IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v57y2006i11d10.1057_palgrave.jors.2602142.html
   My bibliography  Save this article

A rule-based risk decision-making approach and its application in China's customs inspection decision

Author

Listed:
  • Z Hua

    (University of Science & Technology of China)

  • S Li

    (University of Science & Technology of China)

  • Z Tao

    (University of Science & Technology of China)

Abstract

This paper addresses a kind of risk decision-making problem existing widely in public administration and business management, which is characterized by (1) occurrence probabilities of states of nature can be estimated by analysing historical observations, but historical observations of different objects are unhomogeneous, (2) the relation between observations and occurrence probabilities of states of nature are affected by some qualitative and quantitative indicators, (3) it is a real-time decision-making problem, that is, there are many decisions for different objects to be made in a limited time, (4) considering decision's execution, impact of resource constrains is an important issue in decision-making process. In this paper, we develop a rule-based approach to address the problem. In the proposed approach, a two-step clustering method is employed to classify objects into categories, and observations in each category can be approximately viewed as homogeneous. For objects in each category, occurrence probabilities of states of nature are estimated by logistic regression, and the decision rule is obtained through solving an optimization model, which is to minimize the total decision risks while satisfying resource constrains. Effect and efficacy of our approach are illustrated through its application to China's customs inspection decision.

Suggested Citation

  • Z Hua & S Li & Z Tao, 2006. "A rule-based risk decision-making approach and its application in China's customs inspection decision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1313-1322, November.
  • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:11:d:10.1057_palgrave.jors.2602142
    DOI: 10.1057/palgrave.jors.2602142
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602142
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/palgrave.jors.2602142?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Francis C. Chu & Joseph Y. Halpern, 2004. "Great expectations. Part II: Generalized expected utility as a universal decision rule," Game Theory and Information 0411004, University Library of Munich, Germany.
    2. K A Smith & R J Willis & M Brooks, 2000. "An analysis of customer retention and insurance claim patterns using data mining: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 532-541, May.
    3. Brian Everitt, 1980. "Cluster analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 14(1), pages 75-100, January.
    4. Francis Chu & Joseph Halpern, 2008. "Great Expectations. Part I: On the Customizability of Generalized Expected Utility," Theory and Decision, Springer, vol. 64(1), pages 1-36, February.
    5. Brian J. Cohen, 1996. "Is Expected Utility Theory Normative for Medical Decision Making?," Medical Decision Making, , vol. 16(1), pages 1-6, February.
    6. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
    7. Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
    8. Gobin, A. & Campling, P. & Feyen, J., 2001. "Logistic modelling to identify and monitor local land management systems," Agricultural Systems, Elsevier, vol. 67(1), pages 1-20, January.
    9. David Lowe & Jamshid Parvar, 2004. "A logistic regression approach to modelling the contractor's decision to bid," Construction Management and Economics, Taylor & Francis Journals, vol. 22(6), pages 643-653.
    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. Ana Margarida Fernandes & Russell Hillberry & Alejandra Mendoza Alcántara, 2021. "Trade Effects of Customs Reform: Evidence from Albania," The World Bank Economic Review, World Bank, vol. 35(1), pages 34-57.

    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. Francis Chu & Joseph Halpern, 2008. "Great Expectations. Part I: On the Customizability of Generalized Expected Utility," Theory and Decision, Springer, vol. 64(1), pages 1-36, February.
    2. Jabbar, Amina M. & Abelson, Julia, 2011. "Development of a framework for effective community engagement in Ontario, Canada," Health Policy, Elsevier, vol. 101(1), pages 59-69, June.
    3. Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    4. Shern, David L. & Trochim, William M. K. & LaComb, Christina A., 1995. "The use of concept mapping for assessing fidelity of model transfer: An example from psychiatric rehabilitation," Evaluation and Program Planning, Elsevier, vol. 18(2), pages 143-153.
    5. Manuel Chaves-Maza & Eugenio M. Fedriani Martel, 2020. "Entrepreneurship support ways after the COVID-19 crisis," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 662-681, December.
    6. Chen, Shihua & Chen, Yulin & Jebran, Khalil, 2021. "Trust and corporate social responsibility: From expected utility and social normative perspective," Journal of Business Research, Elsevier, vol. 134(C), pages 518-530.
    7. Qiao, Yu & Labi, Samuel & Fricker, Jon D., 2021. "Does highway project bundling policy affect bidding competition? Insights from a mixed ordinal logistic model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 228-242.
    8. Lawrence, John D. & Kaylen, Michael S., 1990. "Risk Management For Livestock Producers: Hedging And Contract Production," Staff Papers 13496, University of Minnesota, Department of Applied Economics.
    9. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    10. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    11. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
    12. repec:onb:oenbwp:y:2005:i:9:b:1 is not listed on IDEAS
    13. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    14. Rosas, Scott R. & Ridings, John W., 2017. "The use of concept mapping in measurement development and evaluation: Application and future directions," Evaluation and Program Planning, Elsevier, vol. 60(C), pages 265-276.
    15. Chollete, Loran & Schmeidler, David, 2014. "Demand-Theoretic Approach to Choice of Priors," UiS Working Papers in Economics and Finance 2014/14, University of Stavanger.
    16. Brown, Jason D. & Ivanova, Viktoria & Mehta, Nisha & Skrodzki, Donna & Gerrits, Julie, 2013. "Social needs of aboriginal foster parents," Children and Youth Services Review, Elsevier, vol. 35(11), pages 1886-1893.
    17. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    18. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    19. Chhaya Dubey & Dharmendra Kumar & Ashutosh Kumar Singh & Vijay Kumar Dwivedi, 2024. "Applying machine learning models on blockchain platform selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3643-3656, August.
    20. Guobin Fu & Stephanie R. Clark & Dennis Gonzalez & Rodrigo Rojas & Sreekanth Janardhanan, 2023. "Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    21. Pérez-Campuzano, Darío & Rubio Andrada, Luis & Morcillo Ortega, Patricio & López-Lázaro, Antonio, 2022. "Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance," Journal of Air Transport Management, Elsevier, vol. 101(C).

    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:pal:jorsoc:v:57:y:2006:i:11:d:10.1057_palgrave.jors.2602142. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.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.