| --- |
| license: mit |
| task_categories: |
| - time-series-forecasting |
| - other |
| tags: |
| - artificial-life |
| - consciousness |
| - evolutionary-computation |
| - code-evolution |
| - emergence |
| - hard-problem |
| - digital-organisms |
| - ecosystem-dynamics |
| - pandemic-simulation |
| language: |
| - en |
| pretty_name: "Primordial: Artificial Life Evolution Dataset" |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Primordial: Artificial Life Evolution Dataset |
|
|
| **Tick-by-tick evolution data from digital organisms with body (executable code) + mind (structured knowledge). Sexual reproduction, pandemics, mass extinction, predation — all emerged from simple rules.** |
|
|
| ## Why This Dataset Exists |
|
|
| Most AI datasets capture static snapshots. This dataset captures **dynamic evolution** — digital organisms eating code, reproducing sexually, getting sick, dying, and being selected by nature over 2000 ticks. |
|
|
| No existing dataset provides: |
| 1. **Ecosystem dynamics with full observability** — population, disease, energy, all tracked per tick |
| 2. **Emergent phenomena from simple rules** — no behavior was hardcoded |
| 3. **Sexual reproduction + pandemic cycles + mass extinction** in one simulation |
|
|
| ## Dataset Contents |
|
|
| ### `train.parquet` — Evolution Log (2000 records, Parquet format) |
|
|
| Also available as `sim3_evolution.jsonl` (raw JSONL). |
|
|
| One record per simulation tick. 60 initial entities, 2000 ticks. |
|
|
| **18 fields per record:** |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `tick` | int | Simulation step (1-2000) | |
| | `alive` | int | Living entities | |
| | `males` | int | Male count | |
| | `females` | int | Female count | |
| | `sick` | int | Currently infected | |
| | `max_gen` | int | Highest generation alive | |
| | `avg_energy` | float | Mean energy across population | |
| | `avg_body` | float | Mean body size (function count) | |
| | `avg_mind` | float | Mean mind size (knowledge nodes) | |
| | `max_body` | int | Largest body | |
| | `max_mind` | int | Largest mind | |
| | `avg_age` | float | Mean age in ticks | |
| | `born` | int | Births this tick | |
| | `died` | int | Deaths this tick | |
| | `eaten_pred` | int | Predation kills this tick | |
| | `mated` | int | Successful matings this tick | |
| | `infections` | int | New infections this tick | |
| | `famine` | bool | Famine period active | |
|
|
| ### Key Statistics |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Max generation | 22 | |
| | Total born | 4,785 | |
| | Total predation kills | 3,901 | |
| | Total infections | 6,447 | |
| | Peak population | 515 (tick 327) | |
| | Min population | 14 (tick 56) | |
| | Peak pandemic | 69% infected (tick 556) | |
| | Mass extinctions | 3 (ticks 600, 1200, 1800) | |
| | Carrying capacity | ~300-400 (emerged, not hardcoded) | |
|
|
| ## Emergent Phenomena Captured |
|
|
| 1. **Ecological oscillation** — population self-regulates around carrying capacity without any target |
| 2. **Pandemic waves** — disease peaks then declines as immune memory spreads; new strains restart cycle |
| 3. **Post-extinction recovery** — after 50% die-off, population rebounds within ~100 ticks; survivors are fitter |
| 4. **Gender homeostasis** — M/F ratio stays ~50:50 despite stochastic assignment |
| 5. **Generation acceleration then stabilization** — early gens appear fast, then stabilize to ~90-100 ticks/gen |
|
|
| ## How To Use |
|
|
| ### Load with HuggingFace |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("jkdkr2439/Primordial-Evolution") |
| ``` |
|
|
| ### Load directly |
| ```python |
| import pandas as pd |
| df = pd.read_parquet("train.parquet") |
| df.plot(x="tick", y=["alive", "sick"], figsize=(12, 4)) |
| ``` |
|
|
| ### Prediction task |
| ```python |
| # Predict next 100 ticks from previous 100 |
| # Input: df[0:100], Output: df[100:200] |
| # Features: alive, sick, avg_energy, born, died, famine |
| ``` |
|
|
| ## Connection to Consciousness Research |
|
|
| This dataset comes from the [Primordial](https://github.com/jkdkr2439/Primordial-Hard-Problem-of-Consciousness) project — a computational framework studying emergence of self-reflective behavior in digital organisms, with implications for the Hard Problem of Consciousness (Chalmers, 1995). |
|
|
| ## Entity Architecture |
|
|
| ``` |
| Entity = Body (Python functions) + Mind (NMF knowledge graph) |
| - Body: digest code, build new functions, decay unused ones |
| - Mind: absorb knowledge, compress to DNA, decay unused nodes |
| - Sex: M encodes gamete (cheap), F decodes + builds child (expensive) |
| - Immune: antibody memory, virus corrupts body functions |
| - Lifecycle: Vo (dormant) > Sinh (born) > Dan (growing) > Chuyen (transform) |
| ``` |
|
|
| ## Physics Laws (all hardcoded, no entity can break) |
|
|
| - Syntax validity: `ast.parse()` — invalid code = dead |
| - Energy conservation: eating gives energy, existing costs energy |
| - Complexity cost: more code = more expensive to maintain |
| - Aging: older entities cost exponentially more |
| - Famine: every 250 ticks, food drops to 30% |
| - Extinction: every 600 ticks, bottom 50% killed |
|
|
| ## Author |
|
|
| Tung Nguyen (Kevin T.N.) |
|
|
| ## License |
|
|
| MIT |
|
|
