Memory and self-induced shocks in an evolutionary population competing for limited resources

Area of Science:

• Evolutionary dynamics
• Complex systems
• Agent-based modeling

Background:

• Builds upon a previously introduced multiagent system known for self-segregation and population freezing.
• The system models populations competing for limited resources, exhibiting complex emergent behaviors.

Purpose of the Study:

• To investigate the role of memory and self-induced shocks in an evolutionary population.
• To understand the mechanisms driving large-scale changes in the multiagent system.

Main Methods:

• Utilizes a multiagent system simulation.
• Analyzes population dynamics based on a 'gene' value (p) ranging from 0 to 1.
• Examines transitions to 'frozen' populations and self-induced macroscopic changes.

Main Results:

• Identified memory as a key factor in population dynamics.
• Demonstrated that macroscopic self-induced shocks are controlled by microscopic changes in extreme subgroups (p ≈ 0 and p ≈ 1).
• Observed population freezing as a function of global resource levels.

Conclusions:

• Microscopic behaviors within extreme population subgroups are critical drivers of large-scale evolutionary dynamics.
• Memory plays a crucial role in the emergence and control of self-induced shocks in resource-limited populations.