Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach
Abstract
1. Introduction
2. Methods
2.1. Model Setup
- 1.
- Hunt: Costs days (e.g., ). The group consumes daily rations during these days. After completing the hunt, they obtain an effective yield proportional to Y, adjusted by the group’s management skill G and cultural complexity C. The effective yield is
- 2.
- Invest (resource management): Costs day. Improves G by a fixed increment .
- 3.
- Culture: Costs day. Increases C by a unit and grants a small immediate reward.
2.2. Propositions and Theorem
2.3. Additional Clarifications of Key Assumptions
3. Experiments
3.1. Simulation Procedure
3.2. Parameter Settings
3.3. Data Collection and Analysis
3.4. Reinforcement-Learning Setting
- Stored food level normalized by the annual requirement .
- Resource management skill level G.
- Cultural complexity C.
- The fraction of remaining time .
- Normalized yield and spoilage probability p. Here, 3000 served as a convenient normalization factor that corresponded to our maximum yield in the experimental range (). Normalizing the yield helped stabilize the agent’s input scale for learning.
- Hunt: Occupied days, with each incurring daily consumption and spoilage. After completing the hunt, the agent obtained units of food.
- Invest: Took 1 day. Improved G by a fixed increment . Daily consumption and spoilage was applied.
- Culture: Took 1 day. Increased C by 1 and gave a small immediate reward. Daily consumption and spoilage was applied.
3.4.1. RL Model and Optimization
3.4.2. Training Details
3.4.3. Parameter Variations
3.4.4. Pseudocode for Agent Decision Algorithm
Algorithm 1 Daily Agent Decision Pseudocode |
|
4. Results
4.1. Overall Patterns
4.2. Statistical Relationships
5. Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Parameter | Coefficient | p-Value | StdErr |
---|---|---|---|
const | 347.45 | <0.000001 | 7.50 |
Y (x1) | 0.0370 | <0.000001 | 0.0024 |
p (x2) | −1134.83 | <0.000001 | 16.23 |
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Lee, M. Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach. Big Data Cogn. Comput. 2025, 9, 34. https://doi.org/10.3390/bdcc9020034
Lee M. Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach. Big Data and Cognitive Computing. 2025; 9(2):34. https://doi.org/10.3390/bdcc9020034
Chicago/Turabian StyleLee, Minhyeok. 2025. "Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach" Big Data and Cognitive Computing 9, no. 2: 34. https://doi.org/10.3390/bdcc9020034
APA StyleLee, M. (2025). Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach. Big Data and Cognitive Computing, 9(2), 34. https://doi.org/10.3390/bdcc9020034