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Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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NOTES

1 Reports of the National Research Council and the National Academies are cited throughout the report as relevant and appear in these endnotes.

2 See http://www.gallup.com/poll/1675/mostimportant-problem.aspx [May 2017].

3 See http://minnesota.cbslocal.com/2017/04/30/vaccine-safety-council-measles-outbreak [May 2017].

4 See https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6406a5.htm?s_cid=mm6406a5_w [May 2017].

5 Cohen, H.S. (1995). Expanding Metropolitan Highways: Implications for Air Quality and Energy. Transportation Research Board Special Report 245. Washington, DC: National Academy Press.

6 Steinberg, R., and Zangwill, W.I. (1983). Prevalence of Braess’ paradox. Transportation Science, 17(3), 301-318.

7 National Academy of Engineering. (2008). Grand Challenges for Engineering. Washington, DC: The National Academies Press.

8 National Academy of Engineering. (2008). Grand Challenges for Engineering. Washington, DC: The National Academies Press.

9 See http://weis2017.econinfosec.org [May 2017].

10 Rovee-Collier, C.K. (1997). Dissociations in infant memory: Rethinking the development of implicit and explicit memory. Psychological Review, 104(3), 467-498.

11 Groves, R.M., Fowler F.J., Jr., Couper, M.P., Lepkowski, J.M., Singer, E., and Tourangeau, R. (2011). Survey Methodology. Vol. 561. Hoboken, NJ: John Wiley & Sons.

12 Mellers, B., Stone, E., Murray, T., et al. (2015). Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science, 10(3), 267-281.

13 Arrow, K., Forsythe, R., Gorham, M., et al. (2008). The promise of prediction markets. Science, 320(5878), 877.

14 Makridakis, S., Hogarth, R.M., and Gaba, A. (2009). Forecasting and uncertainty in the economic and business world. International Journal of Forecasting, 25(4), 794-812.

15 Lazarsfeld, P.F. (1949). The American soldier—An expository review. Public Opinion Quarterly, 13(3), 377-404.

16 Rosenfeld, S.A. (2011). Common Sense. Cambridge, MA: Harvard University Press.

17 Watts, D.J. (2011). Everything Is Obvious: *Once You Know the Answer. New York: Crown Business.

18 Salganik, M.J., Dodds, P.S., and Watts, D.J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854-856.

19 Salganik, M.J., and Watts, D.J. (2008). Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market. Social Psychology Quarterly, 71, 338-355.

20 Frank, R.H. (2016). Success and Luck: Good Fortune and the Myth of Meritocracy. Princeton, NJ: Princeton University Press.

21 Milanovic, B. (2011). Worlds Apart: Measuring International and Global Inequality. Princeton, NJ: Princeton University Press.

22 Nickerson, R.S. (1988). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175.

23 Alba, J.W., and Hasher, L. (1983). Is memory schematic? Psychological Bulletin, 93(2), 203.

24 Wilson, R. (1978). Management and financing of exploration for offshore oil and gas. Public Policy, 26(4), 629-657.

25 Athey, S., and Levin, J. (2001). Information and competition in U.S. Forest Service timber auctions. Journal of Political Economy, 109(21), 375-417.

26 Varian, H.R. (2007). Position auctions. International Journal of Industrial Organization, 25, 1163-1178.

27 See http://38r8om2xjhhl25mw24492dir.wpengine.netdna-cdn.com/wp-content/uploads/2016/10/Behavioral-Insights-for-Cities-2.pdf [May 2017] and http://www.arnoldfoundation.org/initiative/evidence-based-policy-innovation/evidence-based-decision-making [May 2017].

28 See https://www.nsf.gov/about [May 2017].

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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29 See http://www.pewinternet.org/2015/07/01/chapter-3-support-for-government-funding [May 2017].

30 See https://www.everycrsreport.com/files/20161104R44679_4240907afae9830465342a15697ca5b07a694f23.pdf [May 2017].

31 Von Neumann, J., and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.

Nash, J. (1951). Non-cooperative games. The Annals of Mathematics, 54(2), 286-295.

32 Roth, A.E. (2003). The origins, history, and design of the resident match. JAMA, 289(7), 909-912.

33 Roth, A.E., Sönmez, T., and Ünver, M.U. (2005). A kidney exchange clearinghouse in New England. American Economic Review, 95(2), 376-380.

34 Roth, A.E. (2015). Who Gets What—and Why: The New Economics of Matchmaking and Market Design. New York: Houghton Mifflin Harcourt.

35 Roth, A.E. (2015). Who Gets What—and Why: The New Economics of Matchmaking and Market Design. New York: Houghton Mifflin Harcourt.

36 See, for example, Nobelprize.org. (2014). The Prize in Economic Sciences 2012—Advanced Information. Available: http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2012/advanced.html [May 2017].

37 Personal communication from Hal Varian, professor of economics at the University of California, Berkeley, March 27, 2017.

38 Hertzman C., and Boyce, T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329-347.

39 Institute of Medicine and National Research Council. (2014). New Directions in Child Abuse and Neglect Research. Washington, DC: The National Academies Press.

40 Hertzman C., and Boyce, T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329-347.

41 Thompson, R.A., and Haskins, R. (2014). Early stress gets under the skin: Promising initiatives to help children facing chronic adversity. Future of Children, 24(1), 1-6. Available: http://www.futureofchildren.org/sites/futureofchildren/files/media/helping_parents_helping_children_24_01_policy_brief.pdf [June 2017].

42 House, J.S., Landis, K.R., and Umberson, D. (1988). Social relationships and health. Science 241(4865), 540-545.

43 Yang, Y.C., Boen, C., Gerken, K., et al. (2016). Social relationships and physiological determinants of longevity across the human life span. Proceedings of the National Academy of Sciences, 113(3), 578-583.

44 Waldron, H. (2007). Trends in mortality differentials and life expectancy for male Social Security-covered workers, by socieoeconomic status. Social Security Bulletin, 67(3).

45 National Academies of Sciences, Engineering, and Medicine. (2015). The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses. Washington, DC: The National Academies Press.

46 Case, A., and Deaton, A. (2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112(49), 15078-15083.

47 National Academies of Sciences, Engineering, and Medicine. (2015). The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses. Washington, DC: The National Academies Press.

48 National Academies of Sciences, Engineering, and Medicine. (2015). The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses. Washington, DC: The National Academies Press.

49 National Research Council. (2000). From Neurons to Neighborhoods: The Science of Early Childhood Development. Washington, DC: National Academy Press.

50 National Research Council. (2014). The Growth of Incarceration in the United States: Exploring Causes and Consequences. Washington, DC: The National Academies Press.

51 National Research Council. (2013). Reforming Juvenile Justice: A Developmental Approach. Washington, DC: The National Academies Press.

52 National Academies of Sciences, Engineering, and Medicine. (2016). Parenting Matters: Supporting Parents of Children Ages 0-8. Washington, DC: The National Academies Press. doi: 10.17226/21868.

53 National Research Council. (2000). How People Learn: Brain, Mind, Experience, and School:

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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Expanded Edition. Washington, DC: National Academy Press.

54 National Research Council. (2007). Taking Science to School: Learning and Teaching Science in Grades K-8. Washington, DC: The National Academies Press.

55 Investment Company Institute (2006) as cited in Thaler, R.H., and Sunstein, C.R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven, CT: Yale University Press.

56 Thaler, R.H., and Benartzi, S. (2004). Save more tomorrow™: Using behavioral economics to increase employee saving. Journal of Political Economy, 112(S1), S164-S187.

57 Thaler, R.H., and Sunstein, C.R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven, CT: Yale University Press.

58 National Research Council. (2014). Identifying the Culprit: Assessing Eyewitness Identification. Washington, DC: The National Academies Press.

59 Garrett, B.L. (2012). Convicting the Innocent: Where Criminal Prosecutions Go Wrong. Cambridge, MA: Harvard University Press.

60 Afraz, A., Vaziri-Pashkam, M., and Cavanagh, P. (2010). Spatial heterogeneity in the perception of face and form attributes. Current Biology, 20(23), 2112-2116.

61 Wixted, J.T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235-269.

62 Tulving, E., and Thomson, D.M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80(5), 352-373.

63 Dudai, Y. (2006). Reconsolidation: The advantage of being refocused. Current Opinion in Neurobiology, 16(2), 174-178.

64 Loftus, E.F. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learning and Memory, 12(4), 361-366.

65 Bjork, R.A. (1992). Interference and memory. In L.R. Squire (Ed.), Encyclopedia of Learning and Memory (pp. 283-288). New York: Macmillan.

66 McGeoch, J.A. (1932). Forgetting and the law of disuse. Psychological Review, 39(4), 352-370.

67 Jenkins, J.G., and Dallenbach, K.M. (1924). Obliviscence during sleep and waking. The American Journal of Psychology, 35(4), 605-612.

68 Underwood, B.J., and Postman, L. (1960). Extra-experimental sources of interference in forgetting. Psychological Review, 67(2), 73-95.

69 See, for example, Conboy, B.T., and Kuhl, P.K. (2011). Impact of second-language experience in infancy: Brain measures of first-and second-language speech perception. Developmental Science, 14(2), 242-248.

70 National Academies of Sciences, Engineering, and Medicine. (2017). Promoting the Educational Success of Children and Youth Learning English: Promising Futures. Washington, DC: The National Academies Press. doi: 10.17226/24677.

71 Byers-Heinlein, K., Burns, T.C., and Werker, J.F. (2010). The roots of bilingualism in newborns. Psychological Science, 21(3), 343-348.

72 National Academies of Sciences, Engineering, and Medicine. (2017). Promoting the Educational Success of Children and Youth Learning English: Promising Futures. Washington, DC: The National Academies Press. doi: 10.17226/24677.

73 See https://www.goldengooseaward.org/awardees/marshmallowtest [May 2017].

74 Mischel, W., Ayduk, O., Berman, M.G., et al. (2011). “Willpower” over the life span: Decomposing self-regulation. Social Cognitive and Affective Neuroscience, 6(2), 252-256.

75 Mischel, W., Ayduk, O., Berman, M.G., et al. (2011). “Willpower” over the life span: Decomposing self-regulation. Social Cognitive and Affective Neuroscience, 6(2), 252-256.

76 See https://www.goldengooseaward.org/awardees/marshmallowtest [May 2017].

77 Sandler, T., Tschirhart, J.T., and Cauley, J. (1983). A theoretical analysis of transnational terrorism. American Political Science Review, 77(4), 36-54.

78 Enders, W., and Sandler, T. (2012). The Political Economy of Terrorism, Second Edition. Cambridge, UK: Cambridge University Press.

79 Goldstein, J.S. (1992). A conflict-cooperation scale for WEIS events data. Journal of Conflict Resolution, 36(2), 369-385.

80 Wasserman, S., and Faust, K. (1994). Social Network Analysis: Methods and Applications. New York: Cambridge University Press.

81 Benjamin, V., and Chen, H. (2012). Securing cyberspace: Identifying key actors in hacker communities. Intelligence and Security Informatics. Available: http://www.victorbenjamin.com/papers/

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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conference/2012/Securing%20Cyberspace%20Identifying%20Key%20Actors%20in%20Hacker%20Communities.pdf [May 2017].

82 See http://evonomics.com/tragedy-of-the-commons-elinor-ostrom [May 2017] and http://www.aei.org/publication/elinor-ostrom-and-the-solution-to-the-tragedy-of-the-commons [May 2017].

83 Ariely, D. (2008). Predictably Irrational. New York: Harper.

84 Thaler, R.H., and Sunstein, C.R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven, CT: Yale University Press.

85 Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus, and Giroux.

86 Alcott, H., and Rogers, T. (2014). The short-run and long-run effects of behavioral interventions: Experimental evidence from energy conservation. American Economic Review, 104(10), 3003-3037.

87 See http://www.behaviouralinsights.co.uk [May 2017]; https://sbst.gov [May 2017], and https://sbst.gov/download/2016%20SBST%20Annual%20Report.pdf [May 2017].

88 Kaplan, E.H., and Kress, M. (2005). Operational effectiveness of suicide-bomber-detector schemes: A best-case analysis. Proceedings of the National Academy of Sciences, 102, 10399-10404.

89 Lee, R., and Carter, L. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87(419), 659-671.

90 Gerosi, F., and King, G. (2008) Demographic Forecasting. Princeton, NJ: Princeton University Press.

91 International Monetary Fund. (2012). The financial impact of longevity risk. Chapter 4 in Global Financial Stability Report. The Quest for Lasting Stability. Washington, DC: International Monetary Fund. Available: https://www.imf.org/external/pubs/ft/gfsr/2012/01/pdf/c4.pdf [May 2017].

92 Pita, J., Jain, M., Marecki, J., et al. (2008). Deployed ARMOR protection: The application of a game theoretic model for security at the Los Angeles International Airport. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Industrial Track (pp. 125-132). Available: http://teamcore.usc.edu/papers/2008/AAMASind2008Final.pdf [June 2017].

93 Shieh, E., An, B., Yang, R., et al. (2012). PROTECT: A deployed game theoretic system to protect the ports of the United States. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems—Volume 1 (13-20). Available: http://teamcore.usc.edu/papers/2012/protect_aamas_2012_camera_ready_final2_20120109.pdf [June 2017].

94 An, B., Shieh, E., Tambe, M., et al. (2012). PROTECT—A deployed game theoretic system for strategic security allocation for the United States Coast Guard. AI Magazine, 33(4), 96. Available: https://www.aaai.org/ojs/index.php/aimagazine/article/view/2401 [June 2017].

95 Helbing, D., Farkas, I., and Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487-490.

96 Helbing, D., Buzna, L., Johansson, A., and Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 39(1), 1-24.

97 Choi, H. and Varian, H. (2009). Predicting Initial Claims for Unemployment Benefits. Available: https://static.googleusercontent.com/media/research.google.com/en//archive/papers/initialclaimsUS.pdf [May 2017].

98 Goel, S., Hofman, J.M., Lahaie, S., et al. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, 107(41), 17486-17490.

99 Choi, H., and Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2-9.

100 Gelman, A. (2007). Struggles with survey weighting and regression modeling. Statistical Science, 2007, 153-164.

101 Gelman, A., Lax, J., Phillips, J., Gabry, J., and Trangucci, R. (2016). Using Multilevel Regression and Poststratification to Estimate Dynamic Public Opinion. Available: http://www.columbia.edu/~jhp2121/workingpapers/MRT.pdf [May 2017].

102 Wang, W., Rothschild, D., Goel, S., and Gelman, A. (2014). Forecasting elections with non-representative polls. International Journal of Forecasting, 31(3), 980-991.

103 Heckathorn, D.D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174-199.

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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104 Gile, K.J., Johnston, L.G., and Salganik, M.J. (2015). Diagnostics for respondent-driven sampling. Statistics in Society, 178(1), 241-269.

105 Wejnert, C. (2009). An empirical test of respondent-driven sampling: Point estimates, variance, measures of degree, and out-of-equilibrium data. Sociological Methodology, 39(1), 73-116.

106 See https://www.pepfar.gov [May 2017].

107 Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1), 8-37.

108 Milgrom, P., and Weber, R. (1982). A theory of auctions and competitive bidding. Econometrica: Journal of the Econometric Society, 50(5), 1089-1122.

109 Wilson, R. (1992). Strategic analysis of auctions. In R. Aumann and S. Hart (Eds.), Handbook of Game Theory with Economic Applications (Vol. 1, Ch. 8). Amsterdam: North-Holland.

110 See https://www.goldengooseaward.org/awardees/auction-design [May 2017].

111 Falk, G. (1994). Welfare: A review of studies on time spent on welfare. CRS Report for Congress. Washington, DC: Congressional Research Service.

112 See http://greenbook.waysandmeans.house.gov/1994-green-book [May 2017].

113 Harris, K.M. (1993). Work and welfare among single mothers in poverty. American Journal of Sociology, 99(2), 317-352.

114 Acs, G., and Loprest, P. (2001). Final Synthesis Report of Findings from ASPE’s “Leavers” Grants. Washington, DC: U.S. Department of Health and Human Services.

115 Haskins, R. (2001). Effects of welfare reform at four years. In P.L. Chase-Lansdale and G. Duncan (Eds.) For Better and For Worse: Welfare Reform and the Well-Being of Children and Families. New York: Russell Sage Foundation.

116 See Burkhauser, R.V., Ed. (2015). Point/counterpoint: Welfare reform: A 20-year retrospective, Journal of Policy Analysis and Management, 35(1), 223-244.

117 Watts, D.J., and Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440-442.

118 Moon, I-C., and Carley, K.M. (2007). Modeling and simulation of terrorist networks in social and geospatial simensions. IEEE Intelligent Systems: Special Issue on Social Computing, 22(5), 40-49.

119 Berman, E., and Matanock, A.M. (2015). The empiricists’ insurgency. Annual Review of Political Science, 19, 443-464.

120 Mumumuza, R. (2014). Dangerous Practices Spread Ebola in Sierra Leone. Available: https://www.bostonglobe.com/news/world/2014/12/04/dangerous-practices-spread-ebola-sierra-leone/KWHWTzXQQTYHBXrexLvzXN/story.html [May 2017].

121 Fassassi, A. (2014). How Anthropologists Help Medics Fight Ebola in Guinea. Available: http://www.scidev.net/global/cooperation/feature/anthropologists-medics-ebola-guinea.html [May 2017].

122 Médecins Sans Frontières. (2014). Struggling to Contain the Ebola Epidemic in West Africa. Available: http://www.doctorswithoutborders.org/news-stories/voice-field/struggling-contain-ebola-epidemic-west-africa [May 2017].

123 World Health Organization, E-Recruitment. (2015). Ebola Outbreak-Surge Capacity-Anthropologist (AFRO/14/TA187). Available: https://erecruit.who.int/public/hrd-cl-vac-view.asp?o_c=1000&jobinfo_uid_c=30365&vaclng=en [May 2017].

124 Varian, H.R. (2016). The economics of Internet search. In J.M. Bauer and M. Latzer (Eds.), Handbook on the Economics of the Internet. Northampton, MA: Edward Elgar.

125 MacKie-Mason, J.K., and Varian, H.R. (1995). Pricing congestible network resources. IEEE Journal on Selected Areas in Communications, 13(7), 1141-1149.

126 MacKie-Mason, J., Shenker, S., and Varian, H.R. (1996). Service architecture and content provision. The network provider as editor. Telecommunications Policy, 20(3), 203-217.

127 Varian, H.R. (2000). Buying, sharing and renting information goods. The Journal of Industrial Economics, 48(4), 473-488.

128 Weatherford, L.R., and Bodily, S.E. (1992). A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing. Operations Research, 40(5), 831-844.

129 Gallego, G., and Van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
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demand over finite horizons. Management Science, 40(8), 999-1020.

130 Talluri, K., and van Ryzin, G. (2004). The Theory and Practice of Revenue Management. New York: Springer.

131 Elmaghraby, W., and Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287-1309.

132 McAfee, R.P., and Velde, V. (2008). Dynamic pricing with constant demand elasticity. Production and Operations Management, 17(4), 432-438.

133 Ebert, P., and Freibichler, W. (2017). Nudge management: Applying behavioral science to increase the knowledge of worker productivity. Journal of Organization Design, 6(4), 1-6.

134 Thaler, R., and Sunstein, C. (2008). Nudge. New York: Penguin Books.

135 See http://www.internetlivestats.com/google-search-statistics [May 2017].

136 See http://www.business2community.com/online-marketing/how-many-ads-does-google-serve-in-a-day-0322253 [May 2017].

137 Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39-43.

138 Hubbell, C. (1965). An input–output approach to clique identification. Sociometry, 28, 377-399.

139 Mizruchi, M.S., Mariolis, P., Schwartz, M., et al. (1986). Techniques for disaggregating centrality scores in social networks. Sociological Methodology, 16, 26-48.

140 Some of these search engines, such as Inktomi and Lycos, also were supported with funding from the Digital Libraries Initiative.

141 This paragraph drawn from the two summaries of NSF awards: https://www.nsf.gov/news/special_reports/cyber/digitallibraries.jsp [May 2017] and https://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=100660 [May 2017].

142 See https://www.wired.com/2009/05/nep-googlenomics [May 2017].

143 Helmreich, R.L., and Foushee, H.C. (2010). Why CRM? Empirical and theoretical bases of human factors training. In B.G. Kanki, R.L. Helmreich, and J. Anca (Eds.), Crew Resource Management (Ch. 1, pp. 3-58). Cambridge, MA: Academic Press.

144 Helmreich, R.L., and Foushee, H.C. (2010). Why CRM? Empirical and theoretical bases of human factors training. In B.G. Kanki, R.L. Helmreich, and J. Anca (Eds.), Crew Resource Management (Ch. 1, pp. 3-58). Cambridge, MA: Academic Press.

145 Helmreich, R.L., Merritt, A.C., and Wilhelm, J.A. (1999) The evolution of crew resource management training in commercial aviation. The International Journal of Aviation Psychology, 9(1), 19-32. doi: 10.1207/s15327108ijap0901_2.

146 Gaba, D.M., Howard, S.K., Fish, K.J., et al. (2001). Simulation-based training in anesthesia crisis resource management (ACRM): A decade of experience. Simulation & Gaming, 32(2), 175-193.

147 Hagemann, V., Kluge, A., and Greve, J. (2012). Measuring the effects of team resource management training for the fire service. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, USA, 56, 2442-2446. doi:10.1177/1071181312561497.

148 National Academies of Sciences, Engineering, and Medicine. (2016). Commercial Motor Vehicle Driver Fatigue: Long-Term Health, and Highway Safety. Washington, DC: The National Academies Press.

149 National Academies of Sciences, Engineering, and Medicine. (2016). Strengthening the Safety Culture of the Offshore Oil and Gas Industry. Washington, DC: The National Academies Press.

150 Pronovost, P., and Vohr, E. (2010). Safe Patients, Smart Hospitals: How One Doctor’s Checklist Can Help Us Change Health Care from the Inside Out. New York: Plume.

151 Gawande, A. (2007). The checklist. The New Yorker, 83(39), 86-95.

152 See https://www.goldengooseaward.org/awardees/of-maps-and-men [May 2017].

153 Cohen, J.E., and Small, C. (1998). Hypsographic demography: The distribution of human population by altitude. Proceedings of the National Academy of Sciences, 95(24), 14009-14014.

154 U.S. National Science Foundation. (2011). Rebuilding the Mosaic: Fostering Research in the Social, Behavioral, and Economic Sciences at the National Science Foundation in the Next Decade. Directorate for Social, Behavioral, and Economic Sciences. Washington, DC: National Science Foundation.

155 National Institutes of Health. (2017). The Office of Behavioral and Social Sciences Research

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Strategic Plan 2017-2021. Available: https://obssr.od.nih.gov/wp-content/uploads/2016/12/OBSSR-SP-2017-2021.pdf [May 2017].

156 Chetty, R., Hendren, N., Kline, P., et al. (2014). Is the United States still a land of opportunity? Recent trends in intergenerational mobility. The American Economic Review, 104(5), 141-147.

157 See https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/2 [May 2017].

158 Benjamin, V., and Chen, H. (2012). Securing cyberspace: Identifying key actors in hacker communities. Intelligence and Security Informatics, 24-29. Available http://www.victorbenjamin.com/papers/conference/2012/Securing%20Cyberspace%20Identifying%20Key%20Actors%20in%20Hacker%20Communities.pdf [May 2017].

159 See https://www.bloomberg.com/news/articles/2016-10-13/predicting-terrorism-from-big-data-challenges-u-s-intelligence [May 2017].

160 Strang, K.D., and Sun, Z. (2016). Analyzing relationships in terrorism big data using Hadoop and statistics. Journal of Computer Information Systems, 56(6), 55-65.

161 Choi, H. and H. Varian. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2-9.

162 U.S. National Science Foundation. (2011). Rebuilding the Mosaic: Fostering Research in the Social, Behavioral, and Economic Sciences at the National Science Foundation in the Next Decade. Directorate for Social, Behavioral, and Economic Sciences. Washington, DC: National Science Foundation.

163 National Research Council. (2015). Enhancing the Effectiveness of Team Science. Washington, DC: The National Academies Press.

164 National Research Council. (2014). Convergence: Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond. Washington, DC: The National Academies Press.

165 See https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216048 [May 2017].

166 Brummitt, C.D., Barnett, G., and D’Souza, R.M. (2015). Couples catastrophes: Sudden shifts cascade and hop among interdependent systems. Journal of the Royal Society Interface, 12, 1-22.

167 Vijayaraghavan, V.S., Noël, P-A., Maoz, Z., et al. (2015). Quantifying dynamical spillover in co-evolving multiples networks. Scientific Reports, 5(15142), 1-10.

168 National Academies of Sciences, Engineering, and Medicine. (2017). A New Vision for Center-Based Engineering Research. Washington, DC: The National Academies Press.

169 National Academies of Sciences, Engineering, and Medicine. (2017). Information Technology and the U.S. Workforce: Where Are We and Where Do We Go from Here? Washington, DC: The National Academies Press. doi: 10.17226/24649.

170 See http://www.cpc.unc.edu/projects/addhealth [May 2017].

171 Feldman, M., Francis, J. and Bercovitz, J. (2005). Creating a cluster while building a firm: Entrepreneurs and the formation of industrial clusters. Regional Studies, 39, 129-141. Available: http://dx.doi.org/10.1080/0034340052000320888 [May 2017].

172 Graham, S.J.H., and Hancock, G. (2014). The USPTO economics research agenda. Journal of Technology Transfer, 39, 335-344.

173 See http://www.nber.org/callforpapers/CallforProposalsProductivityInnovationEntrepreneurship.html [May2017].

Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 36
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 37
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 38
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 39
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 40
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 41
Suggested Citation:"NOTES." National Academies of Sciences, Engineering, and Medicine. 2017. The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation. Washington, DC: The National Academies Press. doi: 10.17226/24790.
×
Page 42
The Value of Social, Behavioral, and Economic Sciences to National Priorities: A Report for the National Science Foundation Get This Book
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Nearly every major challenge the United States faces—from alleviating unemployment to protecting itself from terrorism—requires understanding the causes and consequences of people’s behavior. Even societal challenges that at first glance appear to be issues only of medicine or engineering or computer science have social and behavioral components. Having a fundamental understanding of how people and societies behave, why they respond the way they do, what they find important, what they believe or value, and what and how they think about others is critical for the country’s well-being in today’s shrinking global world. The diverse disciplines of the social, behavioral, and economic (SBE) sciences ―anthropology, archaeology, demography, economics, geography, linguistics, neuroscience, political science, psychology, sociology, and statistics―all produce fundamental knowledge, methods, and tools that provide a greater understanding of people and how they live.

The Value of Social, Behavioral, and Economic Sciences to National Priorities evaluates whether the federal government should fund SBE research at the National Science Foundation (NSF), and, specifically, whether SBE research furthers the mission of the NSF to advance national priorities in the areas of health, prosperity and welfare, national defense, and progress in science; advances the missions of other federal agencies; and advances business and industry, and to provide examples of such research. This report identifies priorities for NSF investment in the SBE sciences and important considerations for the NSF for strategic planning.

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