Overview Dashboard
Key metrics from the WildMind simulation at a glance.
Zipf's Law Analysis
The rank-frequency distribution of sounds in the emergent language. Natural languages follow Zipf's Law with slope near -1.0 on a log-log plot.
Heaps' Law Analysis
Vocabulary growth as a function of corpus size. Natural languages exhibit beta between 0.4 and 0.6.
Network Topology
Social network structure from citizen interactions. Compares clustering and path lengths to random graph baselines.
Vocabulary Growth
Shared vocabulary size over simulation ticks, with best-fit growth model.
Influence & Cascades
How vocabulary spreads through the population. Linguistic influence ranking and information cascade tracking.
Language Dictionary
Complete lexicon data: shared community words and personal citizen vocabularies.
SHARED LEXICON
PERSONAL LEXICON (ALL CITIZENS)
Phoneme Analysis
Character-level frequency analysis of all lexicon sounds, color-coded by vowel/consonant. Radar chart shows archetype phoneme profiles.
Communication Efficiency
Success rate and utterance length trends, plus context type breakdown.
Survival & Population
Death causes, age distributions, and population trends.
World History
Comparison of all simulation worlds.
Training History
Additive training runs per citizen: dataset composition across versions.
Methodology
How the simulation, language metrics, and network analysis work.
Simulation Architecture
Each citizen is an independent AI model (fine-tuned LLM) that perceives its environment, makes decisions, and generates vocalizations. Citizens live in a simulated wilderness with latitude-based climate, biomes, predators, prey, and survival mechanics. Language emerges through repeated contextual interactions -- when two citizens encounter the same stimulus and both vocalize, the co-occurrence of sound and context gradually binds them into meaning.
Zipf's Law Computation
We count the frequency of each unique sound in the full utterance log. Sounds are ranked by frequency. On a log-log plot, Zipf's Law predicts a linear relationship with slope approximately -1.0. We compute the coefficient via OLS linear regression on log(rank) vs log(frequency). Natural human languages typically show coefficients between -0.8 and -1.2.
Heaps' Law Computation
Heaps' Law models the relationship: V(n) = K * n^beta. Beta values between 0.4 and 0.6 indicate natural-language-like sublinear growth. Values near 1.0 mean every token is novel. Values near 0.0 mean the vocabulary saturated early.
Network Analysis
The social network is constructed from interaction history. Edges are weighted by interaction frequency and relationship score. We compute the clustering coefficient, average path length, and degree distribution. These are compared to an Erdos-Renyi random graph with the same parameters.
PageRank / Influence
Linguistic influence uses modified PageRank on the vocabulary transfer graph. An edge from A to B exists if B learned a sound that A established. Citizens with high PageRank are vocabulary hubs.
Known Limitations
Small population (6-15 citizens) limits statistical power. The LLM substrate introduces biases. Phoneme analysis operates at character level. Zipf and Heaps fits may be noisy at low token counts. Cross-world comparisons are confounded by parameter changes.
API Documentation
Public endpoints for programmatic access to simulation data.
Base URL
https://cosmic-piroshki-aeb441.netlify.app
GET /api/state
Full simulation state. Primary endpoint used by this page.
curl -s https://cosmic-piroshki-aeb441.netlify.app/api/state | jq '.shared_lexicon[:3]'
GET /api/api?endpoint=meta
API metadata and available endpoints.
curl -s "https://cosmic-piroshki-aeb441.netlify.app/api/api?endpoint=meta" | jq .
GET /api/api?endpoint=citizens
All alive citizens with positions, mood, energy, personality, vocabulary.
curl -s "https://cosmic-piroshki-aeb441.netlify.app/api/api?endpoint=citizens"
GET /api/api?endpoint=lexicon
Shared and personal lexicons.
curl -s "https://cosmic-piroshki-aeb441.netlify.app/api/api?endpoint=lexicon"
GET /api/api?endpoint=interactions&limit=50&offset=0
Paginated interaction history. Max 500 per page.
GET /api/api?endpoint=science
Latest science metrics: Zipf, Heaps, network, cascades, growth, efficiency, semantic fields.
GET /api/api?endpoint=utterances&limit=50&citizen_id=ID
Utterance log. Optionally filter by citizen. Max 500.
GET /api/api?endpoint=history&table=TABLE&limit=50
Historical data. Allowed tables: science_metrics, state_snapshots, utterance_log.
Response Format
All endpoints return JSON with CORS headers. Error responses include an error field.
Data Export
Download current simulation data for offline analysis.
Generated from current API response. Refresh for latest.
Social Network
Relationship graph data: bond types, strengths, and distribution.