Computational Social Science for Sustainability

IC2S2 2025 Tutorial

Hello and welcome to the tutorial website. Below is a brief abstract, a syllabus outlining the plan and references for the tutorial, information on the history of the course, and course resources.

The tutorial is under active development. Course content is set to focus on the diffusion of adaptations in social networks through non-adaptive contagion learning and frequency- and success-biased adaptive learning strategies.

The first offering of this material as a graduate- and undergraduate-level course is occurring now, Winter quarter 2025 at the Stanford Doerr School of Sustainability, department of Environmental Social Sciences. This tutorial is a condensed three-hour overview of that material, focused on demonstrating how participants may harness this approach to social science using the socmod library to design, implement, and analyze their own models for their domains of interest.

Abstract

Humans face an existential challenge to transition to sustainable practices that do not exhaust available ecological, economic, and social capital. Computational social-cognitive models can be used to deduce the efficacy of potential training or educational interventions to promote sustainable practices. Tutorial attendees will learn to use the socmod library to create their own models of social learning and social influence to predict the relative success of different intervention strategies. Sustainability motivates this work, but the framework could be used to model related social and behavioral contexts.

Syllabus

The following outlines the three-hour tutorial.

Pre-requesites/pre-workshop

It would help to have installed R and RStudio.

Review the Introduction in the Course Notes. We will not cover material from the cooperation and coordination subsections.

Hour 1: Diffusion of adaptations through social networks

  • socmod and the social behavior modeling framework.
  • Non-adaptive contagion models of social learning.
    • Definition—a person adopts a behavior with some probability if they are exposed as in epidemiology; agent-based formulation.
    • Comparison with well-mixed and large-\(N\) formal calculations.
  • Adaptive success-biased learning can boost diffusion of adaptations.
    • Definition—higher fitness means greater chance to be a teacher/demonstrator; agent-based formulation.
    • Comparison with well-mixed, large-\(N\) formal calculations.
    • Note: the frequency-biased strategy is another type of adaptive learning where a learner is more likely to adopt behaviors they observe to occur more frequently.

Hour 2: Social influence of belief and opinion

  • Opinion dynamics: opinions (or beliefs, etc.) as points in space; discourse as forces acting between opinions.
  • Agent-based modeling applications:
    • Group polarization from stubborn extremism.
    • Understand whcih factors make political polarization more or less random and path dependent (Matthew A. Turner and Smaldino 2018).

Hour 3: Models meet real world

  • Group polarization replications may be marred by high false discovery rates.
  • How minority-majority core-periphery structure can promote sustainable adaptations (Matthew A. Turner et al. 2023).

Resources

  • The socmod R library provides a toolkit for building and running iterative models of social behavior. It focuses on agent-based models, currently on social learning models of the diffusion of adapta tions. It integrates with igraph to enable agent-based model initialization with real-world networks.
  • The Course Notes provide useful information (sometimes) in narrative form explaining the development and theory of this modeling framework.

References

Turner, Matthew A, Alyson L Singleton, Mallory J Harris, Ian Harryman, Cesar Augusto Lopez, Ronan Forde Arthur, Caroline Muraida, and James Holland Jones. 2023. “Minority-group incubators and majority-group reservoirs support the diffusion of climate change adaptations.”
Turner, Matthew A., and Paul E. Smaldino. 2018. “Paths to Polarization: How Extreme Views, Miscommunication, and Random Chance Drive Opinion Dynamics.” Complexity. https://doi.org/10.1155/2018/2740959.