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Simplifying Complexity

The Mathematics of War: Part 2

Dec 11, 2023
45:01
Snipd AI
Neil Johnson delves deeper into the power law describing war casualties. He discusses the interdisciplinary nature of complex systems research. The chapter explores excitons, crowd behavior, financial markets, and the work of Richardson in complex systems. It also explores the formation and collaboration of relationships in wars and insurgencies. Lastly, it discusses how complexity science can enhance various disciplines in understanding cities and conflicts.
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Podcast summary created with Snipd AI

Quick takeaways

  • The distribution of group sizes in conflicts follows a power law with a slope of 2.5, indicating the ongoing dynamics of group formation and dissolution.
  • Complexity science enables the examination of conflict dynamics and the identification of common patterns and principles across different conflicts.

Deep dives

Understanding Conflict as Complex Systems

Conflict is a complex system defined by power laws, as seen in other systems like metabolism and sand-pile models. The post-internet age has brought new dynamics to conflicts. Physics background of studying group behaviors influenced interest in conflict casualties. Group formation and dissolution in conflicts follow similar principles as the formation and dissolution of opinions in financial markets. The distribution of group sizes in conflicts follows a power law with a slope of 2.5, indicating the ongoing dynamics of group formation and dissolution. Online platforms like vKon taktia have provided data on group dynamics in conflicts. The 2.5 power law was observed in conflicts like the Iraq war, narco-guerrilla war in Colombia, and pro-ISIS groups online. The 2.5 power law within a conflict represents the gradual formation and sudden dissociation of groups. Aggregating events from conflicts with 2.5 power law results in a power law with a lower slope, giving insights into conflicts at different scales. The work builds upon Richardson's earlier studies and highlights the importance of crossing disciplinary boundaries in complexity science.

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