IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v261y2018i1d10.1007_s10479-017-2610-4.html
   My bibliography  Save this article

Decomposing risk in an exploitation–exploration problem with endogenous termination time

Author

Listed:
  • Francisco Alvarez

    (Universidad Complutense)

Abstract

An expected utility risk averse maximizer must decide on an investment policy for a set of N projects. The budget for investment is fixed and it is allocated to the projects gradually over time by an endogenously determined amount. We allow for simultaneous investments in different projects as well as investments in the same project at different times. The termination time is endogenous. The problem finishes, at latest, when the budget is fully depleted. Our problem has an exploitation versus exploration trade off. There are unknown relevant characteristics of each project that the decision maker only learns by investing in the corresponding project. We analyze the performance of dynamic programming based policies. Particularly, we use differences in the value functions of N single-project problems to construct the opportunity cost of investing in each project. Those opportunity costs drive the investment policy for the N project problem.

Suggested Citation

  • Francisco Alvarez, 2018. "Decomposing risk in an exploitation–exploration problem with endogenous termination time," Annals of Operations Research, Springer, vol. 261(1), pages 45-77, February.
    • Handle: RePEc:spr:annopr:v:261:y:2018:i:1:d:10.1007_s10479-017-2610-4
    DOI: 10.1007/s10479-017-2610-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2610-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2610-4?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. Robert J. Aumann & Roberto Serrano, 2008. "An Economic Index of Riskiness," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 810-836, October.
    2. Esther Frostig & Gideon Weiss, 2016. "Four proofs of Gittins’ multiarmed bandit theorem," Annals of Operations Research, Springer, vol. 241(1), pages 127-165, June.
    3. Stephen E. Chick & Noah Gans, 2009. "Economic Analysis of Simulation Selection Problems," Management Science, INFORMS, vol. 55(3), pages 421-437, March.
    4. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    5. Dimitris Bertsimas & Adam J. Mersereau, 2007. "A Learning Approach for Interactive Marketing to a Customer Segment," Operations Research, INFORMS, vol. 55(6), pages 1120-1135, December.
    6. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    7. Stephen E. Chick & Peter Frazier, 2012. "Sequential Sampling with Economics of Selection Procedures," Management Science, INFORMS, vol. 58(3), pages 550-569, March.
    8. Amman, Hans M. & Kendrick, David A., 1994. "Active learning Monte Carlo results," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 119-124, January.
    9. Jinn-Tsair Teng & Gerald L. Thompson, 1983. "Oligopoly Models for Optimal Advertising When Production Costs Obey a Learning Curve," Management Science, INFORMS, vol. 29(9), pages 1087-1101, September.
    10. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    11. Zi-Lin He & Poh-Kam Wong, 2004. "Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis," Organization Science, INFORMS, vol. 15(4), pages 481-494, August.
    12. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    13. Springborn, Michael R., 2014. "Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 226-242.
    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. Sinha, Kanhaiya K. & Steel, Piers & Saunders, Chad & Fariborzi, Hadi, 2023. "Balancing entrepreneurial and learning orientations: A meta-analytic approach to understanding performance variability," Journal of Business Venturing Insights, Elsevier, vol. 20(C).

    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. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    2. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    3. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    4. Son K. Lam & Thomas E. DeCarlo & Ashish Sharma, 2019. "Salesperson ambidexterity in customer engagement: do customer base characteristics matter?," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 659-680, July.
    5. Zhang, Feng & Jiang, Guohua & Cantwell, John A., 2015. "Subsidiary exploration and the innovative performance of large multinational corporations," International Business Review, Elsevier, vol. 24(2), pages 224-234.
    6. Mammassis, Constantinos S. & Kostopoulos, Konstantinos C., 2019. "CEO goal orientations, environmental dynamism and organizational ambidexterity: An investigation in SMEs," European Management Journal, Elsevier, vol. 37(5), pages 577-588.
    7. Hu, Jing & Wang, Yilin & Liu, Shengnan & Song, Mingshun, 2023. "Mechanism of latecomer enterprises’ technological catch-up in technical standards alliances – An ambidextrous innovation perspective," Journal of Business Research, Elsevier, vol. 154(C).
    8. Pino G. Audia & Jack A. Goncalo, 2007. "Past Success and Creativity over Time: A Study of Inventors in the Hard Disk Drive Industry," Management Science, INFORMS, vol. 53(1), pages 1-15, January.
    9. M. M. Sulphey, 2019. "Could the Adoption of Organizational Ambidexterity Have Changed the History of Nokia?," South Asian Journal of Business and Management Cases, , vol. 8(2), pages 167-181, August.
    10. Centobelli, Piera & Cerchione, Roberto & Esposito, Emilio & Shashi,, 2019. "Exploration and exploitation in the development of more entrepreneurial universities: A twisting learning path model of ambidexterity," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 172-194.
    11. Flemming Sørensen & Jan Mattsson, 2016. "Speeding Up Innovation: Building Network Structures For Parallel Innovation," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 1-30, February.
    12. Dante I. Leyva-de la Hiz & J. Alberto Aragon-Correa & Andrew G. Earle, 2022. "Innovating for Good in Opportunistic Contexts: The Case for Firms’ Environmental Divergence," Journal of Business Ethics, Springer, vol. 176(4), pages 705-721, April.
    13. Archibugi, Daniele & Filippetti, Andrea & Frenz, Marion, 2013. "Economic crisis and innovation: Is destruction prevailing over accumulation?," Research Policy, Elsevier, vol. 42(2), pages 303-314.
    14. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    15. Frank T. Rothaermel & Maria Tereza Alexandre, 2009. "Ambidexterity in Technology Sourcing: The Moderating Role of Absorptive Capacity," Organization Science, INFORMS, vol. 20(4), pages 759-780, August.
    16. Schnellbaecher, Benedikt & Diefenbach, Ulf & Millemann, Jan, 2015. "Putting the Individual in Ambidexterity – Identifying Activities to Achieve Organisational Ambidexterity and Long-Term Survival," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 57-63, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    17. Jiewei Zu & Jianan Wang & Jun Ma, 2022. "Ambidexterity in a Rapidly Changing Environment of China: Top Management Team Decision Making and Sustained Performance," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    18. Yuk, Hyeyeon & Garrett, Tony C., 2023. "Does customer participation moderate the effects of innovation on cost-based financial performance? An examination of different forms of customer participation," Journal of Business Research, Elsevier, vol. 156(C).
    19. Ayda Amniattalab & Reza Ansari, 2016. "The Effect Of Strategic Foresight On Competitive Advantage With The Mediating Role Of Organisational Ambidexterity," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-18, April.
    20. Huang, Minjie & Kubick, Thomas R. & Tseng, Kevin, 2021. "Technology spillovers and the duration of executive compensation," Journal of Banking & Finance, Elsevier, vol. 131(C).

    More about this item

    Keywords

    Exploitation–exploration; Dynamic programming; Risk;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    Statistics

    Access and download statistics

    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:spr:annopr:v:261:y:2018:i:1:d:10.1007_s10479-017-2610-4. 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.springer.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.