IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i21p8758-d432755.html
   My bibliography  Save this article

Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Author

Listed:
  • Junyi Wu

    (Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan)

  • Shari Shang

    (Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan)

Abstract

Artificial intelligence (AI) has been applied to various decision-making tasks. However, scholars have yet to comprehend how computers can integrate decision making with uncertainty management. Obtaining such comprehension would enable scholars to deliver sustainable AI decision-making applications that adapt to the changing world. This research examines uncertainties in AI-enabled decision-making applications and some approaches for managing various types of uncertainty. By referring to studies on uncertainty in decision making, this research describes three dimensions of uncertainty, namely informational, environmental and intentional. To understand how to manage uncertainty in AI-enabled decision-making applications, the authors conduct a literature review using content analysis with practical approaches. According to the analysis results, a mechanism related to those practical approaches is proposed for managing diverse types of uncertainty in AI-enabled decision making.

Suggested Citation

  • Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8758-:d:432755
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/21/8758/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/21/8758/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    2. Warren E. Walker & Marjolijn Haasnoot & Jan H. Kwakkel, 2013. "Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty," Sustainability, MDPI, vol. 5(3), pages 1-25, March.
    3. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
    4. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    5. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    6. James G. March, 1978. "Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 587-608, Autumn.
    7. George P. Huber, 1991. "Organizational Learning: The Contributing Processes and the Literatures," Organization Science, INFORMS, vol. 2(1), pages 88-115, February.
    8. Nissan-Rozen, Ittay, 2015. "Against Moral Hedging," Economics and Philosophy, Cambridge University Press, vol. 31(3), pages 349-369, November.
    9. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    10. Lipshitz, Raanan & Strauss, Orna, 1997. "Coping with Uncertainty: A Naturalistic Decision-Making Analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(2), pages 149-163, February.
    11. John Rust, 2019. "Has Dynamic Programming Improved Decision Making?," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 833-858, August.
    12. T.K. Das & Bing‐Sheng Teng, 1999. "Cognitive Biases and Strategic Decision Processes: An Integrative Perspective," Journal of Management Studies, Wiley Blackwell, vol. 36(6), pages 757-778, November.
    13. Willcock, Simon & Martínez-López, Javier & Hooftman, Danny A.P. & Bagstad, Kenneth J. & Balbi, Stefano & Marzo, Alessia & Prato, Carlo & Sciandrello, Saverio & Signorello, Giovanni & Voigt, Brian & , 2018. "Machine learning for ecosystem services," Ecosystem Services, Elsevier, vol. 33(PB), pages 165-174.
    14. James Love-Koh & Alison Peel & Juan Carlos Rejon-Parrilla & Kate Ennis & Rosemary Lovett & Andrea Manca & Anastasia Chalkidou & Hannah Wood & Matthew Taylor, 2018. "The Future of Precision Medicine: Potential Impacts for Health Technology Assessment," PharmacoEconomics, Springer, vol. 36(12), pages 1439-1451, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Da Wan & Xiaoyu Zhao & Wanmei Lu & Pengbo Li & Xinyu Shi & Hiroatsu Fukuda, 2022. "A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    2. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anindya Ghosh & Thomas Klueter, 2022. "The Role of Frictions due to Top Management in Alliance Termination Decisions: Insights from Established Bio‐Pharmaceutical Firms," Journal of Management Studies, Wiley Blackwell, vol. 59(5), pages 1315-1353, July.
    2. Michel Barabel & Olivier Meier, 2002. "Biais cognitifs du dirigeant, conséquences et facteurs de renforcement lors de fusions-acquisitions:synthèse et illustrations," Revue Finance Contrôle Stratégie, revues.org, vol. 5(1), pages 5-42, March.
    3. Istipliler, Baris & Ahrens, Jan-Philipp & Bort, Suleika & Isaak, Andrew, 2023. "Is exposure to the family firm always good for the next CEO? How successor pre-succession firm experience affects post-succession performance in family firms," Journal of Business Research, Elsevier, vol. 167(C).
    4. Lindner, Thomas & Puck, Jonas & Stocco, Giulia, 2023. "Asymmetric risk perception and firm financing in the institutional envelope," International Business Review, Elsevier, vol. 32(3).
    5. Ghosh, Anindya & Klueter, Thomas, 2022. "The role of frictions due to top management in alliance termination decisions: Insights from established bio-pharmaceutical firms," Other publications TiSEM 9faa19d2-d1a8-4490-befb-d, Tilburg University, School of Economics and Management.
    6. Randall, M. & Montgomery, J. & Lewis, A., 2022. "Robust temporal optimisation for a crop planning problem under climate change uncertainty," Operations Research Perspectives, Elsevier, vol. 9(C).
    7. Thunström, Linda & Nordström, Jonas & Shogren, Jason F., 2015. "Certainty and overconfidence in future preferences for food," Journal of Economic Psychology, Elsevier, vol. 51(C), pages 101-113.
    8. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    9. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    10. Sylvie Héroux & Mélanie Roussy, 2020. "Three cases of compliance with governance regulation: an organizational learning perspective," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 24(2), pages 449-479, June.
    11. Andrea Francesconi & Enrico Guarini, 2017. "Performance-based funding e sistemi di allocazione delle risorse ai dipartimenti: prime evidenze nelle universit? italiane," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2017(1), pages 113-134.
    12. Pooja Kushwaha & M. K. Rao, 2017. "Integrating the Linkages between Learning Systems and Knowledge Process: An Exploration of Learning Outcomes," Business Perspectives and Research, , vol. 5(1), pages 11-23, January.
    13. Freeman, Steven F., 1997. "Good decisions : reconciling human rationality, evolution, and ethics," Working papers WP 3962-97., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    14. van Riel, A.C.R. & Lievens, A., 2003. "New service development in high tech sectors: a decision making perspective," Research Memorandum 013, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    15. Nicolai J. Foss, 1996. "Firms, Incomplete Contracts and Organizational Learning," DRUID Working Papers 96-2, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    16. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    17. Edna Isabel de la Garza Martinez & Zochitl Araiza Garza & Nidia Estela Hernández Castro & Izamar Guadalupe Amador Charles, 2016. "Relationship Between Levels Of Organization Learning,Relacion Entre Los Niveles De Aprendizaje Organizacional," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 9(3), pages 71-82.
    18. Schweizer, Lars & Patzelt, Holger, 2012. "Employee commitment in the post-acquisition integration process: The effect of integration speed and leadership," Scandinavian Journal of Management, Elsevier, vol. 28(4), pages 298-310.
    19. Pascale Amans & Sylvie Rascol-Boutard, 2006. "Controlling Complex Organizations on the Basis of an Operational Performance Measure," Post-Print hal-01659071, HAL.
    20. Moore, Don A., 1999. "Order Effects in Preference Judgments: Evidence for Context Dependence in the Generation of Preferences, ," Organizational Behavior and Human Decision Processes, Elsevier, vol. 78(2), pages 146-165, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8758-:d:432755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.