2023 Chadefaux T.
Journal of Computational Science
This article introduces an automated pattern recognition system for conflict. The monitoring system aims to uncover, cluster, and classify temporal patterns of escalation to improve future forecasts and better understand the causes of escalation toward war. It identifies important temporal patterns in conflict data using novel pattern detection methods and new data. These patterns are used to forecast conflict, with live predictions released in real time. Finally, the discovery of recurring motifs—prototypes—can inform new or existing theoretical frameworks. In this article, I discuss the methodological innovations required to achieve these goals and the path to creating an autonomous conflict monitoring system. I also report on promising results obtained using these methods, which show that they perform well on true out-of- sample forecasts of the count of the number of fatalities per month from state-based conflict. The monitoring system has important implications for computational diplomacy, as it can alert diplomats of geopolitical risks.
2022 Boussalis C., Chadefaux T., Salvi A. and Decadri S.
International Studies Quarterly 66(4)
Scholars of international conflicts have long emphasized the role of private information in the onset of interstate wars. Yet, the literature lacks direct and systematic evidence of its effect. This is largely due to challenges with accessing decision-makers’ private and often confidential information and opinions. We compile a large corpus of declassified French diplomatic cables that span the period 1871–1914. Using these texts, we estimate a dynamic topic model to generate diplomatic thematic variables, which are then used to forecast the onset of French Militarized Interstate Disputes (MIDs). The inclusion of information from diplomatic correspondence greatly improves estimates of MID timing, compared to models that rely solely on public information such as structural determinants and revealed risk perceptions derived from financial markets or the press. These results emphasize the importance of private information in decisions to go to war and the limitations of empirical work that relies solely on publicly available data.
2022 Chadefaux T.
International Interactions 48(4)
Do conflict processes exhibit repeating patterns over time? And if so, can we exploit the recurring shapes and structures of the time series to forecast the evolution of conflict? Theory has long focused on the sequence of events that precedes conflicts (e.g., escalation or brinkmanship). Yet, current empirical research is unable to represent these complex interactions unfolding over time because it attempts to match cases on the raw value of covariates, and not on their structure or shape. As a result, it cannot easily represent real-world relations which may, for example, follow a long alternation of escalation and détente, in various orders and at various speeds. Here, I aim to address these issues using recent machine-learning methods derived from pattern recognition in time series to study the dynamics of casualties in civil war processes. I find that the methods perform well on out-of-sample forecasts of the count of the number of fatalities per month from state-based conflict. In particular, our results yield Mean Squared Errors that are lower than the competition benchmark. We discuss the implication for conflict research and the importance of comparing entire sequences rather than isolated observations in time.
2022 Turkoglu O., Chadefaux T.
Political Science Research and Methods (First View)
Is terrorism effective as a tool of political influence? In particular, do terrorists succeed in affecting their targets’ attitudes, and how long does the effect last? Existing research unfortunately is either limited to small samples or does not address two main difficulties: issues of endogeneity and the inability to assess the duration of the effect. Here, we first exploit the exogeneity to the selection process of the success or failure of an attack as an identification mechanism. Second, we take advantage of the random allocation of survey respondents to interview times to estimate the duration of the impact of terrorist events on attitudes. Using survey data from 30 European democracies between 2002 and 2017, we find first that terrorism affects people's reported life satisfaction and happiness—a proxy for the cost of terrorism in terms of utility. However, we also find that terrorist attacks do not affect respondents’ attitude toward their government, institutions, or immigrants. This suggests that terrorism is ineffective at translating discontent into political pressure. Importantly, we also find that all effects disappear within less than two weeks.