Heritage dictionary). Technically, recipes are sequences of numerical or alphanumerical codes, reported in vectors, and move from an agent to another determining the events and generating the edges of the emerging networks. A basic code will be shown, useful to manage possible