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2023 | 11 | 37-64

Article title

The Human Governance Problem: Complex Systems and the Limits of Human Cognition

Authors

Content

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Abstracts

EN
The impact of complexity within government and societal systems is considered relative to the limitations of human cognitive bandwidth, and the resulting reliance on cognitive biases and systems of automation when that bandwidth is exceeded. Examples of how humans and societies have attempted to cope with the growing difference between the rate at which the complexity of systems and human cognitive capacities increase respectively are considered. The potential of and urgent need for systems capable of handling the existing and future complexity of systems, utilizing greater cognitive bandwidth through scalable AGI, are also considered, along with the practical limitations and considerations in how those systems may be deployed in real-world conditions. Several paradoxes resulting from the influence of prolific Narrow Tool AI systems manipulating large portions of the population are also noted.

Year

Volume

11

Pages

37-64

Physical description

Dates

published
2023

Contributors

  • AGI Laboratory, Seattle, WA, USA

References

  • Aczel, B., Bago, B., Szollosi, A., Foldes, A. Lukacs, B., Is It Time for Studying Real-Life Debiasing? Evaluation of the Effectiveness of an Analogical Intervention Technique, Frontiers in Psychology, 2015, 6, p. 1120.
  • Alam, S., Gebremichael, M., Ban, Z., Scanlon, B. R., Senay, G., Lettenmaier, D. P., Post‐Drought Groundwater Storage Recovery in California's Central Valley, Water Resources Research, 2021, 57 (10); p.e2021WR030352.
  • Alm, J., Tax Evasion, Technology, and Inequality, Economics of Governance, 2021, 22 (4), pp. 321–343.
  • Atreides, K., E-governance with Ethical Living Democracy, Procedia Computer Science, 2021, 190, pp. 35–39.
  • Atreides, K., Philosophy 2.0: Applying Collective Intelligence Systems and Iterative Degrees of Scientific Validation, Filozofia i Nauka, 2022, 10.
  • Atreides, K., Kelley, D. J., Masi, U., Methodologies and Milestones for the Development of an Ethical Seed, in: Biologically Inspired Cognitive Architectures Meeting, Springer, Cham 2020, November, pp. 15–23.
  • Azzopardi, L., Cognitive Biases in Search: A Review and Reflection of Cognitive Biases in Information Retrieval, in: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval, 2021, March. pp. 27–37.
  • Bar-Yam, Y., Complexity Rising: From Human Beings to Human Civilization, a Complexity Profile, 2000.
  • Bender, E. M., Gebru, T., McMillan-Major, A., Shmitchell, S., On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery 2021, pp. 610–623.
  • Bostrom, N., Yudkowsky, E., Artificial Intelligence Safety and Security, Chapman and Hall/CRC, 2018, pp. 57–69.
  • Bostrom, N., Ethical Issues in Advanced Artificial Intelligence. Science Fiction and Philosophy: From Time Travel to Superintelligence, 2003, 277, p. 284.
  • Bowman, N. D., Cohen, E., Mental Shortcuts, Emotion, and Social Rewards: The Challenges of Detecting and Resisting Fake News, in: Fake News: Understanding Media and Misinformation in the Digital Age, Zimdars, M., McLeod, K. (eds.), MIT Press, 2020, pp. 223–233.
  • Brailovskaia, J., Margraf, J., Schillack, H. Köllner, V., Comparing Mental Health of Facebook Users and Facebook Non-Users in an Inpatient Sample in Germany, Journal of Affective Disorders, 2019, 259, pp. 376–381.
  • Bullock, C., The Cobbler of Preston, 1716.
  • Chua, P.K., Mazmanian, M., Are You One of Us? Current Hiring Practices Suggest the Potential for Class Biases in Large Tech Companies, Proceedings of the ACM on Human-Computer Interaction, 2020, 4 (CSCW2), pp. 1–20.
  • Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W. Starnini, M., The Echo Chamber Effect on Social Media, Proceedings of the National Academy of Sciences, 2021, 118(9), p.e2023301118.
  • Cohen, M.A., Dennett, D.C. Kanwisher, N., What Is the Bandwidth of Perceptual Experience?, Trends in Cognitive Sciences, 2016, 20 (5), pp. 324–335.
  • Collington, R. Mazzucato, M., Britain’s Public Sector Is Paying the Price for the Government’s Consultancy Habit, The Guardian, 2021, September 20th; https://www.theguardian.com/commentisfree/2021/sep/20/britain-public-sector-consultancy-habit-pandemic-private-services
  • Friston, K., Moran, R.J., Nagai, Y., Taniguchi, T., Gomi, H. Tenenbaum, J., World Model Learning and Inference, Neural Networks, 2021, 144, pp. 573–590.
  • Galton, F., Vox Populi (the Wisdom of Crowds), Nature, 1907, 75 (7), pp. 450–451.
  • Haidt, J., The Righteous Mind: Why Good People Are Divided by Politics and Religion, Vintage, 2012.
  • Hanley, H.W., Kumar, D. Durumeric, Z., Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War on Reddit, 2022, arXiv preprint arXiv:2205.14484.
  • Hanson, R., The Great Filter-are We Almost Past It, 1998; preprint available at http://hanson. gmu. edu/greatfilter. html.
  • Harel, T. O., Jameson, J. K. , Maoz, I., The normalization of hatred: Identity, Affective Polarization, and Dehumanization on Facebook in the Context of Intractable Political Conflict, Social Media+ Society, 2020, 6(2); p.2056305120913983.
  • Harish, A., The New Slot Machine: An International Perspective on Why the United States Should Learn to Stop Loving the Loot Box, Emory International Law Review, 2022, 36 (1), p.131.
  • Hayek F. A.V., The Use of Knowledge in Society, The American Economic Review, 1945, 35 (4), pp. 518–530.
  • Herr, P.M., Consequences of Priming: Judgment and Behavior, Journal of Personality and Social Psychology, 1986, 51 (6), p. 1106.
  • Hilbert, M., Big Data for Development: A Review of Promises and Challenges, Development Policy Review, 2016, 34 (1), pp. 135–174.
  • Holzinger, A., Saranti, A., Molnar, C., Biecek, P. Samek, W., Explainable AI Methods-a brief Overview, in: International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, Springer, Cham 2022, pp. 13–38.
  • Hosking, G., The Secline of Trust in Government, in: Trust in Contemporary Society, Brill, 2019, pp. 77–103.
  • James, I., With Severe Drought, an Urgent Call to Rework the Colorado River’s Defining Pact, Los Angeles Times, May 19th 2022; https://www.latimes.com/california/story/2022-05-19/former-interior-secretary-calls-for-revamping-colorado-river-compact
  • Judge, M., Idiocracy, Movie, Ternion Pictures, Hollywood 2006.
  • Kagan, B. J., Kitchen, A. C., Tran, N. T., Parker, B. J., Bhat, A., Rollo, B., Razi, A. Friston, K. J., In Vitro Neurons Learn and Exhibit Sentience When Embodied in a Simulated Game-World, 2021; bioRxiv.
  • Kahneman, D., Sibony, O. Sunstein, C. R., Noise: A Flaw in Human Judgment, Little, Brown, 2021.
  • Kallemeyn, L.M., Hall, J.N. Gates, E., Exploring the Relevance of Complexity Theory for Mixed Methods Research, Journal of Mixed Methods Research, 2020, 14 (3), pp. 288–304.
  • Kao, A. B., Berdahl, A. M., Hartnett, A. T., Lutz, M. J., Bak-Coleman, J. B., Ioannou, C. C., Giam, X.M Couzin, I. D., Counteracting Estimation Bias and Social Influence to Improve the Wisdom of Crowds, Journal of The Royal Society Interface, 2018, 15 (141); p.20180130.
  • Katz, D. M., Bommarito, M. J., Measuring the Complexity of the Law: the United States Code, Artificial intelligence and Law, 2014, 22 (4), pp. 337–374.
  • Kelley, D., Atreides, K., AGI Protocol for the Ethical Treatment of Artificial General Intelligence Systems, Procedia Computer Science, 2020, 169, pp. 501–506.
  • Kelley, D., The Sapient and Sentient Intelligence Value Argument (Ssiva) Ethical Model Theory for Artificial General Intelligence, in: Transhumanist Handbook, Springer, 2019.
  • Kelley, D.J., Artificial General Intelligence (AGI) Protocols: Protocol 2 Addressing External Safety with Research Systems.
  • Kelly, K., What Technology Wants, Penguin, 2011.
  • Kreiss, D., McGregor, S.C., Technology Firms Shape Political Communication: The Work of Microsoft, Facebook, Twitter, and Google with Campaigns during the 2016 Us Presidential Cycle, Political Communication, 2018, 35 (2), pp. 155–177.
  • Kurashima, S., Asahi, Y., Analysis of Declining Fertility Rate in Japan by Focusing on TFR and Women Moving, in: International Conference on Human-Computer Interaction Springer, Cham 2022, pp. 337–353.
  • Lang, T., McKee, M., The Reinvasion of Ukraine Threatens Global Food Supplies, bmj, 2022.
  • Last Week Tonight with John Oliver, Water; accessed July 2nd, 2022; https://www.youtube.com/watch?v=jtxew5XUVbQ
  • Lees, M., Knight, R., Smith, R., Development and Application of a 1D Compaction Model to Understand 65 Years of Subsidence in the San Joaquin Valley, Water Resources Research, 2022; p.e2021WR031390.
  • Liang, C. S., Cross, M. J., White-Crusade, How to Prevent Right-Wing Extremists from Exploiting the Internet, Geneva Centre for Security Policy, 2020, 11.
  • Limberg, J., Knill, C., Steinebach, Y., Condemned to Complexity? Growing State Activity and Complex Policy Systems, Governance, 2022.
  • Liu, H. Y., Lauta, K. C., Maas, M. M., Governing Boring Apocalypses: A New Typology of Existential Vulnerabilities and Exposures for Existential Risk Research, Futures, 2018, 102, pp. 6–19.
  • Lompo, M. L., Ouoba, M. M., How They Hide Money? An Investigation on Tax Evasion of Large Corporations and Wealthy Taxpayers, 2022.
  • Lorenz-Spreen, P., Mønsted, B.M., Hövel, P. et al., Accelerating Dynamics of Collective Attention, Nat Commun, 2019, 10, 1759; https://doi.org/10.1038/s41467-019-09311-w
  • Martin, J., Parenti, M., Toubal, F., Corporate Tax Avoidance and Industry Concentration, 2020.
  • Menczer, F. and Hills, T., Information Overload Helps Fake News Spread, and Social Media Knows It, Scientific American, 2020, 323 (6), pp. 54–61.
  • Miller, E. K., Buschman, T. J., Working Memory Capacity: Limits on the Bandwidth of Cognition, Daedalus, 2015, 144 (1), pp. 112–122.
  • MIT Center for Collective Intelligence; accessed July 2nd 2022; https://cci.mit.edu/
  • Mueller, L., Conceptual Breakthroughs in Evolutionary Ecology, Academic Press, 2019.
  • OECD, Debate the Issues: Complexity and Policy Making; accessed July 2nd, 2022. https://www.oecd.org/naec/complexity_and_policymaking.pdf
  • Olson, P., Facebook and Google’s Ad Addiction Can’t Last Forever, Bloomberg. February 3rd, 2022; accessed July 7th, 2022; https://www.bloomberg.com/opinion/articles/2022-02-03/facebook-and-google-s-ad-addiction-can-t-last-forever-thanks-to-tiktok-web3
  • Orlowski, J., The Social Dilemma. Exposure Labs, Argent Pictures, The Space Program, Los Angeles, CA 2020.
  • Pawson, R., Wong, G., Owen, L., Known Knowns, Known Unknowns, Unknown Unknowns: The Predicament of Evidence-Based Policy, American Journal of Evaluation, 2011, 32 (4), pp. 518–546.
  • Payne, B. K., Hannay, J. W., Implicit Bias Reflects Systemic Racism, Trends in Cognitive Sciences, 2021, 25 (11), pp. 927–936.
  • Ramachandran, V. S., Encyclopedia of Human Behavior, Academic Press, 2012.
  • Ratzke, C., Denk, J., Gore, J., Ecological Suicide in Microbes, Nature Ecology & Evolution, 2(5), 2018, pp. 867–872.
  • Regalado, A., Meet Altos Labs, Silicon Valley’s Latest Wild Bet on Living Forever, MIT Technology Review, September 4th,2021; https://www.technologyreview.com/2021/09/04/1034364/altos-labs-silicon-valleys-jeff-bezos-milner-bet-living-forever/
  • Rodríguez-Ferreiro, J., Barberia, I., The Moral Foundations of Illusory Correlation, Plos One, 12 (10), 2017, p.e0185758.
  • Ross, E., Doomsday Clock Ticks Closer to Apocalypse, Nature, 2017, 26.
  • Rossmo, D. K., Cognitive Biases: Perception, Intuition, and Tunnel Vision, in: Criminal Investigative Failures, Routledge, 2008, pp. 33–46.
  • Russell, S., Human Compatible: Artificial Intelligence and the Problem of Control, Penguin, 2019.
  • Schiffling, S., Valantasis Kanellos, N., Five Essential Commodities That Will Be Hit by War in Ukraine, The Conversation 2022.
  • Schilbach, F., Schofield, H. Mullainathan, S., The Psychological Lives of the Poor, American Economic Review, 106 (5), 2016, pp. 435–440.
  • Şekerli, E.B., Akçetin, E., Diversification Strategy in Internet Industry: Case of Google Inc., Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 20 (3), 2018, pp. 271–289.
  • Simons, D. J., Chabris, C.F., Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events, Perception, 28 (9), 1999, pp. 1059–1074.
  • Sowels, N., A Brief Introduction to Complexity Theory in Managing Public Services, Revue Française de Civilisation Britannique. French Journal of British Studies, 26 (XXVI-2), 2021.
  • Stanchi, K. M., The Power of Priming in Legal Advocacy: Using the Science of First Impressions to Persuade the Reader, Or. L. Rev., 2010, 89.
  • Tetlock, P. E., Expert Political Judgment, in: Expert Political Judgment, Princeton University Press, 2017.
  • Thaler, R. H. Sunstein, C. R., Nudge, Yale University Press, 2021.
  • Tokita, C. K., Guess, A. M. Tarnita, C. E., Polarized Information Ecosystems Can Reorganize Social Networks Via Information Cascades, Proceedings of the National Academy of Sciences, 2021, 118 (50), p.e2102147118.
  • Tversky, A., Kahneman, D., The Framing of Decisions and the Psychology of Choice, in: Behavioral Decision Making, Springer, Boston, MA 1985, pp. 25–41.
  • Twenge, J.M., Joiner, T. E., Rogers, M. L. Martin, G. N., Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates among Us Adolescents after 2010 And Links to Increased New Media Screen Time, Clinical Psychological Science, 2018, 6 (1), pp. 3–17.
  • U.S. International Trade Commission, Harmonized Tariff Schedule and General Notes; accessed July 2nd, 2022; https://hts.usitc.gov/current
  • Urbina, F., Lentzos, F., Invernizzi, C., Ekins, S., Dual Use of Artificial-Intelligence-Powered Drug Discovery, Nature Machine Intelligence, 2022, 4 (3), pp.189–191.
  • US Bureau of Reclamation, Colorado River Basin Natural Flow and Salt Data; accessed July 2nd, 2022; https://www.usbr.gov/lc/region/g4000/NaturalFlow/provisional.html
  • Veldwijk, J., Essers, B. A., Lambooij, M. S., Dirksen, C. D., Smit, H. A., De Wit, G.A., Survival or Mortality: Does Risk Attribute Framing Influence Decision-Making Behavior in a Discrete Choice Experiment?, Value in Health, 2016, 19 (2), pp. 202–209.
  • Wachowski, A., Wachowski, L., Reeves, K., Fishburne, L., Moss, C. A., Weaving, H., Foster, G., Pantoliano, J. Staenberg, Z., Matrix, Warner Home Video, Burbank, CA 1999.
  • White, J. M., Lidskog, R., Ignorance and the regulation of artificial intelligence, Journal of Risk Research, 2022. 25 (4), pp. 488–500.
  • Whitten-Woodring, J., Kleinberg, M.S., Thawnghmung, A., Thitsar, M. T., Poison If You Don’t Know How to Use It: Facebook, Democracy, and Human Rights in Myanmar, The International Journal of Press/Politics, 2020, 25 (3), pp. 407–425.
  • Wiesmann, U. M., Hurni, H. (eds.), Research for Sustainable Development: Foundations, Experiences, and Perspectives, University of Bern, Bern 2011.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
31233773

YADDA identifier

bwmeta1.element.ojs-doi-10_37240_FiN_2023_11_1_4
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