Agent-Based Modeling

Agent-based models can explore patterns that emerge from individual behaviors and realistic geographic or social environments

Agent-based modeling is an approach that allows researchers to gain powerful insights about the world using highly flexible computational simulations. This track will provide an introduction to agent-based modeling methods for students who have never used these methods in their work, but are considering incorporating them directly into their research, are working with interdisciplinary teams including agent-based modelers, or simply want to become informed consumers of research using agent-based modeling. In this track, we will provide an overview of what sorts of problems might benefit from agent-based modeling, discuss key concepts in model design and use, and review available best practices and tools. Throughout, we will make use of many examples of existing agent-based models across research contexts including public health and education, and will provide attendees with opportunities for hands-on engagement with agent-based models.

Helpful preparation for this track includes:

  • Familiarity with basic statistical terminology and techniques
  • One or more research topics in mind to which you might want to apply agent-based modeling

Instructors: Ross Hammond and Matt Kasman


Group Model Building

Group model building is a participatory method for engaging and involving stakeholders in the process of building and using system dynamics models

Group model building is a participatory method for engaging and involving stakeholders in the process of building and using system dynamics models. The group model building (GMB) track will provide an introduction to the structured processes of engaging communities and other stakeholders in system dynamics modeling.  The course will cover approaches and tools for:

  • Participatory problem scoping
  • Facilitation of model development and confidence building
  • Conceptualizing the role of community participation in developing systems insights

The GMB track will place an emphasis on supporting track participants to design group model building approaches in their own lines of research & practice.

Helpful preparation for this track includes:

  • One or more research questions or topics that can be (re-) formulated from a systems perspective
  • Identification of stakeholder group(s) whose involvement would be required to address the question
  • Preliminary exposure or prior reading on qualitative causal mapping or quantitative system dynamics modeling

Instructor: Ellis Ballard and Kelsey Werner


Social Network Analysis

Social network analysis can visualize, describe, and model how information flows across social systems; how organizations, coalitions, communities, or government agencies organize and work together to achieve health or social change; and how complex systems are made up of disparate actors and entities.

The social network analysis (SNA) track will focus on the data and analytic tools used to study and evaluate social relationships and social structures. Network methods are used in public health, medicine, and other social science areas for studying infectious disease transmission, diffusion of innovations, mapping coalition and organizational structures and outcomes, social support, among many other applications. In the network track, trainees will learn four fundamental network analytic practices: network data management, network visualization, network description, and statistical modeling of networks. Participants will learn how to use cutting-edge analytic tools (Statnet and igraph packages in R) on real-world social network datasets. Participants will be provided a copy of A User’s Guide to Network Analysis in R, by Douglas Luke, published by Springer. Examples will emphasize using network methods to study social impacts of policy and practice interventions in the health and social service systems.

Helpful preparation for this track includes:

  • The latest versions of R and RStudio installed on the laptop you bring (instructions will be provided ahead of the training institute)
  • Some familiarity with basic data management and statistical programming procedures
  • One or more research topics or datasets in mind to which you want to apply network analysis

Instructors: Douglas Luke and Todd Combs


System Dynamics

System dynamics is a method for understanding system behavior over time from an endogenous (feedback) perspective using informal causal maps and formal models with computer simulation

The system dynamics (SD) track will provide an introduction to system dynamics and quantitative computer simulation modeling. System dynamics focuses on developing endogenous or feedback explanations of dynamic behavior including individual, aggregate and multilevel models of systems. The SD track, participants will learn how to frame research questions as dynamic problems, conceptualize systems from a feedback perspective, and develop a basic computer simulation model with an online interface for conducting and sharing simulation studies.

Helpful preparation for this track includes:

  • Experience and interest in building quantitative models
  • One or more research questions or topics that can be (re-) formulated from a systems perspective

Instructors: Kristen Hassmiller Lich and Nasim Sabounchi


*NEW* Systems Science Thinking and Principles in Social Systems

Systems science is an interdisciplinary field concerned with understanding systems—from simple to complex—in nature, society, cognition, engineering, technology and science itself

This track is for students who are interested in thinking about social issues from a systems science perspective. Systems science focuses on understanding the nature of systems and system-level behavior. The track will introduce students to common components, mechanisms, and overall behavior of complex systems. Students will have the opportunity to:

  • explore the application of these principles to social systems using real-life examples and a variety of hands-on experiences;
  • apply course content to their own respective areas of interest and expertise; and
  • learn about common methods used to understand systems will be introduced in this track, laying the groundwork for future methodological training and applications

No preparation for this track is needed.

Instructors: Virginia McKay and Alexandra Morshed