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PaCE

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The Patterns of Conflict Emergence Developing an Automated Pattern Recognition...

The Patterns of Conflict Emergence Developing an Automated Pattern Recognition System for Conflict Are there recurring patterns in the escalation and emergence of wars? The idea that history may repeat itself is old. But recent advances overcoming methodological and data barriers present an opportunity to identify these recurre... ver más

31/12/2026

TRINITY COLLEGE DU...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

THE PROVOST FELLOWS FOUNDATION SCHOLARS THE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-03-05

ERC-2020-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 4 años

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2 Participantes

TRINITY COLLEGE DUBLIN

1.95M€ | Lider

DIVASA FARMAVIC

96.00K€ | Participante

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Duración del proyecto: 69 meses Fecha Inicio: 2021-03-05
Fecha Fin: 2026-12-31

Líder del proyecto

THE PROVOST FELLOWS FOUNDATION SCHOLARS THE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Are there recurring patterns in the escalation and emergence of wars? The idea that history may repeat itself is old. But recent advances overcoming methodological and data barriers present an opportunity to identify these recurrences empirically and to examine whether these patterns can be classified to improve forecasts and inform theories of conflict. I propose to combine new methods—using the shape of the sequence of events rather than its raw values—and novel data on conflict from finance, diplomatic cables, and newspapers, to extract typical pre-war motifs. Just as DNA sequencing has been critical to medical diagnoses, PaCE aims to diagnose international politics by uncovering the relevant patterns in the area of conflict. Our goals are to: (i) Identify patterns in the pre-conflict actions using data on conflict events—from the onset of WWI to Hamas’s rocket launches—and in their perceptions using data from financial markets (the crowd’s perception), news articles (the experts), and diplomatic documents (the policy-makers). This will allow us to evaluate the patterns of escalation over different timescales—from the decade to the minute. The similarity between temporal sequences will be measured using algorithms which allow for flexible matching, such as Dynamic Time Warping. (ii) Evaluate the utility of these patterns to improve forecasts of conflict with both historical and live out-of-sample predictions. Our results, using shape-based classification methods, will be made public and evaluated in real time. Moreover, using new measures of complexity to distinguish regular, chaotic, and random behavior, I will measure possible fundamental limits to the predictability of conflict events. (iii) Summarize the core features of dangerous patterns into motifs—recurring patterns—that can help build new theories of conflict emergence and escalation. PaCE will build a repository of shapes—a grammar of patterns—to be used as the building blocks of new theories.

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