Economic Models
Survey of foundational and emerging economic modeling approaches highlighting how interactive, HCI‑driven models seed grassroots transformation.
1. Origins & Interactive Explorations
Predator–Prey Virtual Analog: an interactive web app used coupled differential equations to illustrate resource–consumer cycles, sparking insights into feedback loops and oscillatory dynamics.
Key Insight: Simple, interactive visual models can turn abstract equations into embodied learning experiences, revealing non‑linear behaviors and stability boundaries.
2. System Dynamics Foundations
Jay Forrester’s Legacy: Founder of system dynamics—pioneered feedback‑loop modeling for industrial, urban, and ecological systems.
Donella Meadows & The Limits to Growth: Applied system‑dynamics simulations to global resource use, demonstrating overshoot, collapse, and leverage points for sustainability.
Tools & Practices:
Vensim / InsightMaker for stocks & flows
Causal loop diagramming workshops to map economic feedbacks
3. Doughnut Economics: A New Paradigm
Kate Raworth’s Doughnut Model: Defines a safe and just space for humanity between social foundation and ecological ceiling. Doughnut Economics Action Lab
HCI & Grassroots Impact: Interactive online dashboards and participatory workshops empower communities to localize the Doughnut framework.
Case Example: Amsterdam’s Doughnut City initiative—mapping local data onto the doughnut and co-designing action plans.
Grassroots Economics:
4. Modeling Snapshots
Agent Variables (Predator–Prey): { preyPop: number, predatorPop: number, growthRate: %, predationRate: % }
System Dynamics Loop (Doughnut): Stock: socialCapital; Flow: investment; Constraint: resourceCeiling; Feedback: regulatoryPolicy
Hybrid Approaches: Combining ABM agents for market behaviors with SD loops for macro‑economic flows.
5. Interactive Tools & Resources
Web Simulators: NetLogo predator–prey models, InsightMaker Doughnut templates.
Visualization Libraries: D3.js for live dashboards; P5.js for rhythmic fractal economics visualizations.
Community Platforms: GitHub repos, Miro boards, and Slack channels for open‑source economic model collaboration.
6. Integrating VIM & Doughnut Economics for Trustworthy AI
By combining VIM’s four domains with Doughnut Economics’ grassroots framing, communities can co-design AI ecosystems that stay within both social and planetary boundaries:
Embodied Interaction Workshops: Local labs where participants use biofeedback and VR experiences to sense AI’s impact on wellbeing, grounding abstract AI risks in felt reality.
Relational Mapping Dashboards: Community-curated AI impact maps that track data flows, bias hotspots, and communication norms against the Doughnut’s social foundation and ecological ceiling.
Ethical Alignment Canvases: Values-driven forums where stakeholders identify “shadow” AI harms (e.g., surveillance, disinformation) and prototype restorative feedback loops to realign models.
Adaptive Learning Sprints: Participatory simulations and hackathons where open-source AI models are iteratively tuned on local datasets, with metrics tied to Doughnut indicators like digital inclusion, data sovereignty, and carbon footprint.
Transformative Potential: This synergy empowers decentralized, human-centered AI governance—melding the rigors of system modeling with grassroots empathy to create resilient, equitable, and energy-efficient AI systems that honor universal wellbeing.
7. Further Reading & References
Forrester, J. W. (1961). Industrial Dynamics.
Meadows, D. H., Meadows, D. L., & Randers, J. (1972). The Limits to Growth.
Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st‑Century Economist.
Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World.
J. Sterman, “System Dynamics: Systems Thinking and Modeling for a Complex World,” Massachusetts Institute of Technology. Engineering Systems Division, Working Paper, May 2002. Accessed: Nov. 15, 2025. [Online]. Available: https://dspace.mit.edu/handle/1721.1/102741
Fishwick, P. A. (2007). “Modeling, Simulation, and Graphics for Economics Education.” Simulation & Gaming, 38(4), 432–449.
Wilensky, U., & Rand, W. (2015). An Introduction to Agent‑Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo.
K. Doore and P. Fishwick, “Prototyping an analog computing representation of predator prey dynamics,” in Proceedings of the Winter Simulation Conference 2014, Dec. 2014, pp. 3561–3571a. doi:10.1109/WSC.2014.7020186.
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