نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، دانشکده حکمرانی، دانشگاه تهران، تهران، ایران

چکیده

پژوهش حاضر با هدف فراترکیب پژوهش‌های حکمرانی شناختی به منظور درک پیچیدگی حکمرانی به وسیلۀ همگرایی آن با علوم اعصاب (مطالعات مغز)، شناختی (مطالعات ذهن) و روان‏شناسی (مطالعات روان) انجام شده است. برای این منظور، از رویکرد کیفی و روش سنتزپژوهی سندلوسکی و باروسو (2007) استفاده شده است. جامعۀ پژوهش اسناد علمی قابل دسترس در پایگاه‌های علمی داخلی و خارجی بوده است که از میان آن‏ها، 33 سند علمی به شیوۀ نمونه‌گیری هدف‏مند انتخاب و تحلیل شدند. برای تحلیل یافته‌ها، کدها از متن استخراج شدند و مقوله‌ها شکل گرفتند و در نهایت، الگوی نهایی تدوین شد. در پایان، برای بررسی اعتبار یافته‌های پژوهش از روش گروه کانونی استفاده شد که طی آن، یافته‌های پژوهش در گروه کانونی 5 نفرۀ متخصصان حکمرانی و شناختی مطرح، تصحیح و تکمیل شدند. نتایج پژوهش، حاکی از شناسایی 12 مقوله برای حکمرانی شناختی از جمله رویکرد شناختی، حل مسألۀ شناختی، خط‌مشی‌گذاری شناختی، تنظیم‌گری شناختی، آینده‌پژوهی شناختی، نظارت و ارزیابی شناختی، الگوسازی شناختی، خودحکمرانی شناختی، هوشمندی شناختی، شایستگی‌های شناختی و در نهایت حکمرانی بر علوم شناختی بوده است. می‌توان نتیجه گرفت علوم شناختی با شناسایی نقاط کور و همچنین ساده‌سازی و تجزیه و تحلیل موشکافانه به درک بهتر پیچیدگی حکمرانی کمک می‏ کند و پیشنهادهای شناختی مناسبی را برای حکمرانی ارائه می دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Metasynthesis of cognitive governance; convergence of governance and cognitive sciences in understanding the complexities of governance

نویسنده [English]

  • Ebrahim Mazari

Assistant Professor, Faculty of Governance, University of Tehran, Tehran, Iran.

چکیده [English]

Purpose: The current research was conducted with the goal of meta-synthesis of cognitive governance research to understand the complexity of governance through its convergence with neuroscience (brain studies), cognitive (mind studies), and psychology (mind studies). The complexity of governance, on the one hand, and the new and deep understanding of neuroscience, cognitive science, and psychology, especially when humans are active in this complexity, on the other hand, can serve governance by intersecting and merging, which is called cognitive governance. Cognitive governance includes governance over cognition, governance using cognitive sciences, and governance over cognitive sciences, and it is trying, by highlighting the role of cognition, to address the complexities of governance that are caused by the existence of numerous factors and elements and many relationships between them.
Method: Metasynthesis helps to aggregate and analyze scattered research related to emerging concepts and provides an integrated view of it. For this purpose, the qualitative approach and synthesis research method of Sandelowski and Barroso (2007) have been used. The research community of scientific documents and documents (articles, theses, treatises, books, and work reports) has been available in domestic and foreign reliable scientific databases, from which 33 scientific documents were selected and analyzed by targeted sampling. To analyze the findings, codes were extracted from the text, and categories were formed, and finally, the final model was developed. In the end, to check the validity of the research findings, the focus group method was used, during which the research findings were presented, corrected, and completed in the focus group of 5 people of governance and cognitive experts.
Findings: The findings of the current research indicate the identification of 12 categories for cognitive governance, including cognitive approach, cognitive problem-solving, cognitive policy-making, cognitive regulation, cognitive future research, cognitive monitoring and evaluation, cognitive modeling, cognitive self-governance, cognitive intelligence, cognitive competencies, and finally ruling cognitive sciences. The cognitive approach is the basis of cognitive governance, which solves cognitive issues in governance with the help of cognitive intelligence and cognitive modeling. The functions of cognitive policy-making, cognitive regulation, and cognitive decision-making, which form the main body of cognitive governance to operationalize it, are produced in response to issues and are continuously evaluated and cognitively monitored. Such a level of knowledge and action can visualize the future and use cognitive science for this purpose, so the future of cognitive research looking at the creation of the future with the help of cognitive science is proposed in this model. The highest level of governance, which is cognitive self-governance, makes a cognitive system based on deep, nested, and comprehensive cognitions that realize self-governance. Such entanglement of variables and factors is strengthened by the two arms of cognitive science governance, which is the result of the rapid growth of cognitive technologies, as well as cognitive competencies, which guarantee the efficiency and effectiveness of cognitive governance actors. The cognitive governance model is a set of components and relationships between them that were described.
Discussion and conclusion: It can be concluded that cognitive science helps to better understand the complexity of governance by identifying blind spots, as well as simplification and analysis, and provides appropriate cognitive solutions and suggestions for governance. On the one hand, cognitive governance makes it easier to understand and face the complexities of governance, and on the other hand, the increasing growth of cognitive technologies strengthens the possibility of making cognitive studies and strategies more objective in governance. Cognitive governance aims at the two dimensions of governance being popular and based on smart technologies, and it connects human cognition to machine cognition and emerging technologies. Such cognitive capacity is needed by the phenomenon, concept, and practice of governance.

کلیدواژه‌ها [English]

  • Governance
  • Complexity of governance
  • Cognitive governance
  • Cognitive sciences
  1. Arie, R. (2009). Governance of new and emerging science and technology. In Unnatural Selection: The Challenges of Engineering Tomorrow's People (P. Healey & S. Rayner, Eds.). London: Earthscan.
  2. Beji, R., Yousfi, Q., & Omri, A. (2021). Corporate Social Responsibility and Corporate Governance: A Cognitive Approach. Quantitative Finance. org/10.48550/arXiv.2102.09218
  3. Charreaux, G. (2000). Nouvelle économie et gouvernance. working papers fargo, 1000801.
  4. Chernova, L., Zhuravel, A., Chernova, L., Chernov, S., & Trushliakova, A. (2022). Application of the Cognitive Approach in the Field of Project Management. Proceedings of the 7th International Conference on Digital Technologies in Education, Science and Industry (DTESI 2022), Almaty, Kazakhstan.
  5. Conyon, M.J., & He, L. (2017). Firm performance and boardroom gender diversity: a quantile regression approach. Journal of Business Research, 79, 198–211. org/10.1016/j.jbusres.2017.02.006
  6. Department of Defense Fiscal (2011 a). President’s Budget Estimates, Justification of Estimates: Research Development, Test & Evaluation. Navy, Budget Activity 1–3, February 2010. http://www.secnav.navy.mil/fmc/fmb/ Documents/11pres/RDTEN_BA1-3_Book.pdf.
  7. Department of Defense Fiscal (2011 b). President’s Budget, Air Force Justification Book Volume 1: Research, Development, Test & Evaluation. Air Force–3600, February 2010. http://www.saffm.hq.af.mil/shared/media/ document/AFD-100201-046.pdf.
  8. Department of Defense Fiscal (2011 c). President’s Budget, Defense Advanced Research Projects Agency, Justification Book Volume 1: Research, Development, Test & Evaluation. Defense-Wide–0400, February 2010. http://comptroller.defense.gov/ Portals/45/ Documents/defbud get/fy2011/budget_justification/ pdfs/03 _RDT_and_ 29E/DARP A_RDT_E_PB11.pdf.
  9. Department of the Army, Office of the Secretary of the Army (Financial Management and Controller) (2010). Descriptive Summaries of the Research, Development, Test and Evaluation. Army Appropriations, Volume I–III, February. http://asafm.army.mil/Documents/OfficeDocuments/Budget/ BudgetMaterials/FY11/rforms//vol1.pdf.
  10. Eugster, N., Kowalewski, O., & Spiewanowski, P. (2024). Internal governance mechanisms and corporate misconduct. International Review of Financial Analysis, In Press. org/10.1016/j.irfa.2024.103109
  11. Fukuyama, F., & Wagner, C.S. (2000). Information and Biological Revolutions: Global Governance Challenges—Summary of a Study Group. Santa Monica, CA: RAND Corporation.
  12. Garzarelli, G. (2006). Cognition, Incentives, and Public Governance. Public Finance Revie, 34(3), 235-257. doi.org/10.1177/1091142105285593
  13. Hong, Y., & Fang, Q. (2012). Shift of the focus of global economic governance: the strategy of G20 and big powers. Modern International Relations, 3, 40. https://www.econstor.eu/bitstream/10419/196320/1/GEG-WP-059.pdf
  14. Kenneth, D. (2008). Socio-technical analysis of those concerned with emerging technology, engagement, and governance. In What Can Nanotechnology Learn from Biotechnology? Social and Ethical Lessons for Nanoscience from the Debate over Agrifood, Biotechnology, and GMOs (K. David & P.B. Thompson, Eds.). Burlington, MA: Elsevier Academic Press, p. 8.
  15. Knox, B., Lugo, R., Helkala, K., Sutterlin, S., & Jqsok, Q. (2018). Education for Cognitive Agility: Improved Understanding and Governance of Cyberpower. https://www.researchgate. net/ public ation/331021729.
  16. Kodila-Tedika, O., Rindermann, H., & Christainsen, G. (2016). Cognitive capital, governance, and the wealth of nations. MPRA Paper No. 73484, posted 3 September.
  17. Kosal, M.E., & Huang, J.Y. (2015). Security implications and governance of cognitive neuroscience: An ethnographic survey of researchers. Politics and the Life Sciences, 34(1), 93-108. doi.org/10.1017/pls.2015.4
  18. Lindenberg, S. (2003). The cognitive side of governance. Research in the Sociology of Organizations, 20, 47-76. http://journal.dresmara.ro/issues/volume3_issue1/06_nassreddine_anis.pdf
  19. Lindenberg, S. (2013). Cognition and governance: why incentives have to take a back seat. In Handbook of Economic Organization. Integrating Economic and Organization Theory (A. Grandori, Ed.). Cheltenham: Elgar, 41-61.
  20. Lu, P., Zhou, L., & Fan, X. (2023). Platform governance and sociological participation. The Journal of Chinese Sociology, 10(3), 1-24. doi.org/10.1186/s40711-023-00181-w
  21. Milkoreit, M. (2019). Cognitive capacities for global governance in the face of complexity: the case of climate tipping points.USA: Elgar Publication.
  22. Miller, G.A. (2003). The cognitive revolution: a historical perspective. Trends Cogn Sci, 7(3), 141-4. org/10.1016/S1364-6613(03)00029-9
  23. Naguib, H.M., Kassem, H.M., & Naem, A.M. (2024). The impact of IT governance and data governance on fnancial and non-fnancial performance. Future Business Journal, 10(5), 1-22. doi.org/10.1186/s43093-024-00300-0
  24. Nassreddine, G., & Anis, J. (2012). A cognitive approach to corporate governance: a visualization test of mental models with the cognitive mapping technique. Journal of Defense Resources Management, 3(1), 66-82. file:///C:/Users/Passargad/Downloads/ A_COGNITIVE_APPROACH _TO_CORPORATE_GOVERN.pdf
  25. Nassreddine, G, & Anis, J. (2014). Cognitive governance, cognitive mapping and cognitive conflicts: Structural analysis with the MICMAC method. Cogent Economics & Finance, 2, 1-13. org/10.1080/23322039.2014.922893
  26. Nathan, D. (2013). Precautionary discourse: thinking through the distinction between the precautionary principle and the precautionary approach in theory and practice. Politics and the Life Sciences, 32(1), 2–21. org/10.2990/32_1_2
  27. Pariama, R.E., Albertus Joko Santoso, A.J., & Mudjihartono, P.M. (2021). Study of the effect of cognitive and affective aspects of smart governance in ambon city. 8th International Conference on Information Technology: IoT and Smart City (ICIT '20). Association for Computing Machinery, New York, NY, USA, pp. 169–175. doi.org/10.1145/3446999.3447031
  28. Poincelot, E., & Wegman, G. (2004). Utilisation des critères non fi nanciers pour évaluer ou piloter la performance: analyse théorique - the use of non-fi nancial measures to evaluate or manage the performance: a theoretical analysis. working papers fargo, 1040902.
  29. Przeybilovicz, E., & Cunha, M.A. (2024). Governing in the digital age: the emergence of dynamic smart urban governance modes. Government Information Quarterly, 41, 1-14. doi.org/10.1016/j.giq.2023.101907
  30. Rachlinski, J., & Farina, C. (2002). Cognitive Psychology and Optimal Government Design. Cornell Law Faculty Publications. Paper 755.
  31. Rip, A. (2009). Governance of new and emerging science and technology. In Unnatural Selection: The Challenges of Engineering Tomorrow's People (P. Healey & S. Rayner, Eds.). London: Earthscan.
  32. Roland, C. (2006). Risk management and regulation in an emerging technology. In Nanotechnology: Risk, Ethics, and Law (G. Hunt & M.D. Mehta, Eds.). London: Earthscan.
  33. Rueda, R. (2024). Cognitive Governance and the Historical Distortion of the Norm of Modern Development: A Theory of Political Asymmetry. USA: IGI Global Publishing.
  34. Sandelowski, M., & Barroso, J. (2007). Handbook for synthesizing qualitative research. USA: Springer Publishing Company.
  35. Sellar, S., & Lingard, B. (2013). The OECD and global governance in education. Journal of Education Policy28(5), 710–725. org/10.1080/02680939.2013.779791
  36. Sternberg, R.J., Sternberg, K. (2016). Cognitive Psychology. USA: Wadsworth Publishing.
  37. Sylwester, R. (2008). Alphabetized entries from how to explain a brain (K.W. Fischer & M.H. Immordino-Yang, Eds.). In The Jossey- Bass Reader on Brain and Learning. San Francisco, California. Jossey- Bass.
  38. Torriti, J. (2009). Impact assessments and emerging technologies: From precaution to ‘smart regulation’? In Emerging Technologies: From Hindsight to Foresight (E.F. Einsiedel, Ed.). University of British Columbia Press.
  39. Varela, F.J., Thompson, E., & Rosch, E. (2017). The embodied mind: Cognitive science and human experience. MIT
  40. Wong, M.H.I., Zhao, H., & MacWhinney, B.J.L.L. (2018). A cognitive linguistics application for second language pedagogy. The English Preposition Tutor, 68(2), 438-68. org/10.1111/lang.12278
  41. Wyk, J.V. (2018). Cognitive computing governance and risk management. Southern African Journal of Accountability and Auditing Research, 20(1), 1-18. https://hdl.handle.net/10520/EJC-11fd67a2a5
  42. Yuan, Y. (2021). Smart city next-gen social networks system based on software reconstruction model and cognitive computing. Social Network Analysis and Mining, 11(1), 1–4. https://www.springerprofessional.de/smart-city-next-gen-social-networks-system-based-on-software-rec/19753150.
  43. Zatorre, R.J.C.B. (2018). Brenda Milner and the origins of cognitive neuroscience. Current Biology Magazine, 28(11), 638-9. https://www.cell.com/current-biology/pdf/S0960-9822(18)30520-7.pdf
  44. Zhou, C., Gui, S., Liu, Y., Ma, J., & Wang, H. (2023). Perspectives of Collaborative Governance: Integration of Social and Cognitive Computing with Complex Networks. Mobile Information Systems, (1), 1-6. doi.org/10.1155/2023/2162076