Abstract
Rational social theorists (e.g., game and decision theorists) have failed to confirm that observations of social reality equal social reality. Yet they argue that teams, organizations and social systems should minimize interdependence and competition, echoed by social psychologists to make data iid (i.e., independent and factorable). But the evidence indicates that competitive teams maximize interdependence; self-reports of social reality correlate poorly with social behavior; and only competition measures interdependent social states. Rational expectations aside, we report progress towards a science of interdependence for human–machine teams. Our model of interdependence works like an uncertainty principle in the sense that tradeoffs arise from the uncertainty caused by measuring interdependent actors in orthogonal roles; e.g., in the tradeoff between teams and individuals, teams are more productive but more opaque. Previously, we described interdependence as bistable stories of social reality; the motivation to reject alternative interpretations, increasing uncertainty and errors; and the inability to factor social states. Now we explore education as a surrogate for intelligence in teams. We hypothesized that teams rely on the education (a trained intellect) of its members to produce more patents (a team’s goals). We found that the average schooling in a nation is significantly related to its total patents produced.
Similar content being viewed by others
Notes
A correlation is an association between two factors that does not indicate a causal relationship exists between them. While a zero correlation normally indicates the lack of a relationship between independent entities, the absence of a correlation say between two opposed beliefs about their relationship in the data from orthogonal roles does not indicate the absence of causality; e.g., turn-taking causally coordinates debate; further, debates bound our interpretations of reality; e.g., the debate by Einstein and Bohr over the meaning of quantum uncertainty continues today (Weinberg 2017), preventing a consensus.
World Bank, gdp and populations; https://data.worldbank.org/country.
Algeria, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Palestine, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, United Arab Emirates, Yemen.
HDI; from http://hdr.undp.org/en/composite/HDI.
Transparency Corruptions Perceptions index; https://www.transparency.org.
Global innovation index, https://www.globalinnovationindex.org.
References
Adelson, E. (2000). Lightness perceptions and lightness illusions. In Gazzaniga, M. (Ed.). The new cognitive sciences (2nd ed.). Cambridge: MIT Press.
Akcigit, U., et al. (2017). The rise of American ingenuity. HBS Working Paper 17-063. https://www.hbs.edu/.
Axelrod, R. (1984). The evolution of cooperation. New York: Basic.
Bagarello, F.et al. (2018). Quantifying uncertainty with the aid of Heisenberg–Robertson inequality. Quantum like modeling of decision making. Journal of Mathematical Psychology, 84, 49–56.
Baumeister, R. F., Campbell, J., Krueger, J., & Vohs, K. (2005). Exploding the self-esteem myth. Scientific American, 292(1), 84–91.
Blanton, H., Klick, J., Mitchell, G., Jaccard, J., Mellers, B., & Tetlock, P. (2009). Strong claims and weak evidence: Reassessing the predictive validity of the iat. Journal of Applied Psychology, 94(3), 567–582.
Bloom, J. D. N., Dorgan, S., & Reenen, J. V. (2007). Management practice and productivity. Quarterly Journal of Economics, 122(4), 1351–1408.
Bohr, N. (1955). Science and unity of knowledge. In L. Leary (Ed.), Unity of knowledge (pp. 44–62). New York: Doubleday.
Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–34.
Clarke, K., & Primo, D. (2012). Overcoming ‘physics envy’. New York: New York Times.
Cohen, L. (1995). Time-frequency analysis. Signal processing series. Upper Saddle River: Prentice Hall.
Conant, R. C. (1976). Laws of information which govern systems. IEEE Transaction on Systems, Man, and Cybernetics, 6, 240–255.
Cooke, N., & Hilton, M. E. (2015). Enhancing the effectiveness of team science. Washington, D.C.: National Research Council, National Academies Press.
Cummings, J. (2015) Team science successes and challenges. NSF Workshop Fundamentals of Team Science and the Science of Team Science, Bethesda MD, June 2.
Daft, R., & Weick, K. (1984). Toward a model of organizations as interpretation systems. Academy of Management Review, 9(2), 284–295.
Dang, J. (2018). An updated meta-analyses of the ego-depletion effect. Psychological Research, 82(4), 645–651.
Darley, J. M., & Gross, P. (2000). A Hypothesis-confirming bias in labelling effects. In C. Stangor (Ed.), Stereotypes and prejudice: Essential readings (p. 212). Hove: Psychology Press.
Emerson, J. (2017). Don’t give up on unconscious bias training: Make it better. Harvard Business Review, 4, 28.
Erber, R., & Erber, M. (2016). Intimate relationships: Issues, theories, and research (2nd ed.). Hove: Psychology Press.
Flint, J., & Hagey, K. (2018). Cbs ups stakes in feud with redstones. Wall Street Journal, 5, 14.
Gazzaniga, M. (2011). Who’s in charge? Free will and the science of the brain. New York: Ecco.
Ginsburg, R. (2011). American electric power co., inc., et al. v. connecticut et al. (pp. 10–174). Washington: US Supreme Court.
Grove, T. (2018). Drone attacks on Russian bases in Syria expose security holes the attacks reveal a fresh threat to Moscow from insurgent rebel groups in Syria even after their broad defeat by Russia and its allies. Wall Street Journal, 1, 15.
Haven, E., & Khrennikov, A. (2013). Quantum social science. Cambridge: Cambridge Press.
Jones, E. (1998). Major developments in five decades of social psychology. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (Vol. 1). New York: McGraw-Hill.
Kahneman, D. (2002). Maps of bounded rationality: A perspective on intuitive judgment and choice. Nobel Prize Lecture, 12, 8.
Kelley, H. H. (1979). Personal relationships: Their structure and processes. Hillsdale: Lawrence Earlbaum.
Kenny, D., et al. (1998). Data analyses in social psychology. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 1, pp. 233–65). New York: McGraw-Hill.
Lawless, W. (2017a). The entangled nature of interdependence. Bistability, irreproducibility and uncertainty. Journal of Mathematical Psychology, 78, 51–64.
Lawless, W. (2017b). The physics of teams: Interdependence, measurable entropy and computational emotion. Frontiers of physics, 5, 30.
Lawless, W., Akiyoshi, M., Angjellari-Dajcic, F., & Whitton, J. (2014). Public consent for the geologic disposal of highly radioactive wastes and spent nuclear fuel. International Journal of Environmental Studies, 71(1), 41–62.
Lawless, W. F., Mittu, R., Sofge, D. A., & Hiatt, L. (2019). Introduction to the Special Issue, “Artificial Intelligence (AI), autonomy and human-machine teams: Interdependence, context and explainable AI”. AI Magazine, 40(3), 5–13.
Lewin, K. (1951). Field theory of social science. In D. Cartwright (Ed.), Selected theoretical papers. New York: Harper and Brothers.
Matousek, M. (2018). Tesla is experiencing a painful year: Here’s everything that has gone wrong so far. Business Insider, 10, 30.
NTSB. (2018) Preliminary report released for crash involving pedestrian, uber technologies, inc., test vehicle. Technical report, National Transportation Safety Board.
Nemani, A., et al. (2018). Assessing bimanual motor skills with optical neuroimaging. Science Advances. https://doi.org/10.1126/sciadv.aat3807.
Nosek, B. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), 943.
Pearl, J., & Mackenzie, D. (2018). AI can’t reason why. The current data-crunching approach to machine learning misses an essential element of human intelligence. Wall Street Journal. https://www.wsj.com/articles/ai-cant-reason-why-1526657442.
Posthuma-Coelho, A. (2016). Theoretical vs practical knowledge. Medium, 11, 24.
Salehi-Isfahani, D. (2010) Human development in the Middle East and North Africa. United Nations: Human Development Research Paper 2010/26.
Shaked, A. (2016) Israel patent office annual report, p. 73, table 11. Technical report, Justic State of Israel.
Shambaugh, J., et al. (2017). Eleven facts about innovation and patents (p. 5). Washington: Brookings Institute.
Shannon, C. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423 and 623–656.
Simon, H. (1989) Bounded rationality and organizational learning. Technical Report AIP 107, CMU, Pittsburgh, PA, 9/23.
Soros, G. (2019). Europe, please wake up. Project Syndicate, 2, 11.
Taplin, N. (2018). Can china’s red capital really innovate? Wall Street Journal. https://www.wsj.com/articles/can-chinas-red-capital-really-innovate-1526299173.
Tetlock, P., & Gardner, D. (2015). Superforecasting: The art and science of prediction. New York: Crown.
WP. (2001). White Paper: European governance (COM.428 final; Brussels). Brussels, Commission of the European Community, July 25.
Weinberg, S. (2017). The trouble with quantum mechanics. New York: The New York Review of Books.
Wilkes, W. (2018). How the world’s biggest companies are fine-tuning the robot revolution. Wall Street Journal, 5, 14.
Willinsky, J. (2017). Intellectual property and education. Oxford: Oxford Research Encyclopedia.
Wissner-Gross, A., & Freer, C. (2013). Causal entropic forces. Physical Review Letters, 110(168702), 1–5.
Zell, E., & Krizan, Z. (2014). Do people have insight into their abilities? A metasynthesis? Perspectives on Psychological Science, 9(2), 111–125.
Zumbrun, J. (2018). Should the U.S. worry that china is closing in on its lead in research and development? Amid a productivity slump, the IMF sees benefits from Chinese and South Korean innovation. Wall Street Journal. https://blogs.wsj.com/economics/2018/04/10/should-the-us-worry-aboutchina-rd/.
Acknowledgements
The author thanks the Office of Naval Research for funding my research at the Naval Research Laboratory where I worked for Ranjeev Mittu during the past two summers and where parts of this manuscript were completed. The author also thanks the reviewers for their helpful comments and suggestions and the editor, Tomas Veloz, for his support.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lawless, W.F. Towards an Epistemology of Interdependence Among the Orthogonal Roles in Human–Machine Teams. Found Sci 26, 129–142 (2021). https://doi.org/10.1007/s10699-019-09632-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10699-019-09632-5