Policy Modelling for Complex Issues

Category: Workshop
Posted by: Aaron

The world around us is filled with independent and interacting elements; elements that adapt to their environments and change their behavior in unexpected ways; elements that learn and deceive and plan for the future. In this world, a small perturbation can cascade into a global phenomenon, and major incidents can be muted by systemic absorbers and barriers. Incentives are unclear, motivations are constantly changing, behaviors are both erratic and dogmatic, and the future is not like the past. To achieve your desired results in this world, you need to understand its complexity.

This workshop will provide an introductory treatment of complexity theory and the application of formal models to better understand the causal structures and dynamics of complex adaptive systems. The focus will be on complexity in social systems (public policy, economy, group dynamics, peer influence, etc.), but a significant amount of material will be brought in from all disciplines. This is crucial to understanding complexity in general, and reflects the essentially interdisciplinary nature of complex systems science.

Although a breadth of methodologies will be surveyed to understand the foundations of complexity, we will focus on computer models and simulations because of their close ties to complexity. Agent-based modeling is a research technique that allows researchers to directly encode their theories regarding the causal interactions of system elements and discover what is entailed by those interactions. It is especially helpful for understanding systems that are non-linear, are non-equilibrium, have intermediate numbers of agents, incorporate spatial information, have networked interactions, and/or have dynamic features difficult or impossible to model with other techniques.

Netlogo Software:
We will make extensive use of the Netlogo software package for running demonstrations and participatory models. It can be downloaded for free from the Netlogo website.

Recommended Reading:
John H. Miller and Scott E. Page. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.

Joshua M. Epstein and Robert L. Axtell. (1996). Growing Artificial Societies: Social Science From the Bottom Up. Cambridge MA: MIT/Brookings Institution.

Thomas C. Schelling. (1978). Micromotives and Macrobehavior, Norton. (Especially Chapters 1-4)