Inspiration
The nature vs nurture debate is often treated as static. We wanted to model identity as a dynamic process, shaped by innate ability, socioeconomic context, and cumulative feedback from success and failure.
What it does
Nature vs Nurture Simulation is an agent-based model where individuals with fixed talent and varying class backgrounds make decisions over time. Agents choose tasks, experience stochastic outcomes, and update confidence, competence, aspiration, and risk tolerance as they age. An interactive frontend visualizes these trajectories in real time.
How we built it
We implemented a Python-based agent simulation with feedback-driven identity updates, age-dependent learning decay, and class-based asymmetries in risk and reward. The system is visualized using a React + Three.js frontend that renders agents and tasks interactively.
Challenges
Balancing psychological realism with mathematical stability, preventing degenerate strategies, and translating abstract identity variables into interpretable visual behavior.
Accomplishments
A stable identity model that produces emergent inequality without deterministic rules, paired with an interactive visualization that makes complex dynamics intuitive.
What we learned
Small structural biases and feedback loops can dominate long-term outcomes, even when individual talent is held constant.
What’s next
Social interactions, policy interventions, and deeper user control over agent identity and environment.
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