Challenges for complexity measures: A perspective from social dynamics and collective social computation

Area of Science:

• Social Dynamics and Complexity Science
• Evolutionary Theory

Background:

• Collective properties emerge from individual interactions in social systems.
• Understanding the relationship between micro-level rules and macro-level behavior is a key challenge.

Purpose of the Study:

• To review an empirically grounded approach for studying the emergence of collective properties from individual interactions.
• To define complexity measures applicable to social systems at multiple analytical levels.
• To facilitate quantitative comparisons across diverse social systems.

Main Methods:

• Extracting individual decision-making rules (strategies) from time-series data.
• Constructing adaptive social circuits to model collective effects.
• Developing complexity measures guided by biological and social system structures.

Main Results:

• Social circuits offer a compact description of collective effects by mapping individual rules to aggregate statistical properties.
• Empirically grounded complexity measures are more likely to be applicable to real-world data.
• Principled complexity measures allow rigorous investigation of adaptive features across micro, meso, and macro scales.

Conclusions:

• This approach provides a simple form of social computation.
• Complexity measures grounded in system properties facilitate quantitative comparisons across social systems.
• The framework supports a long-standing goal of evolutionary theory to link adaptive features across scales.