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This study shows how herd instinct can emerge spontaneously in artificial populations. Environmental factors like food scarcity and threats drive the evolution of gregarious behavior over generations.

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Area of Science:

  • Artificial intelligence
  • Evolutionary biology
  • Computational social science

Background:

  • Herd instinct, or gregarious behavior, is crucial for survival in many species.
  • Understanding the evolutionary origins of social behaviors is a key challenge in biology.
  • Previous models often impose social instincts, rather than simulating their emergence.

Purpose of the Study:

  • To investigate the spontaneous emergence of herd instinct in an artificial population.
  • To identify the key environmental drivers for the evolution of gregarious behavior.
  • To simulate the inheritance and optimization of herd instinct over generations.

Main Methods:

  • An agent-based model was developed with agents exhibiting basic survival behaviors (movement, foraging, reproduction, threat evasion, death).
  • No explicit herd instinct was programmed; its evolution was observed under simulated environmental pressures.
  • The model tracked the inheritance of herd instinct traits with random variations across several hundred agent generations.

Main Results:

  • Herd instinct emerged spontaneously in the artificial population without explicit programming.
  • Both the search for food and the evasion of threats were identified as significant factors driving the rise of herd instinct.
  • The evolution of herd instinct was a slow process, requiring extensive simulation time and multiple generations.

Conclusions:

  • Environmental pressures, specifically resource acquisition and predator avoidance, can lead to the natural evolution of herd instinct.
  • Inheritance with random deviation allows herd instinct to evolve towards an optimal level for population survival.
  • Agent-based modeling provides a powerful tool for studying the emergence of complex social behaviors.