Computational Model Library

Environmental stochasticity, resource heterogeneity, and the evolution of cooperation (1.2.0)

The emergence of cooperation in human societies is often linked to environmental constraints, yet the specific conditions that promote cooperative behavior remain an open question. This study examines how resource unpredictability and spatial dispersion influence the evolution of cooperation using an agent-based model (ABM). Our simulations test the effects of rainfall variability and resource distribution on the survival of cooperative and non-cooperative strategies. The results show that cooperation is most likely to emerge when resources are patchy, widely spaced, and rainfall is unpredictable. In these environments, non-cooperators rapidly deplete local resources and face high mortality when forced to migrate between distant patches. In contrast, cooperators—who store and share resources—can better endure extended droughts and irregular resource availability. While rainfall stochasticity alone does not directly select for cooperation, its interaction with resource patchiness and spatial constraints creates conditions where cooperative strategies provide a survival advantage. These findings offer broader insights into how environmental uncertainty shapes social organization in resource-limited settings. By integrating ecological constraints into computational modeling, this study contributes to a deeper understanding of the conditions that drive cooperation across diverse human and animal systems.

Release Notes

Release Notes: Random Rainfall and Cooperation Model

Version: 1.2
Date: June 2025
Authors: Colin M. Lynch, M. Starkey, C. P. Lipo, T. L. Hunt
Manuscript: Environmental stochasticity, resource heterogeneity, and the evolution of cooperation (in submission)


Overview

This agent-based model explores how environmental unpredictability and resource dispersion shape the evolution of cooperation. Agents with cooperative or non-cooperative strategies compete for survival and reproduction in a dynamic, rain-fed landscape. The model formalizes archaeological observations of cooperation in marginal environments.


New in Version 1.2

  • Built-in Rainfall Generation:
    Rainfall is now generated within NetLogo via a truncated geometric distribution—no R preprocessing needed.

  • Improved Documentation:
    Code now includes expanded comments and a clearer Info tab following the ODD protocol.


How to Run

Requirements

  • NetLogo 6.0.4 or later
  • No external files required

Instructions

  1. Open periodicRainfallCooperation.nlogo in NetLogo.
  2. Set parameters in the Interface:
    - gap-width, patch-width, minimum-plant-number
    - P (rainfall periodicity), average-rain, average-drought
    - N (initial population), logistic-growth
    - energy-transfer-cooperator, energy-transfer-non-cooperator
  3. Click Setup, then Go.
  4. Watch blue (cooperators) and red (non-cooperators) agents interact with the environment.
  5. Use plots or BehaviorSpace to analyze survival, lifespan, and movement.

Associated Publications

Environmental stochasticity, resource heterogeneity, and the evolution of cooperation 1.2.0

The emergence of cooperation in human societies is often linked to environmental constraints, yet the specific conditions that promote cooperative behavior remain an open question. This study examines how resource unpredictability and spatial dispersion influence the evolution of cooperation using an agent-based model (ABM). Our simulations test the effects of rainfall variability and resource distribution on the survival of cooperative and non-cooperative strategies. The results show that cooperation is most likely to emerge when resources are patchy, widely spaced, and rainfall is unpredictable. In these environments, non-cooperators rapidly deplete local resources and face high mortality when forced to migrate between distant patches. In contrast, cooperators—who store and share resources—can better endure extended droughts and irregular resource availability. While rainfall stochasticity alone does not directly select for cooperation, its interaction with resource patchiness and spatial constraints creates conditions where cooperative strategies provide a survival advantage. These findings offer broader insights into how environmental uncertainty shapes social organization in resource-limited settings. By integrating ecological constraints into computational modeling, this study contributes to a deeper understanding of the conditions that drive cooperation across diverse human and animal systems.

Release Notes

Release Notes: Random Rainfall and Cooperation Model

Version: 1.2
Date: June 2025
Authors: Colin M. Lynch, M. Starkey, C. P. Lipo, T. L. Hunt
Manuscript: Environmental stochasticity, resource heterogeneity, and the evolution of cooperation (in submission)


Overview

This agent-based model explores how environmental unpredictability and resource dispersion shape the evolution of cooperation. Agents with cooperative or non-cooperative strategies compete for survival and reproduction in a dynamic, rain-fed landscape. The model formalizes archaeological observations of cooperation in marginal environments.


New in Version 1.2

  • Built-in Rainfall Generation:
    Rainfall is now generated within NetLogo via a truncated geometric distribution—no R preprocessing needed.

  • Improved Documentation:
    Code now includes expanded comments and a clearer Info tab following the ODD protocol.


How to Run

Requirements

  • NetLogo 6.0.4 or later
  • No external files required

Instructions

  1. Open periodicRainfallCooperation.nlogo in NetLogo.
  2. Set parameters in the Interface:
    - gap-width, patch-width, minimum-plant-number
    - P (rainfall periodicity), average-rain, average-drought
    - N (initial population), logistic-growth
    - energy-transfer-cooperator, energy-transfer-non-cooperator
  3. Click Setup, then Go.
  4. Watch blue (cooperators) and red (non-cooperators) agents interact with the environment.
  5. Use plots or BehaviorSpace to analyze survival, lifespan, and movement.

Version Submitter First published Last modified Status
1.2.0 Colin Lynch Thu Jun 19 15:44:48 2025 Thu Jun 19 15:45:44 2025 Published
1.0.0 Colin Lynch Fri Mar 14 22:53:08 2025 Fri Mar 14 22:53:08 2025 Published

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