How do humans change their mind?
We implemented a tier of psychological archetypes. Probabilistic Bayesians rationally update their beliefs based on ground-truth market signals. However, Loss-Aversion agents are paralyzed by the sunk-cost fallacy of their past education, and Anchored Extrapolators refuse to accept that their current skills are becoming obsolete until they are structurally unemployed.How does information propagate? We modeled society as a small-world network graph. When an agent gets laid off due to AI displacement, panic spreads to their immediate neighbors. We coded a "Contagion Penalty" so that if your peers are unemployed, your own perception of the market crashes, suppressing your wage discovery and discouraging you from taking risks to reskill.
Are all humans created equal? No. The simulation strictly models information asymmetry. Agents in "Corporate" or "Credentialed" paths have access to cleaner signals about the Ground Truth of labor demand. Agents in lower-tier paths are fed "Public Narrative" noise, which severely delays their ability to recognize that they need to reskill, mathematically trapping them in a cycle of frictional unemployment.
Quantifying Predictions It's not just a visualization but also a causal inference tool. The engine supports headless, multi-seed "sweeps" that generate longitudinal .csv reports. We can quantitatively prove, across thousands of ticks, exactly which cognitive archetypes/initial jobs are most vulnerable to structural disruption, and at what conditions the labor market collapses.
Why I Built It This Way I chose to build a native Python physics and economic engine because it allowed me to execute thousands of complex psychological micro-decisions per second. By grounding our agents in the mathematics of prospect theory rather than natural language generation, I produced a reproducible simulation of humanity colliding with the AI frontier that isn't a total black box.
Built With
- css
- economics
- html
- javascript
- math
- python
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