IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v21y2010i1p170-189.html
   My bibliography  Save this article

Induction over Strategic Agents

Author

Listed:
  • Fidan Boylu

    (Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Haldun Aytug

    (Information Systems and Operations Management Department, The Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611)

  • Gary J. Koehler

    (Information Systems and Operations Management Department, The Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611)

Abstract

We study the problem where a decision maker needs to discover a classification rule to classify intelligent, self-interested agents. Agents may engage in strategic behavior to alter their characteristics for a favorable classification. We show how the decision maker can induce a classification rule that anticipates such behavior while still satisfying an important risk minimization principle.

Suggested Citation

  • Fidan Boylu & Haldun Aytug & Gary J. Koehler, 2010. "Induction over Strategic Agents," Information Systems Research, INFORMS, vol. 21(1), pages 170-189, March.
  • Handle: RePEc:inm:orisre:v:21:y:2010:i:1:p:170-189
    DOI: 10.1287/isre.1090.0272
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.1090.0272
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.1090.0272?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
    ---><---

    References listed on IDEAS

    as
    1. Zhengrui Jiang & Vijay S. Mookerjee & Sumit Sarkar, 2005. "Lying on the Web: Implications for Expert Systems Redesign," Information Systems Research, INFORMS, vol. 16(2), pages 131-148, June.
    2. Srinivasan, Venkat & Kim, Yong H, 1987. "Credit Granting: A Comparative Analysis of Classification Procedures," Journal of Finance, American Finance Association, vol. 42(3), pages 665-681, July.
    3. Eisenbeis, Robert A, 1987. "Credit Granting: A Comparative Analysis of Classification Procedures: Discussion," Journal of Finance, American Finance Association, vol. 42(3), pages 681-683, July.
    4. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    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. Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
    2. Vijay Mookerjee & Radha Mookerjee & Alain Bensoussan & Wei T. Yue, 2011. "When Hackers Talk: Managing Information Security Under Variable Attack Rates and Knowledge Dissemination," Information Systems Research, INFORMS, vol. 22(3), pages 606-623, September.
    3. Asunur Cezar & Srinivasan Raghunathan & Sumit Sarkar, 2020. "Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2789-2807, December.
    4. Yuanfeng Cai & Zhengrui Jiang & Vijay Mookerjee, 2017. "How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 268-286, May.
    5. Zhang, Juheng & Aytug, Haldun, 2016. "Comparison of imputation methods for discriminant analysis with strategically hidden data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 522-530.
    6. Juheng Zhang & Xiaoping Liu & Xiao-Bai Li, 2020. "Predictive Analytics with Strategically Missing Data," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1143-1156, October.
    7. Juheng Zhang & Haldun Aytug & Gary J. Koehler, 2014. "Research Note —Discriminant Analysis with Strategically Manipulated Data," Information Systems Research, INFORMS, vol. 25(3), pages 654-662, September.

    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. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    2. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    3. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
    4. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
    5. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
    7. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    8. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    9. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    10. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    11. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    12. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    13. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    14. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    15. repec:hum:wpaper:sfb649dp2013-037 is not listed on IDEAS
    16. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    17. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    18. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    19. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    20. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    21. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.

    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:inm:orisre:v:21:y:2010:i:1:p:170-189. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.