Biased imitation in coupled evolutionary games in interdependent networks.

Area of Science:

• Evolutionary game theory
• Network science
• Computational social science

Background:

• Interdependent networks create complex interaction structures.
• Strategy evolution depends on network topology and update rules.
• Cross-layer interactions can significantly alter evolutionary dynamics.

Purpose of the Study:

• To investigate the impact of cross-layer imitation on cooperation in interdependent networks.
• To analyze the evolutionary dynamics of the Prisoner's Dilemma and Snowdrift Game under biased imitation.
• To compare the effects of strategy transfer between network layers on different game types.

Main Methods:

• Agent-based simulations on regular random networks.
• Analytical modeling of coupled well-mixed populations.
• Biased imitation update rule with probability p for same-layer and 1-p for cross-layer imitation.

Main Results:

• A slight decrease in same-layer imitation (p<1) promotes cooperation in the Prisoner's Dilemma.
• Conversely, this decrease in p is detrimental to cooperation in the Snowdrift Game.
• Results show contrasting effects of cross-layer interactions on different social dilemmas.

Conclusions:

• The degree of strategy transfer between interdependent networks critically shapes cooperation levels.
• Network structure and game type determine the net effect of cross-layer interactions.
• Understanding these dynamics is crucial for designing effective cooperation strategies in complex systems.