From Agent-based models to network analysis (and return): the policy-making perspective
Magda Fontana and
Pietro Terna ()
Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin
An important perspective use of Agent-based models (ABMs) is that of being employed as tools to support 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 applications in different fields: production, health-care scenarios, paper co-authorship, opinion spreading, etc.
Pages: 21 pages
New Economics Papers: this item is included in nep-hme and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://www.est.unito.it/do/home.pl/Download?doc=/a ... 015dip/wp_7_2015.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:uto:dipeco:201507
Access Statistics for this paper
More papers in Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin Contact information at EDIRC.
Bibliographic data for series maintained by Piero Cavaleri () and Marina Grazioli ().