In canonical accounts of war, conflict outcomes are inherently uncertain. Contesting literatures posit that this uncertainty, arising from stochastic elements of the war- fighting process, may induce conflict due to greater risks of miscalculation or foster peace by breeding caution. We theorize that states, on average, exhibit prudence when confronting greater uncertainty. Despite its conceptual importance, extant proxies for uncertainty at various levels of analysis—such as polarity, balance-of-power, system concentration, and dyadic relative capabilities—are imprecise indicators. To overcome this shortcoming, we clarify the concept’s theoretical foundations and introduce a novel measure that captures the uncertainty over conflict outcomes within any k-state system. Through extensive empirical analysis, we confirm uncertainty’s pacifying effect, and show how this effect operates at different levels of analysis.
In paired randomized experiments units are grouped in pairs, often based on covariate information, with random assignment within the pairs. Average treatment effects are then estimated by averaging the within-pair differences in outcomes. Typically the variance of the average treatment effect estimator is estimated using the sample variance of the within-pair differences. However, conditional on the covariates the variance of the average treatment effect estimator may be substantially smaller. Here we propose a simple way of estimating the conditional variance of the average treatment effect estimator by forming pairs-of-pairs with similar covariate values and estimating the variances within these pairs-of-pairs. Even though these within-pairs-of-pairs variance estimators are not consistent, their average is consistent for the conditional variance of the average treatment effect estimator and leads to asymptotically valid confidence intervals.