Aspiration dynamics generate robust predictions in heterogeneous populations

Area of Science:

• Evolutionary game theory
• Social dynamics
• Network science

Background:

• Update rules govern behavioral adjustments in social interactions.
• Imitation-based rules show sensitivity to model details, while self-evaluation rules are robust.
• Previous self-evaluation studies assumed homogeneous populations.

Purpose of the Study:

• To investigate self-evaluation update rules in heterogeneous population structures (weighted networks).
• To analytically derive conditions for strategy success under weak selection.
• To demonstrate the universality of robustness for self-evaluation rules.

Main Methods:

• Analytical derivation of strategy success conditions.
• Modeling heterogeneous populations using weighted networks.
• Analysis under weak selection and varying aspiration levels.

Main Results:

• The condition for strategy success under self-evaluation rules is risk-dominance.
• This condition is universally applicable across weighted networks and aspiration distributions.
• Previous findings are recovered as special cases, confirming robustness.

Conclusions:

• Self-evaluation update rules exhibit robust evolutionary dynamics irrespective of network heterogeneity.
• This robustness highlights a fundamental difference compared to imitation-based update rules.
• The findings generalize the understanding of evolutionary dynamics in complex social structures.