注定一戰？中美能否避免修昔底德陷阱

包淳亮譯，注定一戰？中美能否避免修昔底德陷阱，八旗文化 ，2018

Allison Graham, Destined for War: Can America and China Escape Thucydides’s Trap?, Mariner Books, 2018

◎從古希臘到美蘇冷戰，從兩千年人類戰爭史出發，預測美、中國不安的未來！ 

　　西元前五世紀的希臘史學家修昔底德記錄了摧毀整個希臘世界的「伯羅奔尼撒戰爭」，他將戰爭的起因總結為「雅典的崛起，以及斯巴達揮之不去的恐懼，使戰爭不可避免」。本書作者格雷厄姆・艾利森把當時斯巴達與雅典面臨的困境稱之為「修昔底德陷阱」：在原本的權力平衡面臨改變時，既有的統治強權可能為了捍衛地位而出手訓誡、扼殺後起的挑戰者，挑戰者也可能不甘屈居人下、試圖改變遊戲規則而「問鼎中原」。在過去500年中，崛起強權挑戰統治強權的案例有16起，其中12起爆發戰爭。「修昔底德陷阱」像幽魂一再地將大國推向毀滅的深淵。俾斯麥在普法戰爭中挑戰歐陸霸主法國，德皇威廉二世在一戰中挑戰英國海軍，日本自認應該享有平等的尊嚴而發動日俄戰爭，又因恐懼美國的經濟封鎖扼殺它的發展而襲擊珍珠港。種種盲目不理智的行為，都可以透過「修昔底德陷阱」得到解釋。 

　　◎南海衝突、台灣獨立、網路攻擊、北韓崩潰、貿易戰爭……誰將引爆美中大戰，又該如何避免？ 

　　21世紀初的中國與美國恰恰再度落入「修昔底德陷阱」的模式，彷彿難逃「注定一戰」。中國的飛速崛起為二戰後美國主導的國際秩序與美國的軍事霸權構成嚴重挑戰。二戰後的美國占全球經濟的50％，如今已下滑至16％。同一時期，中國的比例從1980年的2％飆升至2016年的18％。雪上加霜的是，標榜「中國夢」的習近平與「美國第一」的川普不僅都誓言恢復國家的偉大光榮，也都認為對方是實現目標的障礙。沒有另外兩個領導者比習、川更可能把美中帶向戰爭。 

　　格雷厄姆・艾利森是全球知名的國際關係學者，憑1970年代對古巴飛彈危機的深刻研究奠定其不可動搖的大師地位。他透過對歷代戰爭提綱挈領地分析建立了「修昔底德陷阱」的理論基礎，並以此預測美中爆發衝突的各種可能途徑，在書中列舉了5種爆發戰爭的可能，以及12條趨吉避凶的和平線索，並針對美國政府提出懇切建言，一方面呼籲美國嚴肅看待中國崛起的事實與恢復民族光榮的決心，一方面諄諄勸誡美國外交決策圈應重拾美蘇冷戰時代的宏觀戰略思維，以面對從所未見的安全威脅。本書未上市就已在全球政治、學術、新聞界造成轟動，《注定一戰？》成為所有關心美中未來人士的話題。連習近平都親自表示：「我們都應該努力避免陷入修昔底德陷阱！」

Globalization in transition: The future of trade and value chains

Susan Lund, James Manyika, Jonathan Woetzel, Jacques Bughin, Mekala Krishnan, Jeongmin Seong, and Mac Muir, Globalization in transition: The future of trade and value chains, McKinsey Global Institute, January 2019.

Although output and trade continue to increase in absolute terms, trade intensity (that is, the share of output that is traded) is declining within almost every goods-producing value chain. Flows of services and data now play a much bigger role in tying the global economy together. Not only is trade in services growing faster than trade in goods, but services are creating value far beyond what national accounts measure. Using alternative measures, we find that services already constitute more value in global trade than goods. In addition, all global value chains are becoming more knowledge-intensive. Low-skill labor is becoming less important as factor of production. Contrary to popular perception, only about 18 percent of global goods trade is now driven by labor-cost arbitrage. 

Learning Scheduling Algorithms for Data Processing Clusters

Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh, Learning Scheduling Algorithms for Data Processing Clusters, SIGCOMM '19 Proceedings of the ACM Special Interest Group on Data Communication, Pages 270-288. 

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load.

Codes and more information. 

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Food Discovery with Uber Eats

Ferras Hamad, Isaac Liu, and Xian Xing Zhang, Food Discovery with Uber Eats: Building a Query Understanding Engine, Uber, June 10, 2018

Choice is fundamental to the Uber Eats experience. At any given location, there could be thousands of restaurants and even more individual menu items for an eater to choose from. Many factors can influence their choice. For example, the time of day, their cuisine preference, and current mood can all play a role. At Uber Eats, we strive to help eaters find the exact food they want as effortlessly as possible....