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The Patterns of Conflict Emergence: Developing an Automated Pattern Recognition System for Conflict

Project description

Do past events return as events of the present?

History, it has been widely said, has a tendency to repeat itself. What about in the case of war? The EU-funded PaCE project will study recurring patterns in the escalation and emergence of wars. It will combine new methods (using the shape of the sequence of events rather than its raw values) and data on conflict to extract typical pre-war motifs. The project will identify patterns from the onset of World War I to Hamas’ rocket launches. It will study the perceptions using data from financial markets, news articles and diplomatic documents. PaCE will also assess possible fundamental limits to the predictability of conflict events.

Objective

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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Programme(s)

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Topic(s)

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Funding Scheme

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ERC-COG - Consolidator Grant

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Call for proposal

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(opens in new window) ERC-2020-COG

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Host institution

THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD, OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 952 848,00
Address
COLLEGE GREEN TRINITY COLLEGE
D02 CX56 Dublin
Ireland

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Region
Ireland Eastern and Midland Dublin
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 952 848,00

Beneficiaries (1)

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