Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Simple fusion-fission quantifies Israel-Palestine violence and suggests multi-adversary solution

Frank Yingjie Huo, Pedro D. Manrique, Dylan J. Restrepo, Gordon Woo, Neil F. Johnson

TL;DR

This paper tackles how to quantify casualty risk in protracted Israel–Palestine violence by positing fusion-fission cluster dynamics among fighter forces. It develops a mesoscopic rate-equation framework in which cluster fusion depends on the product of cluster sizes, extended to $D$ adversarial species with coupling matrix $F$, and derives three testable predictions, including a casualty distribution that follows $x^{-eta}$ with $2.0\le\alpha\le2.5$, a giant-cluster onset time $t_c\approx N/(2F)$, and a future multi-adversary super-shock that arrives earlier as inter-species couplings grow, $t_c^{\text{super-shock}}=\left( \frac{f}{f+(D-1)\epsilon} \right) t_c^{\text{Oct7}}$. The authors validate Prediction 1 against GED/GTD casualty data showing a post-October shift to $\alpha=2.0$, and Prediction 2 against October 7 fighter data indicating $D\geq3$, while Prediction 3 highlights how larger inter-species couplings could yield earlier, more lethal attacks. They provide a plug‑and‑play NetLogo simulator to explore interventions that reduce inter-adversary couplings $\epsilon$, offering concrete risk assessments and policy guidance for multi-adversary scenarios.

Abstract

Why humans fight has no easy answer. However, understanding better how humans fight could inform future interventions, hidden shifts and casualty risk. Fusion-fission describes the well-known grouping behavior of fish etc. fighting for survival in the face of strong opponents: they form clusters ('fusion') which provide collective benefits and a cluster scatters when it senses danger ('fission'). Here we show how similar clustering (fusion-fission) of human fighters provides a unified quantitative explanation for complex casualty patterns across decades of Israel-Palestine region violence, as well as the October 7 surprise attack -- and uncovers a hidden post-October 7 shift. State-of-the-art data shows this fighter fusion-fission in action. It also predicts future 'super-shock' attacks that will be more lethal than October 7 and will arrive earlier. It offers a multi-adversary solution. Our results -- which include testable formulae and a plug-and-play simulation -- enable concrete risk assessments of future casualties and policy-making grounded by fighter behavior.

Simple fusion-fission quantifies Israel-Palestine violence and suggests multi-adversary solution

TL;DR

This paper tackles how to quantify casualty risk in protracted Israel–Palestine violence by positing fusion-fission cluster dynamics among fighter forces. It develops a mesoscopic rate-equation framework in which cluster fusion depends on the product of cluster sizes, extended to adversarial species with coupling matrix , and derives three testable predictions, including a casualty distribution that follows with , a giant-cluster onset time , and a future multi-adversary super-shock that arrives earlier as inter-species couplings grow, . The authors validate Prediction 1 against GED/GTD casualty data showing a post-October shift to , and Prediction 2 against October 7 fighter data indicating , while Prediction 3 highlights how larger inter-species couplings could yield earlier, more lethal attacks. They provide a plug‑and‑play NetLogo simulator to explore interventions that reduce inter-adversary couplings , offering concrete risk assessments and policy guidance for multi-adversary scenarios.

Abstract

Why humans fight has no easy answer. However, understanding better how humans fight could inform future interventions, hidden shifts and casualty risk. Fusion-fission describes the well-known grouping behavior of fish etc. fighting for survival in the face of strong opponents: they form clusters ('fusion') which provide collective benefits and a cluster scatters when it senses danger ('fission'). Here we show how similar clustering (fusion-fission) of human fighters provides a unified quantitative explanation for complex casualty patterns across decades of Israel-Palestine region violence, as well as the October 7 surprise attack -- and uncovers a hidden post-October 7 shift. State-of-the-art data shows this fighter fusion-fission in action. It also predicts future 'super-shock' attacks that will be more lethal than October 7 and will arrive earlier. It offers a multi-adversary solution. Our results -- which include testable formulae and a plug-and-play simulation -- enable concrete risk assessments of future casualties and policy-making grounded by fighter behavior.
Paper Structure (8 sections, 1 equation, 3 figures)

This paper contains 8 sections, 1 equation, 3 figures.

Table of Contents

  1. Introduction
  2. Results
  3. Fusion-fission empirical evidence
  4. Fusion-fission mathematics and its predictions
  5. Prediction 1 matches existing casualty data (Fig. 1C) and provides risk estimates for future casualties
  6. Prediction 2 matches October 7 fighter data (Fig. 2)
  7. Prediction 3: future super-shock clusters and attacks (Fig. 3)
  8. Discussion

Figures (3)

  • Figure 1: Prediction 1 vs. empirical data. A: Consecutive time windows show clusters of fighters forming and breaking up (fusion-fission) in a fighter force that is a strong proxy for Israel's opponents B2BGill2. Fighter's (node) attributes shown by shape (faction), size (role), color (skill). A link denotes two fighters (nodes) with a strong personal connection (see text) and hence interactions that promote operational cohesion B2BGill2. B: Daily-scale fusion and fission among pro-IS (Islamic State) fighters online. Small white circle is 1 fighter (see SI Fig. 1 for typical profile). Colored circle: fighter community PRL2023. Links that disappear (appear) shown as yellow (blue). C. Empirical exponent values $\alpha$ (black circles) for approximate power-law casualty distributions (GED conflict data italics; GTD terrorism data non-italics) across Israel-Palestine region Stijn. Prediction 1 matches this, i.e. $2.0\leq \alpha\leq 2.5$. Data-point for Gaza-West Bank casualties shifts to $\alpha=2.0$ after 7 October 2023 (see text and Methods).
  • Figure 2: Prediction 2 vs. empirical data. A: Empirical data for membership of anti-Israel military wing communities on Telegram. It plots Hamas and Hezbollah communities combined since they dominate, but these communities also include those with allegiance to PIJ etc. that may lack their own Telegram communities. Inset: glimpse of underlying mesoscale fusion. B: Rate of change of empirical data in A (dashed gray). Solid line is mathematical prediction (Prediction 2) for $D=3$ adversarial species (formulae in SI Secs. 3,4).
  • Figure 3: Prediction 3 of a future super-shock attack. A: Snapshot from the plug-and-play simulation for $D=3$ adversarial species (e.g. blue Hamas, red PIJ, green Fatah). The mathematical curves in B and C show the super-shock emerging as the couplings (links/interactions) between species become large. B shows its formation time and C shows its relative composition in terms of the $D=3$ adversarial species (blue, red, green as used in the plug-and-play simulation). The October 7 values (dots) are all non-zero but appear near 1 or 0 because of the large scales on the axes. These curves also show the October 7 attack would have occurred earlier and been more lethal if the couplings had been larger. Interventions can be explored using the plug-and-play simulation.