Dutch Navigation Model of Decision Making
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Abstract
In the contemporary consumer-oriented world, where desire often outweighs rationality, there is a growing significance attributed to specific-historical decision-making models, among which the Dutch navigational model stands out. The article explores the primary dimensions of importance and universality inherent in decision-making mechanisms within human life. Specifically, it examines the case of the historical-cultural, semantic, and environmental legacy of the East and West India Company in Amsterdam through the application of visual sociology techniques related to the environment and decision-making culture in the field stage. The examination of culture and the decision-making process, particularly within historical expansion processes as a scaling of outcomes, is intertwined with crucial dimensions of decision-making. These include aspects related to activity, volition, personal-status (social-power) attributions, and the consequential significance of decisions as agents shaping fate in history. Political-strategic decisions made collectively at the highest echelons were harmonized through a unified command structure with hierarchical elements, adherence to the ship's charter, the rationality inherent in navigational logic, and a system celebrating the success of the ship's crew upon the venture's completion. The core of the navigational decision system revolved around key inquiries that mirrored crucial stages of decision-making: Where am I (reconnaissance)? Where do I want to go (goal setting)? Which direction to go (orienteering)? How do I get there (tactics and ship navigation)?
Multimedia thinking, characterized by visual clarity and the openness associated with terrestrial geography, necessitated the integration of virtual marine geography. This marine geography not only reflected concealed but quantifiable underwater factors but also aligned with the invisible environment, reconstructed on maps. Additionally, it harmonized with subjective-architectural construction cartography, collectively fostering flexibility in decision-making.
DOI:
https://doi.org/10.61439/SEEH6417
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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Couck, M., Caers, R., Musch, L., Fliegauf, J., Giangreco, A., & Gidron, Y. (2019). How breathing can help you make better decisions: Two studies on the effects of breathing patterns on heart rate variability and decision-making in business cases. International Journal of Psychophysiology, 139, 1–9. https://doi.org/10.1016/j.ijpsycho.2019.02.011
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Krupiy, T. (2020). A vulnerability analysis: Theorising the impact of artificial intelligence decision-making processes on individuals, society and human diversity from a social justice perspective. Computer Law & Security Review, 38, 105429. https://doi.org/10.1016/j.clsr.2020.105429
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Simon, H. A. (1959). Theories of Decision-Making in Economics and Behavioral Science. The American Economic Review, 49(3), 253–283.
Skrynnikov, R. G. (2005). Mihail Romanov [Mikhail Romanov]. AST.
Stanton, M. C. B., & Roelich, K. (2021). Decision making under deep uncertainties: A review of the applicability of methods in practice. Technological Forecasting and Social Change, 171, 120939. https://doi.org/10.1016/j.techfore.2021.120939
Turner, J. R. (2020). Investigating how governmentality and governance influence decision making on projects. Project Leadership and Society, 1, 100003. https://doi.org/10.1016/j.plas.2020.100003
Vergerio, G., Becchio, C., Delmastro, C., Lanzini, A., Corgnati, S. P., & Borchiellini, R. (2018). A decision-making process to support public administrations in defining local energy policies. Thermal Science and Engineering Progress, 6, 398–409. https://doi.org/10.1016/j.tsep.2018.01.016
Vincent, V. (2021). Integrating intuition and artificial intelligence in organizational decision-making. Business Horizons, 64(4), 425–438. https://doi.org/10.1016/j.bushor.2021.02.008
Von Wallhausen, J. J. (1647). Doctrine and cunning of infantry men’s formation. Moscow Print Yard. (Original work published 1615)
Wang, Y. (2021). When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale. Studies in Educational Evaluation, 69, 100872. https://doi.org/10.1016/j.stueduc.2020.100872
Webb, P., Davidson, G., Edge, R., Falls, D., Keenan, F., Kelly, B., McLaughlin, A., Montgomery, L., Mulvenna, C., Norris, B., Owens, A., & Irvine, R. S. (2020). Key components of supporting and assessing decision making ability. International Journal of Law and Psychiatry, 72, 101613. https://doi.org/10.1016/j.ijlp.2020.101613
Wieder, B., & Ossimitz, M. (2015). The Impact of Business Intelligence on the Quality of Decision Making – A Mediation Model. Procedia Computer Science, 64, 1163–1171. https://doi.org/10.1016/j.procs.2015.08.599
Yang, M., & Gabrielsson, P. (2017). Entrepreneurial marketing of international high-tech business-to-business new ventures: A decision-making process perspective. Industrial Marketing Management, 64, 147–160. https://doi.org/10.1016/j.indmarman.2017.01.007
Yoon, H., Scopelliti, I., & Morewedge, C. K. (2021). Decision making can be improved through observational learning. Organizational Behavior and Human Decision Processes, 162, 155–188. https://doi.org/10.1016/j.obhdp.2020.10.011
Alekseev, T. V. (2019). Oruzhejnoe proizvodstvo Rossii s XVI veka do 1917 goda v otechestvennoj dorevolyucionnoj istoriografii [Weapon production of Russia from the XVI century to 1917 in the domestic pre-revolutionary historiography]. In Vojna i oruzhie: Novye issledovaniya i materialy. Trudy devyatoj mezhdunarodnoj nauchno-prakticheskoj konferencii. Maj 15-17, 2019 [War And Weapons: New Research and Materials. Proceedings of the Ninth International Scientific and Practical Conference. May 15-17, 2019]. The Military Historical Museum of Artillery, Engineers and Signal Corps. https://artillery-museum.ru/assets/files/konferencziya-vio-2019-i-tom-czvet.pdf
Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
Altshuller, G. S. (1996). And suddenly the inventor appeared: TRIZ, the Theory of Inventive Problem solving (L. A. Shulyak, Trans.). Technical Innovation Center. (Original work published 1984)
Ariely, D. (2008). Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions. HarperCollins.
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766. https://doi.org/10.1016/j.techfore.2021.12076
Bagozzi, R. P., Sekerka, L. E., & Sguera, F. (2018). Understanding the consequences of pride and shame: How self-evaluations guide moral decision making in business. Journal of Business Research, 84, 271–284. https://doi.org/10.1016/j.jbusres.2017.11.036
Balbontin, C., & Hensher, D. A. (2021). Understanding business location decision making for transport planning: An investigation of the role of process rules in identifying influences on firm location. Journal of Transport Geography, 91, 102955. https://doi.org/10.1016/j.jtrangeo.2021.102955
Bernoulli, D. (1954). Exposition of a New Theory on the Measurement of Risk. Econometrica, 22(1), 23. (Original work published 1738) https://doi.org/10.2307/1909829
Boffelli, A., Golini, R., Orzes, G., & Dotti, S. (2020). Open the box: A behavioural perspective on the reshoring decision-making and implementation process. Journal of Purchasing and Supply Management, 26(3), 100623. https://doi.org/10.1016/j.pursup.2020.100623
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312
Chang, A. (2016). Big data in medicine: The upcoming artificial intelligence. Progress in Pediatric Cardiology, 43, 91–94. https://doi.org/10.1016/j.ppedcard.2016.08.021
Couck, M., Caers, R., Musch, L., Fliegauf, J., Giangreco, A., & Gidron, Y. (2019). How breathing can help you make better decisions: Two studies on the effects of breathing patterns on heart rate variability and decision-making in business cases. International Journal of Psychophysiology, 139, 1–9. https://doi.org/10.1016/j.ijpsycho.2019.02.011
De Spinoza, B. (1996). Ethics. Penguin Classics. (Original work published 1676).
Ding, R., Palomares, I., Wang, X., Yang, G., Liu, B., Dong, Y., Herrera–Viedma, E., & Herrera, F. (2020). Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion, 59, 84–102. https://doi.org/10.1016/j.inffus.2020.01.006
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Erasmus, D. (2019). Collected works of Erasmus. (Vols. 1–75). University of Toronto Press.
Foresti, R., Rossi, S., Magnani, M., Lo Bianco, C. G., & Delmonte, N. (2020). Smart Society and Artificial intelligence: big data scheduling and the global standard method applied to smart maintenance. Engineering, 6(7), 835–846. https://doi.org/10.1016/j.eng.2019.11.014
Gigerenzer, G. (2014). Risk Savvy: How To Make Good Decisions. Penguin UK.
Kahneman, D. (2011). Thinking, Fast and Slow. Penguin UK.
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Bias. Cambridge University Press.
Killen, C. P., Geraldi, J., & Kock, A. (2020). The role of decision makers’ use of visualizations in project portfolio decision making. International Journal of Project Management, 38(5), 267–277. https://doi.org/10.1016/j.ijproman.2020.04.002
Kittel, A., Cunningham, I., Larkin, P., Hawkey, M., & Rix‐Lièvre, G. (2021). Decision-making training in sporting officials: Past, present and future. Psychology of Sport and Exercise, 56, 102003. https://doi.org/10.1016/j.psychsport.2021.102003
Krupiy, T. (2020). A vulnerability analysis: Theorising the impact of artificial intelligence decision-making processes on individuals, society and human diversity from a social justice perspective. Computer Law & Security Review, 38, 105429. https://doi.org/10.1016/j.clsr.2020.105429
Leontiev, A. N. (1999). Volya. Moscow University Psychology Bulletin. Series 14. Psychology., 2, 3–14.
Mangutova, S., & Nikolaeva, T. (2008, November 2). From the History of the Rare Fund of the Scientific Library of the RGO. Russian Geographical Society. https://rgo.ru/activity/redaction/news/iz-istorii-redkogo-fonda-nauchnoy-biblioteki-rgo/
Murray, D. J., Boyle, W. A., Beyatte, M. B., Knittel, J., Kerby, P., Woodhouse, J., & Boulet, J. R. (2018). Decision-making skills improve with critical care training: Using simulation to measure progress. Journal of Critical Care, 47, 133–138. https://doi.org/10.1016/j.jcrc.2018.06.021
New American Bible. (2002). United States Conference of Catholic Bishops. https://www.vatican.va/archive/ENG0839/_INDEX.HTM
Reynaldo, C., Christian, R., Hosea, H., & Gunawan, A. a. S. (2021). Using video games to improve capabilities in decision making and cognitive skill: a literature review. Procedia Computer Science, 179, 211–221. https://doi.org/10.1016/j.procs.2020.12.027
Rubinstein, S. (2000). Fundamentals of General Psychology. Piter. (Original work published 1940).
Rubinstein, S. (2002). Bytie i soznanie. Chelovek i mir [Being and consciousness. Man and the world]. Collection. Piter.
Sallust. (1964). The Jugurthine War. . . Penguin Books. (Original work published 40 B.C.E.)
Sigmond, J. P., & Zuiderbaan, L. H. (1979). Dutch Discoveries of Australia: Shipwrecks, Treasures and Early Voyages Off the West Coast. Rigby.
Simon, H. A. (1959). Theories of Decision-Making in Economics and Behavioral Science. The American Economic Review, 49(3), 253–283.
Skrynnikov, R. G. (2005). Mihail Romanov [Mikhail Romanov]. AST.
Stanton, M. C. B., & Roelich, K. (2021). Decision making under deep uncertainties: A review of the applicability of methods in practice. Technological Forecasting and Social Change, 171, 120939. https://doi.org/10.1016/j.techfore.2021.120939
Turner, J. R. (2020). Investigating how governmentality and governance influence decision making on projects. Project Leadership and Society, 1, 100003. https://doi.org/10.1016/j.plas.2020.100003
Vergerio, G., Becchio, C., Delmastro, C., Lanzini, A., Corgnati, S. P., & Borchiellini, R. (2018). A decision-making process to support public administrations in defining local energy policies. Thermal Science and Engineering Progress, 6, 398–409. https://doi.org/10.1016/j.tsep.2018.01.016
Vincent, V. (2021). Integrating intuition and artificial intelligence in organizational decision-making. Business Horizons, 64(4), 425–438. https://doi.org/10.1016/j.bushor.2021.02.008
Von Wallhausen, J. J. (1647). Doctrine and cunning of infantry men’s formation. Moscow Print Yard. (Original work published 1615)
Wang, Y. (2021). When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale. Studies in Educational Evaluation, 69, 100872. https://doi.org/10.1016/j.stueduc.2020.100872
Webb, P., Davidson, G., Edge, R., Falls, D., Keenan, F., Kelly, B., McLaughlin, A., Montgomery, L., Mulvenna, C., Norris, B., Owens, A., & Irvine, R. S. (2020). Key components of supporting and assessing decision making ability. International Journal of Law and Psychiatry, 72, 101613. https://doi.org/10.1016/j.ijlp.2020.101613
Wieder, B., & Ossimitz, M. (2015). The Impact of Business Intelligence on the Quality of Decision Making – A Mediation Model. Procedia Computer Science, 64, 1163–1171. https://doi.org/10.1016/j.procs.2015.08.599
Yang, M., & Gabrielsson, P. (2017). Entrepreneurial marketing of international high-tech business-to-business new ventures: A decision-making process perspective. Industrial Marketing Management, 64, 147–160. https://doi.org/10.1016/j.indmarman.2017.01.007
Yoon, H., Scopelliti, I., & Morewedge, C. K. (2021). Decision making can be improved through observational learning. Organizational Behavior and Human Decision Processes, 162, 155–188. https://doi.org/10.1016/j.obhdp.2020.10.011