As the United States struggles to emerge from recession, India and China's continued robust growth is the subject of much interest and concern. Council on Foreign Relations (CFR) Senior Fellow Adam Segal will talk about his new book Advantage: How American Innovation Can Overcome the Asian Challenge, analyzing Asia's technological rise, questioning assumptions about the United States inevitable decline, and explaining how America can preserve and improve its position in the global economy by optimizing its strength of moving ideas from the lab to the marketplace.

In his book, Segal argues that the emergence of India and China does not mean the end of American economic and technological power. Instead, the United States should now leverage its many advantages.

Through his research, Segal concludes the United States has an advantage over Asia in the realm of the software of innovation. “In America, your ideas can make you rich. Intellectual property is protected, and individual scientists are able to exploit their breakthroughs for commercial gains,” he writes. “It is time to realize that software in its most expansive sense offers the most opportunities for the United States to ensure its competitive place in the world.” The challenge is “to recover a culture of innovation that was driven underground, overshadowed by sexy credit default swaps and easy spending.”

Speaker

Adam Segal is the Ira A. Lipman senior fellow for counterterrorism and national security studies at the Council on Foreign Relations (CFR). An expert on security issues, technology development, and Chinese domestic and foreign policy, Dr. Segal currently leads study groups on cybersecurity and cyber conflict as well as Asian innovation and technological entrepreneurship. His new book Advantage: How American Innovation Can Overcome the Asian Challenge (W.W. Norton, 2011) looks at the technological rise of Asia. Dr. Segal is a research associate of the National Asia Research Program and was the project director for a CFR-sponsored independent task force on Chinese military modernization.

Before coming to CFR, Dr. Segal was an arms control analyst for the China Project at the Union of Concerned Scientists. There, he wrote about missile defense, nuclear weapons, and Asian security issues. Dr. Segal has been a visiting scholar at the Massachusetts Institute of Technology’s Center for International Studies, Shanghai Academy of Social Sciences, and Tsinghua University in Beijing. He has taught at Vassar College and Columbia University. Dr. Segal is the author of Digital Dragon: High-Technology Enterprises in China (Cornell University Press, 2003), as well as several articles and book chapters on Chinese technology policy. His work has recently appeared in the International Herald Tribune, Financial Times, Washington Quarterly, Los Angeles Times, and Foreign Affairs. Dr. Segal currently writes for the CFR blog, “Asia Unbound".

Dr. Segal has a BA and PhD in government from Cornell University, and an MA in international relations from the Fletcher School of Law and Diplomacy, Tufts University. He reads and speaks Chinese.

Abstract
I will begin this talk with a short discussion of the function of warning in the US national security community, and the analytic methodology used by US intelligence agencies (in 1941 and since) to address the problem of warning. I will then present a formal model for crisis warning consisting of a Partially Observable Markov Decision Process (POMDP) intended to assist an intelligence analyst in deciding when to issue an alert to a foreign policy principal decision maker such as the President. The lead time demanded by the principal is a key element in the model. I will spend the remainder of the talk illustrating this warning model in the context of the brewing crisis in the Pacific from July to December 1941, and present results from test runs of the model using historical raw intelligence data from that period. While a probabilistic approach to warning is not a new idea, this research addresses three outstanding issues left unresolved from past efforts to develop such an approach:

1. The need to process multiple dependent signals in a manner that is combinatorially feasible;
2. Incorporation of the time dimension in which intelligence data is received into the inference, and the effect of dynamics on a warning decision where a finite horizon is imposed;
3. Consideration of the fact that the analyst serves as an advisor to the principal decision maker but is not completely aware of the principal’s preference set.

Together with my thesis advisor, Prof Elisabeth Pate-Cornell, I am currently writing a paper that covers the presented material, and I hope to incorporate feedback from this presentation into the paper. Because the paper is currently a work in progress, I am not distributing it at this time.

David Blum attends Stanford University, where he is a 3rd year Ph.D. student in the Department of Management Science & Engineering as well as a U.S. Department of Defense SMART Scholar. He is currently developing a probabilistic model of national security crises, with the goal of improving crisis early warning. His interests also include targeting in counter-terrorism, signatures of WMD proliferation, and models of decisions made by adversarial actors as games with incomplete information. He is a graduate intern in the Counter-Proliferation Operations-Intelligence Support program at Lawrence Livermore National Laboratory.

Between 2004 and 2008 David worked at the U.S. Department of Defense as an operations research analyst. He deployed twice to Iraq, in 2007 and 2008, where,  as member of Multi-National Corps Iraq, he provided direct analytic support to conventional and special operations units. He received his Master's degree from MIT in political science, concentrating in security studies, and his Bachelor's degree from Columbia University in history and physics.