DOI

Summary
The chapter provides an introduction to complex systems modelling for public policies analysis and development. It opens with a brief definition of complex systems and it subsequently shows some examples of methods and methodologies that can be applied to the study of complex social systems. These methods comprehend:
- Network analysis
- Information theory
- Cellular Automata (CA)
- Agent-based models (ABM)
- Data mining
It also suggests some software that can be successfully used for carrying out these researches:
- Python
- QGIS
- NetLogo
- R
Moving forward, the author provides some examples of “complex objects” that can be analyzed thanks to these instruments:
- Societies
- Economy
- Cities
- The Environment
- Education
- Transports
- The legislative process
Finally, the chapter concludes with some suggestions for the policy makers, who, when studying new policies and devising new models of analysis, should always bear in mind that:
- Agents are heterogeneous, meaning that models cannot take into account a “general/average person, institution or firm”.
- Everything is interconnected, which, in terms of public policy, brings awareness to the fact that linear type analyses might be inadequate or insufficient
- Policies does not work with clear, linear or immediate cause and effects, which means that policy makers should always proceed with caution and follow a process of “trial and error” based on a grassroot/bottom-up strategy. In the authors words: “policy might be more effective if geared towards: i) improving the resilience of the system and decreasing its vulnerability, ii) avoiding dangerous tipping points, iii) identifying the key actors in a network that can promote changes in the system”.

Relevance for Complex Systems Knowledge
The text call for a stronger collaboration between scientists (especially computer scientists), experts of social sciences and policy makers. It provides valuable information by presenting a list of methods and methodologies that can be readily applied for a wide spectrum of “complex social objects”. It also briefly presents some software that have proven successful in designing models of complex systems. Finally, it provides a list of benefits and important insights that policy makers and scientists should bear in mind when designing models of complex systems.

Point of Strength
- It presents some software
- It has a clear interdisciplinary perspective
- It presents differences and similarities between hard sciences models and social systems models
- It provides clear methodologies and tools to be used when designing complex social models
- It provides suggestions for policy makers