Trading on Preconceptions: Why World War I Was Not a Failure of Economic Interdependence

World War I is generally viewed by both advocates and critics of commercial liberal theory as the quintessential example of a failure of economic integration to maintain peace. Yet this consensus relies on both methodologically flawed inference and an incomplete accounting of the antecedents to the war. Crucially, World War I began in a weakly integrated portion of Europe with which highly integrated powers were entangled through the alliance system. Crises among the highly interdependent European powers in the decades leading up to the war were generally resolved without bloodshed. Among the less interdependent powers in Eastern Europe, however, crises regularly escalated to militarized violence. Moreover, the crises leading to the war created increased incentives for the integrated powers to strengthen commitments to their less interdependent partners. In attempting to make these alliances more credible, Western powers shifted foreign policy discretion to the very states that lacked strong economic disincentives to fight. Had globalization pervaded Eastern Europe, or if the rest of Europe had been less locked into events in the east, Europe might have avoided a “Great War.” </jats:p

Root-to-Leaf Scheduling in Write-Optimized Trees

International audienceWrite-optimized dictionaries are a class of cache-efficient data structures that buffer updates and apply them in batches to optimize the amortized cache misses per update. For example, a B^epsilon tree inserts updates as messages at the root. B^epsilon trees only move ("flush") messages when they have total size close to a cache line, optimizing the amount of work done per cache line written. Thus, recently-inserted messages reside at or near the root and are only flushed down the tree after a sufficient number of new messages arrive. Although this lazy approach works well for many operations, some types of updates do not complete until the update message reaches a leaf. For example, deferred queries and secure deletes must flush through all nodes along their root-to-leaf path before taking effect. What happens when we want to service a large number of (say) secure deletes as quickly as possible? Classic techniques leave us with an unsavory choice. On the one hand, we can group the delete messages using a write-optimized approach and move them down the tree lazily. But then many individual deletes may be left incomplete for an extended period of time, as their messages wait to be grouped with a sufficiently large number of related messages. On the other hand, we can ignore cache efficiency and perform a root-to-leaf flush for each delete. This begins work on individual deletes immediately, but harms system throughput. This paper investigates a new framework for efficiently flushing collections of messages from the root to their leaves in a write-optimized data structure. Our goal is to minimize the average time that messages reach the leaves. We give an algorithm that O(1)-approximates the optimal average completion time in this model. Along the way, we give a new 4-approximation algorithm for scheduling parallel tasks for weighted completion time with tree precedence constraints