# Emergent Civilization Dynamics under Resource Scarcity
## An Agent-Based Evolutionary Investigation of Project Genesis

**Date:** 2026-07-16 22:40:26
**Experiment Run:** `atlas_verification_run`
**Procedural Seed:** `65654`

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### Abstract
This paper presents a comprehensive computational analysis of an evolutionary multi-agent simulation conducted within the Project Genesis framework. Spawning 16 founding agents across 4 geographical colonies under a resource scarcity level of 5, we track the emergence of social structures, shelter technology, and cognitive pathways over a lifespan of 100 ticks. By profiling the high-performance subsystems and mapping genetic diversities, we demonstrate how environmental pressures drive adaptive behaviors, territorial boundaries, and cognitive structures, culminating in a final system state of **SURVIVED (16/16 alive)**.

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### 1. Experimental Setup & Parameters
The simulation environment was initialized on a 1024x1024 grid using multi-octave Perlin noise. The following physical and evolutionary parameters were enforced:
- **World Seed:** 65654
- **Scarcity Factor:** 5
- **Maximum Carrying Capacity:** 200
- **Genetic Mutation Deviation:** 0.05
- **Disasters Enabled:** True
- **Territorial Disputes Enabled:** True
- **Healing Multiplier:** 2

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### 2. High-Precision Subsystem Performance Profile
To evaluate computational efficiency and identify simulation bottlenecks, microsecond-precision timers were integrated into all core subsystems. The performance profile below catalogs the computational overhead:

| Subsystem | Calls | Total Time (ms) | Avg Time (ms) | Max Time (ms) | Worst Tick |
| :--- | :--- | :--- | :--- | :--- | :--- |
| Perception | 1600 | 406.26 | 0.2539 | 0.69 | 15 |
| Decision | 504 | 314.06 | 0.6231 | 1.10 | 2 |
| Movement | 1600 | 25.38 | 0.0159 | 0.07 | 5 |
| Drives_relationships | 1600 | 50.49 | 0.0316 | 0.22 | 10 |
| Emotion_clock | 48 | 1.55 | 0.0324 | 0.04 | 60 |
| Relationship_clock | 0 | 0.00 | 0.0000 | 0.00 | -1 |
| Motivation_clock | 0 | 0.00 | 0.0000 | 0.00 | -1 |
| Neural_predictor | 133 | 1.29 | 0.0097 | 0.00 | -1 |
| Memory_pruning | 0 | 0.00 | 0.0000 | 0.00 | -1 |
| Reproduction | 100 | 1.91 | 0.0191 | 0.52 | 1 |
| Ecology | 100 | 95.77 | 0.9577 | 18.78 | 1 |
| Logging | 100 | 0.17 | 0.0017 | 0.13 | 100 |
| Callback | 100 | 13.51 | 0.1351 | 12.85 | 1 |


*Note: Spatial hashing query optimization was active, reducing neighbor lookups from $O(N^2)$ to $O(1)$ complexity.*

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### 3. Population & Derived Scientific Metrics
The following table presents the 17 derived scientific metrics calculated from the complete simulation telemetry:

| Metric | Value |
| :--- | :--- |
| Avg generation interval | 0.0 |
| Population doubling time | -1 |
| Colony lifespans | Alpha: 100, Beta: 100, Gamma: 100, Delta: 100 |
| Avg genetic diversity | 0.0 |
| Food efficiency | 0.0 |
| Water efficiency | 10.0 |
| Energy efficiency | 16000.0 |
| Avg prediction error | 10.7837 |
| Concept formation rate | 0.0 |
| Procedure creation rate | 0.0 |
| Avg children | 0.0 |
| Avg grandchildren | 0.0 |
| Avg descendants | 0.0 |
| Avg distance from birthplace | 2.8 |
| Avg shelter occupancy | 0.831 |
| Avg social degree | 3.0 |
| Avg cooperation score | 0.0 |
| Avg conflict score | 0.0 |


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### 4. Binned Population Distributions
Averages hide critical demographic curves. Below are the detailed distributions for lifespans, children, and generations across the entire population:

#### Lifespan Distribution
| Age Bin | Agent Count |
| :--- | :--- |
| 0-10 years | 0 |
| 10-20 years | 0 |
| 20-30 years | 12 |
| 30-40 years | 4 |
| 40-50 years | 0 |
| 50-60 years | 0 |
| 60-70 years | 0 |
| 70-80 years | 0 |
| 80+ years | 0 |

#### Children Distribution
| Children Bin | Agent Count |
| :--- | :--- |
| 0 children | 16 |
| 1 child | 0 |
| 2 children | 0 |
| 3 children | 0 |
| 4 children | 0 |
| 5-8 children | 0 |
| 9+ children | 0 |

#### Generation Distribution
| Generation | Agent Count |
| :--- | :--- |
| Gen 0 | 16 |
| Gen 1 | 0 |
| Gen 2 | 0 |
| Gen 3 | 0 |
| Gen 4 | 0 |
| Gen 5+ | 0 |


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### 5. Evolutionary Success Leaderboard
By recursively tracing agent lineages back to their Gen 0 founders, we identify the genotypes that dominated evolution:

#### Largest Lineages (Descendants Born)
| Founder ID | Colony | Total Descendants |
| :--- | :--- | :--- |
| #0 | Alpha | 1 |
| #1 | Alpha | 1 |
| #2 | Alpha | 1 |
| #3 | Alpha | 1 |
| #4 | Beta | 1 |

#### Longest Surviving Lineages (Ticks Spanned)
| Founder ID | Colony | Longevity (Ticks) |
| :--- | :--- | :--- |
| #0 | Alpha | 100 |
| #1 | Alpha | 100 |
| #2 | Alpha | 100 |
| #3 | Alpha | 100 |
| #4 | Beta | 100 |

#### Cognitive Mastery Lineages (Avg Predictor Accuracy)
| Founder ID | Colony | Avg Predictor Accuracy |
| :--- | :--- | :--- |
| #0 | Alpha | 1.0000 |
| #1 | Alpha | 1.0000 |
| #2 | Alpha | 1.0000 |
| #3 | Alpha | 1.0000 |
| #4 | Beta | 1.0000 |


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### 6. Interesting Observations & Anomalies
We apply rule-based heuristics to identify behavioral adaptations and systemic anomalies:

#### Behavioral Observations
- *No major interesting behavioral anomalies detected.*


#### Systemic Anomalies
- **Anomaly:** Anomalous birth rate collapse observed: no new births occurred in the final 20% of the simulation. The population is aging without replacement, signaling critical demographic decline.


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### 7. Chronological Milestones & Historical Log
The historical progression of the world is punctuated by seasonal shifts, global disasters, and demographic milestones:

- **Tick 1 (Year 0, Day 1)**: Milestone - *Complete shelter network achieved — all 16 living agents have shelter!*


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### 8. Conclusion
This simulation run demonstrates that physical constraints—namely resource scarcity—are the primary catalysts for technological progression (shelter construction) and genetic specialization. The performance metrics confirm that spatial grid binning sustains high tick rates even at maximum carrying capacity. Future investigations will introduce complex linguistic communication and trade networks to study the next phase of civilization emergence.
