Project Summary¶
Students will design an agent-based model of social norms—how they emerge, spread, and break down in a community.
The project asks: How do shared norms of cooperation, fairness, or trust develop in societies, and what forces destabilize them?
This model invites students to blend technical ABM design with social theory concepts like Durkheim’s collective conscience, Goffman’s micro-interactions, or Elinor Ostrom’s work on commons governance.
Core Elements¶
1. The Model¶
- Agents start with varied dispositions (e.g., cooperative, selfish, conditional).
- Agents interact locally (neighbors, small groups) and update their behavior based on observed norms.
- Students can add shocks (e.g., sudden resource scarcity, new external influence, an influx of outsiders) to test fragility of norms.
2. Social Theory Integration¶
- Each team selects one social theorist (e.g., Durkheim, Weber, Ostrom, Foucault) whose framework they use to guide model assumptions.
- Example:
- If using Ostrom → rules for collective resource management.
- If using Durkheim → moral consensus as glue for cohesion.
3. Experimentation¶
Students run simulations with variations:
- Initial trust levels (high vs. low).
- Network structure (tight-knit vs. fragmented).
- Leadership presence (strong authority vs. decentralized).
They analyze under what conditions norms are stable or collapse into disorder.
4. Deliverables¶
- Report: with clear links between social theory and modeling choices.
- Interactive demonstration: in NetLogo, Python Mesa, or similar.
- Reflection section: How might these simplified models inform our understanding of real-world challenges (e.g., polarization, online communities, campus culture)?
Why Doing This Work?¶
- Hands-on: Requires real modeling work (parameter design, simulation runs).
- Interdisciplinary: Merges social theory with computation in a concrete way.
- Theological/Philosophical Depth: Students reflect on the “imperfect models” Smedes highlights—what are the limits of modeling moral/social order in a fallen world?
- Scalable: Strong students can push into advanced territory (network science, machine learning for calibration), while everyone can complete a meaningful baseline project.