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Below results based on the criteria 'Conflict'
Total number of records returned: 18

Automated Production of High-Volume, Near-Real-Time Political Event Data
Schrodt, Philip

Uploaded 08-30-2010
Keywords event data
natural language processing
open source
Abstract This paper summarizes the current state-of-the-art for generating high-volume, near-real-time event data using automated coding methods, based on recent efforts for the DARPA Integrated Crisis Early Warning System (ICEWS) and NSF-funded research. The ICEWS work expanded by more than two orders of magnitude previous automated coding efforts, coding of about 26-million sentences generated from 8-million stories condensed from around 30 gigabytes of text. The actual coding took six minutes. The paper is largely a general ``how-to'' guide to the pragmatic challenges and solutions to various elements of the process of generating event data using automated techniques. It also discusses a number of ways that this could be augmented with existing open-source natural language processing software to generate a third-generation event data coding system.

Racing Horses: Constructing and Evaluating Forecasts in Political Science
Brandt, Patrick
Freeman, John R.
Schrodt, Philip

Uploaded 07-27-2011
Keywords forecasting
political conflict
scoring rules
model training
forecast density
verification rank histogram
probability integral transform
Abstract We review methods for forecast evaluations and how they can be used in political sciences. We examine how forecast densities are more useful summaries of forecasted variables than point metrics. We also cover how continuous rank probability scores, probability integral transforms, and verification rank histograms can be used to calibrate and evaluate forecast performance. Finally, we present two illustrations, one a simulation and the other a comparison of forecasting models for the China-Taiwan (cross-straits) conflict.

Armed Conflict as a Public Health Problem
Murray, Christopher J. L.
King, Gary
Lopez, Alan D.
Tomijima, Niels
Krug, Etienne

Uploaded 02-25-2002
Keywords International Conflict Data
public health
Abstract Armed conflict is a major cause of injury and death worldwide, but we need much better methods of quantification before we can accurately assess its effect. Armed conflict between warring states and groups within states have been major causes of ill health and mortality for most of human history. Conflict obviously causes deaths and injuries on the battlefield, but also health consequences from the displacement of populations, the breakdown of health and social services, and the heightened risk of disease transmission. Despite the size of the health consequences, military conflict has not received the same attention from public health research and policy as many other causes of illness and death. In contrast, political scientists have long studied the causes of war but have primarily been interested in the decision of elite groups to go to war, not in human death and misery. We review the limited knowledge on the health consequences of conflict, suggest ways to improve measurement, and discuss the potential for risk assessment and for preventing and ameliorating the consequences of conflict.

An Automated Information Extraction Tool For International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design
King, Gary
Lowe, Will

Uploaded 02-25-2002
Keywords rare events
international conflict data
Abstract Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or occasionally monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the last two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by ``reading'' news stories without human coders. We design a method that makes it feasible for the first time to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large scale data collections, the program dominates human coding. Our new evaluative method should be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we will be releasing with this article data on 4.3 million international events, covering the entire world for the last decade.

Automated Coding of International Event Data Using Sparse Parsing Techniques
Schrodt, Philip A.

Uploaded 06-28-2001
Keywords event data
natural language processing
content analysis
open source
Abstract "Event data" record the interactions of political actors reported in sources such as newspapers and news services; this type of data is widely used in research in international relations. Over the past ten years, there has been a shift from coding event data by humans -- typically university students -- to using computerized coding. The automated methods are dramatically faster, enabling data sets to be coded in real time, and provide far greater transparency and consistency than human coding. This paper reviews the experience of the Kansas Event Data System (KEDS) project in developing automated coding using "sparse parsing" machine coding methods, discusses a number of design decisions that were made in creating the program, and assesses features that would improve the effectiveness of these programs.

Monitoring conflict using automated coding of newswire reports
Schrodt, Philip A.
Gerner, Deborah J.
Simpson Gerner, Erin M.

Uploaded 06-28-2001
Keywords event data
natural language processing
Abstract his paper discusses the experience of the Kansas Event Data System (KEDS) project in developing event data sets for monitoring conflict levels in five geographical areas: the Levant (Arab-Israeli conflict), Persian Gulf, former Yugoslavia, Central Asia (Afghanistan, Armenia-Azerbijan, former Soviet republics), and West Africa (Liberia, Sierra Leone). These data sets were coded from commercial news sources using the KEDS and TABARI automated coding systems. The paper discusses our experience in developing the dictionaries required for this coding, the problems with the number of reported events in the various areas, and provides examples of the statistical summaries that can be produced from event data. We also compare the coverage of the Reuters and Agence France Presse news services for selected years in the Levant and former Yugoslavia. We conclude with suggestions for four topics where additional efforts that could be usefully undertaken by multiple research projects.

Analyzing the Dynamics of International Mediation Processess in the Middle East and the former Yugoslavia
Gerner, Deborah J.
Schrodt, Philip A.

Uploaded 06-28-2001
Keywords mediation
event data
Middle East
Abstract This paper discusses a new National Science Foundation-funded project that will examine the dynamics of third-party international mediation using statistical time-series analyses of political event data. Third-party mediation was attempted in over half of the conflicts in the post-WWII period and it is likely that the use of mediation has increased following the end of the Cold War. Surprisingly, there have been few systematic studies on mediation. Those that do exist have generally focused on relatively static contextual factors such as the the conflict's attributes and the prior relationship between the mediator and protagonists rather than on dynamic factors' both contextual and process that may contribute to the success or failure of mediation activities. In contrast, the extensive qualitative literature provides numerous hypotheses about dynamic aspects of mediation. This, however, primarily consists of case studies, often by mediation practitioners, that exhibit little cumulation and, when taken as a whole, are rife with contradictory assertions. The project will formally test a number of the hypotheses embedded in the theoretical and qualitative literatures on mediation, using automated coding of event data from news-wire sources and employing time-series and event- history methods. A system of specialized event codes that a sensitive to mediation activities will be developed, then events will be coded from news reports using the TABARI machine coding program. The research will look at the factors that influence (1) whether mediation is accepted by the parties in a conflict, (2) whether formal agreements are reached, and (3) whether the agreements actually reduce the level of conflict. The project will initially focus on conflicts in the Middle East, a region where the principal investigators have substantial field experience. After refining the statistical tests on the Middle East case, the analysis will be extended to event data on conflicts in the former Yugoslavia and West Africa. The paper presents the results of an empirical "plausibility probe" based on existing WEIS-coded event data for the Levant and the former Yugoslavia. It employs a simple measure of third-party mediation efforts as the independent variables and Goldstein-scaled cooperation as the dependent variable. In the Levant, we find a weak but consistent pattern of mediation correlating with past conflictual activity, and resulting in later increases in cooperation. In the former Yugoslavia, the analysis shows strikingly different results for the mediation efforts the UN, European states, and the US. All three respond to increased conflict, but the UN efforts correlate with greater conflict, the US efforts with greater cooperation, and the European efforts have no effect. These results are consistent with many of the qualitative assessments of these efforts, and suggest that the event data approach will produce credible results

Forecasting Conflict in the Balkans using Hidden Markov Models
Schrodt, Philip A.

Uploaded 08-24-2000
Keywords forecasting
event data
hidden Markov models
Abstract This study uses hidden Markov models (HMM) to forecast conflict in the former Yugoslavia for the period January 1991 through January 1999. The political and military events reported in the lead sentences of Reuters news service stories were coded into the World Events Interaction Survey (WEIS) event data scheme. The forecasting scheme involved randomly selecting eight 100-event "templates" taken at a 1-, 3- or 6-month forecasting lag for high-conflict and low-conflict weeks. A separate HMM is developed for the high-conflict-week sequences and the low-conflict-week sequences. Forecasting is done by determining whether a sequence of observed events fit the high-conflict or low-conflict model with higher probability. Models were selected to maximize the difference between correct and incorrect predictions, evaluated by week. Three weighting schemes were used: unweighted (U), penalize false positives (P) and penalize false negatives (N). There is a relatively high level of convergence in the estimatesčthe best and worst models of a given type vary in accuracy by only about 15% to 20%. In full-sample tests, the U and P models produce at overall accuracy of around 80%. However, these models correctly forecast only about 25% of the high-conflict weeks, although about 60% of the cases where a high-conflict week has been forecast turn out to have high conflict. In contrast, the N model has an overall accuracy of only about 50% in full-sample tests, but it correctly forecasts high-conflict weeks with 85% accuracy in the 3- and 6-month horizon and 92% accuracy in the 1-month horizon. However, this is achieved by excessive predictions of high-conflict weeks: only about 30% of the cases where a high-conflict week has been forecast are high-conflict. Models that use templates from only the previous year usually do about as well as models based on the entire sample. The models are remarkably insensitive to the length of the forecasting horizončthe drop-off in accuracy at longer forecasting horizons is very small, typically around 2%-4%. There is also no clear difference in the estimated coefficients for the 1-month and 6-month models. An extensive analysis was done of the coefficient estimates in the full-sample model to determine what the model was "looking at" in order to make predictions. While a number of statistically significant differences exist between the high and low conflict models, these do not fall into any neat patterns. This is probably due to a combination of the large number of parameters being estimated, the multiple local maxima in the estimation surface, and the complications introduced by the presence of a number of very low probability event categories. Some experiments with simplified models indicate that it is possible to use models with substantially fewer parameters without markedly decreasing the accuracy of the predictions; in fact predictions of the high conflict periods actually increase in accuracy quite substantially.

Time Remembered: A Dynamic Model of Interstate Interaction
Crescenzi, Mark J. C.
Enterline, Andrew J.

Uploaded 05-23-2000
Keywords dynamic model
Abstract Over time, states form relationships. These relationships, mosaics of past interactions, provide political leaders with information about how states are likely to behave in the future. Although simple, this claim holds important implications for the manner in which we construct and test empirically our expectations about interstate behavior. Empirical analyses of interstate relations implicitly assume that the units of analysis, principally dyad-years, are independent. Formal models of interstate interaction are often cast in the absence of historical context. In the following paper, we construct a dynamic model of interstate interaction that we believe will assist scholars employing empirical and formal methods by incorporating history into their models of interstate relations. Our conceptual model includes both conflictual and cooperative components, and exhibits the basic properties of growth and decay indicative of a dyadic behavioral history. In an empirical exposition, we derive a continuous measure of interstate conflict from the conflictual component of the model. Turning to Oneal and Russett's (1997) analysis of dyadic conflict for the period 1950-85 as a benchmark, we examine whether the inclusion of our measure of interstate conflict significantly improves our ability to predict militarized conflict. We find empirical support for this hypothesis, indicating that our continuous measure of interstate conflict significantly augments a fully specified statistical model of dyadic militarized conflict. We conclude that our research underscores the considerable purchase gained by incorporating historical context into models of interstate relations.

Trade and Conflict in the Cold War Era: An Empirical Analysis
Beck, Nathaniel

Uploaded 08-30-1999
Keywords trade
generalized additive model
Abstract What is the relationship between trade and conflict in the post-World War II era. Using a dyad-year design, and studying both all dyads and politically relevant dyads, this paper uses the generalized additive model to study the relationship between dyadic trade and militarized interstate disputes (both all disputes and those involving casualties only). For all dyads, moving from no trade to a small amount of trade increases the likelihood of conflict, though that mostly reflects the fact that non-traders also are likely to have little conflict in any arena. Moving from zero to low trade decreases the likelihood of conflict among politically relevant dyads, though this may simply reflect the nature of the Cold War world where dyads made up of Cold War opponents did not trade but did fight. In any event, there is little evidence for a causal pacific impact of trade, but also little evidence that trade is inherently conflictual, other than being an obvious necessary condition for trade disputes and also signalling that dyadic partners are in some interesting relationship.

The Problem with Quantitative Studies of International Conflict
Beck, Nathaniel
King, Gary
Zeng, Langche

Uploaded 07-15-1998
Keywords Conflict
neural networks
Bayesian analysis
Abstract Despite immense data collections, prestigious journals, and sophisticated analyses, empirical findings in the literature on international conflict are frequently unsatisfying. Statistical results appear to change from article to article and specification to specification. Very few relationships hold up to replication with even minor respecification. Accurate forecasts are nonexistent. We provide a simple conjecture about what accounts for this problem, and offer a statistical framework that better matches the substantive issues and types of data in this field. Our model, a version of a ``neural network'' model, forecasts substantially better than any previous effort, and appears to uncover some structural features of international conflict.

Time Series Models for Discrete Data: solutions to a problem with quantitative studies of international conflict
Jackman, Simon

Uploaded 07-21-1998
Keywords categorical time series
dependent binary data
Markov regression models
latent autoregressive process
Markov Chain Monte Carlo
international conflict
democratic peace
Abstract Discrete dependent variables with a time series structure occupy something of a statistical limbo for even well-trained political scientists, prompting awkward methodological compromises and dubious substantive conclusions. An important example is the use of binary response models in the analysis of longitudinal data on international conflict: researchers understand that the data are not independent, but lack any way to model serial dependence in the data. Here I survey methods for modeling categorical data with a serial structure. I consider a number of simple models that enjoy frequent use outside of political science (originating in biostatistics), as well as a logit model with an autoregressive error structure (the latter model is fit via Bayesian simulation using Markov chain Monte Carlo methods). I illustrate these models in the context of international conflict data. Like other re-analyses of these data addressing the issue of serial dependence, citeaffixed{beck:btscs}{e.g.,}, I find economic interdependence does not lessen the chances of international conflict. Other findings include a number of interesting asymmetries in the effects of covariates on transitions from peace to war (and vice versa). Any reasonable model of international conflict should take into account the high levels of persistence in the data; the models I present here suggest a number of methods for doing so.

Uncertainty and Ambivalence in the Ecology of Race
Alvarez, R. Michael
Brehm, John

Uploaded 08-22-1996
Keywords racial policy
affirmative action
ecological inference
heteroskedastic ordered logit
value conflict
Abstract Since Myrdal (1944), scholars have regarded American attitudes towards racial policy as a conflict between values, groups, and interests. Although Myrdal viewed the conflict as a state internal to individuals, it begins as aggregate conflict. This mix of ecologies---individual and aggregate---carries forth to the present. This paper takes the question of different ecologies for racial politics seriously, developing tools to compare conflict at individual and aggregate level. We demonstrate that individual racial policy choices stems principally from racial resentment, and that the variability of that choice indicates a state of uncertainty, not ambivalence or equivocation. We further demonstrate that racial resentment does not surface as a predictor of aggregate racial policy choice, even though individual choices about racial policies appear to be more strongly influenced by the level of political informedness.

Using Cluster Analysis to Derive Early Warning Indicators for Political Change in the Middle East, 1979-1996
Schrodt, Philip A.
Gerner, Deborah J.

Uploaded 08-22-1996
Keywords event data
early warning
Middle East
cluster analysis
genetic algorithms
Abstract This paper uses event data to develop an early warning model of major political changes in the Levant for the period April 1979 to July 1996. Following a general review of statistical early warning research, the analysis focuses on the behavior of eight Middle Eastern actors—Egypt, Israel, Jordan, Lebanon, the Palestinians, Syria, the United States and USSR/Russia—using WEIS-coded event data generated from Reuters news service lead sentences with the KEDS machine-coding system. The analysis extends earlier work (Schrodt and Gerner 1995) demonstrating that clusters of behavior identified by conventional statistical methods correspond well with changes in political behavior identified a priori. We employ a new clustering algorithm that uses the correlation between the dyadic behaviors at two points in time as a measure of distance, and identifies cluster breaks as those time points that are closer to later points than to preceding points. We also demonstrate that these data clusters begin to "stretch" prior to breaking apart; this characteristic is used as an early-warning indicator. A Monte- Carlo analysis shows that the clustering and early warning measures perform very differently in simulated data sets having the same mean, variance, and autocorrelation as the observed data (but no cross-correlation) which reduces the likelihood that the clustering patterns are due to chance. The initial analysis uses Goldstein's (1992) weighting system to aggregate the WEIS-coded data. In an attempt to improve on the Goldstein scale, we use a genetic algorithm to optimize the weighting of the WEIS event categories for the purpose of clustering. This does not prove very successful and only differentiates clusters in the first half of the data set, a result similar to one we obtained using the cross-sectional K- Means clustering procedure. Correlating the frequency of events in the twenty-two 2-digit WEIS categories, on the other hand, gives clustering and early warning results similar to those produced by the Goldstein scale. The paper concludes with some general remarks on the role of quantitative early warning and directions for further research. This paper was presented at the American Political Science Association, San Francisco, 28 August - 1 September 1996

Trade and Militarized Conflict: How Modeling Strategic Interactions Between States Makes a Difference
Rowan, Shawn E.

Uploaded 07-19-2005
Keywords trade
Abstract The study between the interaction of war and foreign trade has occupied scholars from political science and economics for thousands of years. I contribute to the trade and conflict debate by accounting for the strategic interaction between states that most or all theories in international relations (IR) assume. I use a strategic statistical model (Signorino 1999, 2003b) that endogenizes the actions that leads states to militarized conflict and peace. The results of the strategic probit model reveal non-linear, asymmetric relationships between trade dependence and militarized conflict for each state in the dyad. Not only are these effects non-linear, but, in equilibrium, also depend on the actions taken by the other state in the dyad. The trade dependence of one state on another can have either a pacifying or a positive effect on militarized conflict. Additionally, these effects are only realized for initial increases in trade dependence and that once a threshold is reached, the effects of trade dependence are constant.

Modeling History Dependence in Network-Behavior Coevolution
Franzese, Robert
Hays, Jude
Kachi, Aya

Uploaded 07-21-2010
Keywords path dependence
history dependence
spatial econometrics
markov chain
military alliance
conflict behavior
Abstract Spatial interdependence--the dependence of outcomes in some units on those in others--is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes: common exposure of actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the variable along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, we can identify the same three processes as potential sources of network effects and network formation. From that perspective, actors' self-selection into networks (by, e.g., behavioral homophily) and actors' behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behavior coevolve over time. This paper begins building such modeling by, on the theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on the empirical side, merging a simple spatial-lag logit model of contagious behavior with a simple p-star logit model of network formation, building this synthetic discrete-time empirical model from the theoretical base of the modified Markov type-interaction model. One interesting consequence of network-behavior coevolution--identically: endogenous patterns of spatial interdependence--emphasized here is how it can produce history-dependent political dynamics, including equilibrium phat and path dependence (Page 2006). The paper explores these implications, and then concludes with a preliminary demonstration of the strategy applied to alliance formation and conflict behavior among the great powers in the first half of the twentieth century.

Moments in Time: Studying European Conflict using a Change-Point Model
Nieman, Mark

Uploaded 07-31-2011
Keywords change-point
time regime
structural break
Abstract Constructivist theories provide insights into understanding systemic violence in Europe by accounting for preference formation in multiple time periods. Thus, constructivism explains how the influence of a set of independent variables on a dependent variable can change over time. By allowing preferences to change, constructivist theories accounts for changes in the direction and statistical significance of weakly exogenous explanatory variables over multiple time periods. Owing to this, different rationalist theories may be appropriate to explain different time periods. To test this, counts of militarized interstate dispute involving European states from 1870 to 2001 are analyzed. Bayesian MCMC change-point models provide an effective tool for identifying multiple time periods and generating unbiased and efficient estimates of explanatory variables. Because change-points are calculated probabilistically, the determinates of structural breaks are also examined by testing for Granger causality. Results generate support for constructivist theories because the influence of variables and statistical significance change depending on the time period. These results differ from tradition models which ignore structural breaks in the dependent variable. Lastly, Granger causality tests indicate that changes in systemic power and democratization are determinates of these structural breaks.

Direct Sports Competition and Interstate Conflict: Investigating a Randomized Natural Experiment
Bertoli, Andrew
Zhang, Baobao
Kalla, Josh

Uploaded 07-22-2014
Keywords International Relations
World Cup
Natural Experiment
Abstract Past studies have found qualitative and quantitative evidence suggesting that international sports can increase tensions between countries and lead to military conflict. We provide a new test of this theory by taking advantage of the random assignment of countries to compete against each other in the group stage of the World Cup from 1930-2010. We first briefly explain how countries were randomly assigned to World Cup groups over this period. We then use randomization inference to show that the number of military disputes between pairs of countries that were assigned to the same group was much larger than would be expected if World Cup competition had no impact on international conflict. The results hold under many important robustness checks, including a placebo test on the previous outcome. To further illustrate this effect, we use Twitter data to show that competition on the playing field increased expressions of nationalism during the 2014 World Cup.

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