Advertisement

Enhancing Group and Societal Outcomes

  • James S. Albus
  • William Sims Bainbridge
  • Kathleen M. Carley
  • R. Price
  • Gary W. Strong
  • Philip Rubin
  • William A. Wallace
  • Jill Banfield
  • Murray Hirschbein
  • Tina Masciangioli
  • Tom Miller
  • Cherry Murray
  • R. L. Norwood
  • John Sargent
  • S. Venneri
  • M. Dastoor
  • M. C. Roco
Chapter

Abstract

The third multidisciplinary theme is concerned with NBIC innovations whose benefits would chiefly be beyond the individual level, for groups, the economy, culture, or society as a whole. It naturally builds on the human cognition and physical capabilities themes and provides a background for the national security and scientific unification panels. In particular, it is focused on a nexus issue that relates logically to most technological applications discussed in this report and that connects all four NBIC scientific and technological realms — that is, how to enhance group human productivity, communication, and cooperation.

Keywords

Cognitive Science Intelligent System Intelligent Machine Intelligent Agent Social Intelligence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. Roco, M.C. and W.S. Bainbridge, eds. 2001. Societal Implications of Nanoscience and Nanotechnology. Dordrecht, Netherlands: Kluwer.Google Scholar
  2. Biber, D., S. Conrad, and R. Reppen. 1998. Corpus linguistics: Investigating language structure and use. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  3. Biederman, I. 1995. Visual object recognition. Chapter 4 in An invitation to cognitive science, 2nd ed, Vol. 2, Visual cognition, S.M. Kosslyn and D.N. Osherson, eds. Cambridge, MA: MIT Press.Google Scholar
  4. Bregman, A.S. 1994. Auditory scene analysis. Cambridge, MA: MIT Press.Google Scholar
  5. Cassell, J., J. Sullivan, S. Prevost, and E. Churchill. 2000. Embodied conversational agents. Cambridge, MA: MIT Press.Google Scholar
  6. Clark, H.H. 1996. Using language. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Gazzaniga, M.S., R.B. Ivry, and G.R. Mangun. 1998. Cognitive neuroscience: The biology of the mind. New York: W.W. Norton and Company.Google Scholar
  8. Golledge, R.G., ed. 1999. Wayfinding behavior: Cognitive mapping and other spatial processes. Baltimore, MD: John Hopkins University Press.Google Scholar
  9. Golledge, R.G., X 1995. Hidden order: How adaptation builds complexity. New York: Addison-Wesley. Kauffman, S. 1995. At home in the universe: The search for the laws of self-organization and complexity. Oxford: Oxford University Press.Google Scholar
  10. Golledge, R.G., 2000. Investigations. Oxford: Oxford University Press.Google Scholar
  11. Kelso, J., and A. Scott. 1997. Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.Google Scholar
  12. Loomis, J.M. and A. Beall. 1998. Visually controlled locomotion: Its dependence on optic flow, three-dimensional space perception, and cognition. Ecological Psychology 10: 271285.Google Scholar
  13. Lyon, G.R. and J.M. Rumsey. 1996. Neuroimaging: A window to the neurological foundations of learning and behavior in children. Baltimore, MD: Paul H. Brookes Publishing Co.Google Scholar
  14. Manning, C.D., and H. Schutze. 1999. Foundations of statistical natural language processing. Cambridge, MA: MIT Press.Google Scholar
  15. Marantz, A., Y. Miyashita, and W. O’Neil, eds. 2000. Image, language, brain. Cambridge, MA: MIT Press.Google Scholar
  16. Posner, M.I. and M.E. Raichie. 1997. Images of mind. New York: W.H. Freeman and Co. Turvey, M.T. 1996. Dynamic touch. American Psychologist 51: 1134–1152.Google Scholar
  17. Turvey, M.T. and R.E. Remez. 1970. Visual control of locomotion in animals: An overview. In Interrelations of the communicative senses, L. Harmon, Ed. Washington, D.C.: National Science Foundation.Google Scholar
  18. Varela, F.J., E. Thompson, and E. Rosch. 1991. The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.Google Scholar
  19. Waldrop, M.M. 1992. Complexity: The emerging science at the edge of order and chaos. New York: Simon and Schuster.Google Scholar
  20. Warren, W.H. 1988. Action modes and laws of control for the visual guidance of action. In Complex movement behaviour: The motor-action controversy, O.G. Meijer and K. Roth, eds. Amsterdam: North-Holland.Google Scholar
  21. Albus, J.S. and A.M. Meystel. 2001. Engineering of mind: An introduction to the science of intelligent systems. New York: John Wiley and Sons.Google Scholar
  22. Albus, J.S. 1976. Peoples’ capitalism: The economics of the robot revolution Kensington, MD: New World Books. See also Peoples’ Capitalism web page at http://www.peoplescapitalism.org.Google Scholar
  23. Bluestone, B., and B. Harrison. 2000. Growing prosperity: The battle for growth with equity in the twenty-first century. New York: Houghton Miffl in Co.Google Scholar
  24. Carter, R. 1998. Mapping the mind University of California Press.Google Scholar
  25. Edelman, G. 1999. Proceedings of International Conference on Frontiers of the Mind in the 21st Century, Library of Congress, Washington D.C., June 15Google Scholar
  26. Gourley, S.R. 2000. Future combat systems: A revolutionary approach to combat victory. Army 50(7):23–26 (July).Google Scholar
  27. Kelso, L., and P. Hetter. 1967. Two factor theory: The economics of reality. New York: Random House.Google Scholar
  28. Maggart, L.E., and R.J. Markunas. 2000. Battlefield dominance through smart technology. Army 50 (7).Google Scholar
  29. Mankiw, G.N. 1992. Macroeconomics. New York: Worth Publishers.Google Scholar
  30. Moravec, H. 1999. Robot: Mere machine to transcendent mind. Oxford: Oxford University Press.Google Scholar
  31. Samuelson, P., and W. Nordhaus. 1989. Economics,13th ed. New York: McGraw-Hill. Symposia. 1988. The slowdown in productivity growth. Journal of Economic Perspectives 2 (Fall).Google Scholar
  32. Toffler, A. 1980. The third wave. New York: William Morrow and Co.Google Scholar
  33. von Neumann, J. 1966. Theory of self-reproducing automata (edited and completed by A. Burks). Urbana: University of Illinois Press.Google Scholar
  34. Alstyne, M.v., and E. Brynjolfsson. 1996. Wider access and narrower focus: Could the Internet Balkanize science? Science 274 (5292): 1479–1480.CrossRefGoogle Scholar
  35. Carley, K.M. forthcoming, Smart agents and organizations of the future. In The handbook of new media,ed. L. Lievrouw and S. Livingstone.Google Scholar
  36. Carley, K.M. . forthcoming. Computational organization science: A new frontier. In Proceedings, Arthur M. Sackler Colloquium Series on Adaptive Agents, Intelligence and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling, October 46, 2001; Irvine, CA: National Academy of Sciences Press.Google Scholar
  37. Carley, K.M. . forthcoming, Intra-Organizational Computation and Complexity. In Companion to Organizations,ed. J.A.C. Baum. Blackwell Publishers.Google Scholar
  38. Carley, K.M., and V. Hill. 2001. Structural change and learning within organizations. In Dynamics of organizations: Computational modeling and organizational theories, ed. A. Lomi and E.R. Larsen. MIT Press/AAAI Press/Live Oak.Google Scholar
  39. Carley, K.M. 1999. Organizational change and the digital economy: A computational organization science perspective. In Understanding the Digital Economy: Data, Tools, Research, ed. E. Brynjolfsson, and B. Kahin. Cambridge, MA: MIT Press.Google Scholar
  40. Carley, K.M., and A. Newell. 1994. The nature of the social agent. J. of Mathematical Sociology 19 (4): 221–262.CrossRefGoogle Scholar
  41. CSI. 2000. CSI/FBI computer crime and security survey. Computer Security Issues and Trends.Google Scholar
  42. Epstein, J., and R. Axtell. 1997. Growing artificial societies Boston, MA: MIT Press. ICSA. 2000. ICSA Labs 6th Annual Computer Virus Prevalence Survey 2000. ICSA.net.Google Scholar
  43. Kephart, J.O. 1994. How topology affects population dynamics. In Artificial life III, ed. C.G. Langton. Reading, MA: Addison-Wesley.Google Scholar
  44. Kurzweil, R. 1988. The age of intelligent machines. Cambridge, MA: MIT Press.Google Scholar
  45. Lomi, A., and E.R. Larsen, eds. 2001. Dynamics of organizations: Computational modeling and organizational theories MIT Press/AAAI Press/Live Oak.Google Scholar
  46. Nixon, P., G. Lacey, and S. Dobson, eds. 1999. Managing interactions in smart environments. In Proceedings, 1“ International Workshop on Managing Interactions in Smart Environments (MANSE `99), Dublin, Ireland, December 1999.Google Scholar
  47. Nohira, N. and R. Eccles, eds. 1992. Organizations and networks: Theory and practice. Cambridge, MA: Harvard Business School Press.Google Scholar
  48. Pastor-Satorras, R., and A. Vespignani. 2001. Epidemic dynamics and endemic states in complex networks. Barcelona, Spain: Universitat Politecnica de Catalunya.Google Scholar
  49. Samuelson, D. 2000. Designing organizations. OR/MS Today. December: 1–4. See also http://www.lionhrtpub.com/orms/orms-12–00/samuelson.html.Google Scholar
  50. Spafford, E.H. 1994. Computer viruses as artificial life. Journal of Artificial Life.Google Scholar
  51. Thomas, P. and H.-W. Gellersen, eds. 2000. Proceedings of the International Symposium on Handheld and Ubiquitous Computing: Second International Symposium, HUC 2000, Bristol, UK, September 25–27, 2000.Google Scholar
  52. Wang, C., J.C. Knight, and M.C. Elder. 2000. On computer viral infection and the effect of immunization. In Proceedings, IEEE 16th Annual Computer Security Applications Conference.Google Scholar
  53. Wasserman, S. and K. Faust. 1994 Social Network Analysis. New York: Cambridge University.Google Scholar
  54. Weiss, G., ed. 1999. Distributed artificial intelligence. Cambridge, MA: MIT Press.Google Scholar
  55. Aunger, R., ed. 2000. Darwinizing culture: The status of memetics as a science. Oxford: Oxford University Press.Google Scholar
  56. Axelrod, R. 1990. The evolution of cooperation. New York: Penguin.Google Scholar
  57. Bainbridge, W.S. 1985. Cultural genetics. In Religious movements, ed. R. Stark. New York: Paragon.Google Scholar
  58. Boyd, R. and P.J. Richerson. 1985. Culture and the evolutionary process. Chicago: University of Chicago Press.Google Scholar
  59. Dawkins, R. 1976. The selfish gene. Oxford: Oxford University Press.Google Scholar
  60. Dennett, D.C. 1995. Darwin’s dangerous idea. New York: Simon and Schuster.Google Scholar
  61. Diamond, J. 1997. Guns, germs, and steel: The fates of human societies. New York: Norton. El-Affendi, A. 1999. Islam and the future of dissent after the “end of history.” Futures 31: 191–204.Google Scholar
  62. Harum, S.L., W.H. Mischo, and B.R. Schatz. 1996. Federating repositories of scientific literature: An update on the Digital Library Initiative at the University of Illinois at Urbana-Champaign. D-Lib Magazine, July/August, www.dlib.org.Google Scholar
  63. Keyfitz, N. 1986. The family that does not reproduce itself. In Below-replacement fertility in industrial societies: Causes, consequences, policies, ed. K. Davis, M.S. Bernstam, and R. Campbell. (A supplement to Population and Development Review).Google Scholar
  64. Kim, K.K., R. Kim, and S.-H. Kim. 1998. Crystal structure of a small heat-shock protein. Nature 394: 595–599.CrossRefGoogle Scholar
  65. Levi-Strauss, C. 1966. The savage mind. Chicago: University of Chicago Press.Google Scholar
  66. Lyman, R.L., and M.J. O’Brien. 1998. The goals of evolutionary archaeology: History and explanation. Current Anthropology 39: 615–652.CrossRefGoogle Scholar
  67. Maynard Smith, J. 1982. Evolution and the theory of games. New York: Cambridge University Press.CrossRefGoogle Scholar
  68. Reiss, D., B. McCowan, and L. Marino. 1997. Communicative and other cognitive characteristics of bottlenose dolphins. TICS 140–145.Google Scholar
  69. Strong, G.W. 1990. Neo-Lamarckism or the rediscovery of culture. Behavioral and Brain Sciences 13: 92.CrossRefGoogle Scholar
  70. Parsons, T. 1964. Evolutionary universals in society. American Sociological Review 29: 339357.Google Scholar
  71. Rappaport, R. 1988. Ecology, meaning, and religion. Richmond, California: North Atlantic Books.Google Scholar
  72. Sagan, C. 1997. The demon-haunted world: Science as a candle in the dark. New York: Ballantine Books.Google Scholar
  73. Schermer, M. 2002. Why people believe weird things: Pseudoscience, superstition, and other confusions of our time. New York: H. Holt.Google Scholar
  74. Stark, R. and W.S. Bainbridge. 1996. A theory of religion. New Brunswick, NJ: Rutgers University Press.Google Scholar
  75. Tschauner, H. 1994. Archaeological systematics and cultural evolution: Retrieving the honour of culture history. Man 29: 77–93.CrossRefGoogle Scholar
  76. Wallace, A.F.C. 1956. Revitalization movements. American Anthropologist 58: 264–281.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • James S. Albus
    • 2
  • William Sims Bainbridge
    • 8
  • Kathleen M. Carley
    • 5
  • R. Price
  • Gary W. Strong
    • 7
    • 8
  • Philip Rubin
    • 1
  • William A. Wallace
    • 7
  • Jill Banfield
    • 3
  • Murray Hirschbein
    • 6
  • Tina Masciangioli
  • Tom Miller
  • Cherry Murray
  • R. L. Norwood
  • John Sargent
  • S. Venneri
    • 6
  • M. Dastoor
    • 6
  • M. C. Roco
    • 4
  1. 1.The National Science FoundationUSA
  2. 2.National Institute of Standards and TechnologyUSA
  3. 3.University of CaliforniaBerkeleyUSA
  4. 4.National Science FoundationNanoscale Science, Engineering, and Technology (NSET)USA
  5. 5.Carnegie Mellon UniversityUSA
  6. 6.National Aeronautics and Space AdministrationUSA
  7. 7.Rensselaer Polytechnic InstituteUSA
  8. 8.National Science FoundationUSA

Personalised recommendations