Analysis, War, and Decision: Why Intelligence Failures Are Inevitable

1978 ◽  
Vol 31 (1) ◽  
pp. 61-89 ◽  
Author(s):  
Richard K. Betts

Strategic intelligence failures cannot be prevented by organizational solutions to problems of analysis and communication. Analytic certainty is precluded by ambiguity of evidence, ambivalence of judgment, and atrophy of institutional reforms designed to avert failures. Many sources of error are unresolvable paradoxes and dilemmas rather than curable pathologies. Major failures in attack warning, operational evaluation, and intelligence for strategic planning are due primarily to leaders’ psychological attributes rather than to analysts’ failures to detect relevant data. Since analysis and decision are interactive rather than sequential processes, and authorities often hear but dismiss correct estimates, intelligence failure is inseparable from policy failure. Solutions most often proposed—worst-case analysis, multiple advocacy, devil's advocacy, organizational consolidation, sanctions and incentives for analysts, and cognitive rehabilitation—are either impractical because of constraints on the leaders’ time, or they are mixed blessings because they create new problems in the course of solving old ones.

1976 ◽  
Vol 28 (3) ◽  
pp. 348-380 ◽  
Author(s):  
Avi Shlaim

The principal question which this article seeks to answer is: Why was the intention of the Arabs to launch the Yom Kippur War misperceived despite the fact that Israeli Intelligence had ample and accurate information on enemy moves and dispositions? In this anatomy of the Israeli intelligence failure, extensive use is made of the report of the official commission of inquiry that investigated the events leading up to the war. The article is equally concerned with the phenomenon of strategic surprise in general, and this case study is used to explore the psychological and organizational roots of intelligence failures. Some safeguards and institutional reforms for reducing the frequency of failure are examined. However, there is no suggestion that surprise can ever be eliminated altogether. In conclusion a case is made for developing a theory of intelligence through case studies and systematic research.


Author(s):  
Hatim Djelassi ◽  
Stephane Fliscounakis ◽  
Alexander Mitsos ◽  
Patrick Panciatici

2013 ◽  
Vol 21 (10) ◽  
pp. 1823-1836 ◽  
Author(s):  
Yiyuan Xie ◽  
Mahdi Nikdast ◽  
Jiang Xu ◽  
Xiaowen Wu ◽  
Wei Zhang ◽  
...  

2010 ◽  
Vol 43 (15) ◽  
pp. 321-326 ◽  
Author(s):  
Wenfei Wang ◽  
Prathyush P. Menon ◽  
Nuno M. Gomes Paulino ◽  
Emanuele Di Sotto ◽  
Sohrab Salehi ◽  
...  

Algorithmica ◽  
2021 ◽  
Author(s):  
Jie Zhang

AbstractApart from the principles and methodologies inherited from Economics and Game Theory, the studies in Algorithmic Mechanism Design typically employ the worst-case analysis and design of approximation schemes of Theoretical Computer Science. For instance, the approximation ratio, which is the canonical measure of evaluating how well an incentive-compatible mechanism approximately optimizes the objective, is defined in the worst-case sense. It compares the performance of the optimal mechanism against the performance of a truthful mechanism, for all possible inputs. In this paper, we take the average-case analysis approach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design—the scheduling problem (Nisan and Ronen, in: Proceedings of the 31st annual ACM symposium on theory of computing (STOC), 1999). One version of this problem, which includes a verification component, is studied by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014). It was shown that the problem has a tight approximation ratio bound of $$(n+1)/2$$ ( n + 1 ) / 2 for the single-task setting, where n is the number of machines. We show, however, when the costs of the machines to executing the task follow any independent and identical distribution, the average-case approximation ratio of the mechanism given by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014) is upper bounded by a constant. This positive result asymptotically separates the average-case ratio from the worst-case ratio. It indicates that the optimal mechanism devised for a worst-case guarantee works well on average.


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