Michael Colaresi

The dynamics of reciprocity, accountability, and credibility

Do public opinion dynamics play an important role in understanding conflict trajectories between democratic governments and other rival groups? The authors interpret several theories of opinion dynamics as competing clusters of contemporaneous causal links connoting reciprocity, accountability, and credibility. They translate these clusters into four distinct Bayesian structural time series models fit to events data from the Israeli—Palestinian conflict with variables for U.S. intervention and Jewish public opinion about prospects for peace. A credibility model, allowing Jewish public opinion to influence U.S., Palestinian, and Israeli behavior within a given month, fits best. More pacific Israeli opinion leads to more immediate Palestinian hostility toward Israelis. This responses direction suggests a negative feedback mechanism in which low-level conflict is maintained and momentum toward either all-out war or dramatic peace is slowed. In addition, a forecasting model including Jewish public opinion is shown to forecast ex ante better than a model without this variable.

Alliances, Arms Buildups and Recurrent Conflict: Testing a Steps-to-War Model

Alliances and arms races have received considerable attention in the causes-of-war literature. While a large amount of empirical research has pursued these topics separately, multivariate conditional combinations of these processes have been relatively scarce to date. An argument for doing so is provided by Vasquezs steps-to-war theory which organizes international relations into an interactive complex of factors, including territorial disputes, interstate rivalry, recurrent crises, alliances, military buildups, and war onset. A model linking indicators of these processes is developed and tested for the 1919–95 era. Substantial empirical support for their interactions emerges. Territorial disputes in the context of rivalry and recurrent crises, aggravated by military buildups and asymmetrical external alliance situations, combine to make escalations to war more probable. Hopefully, an improved understanding of interstate escalatory dynamics can serve as a foundation and stimulus for more interactive attempts to unravel the puzzle of war causation.

Governments, Informal Links to Militias, and Accountability

From Syria to Sudan, governments have informal ties with militias that use violence against opposition groups and civilians. Building on research that suggests these groups offer governments logistical benefits in civil wars as well as political benefits in the form of reduced liability for violence, we provide the first systematic global analysis of the scale and patterns of these informal linkages. We find over 200 informal state–militia relationships across the globe, within but also outside of civil wars. We illustrate how informal delegation of violence to these groups can help some governments avoid accountability for violence and repression. Our empirical analysis finds that weak democracies as well as recipients of financial aid from democracies are particularly likely to form informal ties with militias. This relationship is strengthened as the monitoring costs of democratic donors increase. Out-of-sample predictions illustrate the usefulness of our approach that views informal ties to militias as deliberate government strategy to avoid accountability.

To Kill or to Protect Security Forces, Domestic Institutions, and Genocide

Contemporary studies of genocide have found military capabilities to be inconsistent predictors of state-sponsored killings. We suggest that these empirical inconsistencies stem from the fact that government strength can serve two opposing purposes. Some level of armed capabilities is necessary for a state to remain viable and to provide internal and external security. Yet armed government personnel can be deployed to repress and destroy segments of the public. We identify conditions under which an executive is more likely to use security forces for private-interest killing rather than public protection. We hypothesize that unconstrained leaders are more likely to use their putative security forces to initiate genocide and remain in power. An analysis of state failures that lead to genocide robustly supports the idea that the effect of increased security forces on the risk of genocide is conditional on institutional executive constraints.

Do the robot: Lessons from machine learning to improve conflict forecasting

Increasingly, scholars interested in understanding conflict processes have turned to evaluating out-of-sample forecasts to judge and compare the usefulness of their models. Research in this vein has made significant progress in identifying and avoiding the problem of overfitting sample data. Yet there has been less research providing strategies and tools to practically improve the out-of-sample performance of existing models and connect forecasting improvement to the goal of theory development in conflict studies. In this article, we fill this void by building on lessons from machine learning research. We highlight a set of iterative tasks, which David Blei terms ‘Box’s loop’, that can be summarized as build, compute, critique, and think. While the initial steps of Box’s loop will be familiar to researchers, the underutilized process of model criticism allows researchers to iteratively learn more useful representations of the data generation process from the discrepancies between the trained model and held-out data. To benefit from iterative model criticism, we advise researchers not only to split their available data into separate training and test sets, but also sample from their training data to allow for iterative model development, as is common in machine learning applications. Since practical tools for model criticism in particular are underdeveloped, we also provide software for new visualizations that build upon already existing tools. We use models of civil war onset to provide an illustration of how our machine learning-inspired research design can simultaneously improve out-of-sample forecasting performance and identify useful theoretical contributions. We believe these research strategies can complement existing designs to accelerate innovations across conflict processes.

Tracking the Dynamic Evolution of Participants Salience in a Discussion

We introduce a technique for analyzing the temporal evolution of the salience of participants in a discussion. Our method can dynamically track how the relative importance of speakers evolve over time using graph based techniques. Speaker salience is computed based on the eigenvector centrality in a graph representation of participants in a discussion. Two participants in a discussion are linked with an edge if they use similar rhetoric. The method is dynamic in the sense that the graph evolves over time to capture the evolution inherent to the participants salience. We used our method to track the salience of members of the US Senate using data from the US Congressional Record. Our analysis investigated how the salience of speakers changes over time. Our results show that the scores can capture speaker centrality in topics as well as events that result in change of salience or influence among different participants.

With friends like these, who needs democracy? The effect of transnational support from rivals on post-conflict democratization

Previous research has uncovered only ambiguous evidence of the mechanisms that support or inhibit democratic trajectories in the aftermath of civil war. Here I suggest that one specific form of transnational aid during a civil war may have reverberating consequences after the fighting stops. Specifically, when a state emerges to control the executive after a conflict with the help of a previous interstate enemy, the leadership is vulnerable to political attacks on their patriotism and judgment. As such, open democracy becomes a less attractive option for these executives. I investigate this proposition using difference-in-difference matching estimation, as well as several alternative specifications. The findings strongly suggest the presence of disincentives to democratize for those executives that received help from external rivals. This research provides a new set of tools for identifying the causes and potential remedies to deficient democracy after civil wars.

Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale

Sentiment, judgments and expressed positions are crucial concepts across international relations and the social sciences more generally. Yet, contemporary quantitative research has conventionally avoided the most direct and nuanced source of this information: political and social texts. In contrast, qualitative research has long relied on the patterns in texts to understand detailed trends in public opinion, social issues, the terms of international alliances, and the positions of politicians. Yet, qualitative human reading does not scale to the accelerating mass of digital information available currently. Researchers are in need of automated tools that can extract meaningful opinions and judgments from texts. Thus, there is an emerging opportunity to marry the model-based, inferential focus of quantitative methodology, as exemplified by ideal point models, with high resolution, qualitative interpretations of language and positions. We suggest that using alternatives to simple bag of words (BOW) representations and re-focusing on aspect-sentiment representations of text will aid researchers in systematically extracting people’s judgments and what is being judged at scale. The experimental results below show that our approach which automates the extraction of aspect and sentiment MWE pairs, outperforms BOW in classification tasks, while providing more interpretable parameters. By connecting expressed sentiment and the aspects being judged, PULSAR (Parsing Unstructured Language into Sentiment-Aspect Representations) also has deep implications for understanding the underlying dimensionality of issue positions and ideal points estimated with text. Our approach to parsing text into aspects-sentiment expressions recovers both expressive phrases (akin to categorical votes), as well as the aspects that are being judged (akin to bills). Thus, PULSAR or future systems like it, open up new avenues for the systematic analysis of high-dimensional opinions and judgments at scale within existing ideal point models.