Statistical sightings of better angels: Analysing the distribution of battle-deaths in interstate conflict over time

Abstract

Have great wars become less violent over time, and is there something we might identify as the long peace? We investigate statistical versions of such questions, by examining the number of battle-deaths in the Correlates of War dataset, with 95 interstate wars from 1816 to 2007. Previous research has found this series of wars to be stationary, with no apparent change over time. We develop a framework to find and assess a change-point in this battle-deaths series. Our change-point methodology takes into consideration the power law distribution of the data, models the full battle-deaths distribution, as opposed to focusing merely on the extreme tail, and evaluates the uncertainty in the estimation. Using this framework, we find evidence that the series has not been as stationary as past research has indicated. Our statistical sightings of better angels indicate that 1950 represents the most likely change-point in the battle-deaths series – the point in time where the battle-deaths distribution might have changed for the better.

Lewis Fry Richardson: His Intellectual Legacy and Influence in the Social Sciences

“…Until recently scholars have tended to assume that the Second World War is the obvious candidate for a break point into a more peaceful world. However, recent papers by Fagan et al (2018) and Cunen et al (2018) start from an agnostic position on potential break points and use statistical methods to detect convincing ones. Both papers find substantial evidence for a change at 1950 although they identify other candidate break points including 1912 (Fagan et al, 2018) and 1965 (Cunen et al, 2018).…”

Confidence Distributions for FIC Scores

“…This framework, having started with Hjort and Claeskens (2003a) and Claeskens and Hjort (2003), has been demonstrated to be very useful, leading to various FIC procedures in the literature, and now also to the extended and finessed FIC procedures of the present paper. A different and in some situations more satisfactory framework involves starting with a fixed wide model, and with no 'local asymptotics' involved; see the review paper Claeskens et al (2019) for general regression models and Cunen et al (2020) for classes of linear mixed models. The key results involve different approximations to mse quantities, along the lines of…”

“…Later extensions include Claeskens et al (2007) for time series models, Gueuning and Claeskens (2018) for high-dimensional setups, Hjort and Claeskens (2006) and for semiparametric and nonparametric survival regression models, Zhang and Liang (2011) for generalised additive models, Zhang et al (2012) for tobit models, Ko et al (2019) for copulae with two-stage estimation methods. Recent methodological extensions and advances also include setups centred on a fixed wide model, with large-sample approximations not depending on the local asymptotics methods; see Claeskens et al (2019); Hjort (2017, 2019), along with Cunen et al (2020) for linear mixed models. There is a growing list of application domains where FIC is finding practical and context-relevant use, such as finance and economics (Behl et al 2012;Brownlees and Gallo 2008), peace research and political science (Cunen et al 2020), sociology (Zhang et al 2012), marine science (Hermansen et al 2016), etc.…”

“…Recent methodological extensions and advances also include setups centred on a fixed wide model, with large-sample approximations not depending on the local asymptotics methods; see Claeskens et al (2019); Hjort (2017, 2019), along with Cunen et al (2020) for linear mixed models. There is a growing list of application domains where FIC is finding practical and context-relevant use, such as finance and economics (Behl et al 2012;Brownlees and Gallo 2008), peace research and political science (Cunen et al 2020), sociology (Zhang et al 2012), marine science (Hermansen et al 2016), etc. There is similarly a rapidly expanding literature on frequentist model averaging procedures, as partly contrasted with Bayesian versions; perspectives for the latter are summarised in Hoeting et al (1999).…”

Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution

“…The development represents an extension of earlier work of Hjort (2017, 2019) for i.i.d. data and survival analysis, Ko et al (2019) for copulae models, Cunen et al (2019) for power-law distributions (with applications to war and conflict data) and Cunen et al (in review) 1,2 for linear mixed effects models (with application to whale ecology).…”