Phase Diagram and Optimal Information Use in a Collective Sensing System

Area of Science:

• Complex Systems
• Social Dynamics
• Information Theory

Background:

• Individuals in uncertain environments rely on noisy observations and communication.
• Group consensus formation is crucial for collective decision-making and adaptation.
• Understanding the impact of communication noise on consensus is vital for predicting population behavior.

Purpose of the Study:

• To investigate the relationship between communication noise and consensus formation in a population.
• To identify conditions that maximize population inference capability in dynamic environments.
• To explore the role of noise in enhancing population responsiveness to environmental changes.

Main Methods:

• Agent-based modeling of a population with noisy observations and communication.
• Analysis of phase transitions (order-disorder) based on communication noise levels.
• Characterization of consensus states: informed, misinformed, and bistable.

Main Results:

• An order-disorder transition occurs with varying communication noise, affecting consensus formation.
• The population's inference capability is maximized at the edge of bistability, balancing informed and misinformed consensuses.
• An optimal level of communication noise enhances population responsiveness, akin to a resonance phenomenon.

Conclusions:

• Communication noise critically shapes population consensus and collective intelligence.
• Environmental change responsiveness is optimized at specific noise levels, particularly in fast-changing scenarios.
• The study highlights the non-intuitive benefit of noise in improving adaptation and inference in dynamic systems.