Simple mathematical law benchmarks human confrontations.

Area of Science:

• Social Sciences
• Behavioral Economics
• Network Science

Background:

• Societal problems frequently involve repeated aggression between individuals or groups.
• Existing methods lack quantitative tools to predict the severity, timing, and perpetrators of future aggressive acts.
• Diverse domains, including interpersonal violence and cyber-attacks, share underlying patterns of aggression.

Purpose of the Study:

• To develop a unified mathematical framework for benchmarking diverse human aggression events.
• To provide quantitative predictions for future attacks and identify potential perpetrators.
• To inform intervention strategies for mitigating aggression at individual and group levels.

Main Methods:

• Combined analysis of over 100,000 events across multiple domains of human aggression.
• Derivation of a simple mathematical law to benchmark these events.
• Interpretation using a minimal mechanistic model grounded in fieldwork.

Main Results:

• A universal mathematical law effectively benchmarks a wide range of aggressive behaviors.
• Quantitative predictions for future attacks and perpetrator detection are established.
• Insights into lone wolf trajectories and the spread of ideas among perpetrators are provided.

Conclusions:

• The findings offer a quantitative starting point for understanding and intervening in human aggression.
• A unified approach can be applied to aggression across real-world and online contexts.
• The mathematical benchmark aids in detecting abnormal behaviors and developing targeted interventions.