Periodic Reporting for period 2 - PaCE (The Patterns of Conflict Emergence: Developing an Automated Pattern Recognition System for Conflict)
Período documentado: 2023-07-01 hasta 2024-12-31
There have been more than 200 wars since the start of the 20th century, leading to 35 million battle deaths and countless more civilian casualties. International conflicts and civil wars also lead to forced migration, disastrous economic consequences, weakened political systems, and poverty.
The recurrence of wars despite their tremendous economic, social, and institutional costs, may suggest that we are doomed to repeat the errors of the past. Does history indeed repeat itself? Are there particular temporal patterns which occur in the build up to the onset of war? Would better understanding of these patterns help us to avoid such conflicts? This project aims to uncover, cluster, and classify such patterns in meaningful ways to help us improve future forecasts. In particular, it addresses three related central research questions:
1. What are the recurring pre-conflict patterns? Certain indicators may follow a typical path—a motif— prior to conflict events (inter- or intra-state). Or are the variables associated with conflict chaotic and therefore inherently unpredictable? Using novel methods, we search for patterns in the observable actions that international leaders and actors take prior to conflict events, as well as in their perceptions.
2. Can we exploit these patterns for prediction purposes? Can we cluster and classify sequences to understand where tensions are headed—escalation, diffusion, or decline? Answers to these questions could improve our ability to forecast wars by building an automated pattern recognition system for conflict and geopolitical crises. It will also help political scientists to answer theoretical questions that have so far remained out of reach because of the limits of existing methodological approaches and data.
3. Finally, can we apply the patterns to generate new theories about conflict processes, by identifying the key sequences and combinations that are particularly dangerous. To our knowledge, no current system directly addresses these questions. Real-time crisis monitoring systems have emerged both in academia and in policy arena, but they rely on methods which (i) do not attempt to measure how fundamentally chaotic (and therefore predictable) their data is, and (ii) do not treat entire sequences as units of analysis and hence may fail to identify important geometric shapes and redundancies in the data.
(Grant agreement no:101002240)
The recurrence of wars despite their tremendous economic, social, and institutional costs, may suggest that we are doomed to repeat the errors of the past. Does history indeed repeat itself? Are there particular temporal patterns which occur in the build up to the onset of war? Would better understanding of these patterns help us to avoid such conflicts? This project aims to uncover, cluster, and classify such patterns in meaningful ways to help us improve future forecasts. In particular, it addresses three related central research questions:
1. What are the recurring pre-conflict patterns? Certain indicators may follow a typical path—a motif— prior to conflict events (inter- or intra-state). Or are the variables associated with conflict chaotic and therefore inherently unpredictable? Using novel methods, we search for patterns in the observable actions that international leaders and actors take prior to conflict events, as well as in their perceptions.
2. Can we exploit these patterns for prediction purposes? Can we cluster and classify sequences to understand where tensions are headed—escalation, diffusion, or decline? Answers to these questions could improve our ability to forecast wars by building an automated pattern recognition system for conflict and geopolitical crises. It will also help political scientists to answer theoretical questions that have so far remained out of reach because of the limits of existing methodological approaches and data.
3. Finally, can we apply the patterns to generate new theories about conflict processes, by identifying the key sequences and combinations that are particularly dangerous. To our knowledge, no current system directly addresses these questions. Real-time crisis monitoring systems have emerged both in academia and in policy arena, but they rely on methods which (i) do not attempt to measure how fundamentally chaotic (and therefore predictable) their data is, and (ii) do not treat entire sequences as units of analysis and hence may fail to identify important geometric shapes and redundancies in the data.
(Grant agreement no:101002240)
The PaCE Project has made significant progress both in developing novel methodologies and advancing the understanding of conflict dynamics. The project has focused on identifying and analysing temporal patterns in conflict data, improving conflict forecasting models, and disseminating these findings through publications, workshops, and collaborations. Achievements include:
1. Development of Novel Methodologies:
a. Dynamic Synthetic Controls: Development of dynamic synthetic control methods, improving causal inference by accounting for varying response speeds in time series data.
b. Shape-Based Conflict Prediction: Introduction of "Shape Finder" methodology, significantly enhancing conflict forecasting by capturing complex interdependencies in conflict fatalities.
c. Quantifying Temporal Redundancy: Advancing understanding of the predictability of conflicts by quantifying temporal redundancy in conflict data. Provides new insights into whether wars follow repetitive patterns.
2. Research Outputs:
a. Publications: Nine articles either published or are close to publication, including significant contributions to journals such as Political Analysis and the Journal of Peace Research.
b. Ongoing Research: Three articles under review, and six additional projects are close to completion.
3. Exploitation and Dissemination:
a. Open-Source Tools: Development and release of an open-source R package, dsc, which implements the dynamic synthetic control methods.
b. Workshops and Conferences: Hosting major workshop in March 2024, bringing together 20 leading researchers in conflict forecasting. The event wasa significant dissemination effort, facilitating the sharing of the latest advancements and fostering new collaborations. Other workshops with prominent visiting scholars have enhanced the project’s visibility and impact.
c. Live Predictions: Publishing monthly live predictions since January 2024, providing real-time insights into conflict dynamics and demonstrating the practical applicability of the project’s methodologies.
4. Impact on the Research Community:
Already influencing the broader field of conflict forecasting and related disciplines. The project’s work has been widely disseminated through peer-reviewed publications, policy papers, and academic workshops, ensuring that these advancements reach a broad audience of researchers, policymakers, and practitioners.
1. Development of Novel Methodologies:
a. Dynamic Synthetic Controls: Development of dynamic synthetic control methods, improving causal inference by accounting for varying response speeds in time series data.
b. Shape-Based Conflict Prediction: Introduction of "Shape Finder" methodology, significantly enhancing conflict forecasting by capturing complex interdependencies in conflict fatalities.
c. Quantifying Temporal Redundancy: Advancing understanding of the predictability of conflicts by quantifying temporal redundancy in conflict data. Provides new insights into whether wars follow repetitive patterns.
2. Research Outputs:
a. Publications: Nine articles either published or are close to publication, including significant contributions to journals such as Political Analysis and the Journal of Peace Research.
b. Ongoing Research: Three articles under review, and six additional projects are close to completion.
3. Exploitation and Dissemination:
a. Open-Source Tools: Development and release of an open-source R package, dsc, which implements the dynamic synthetic control methods.
b. Workshops and Conferences: Hosting major workshop in March 2024, bringing together 20 leading researchers in conflict forecasting. The event wasa significant dissemination effort, facilitating the sharing of the latest advancements and fostering new collaborations. Other workshops with prominent visiting scholars have enhanced the project’s visibility and impact.
c. Live Predictions: Publishing monthly live predictions since January 2024, providing real-time insights into conflict dynamics and demonstrating the practical applicability of the project’s methodologies.
4. Impact on the Research Community:
Already influencing the broader field of conflict forecasting and related disciplines. The project’s work has been widely disseminated through peer-reviewed publications, policy papers, and academic workshops, ensuring that these advancements reach a broad audience of researchers, policymakers, and practitioners.
Progress Beyond the State of the Art
The PaCE project has made significant strides in advancing the field of conflict forecasting and analysis, pushing the boundaries of current methodologies and contributing new insights that go beyond the state of the art. Progress includes:
Dynamic Synthetic Controls: The development of dynamic synthetic controls addresses a critical gap in causal inference models by accommodating varying response speeds in time series data. . The approach has already been applied to re-estimate the effects of terrorism, tobacco laws, and political reunifications, demonstrating its broad applicability.
Shape-Based Conflict Prediction: The introduction of the "Shape Finder" methodology represents a breakthrough in conflict forecasting. By capturing complex interdependencies and temporal patterns in conflict fatalities, this method allows for more dynamic and accurate predictions, particularly in contexts where traditional models fail to account for sudden surges or declines. This approach not only enhances prediction accuracy but also deepens our understanding of conflict dynamics.
Quantification of Temporal Redundancy: The project has made pioneering contributions to the quantification of temporal redundancy in conflict data. By comparing conflict patterns to those in natural phenomena like earthquakes and epidemics, the project has provided valuable insights into the extent to which conflicts can be anticipated based on historical data.
Interdisciplinary Integration: The PaCE project has successfully integrated methodologies from political science, economics, machine learning, and applied mathematics. This integration has allowed the project to tackle complex problems with innovative solutions.
Expected Results
Several key results are expected to be achieved by the end of the project:
• Completion and Publication of Ongoing Research
• Expansion of Open-Source Tools
• Further Development of Real-Time Forecasting Models
• Broader Dissemination and Policy Impact
• Strengthening Collaborative Networks
In summary, the PaCE project has already achieved significant progress beyond the state of the art, with several innovative methodologies and insights that are reshaping the field of conflict forecasting. By the end of the project, these advancements are expected to culminate in a robust set of tools, publications, and collaborative networks.
The PaCE project has made significant strides in advancing the field of conflict forecasting and analysis, pushing the boundaries of current methodologies and contributing new insights that go beyond the state of the art. Progress includes:
Dynamic Synthetic Controls: The development of dynamic synthetic controls addresses a critical gap in causal inference models by accommodating varying response speeds in time series data. . The approach has already been applied to re-estimate the effects of terrorism, tobacco laws, and political reunifications, demonstrating its broad applicability.
Shape-Based Conflict Prediction: The introduction of the "Shape Finder" methodology represents a breakthrough in conflict forecasting. By capturing complex interdependencies and temporal patterns in conflict fatalities, this method allows for more dynamic and accurate predictions, particularly in contexts where traditional models fail to account for sudden surges or declines. This approach not only enhances prediction accuracy but also deepens our understanding of conflict dynamics.
Quantification of Temporal Redundancy: The project has made pioneering contributions to the quantification of temporal redundancy in conflict data. By comparing conflict patterns to those in natural phenomena like earthquakes and epidemics, the project has provided valuable insights into the extent to which conflicts can be anticipated based on historical data.
Interdisciplinary Integration: The PaCE project has successfully integrated methodologies from political science, economics, machine learning, and applied mathematics. This integration has allowed the project to tackle complex problems with innovative solutions.
Expected Results
Several key results are expected to be achieved by the end of the project:
• Completion and Publication of Ongoing Research
• Expansion of Open-Source Tools
• Further Development of Real-Time Forecasting Models
• Broader Dissemination and Policy Impact
• Strengthening Collaborative Networks
In summary, the PaCE project has already achieved significant progress beyond the state of the art, with several innovative methodologies and insights that are reshaping the field of conflict forecasting. By the end of the project, these advancements are expected to culminate in a robust set of tools, publications, and collaborative networks.