This company's main purpose is to help forward the fields of complex systems and agent-based modeling. In many cases this is best done by constructing models in collaboration with individuals and organizations that lack this capability. In other cases the best approach is to teach the theories and techniques directly to practitioners so they can build models on their own. Our group specializes in small-to-medium scale workshops for graduate students, professors, engineers, and managers.

We offer a variety of workshops, and some of the standard modules are described briefly below. These can be combined, abbreviated, and extended as desired; furthermore we can adapt the material (and instructors) to your individual needs (e.g., your department's research domain or business applications). These can be anywhere from 2 hours to a full month and they can be arranged at any time of the year, anywhere on the globe. If you are interested in learning more about hosting a workshop at your institution, then please feel free to contact us.

Understanding Complexity and Agent-Based Models

Decision makers are increasingly faced with a complex world with uncertain consequences of their actions. To cope with this dynamic and adapting world, we are increasingly relying on models of these complex phenomena to inform us in situations where our intuitions fail us. 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 (e.g. public policy, economy, group dynamics, peer influence), but examples will pull from all domains due to the essentially interdisciplinary nature of complex systems science. Through examples and hands-on exercises, participants will learn to interact with and interpret agent-based models for making better decisions in a complex world.

Introduction to Agent-Based Modeling

This workshop module is designed to get individuals with no coding experience started in writing agent-based model. The only way to learn coding is to write code, and therefore we get the participants to start coding through structured incremental changes to the Schelling Segregation model using Netlogo. These exercises introduce students to finding and adapting existing code, locating help from the programming dictionary, writing fresh code, and eventually to conceptualizing and building their own extensions. This is provides a foundation from which the participants can grow on their own. We also cover the basics in collecting and analyzing data, as well as other components depending on the time available and the audience's needs.

Complex Systems Theory and Practice

The term "complexity theory" is used to refer to a loose collection of ideas about how the order we see all around us is generated by the behaviors and interactions of small constituent parts. There is no straightforward definition of complexity, though there is a general consensus over what objects of study are included: subatomic particles, molecules and genes, organisms, societies, organizations, ecologies, economies, political structures, weather and climate, and astronomical bodies to name a few. These systems undergo chaotic behavior, path dependencies, tipping points, and collapse, and yet through their evolutionary processes they are also self-organizing and robust. This workshop presents the core tenets and major results from complexity theory across all domains, making ties among them to strengthen your understanding of how the world works. It is usually taught alongside one of the methodological modules as a survey of complex systems in its many forms.

Network Theory and Network Models

There is a large and growing interest in using networks to represent all kinds of systems: infrastructure, circuits, interaction networks, social ties, political affiliations, conceptual similarity, and bibliometric data to name a few. The representative power of the graph structure combined with a reasonable number of intuitive measures and freely available software have combined to make networks a standard technique for capturing relational data and structuring interactions in simulations. The ubiquity and usefulness of networks make understanding them essential for anybody, and this workshop module trains participants in recognizing the distinct characteristics of networks and (if desired) how to build and measure them.

Visualization and Analysis of Simulation Data

The ability to build an agent-based or other simulation model is only the first step in process. After you run experiments with that model, sweeping across parameters and doing hundreds of runs with each combination, you are left with an enormous dataset. What do you do with this dataset? The typical approaches to analyzing ABM data are completely wrong: averaging, trending, curve-fitting, and showing representative runs. This workshop module teaches participants what they should do with their data: nonparametric nonaggregated comparisons, Bayesian estimation and inference, interactive plotting, and measures of dynamical properties.

Advanced Agent-Based Modeling

People who have taken the Introduction to Agent-Based Modeling workshop module, or who have studied computer modeling before, may be ready to take their modeling to the next level. Some aspects of modeling can only be learned through experience, but other abilities (which can take years to discover and develop without guidance) can be taught. This module is designed to get participants who are already building their own agent-based models and teach them some advanced techniques and tools to amplify their skills and expand their range.

© 2010-2012 Complexity Research Corporation