Patterns in Conflict Dynamics in Yemen and Syria

TL;DR

The paper investigates heavy-tailed casualty events in contemporary conflicts and tests a fusion–fission framework that predicts a power-law distribution of event sizes with exponent $\alpha$ near $2.5$. Using ACLED fatality data for Yemen (2023–2025) and Syria (2023–2025), it estimates time-varying best-fit $\alpha$ via Clauset–Shalizi–Newman power-law fitting and rolling-window analysis, interpreting temporal changes as shifts in fighter-cluster robustness. The main finding is that $\alpha$ stays largely between $2.5$ and $3.5$, with temporary reductions toward $2.5$ during major crises that precede large-scale battles, suggesting a reorganization toward larger, more cohesive clusters. This cross-conflict pattern provides a potential early-warning signal and a quantitative tracer of organizational dynamics in modern wars, with implications for monitoring escalation risk in ongoing hotspots like Yemen and Syria.

Abstract

Conflict fatalities tend to follow heavy-tailed statistical distributions. A 2005 fusion-fission theory predicts mathematically that for armed groups operating in dynamically evolving clusters within a given conflict, the number of fatalities per conflict event will follow an approximate power-law distribution with exponent near 2.5, with the specific exponent value offering insight into the relative robustness of larger versus smaller clusters of fighters in that armed group. Since Yemen and Syria are current hotspots for future conflict, yet their most recent conflicts (2023-2025) have not been studied at the event level, we use ACLED data to determine their best-fit exponent value as each conflict evolved. We find that the exponent lies between 2.5 and 3.5 predominantly throughout each conflict, which suggests that the fighters in each of these conflicts continued to operate in smaller clusters as the conflict evolved. Moreover, temporary reductions in the exponent value -- which suggests a temporary increase in the robustness and involvement of larger clusters of fighters -- appear to arise during major crises ahead of the largest battles. Though the lack higher-quality data for these conflicts prevents us from establishing this more firmly, such a temporary reduction in the exponent value hints at its potential use as an early-warning signature.

Figures (4)

• Figure 1: A military truck and uniform belonging to former Assad regime soldiers, photographed after the fall of the regime. The abandoned equipment supports the notion that there are moments of rapid fragmentation and collapse of centralized military organization as a conflict evolves. Source: Wikimedia Commons wikimedia_assad_equipment_2024.
• Figure 2: An archway in Damascus that previously displayed the slogan “Assad Forever,” shown blacked out following regime collapse in 2025. The image illustrates the symbolic erasure of state authority and the reconfiguration of political space during prolonged conflict. Source: Wikimedia Commons wikimedia_damascus_archway_2025
• Figure 3: Power-law exponent $\alpha$ estimates for conflict fatalities during the Yemen conflict. Upper panel shows estimates for $\alpha$ within the periods defined by the specific events in Table 1. Vertical lines mark major conflict events. Lower panel shows rolling estimates for $\alpha$ using a 180-day window. Solid lines denote maximum-likelihood estimates with 95% confidence intervals. As a guide, the shaded band marks $\alpha \in [2.0, 3.0]$. Pronounced downward (or upward) movements in $\alpha$ can be interpreted as periods of heightened (or reduced) coordination and hence events with typically more (or less) casualties. It seems that large downward movements in $\alpha$ can follow major regional shocks -- however this is speculative, and confirming any such association more rigorously will require the availability of higher quality data hopefully in the future.
• Figure 4: Power-law exponent $\alpha$ estimates for conflict fatalities during the Syria conflict. Upper panel shows estimates for $\alpha$ within the periods defined by the specific events in Table 1. Vertical lines mark major conflict events. Lower panel shows rolling estimates for $\alpha$ using a 180-day window. Solid lines denote maximum-likelihood estimates with 95% confidence intervals. As a guide, the shaded band marks the regime $\alpha \in [2.0, 3.0]$.