Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101002240 |
| Start date: | 01-01-2022 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | 1 952 848,00 Euro - 1 952 848,00 Euro |
Cordis data
Original description
Are there recurring patterns in the escalation and emergence of wars? The idea that history may repeat itself is old. Butrecent 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.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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