IDEAS home Printed from https://ideas.repec.org/p/mal/wpaper/2017-1.html
   My bibliography  Save this paper

Revisiting the Battle of Midway: A counterfactual analysis

Author

Listed:
  • Anelí Bongers

    (Department of Economics, University of Málaga)

  • José L. Torres

    (Department of Economics, University of Málaga)

Abstract

This paper uses a stochastic salvo combat model to study the Battle of Midway. The parameters of the model are calibrated accordingly to the historical outcome and thus, the model can be used to study alternative scenarios. Contrary to the common wisdom that the result of the Battle was an "incredible" American victory, the model shows that the probability for Japanese to win were very low and indeed close to zero. We carry on four alternative counterfactual analyses: (i) All launched American attack aircraft reach to the Japanese carriers; ii) An additional Japanese carrier; iii) Not to wait to launch Japanese attack aircraft; and iv) American carriers spotted earlier. Including the most favorable scenario for the Japanese, the Battle of Midway remains an American victory.

Suggested Citation

  • Anelí Bongers & José L. Torres, 2017. "Revisiting the Battle of Midway: A counterfactual analysis," Working Papers 2017-01, Universidad de Málaga, Department of Economic Theory, Málaga Economic Theory Research Center.
  • Handle: RePEc:mal:wpaper:2017-1
    as

    Download full text from publisher

    File URL: https://theeconomics.uma.es/malagawpseries/Papers/METCwp2017-1.pdf
    File Function: First version, 2017
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wayne P. Hughes, 1995. "A salvo model of warships in missile combat used to evaluate their staying power," Naval Research Logistics (NRL), John Wiley & Sons, vol. 42(2), pages 267-289, March.
    2. David Connors & Michael J. Armstrong & John Bonnett, 2015. "A Counterfactual Study of the Charge of the Light Brigade," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 48(2), pages 80-89, June.
    3. Michael J Armstrong, 2014. "The salvo combat model with a sequential exchange of fire," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(10), pages 1593-1601, October.
    4. Michael J. Armstrong, 2005. "A Stochastic Salvo Model for Naval Surface Combat," Operations Research, INFORMS, vol. 53(5), pages 830-841, October.
    5. Niall MacKay & Christopher Price & A. Jamie Wood, 2016. "Weighing the fog of war: Illustrating the power of Bayesian methods for historical analysis through the Battle of the Dogger Bank," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 49(2), pages 80-91, April.
    Full references (including those not matched with items on IDEAS)

    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. Younglak Shim & Michael P. Atkinson, 2018. "Analysis of artillery shoot‐and‐scoot tactics," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(3), pages 242-274, April.
    2. Michael J. Armstrong, 2007. "Effective attacks in the salvo combat model: Salvo sizes and quantities of targets," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(1), pages 66-77, February.
    3. Michael J. Armstrong, 2013. "The salvo combat model with area fire," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(8), pages 652-660, December.
    4. Kolebaje, Olusola & Popoola, Oyebola & Khan, Muhammad Altaf & Oyewande, Oluwole, 2020. "An epidemiological approach to insurgent population modeling with the Atangana–Baleanu fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Michael J. Armstrong, 2014. "Modeling Short-Range Ballistic Missile Defense and Israel's Iron Dome System," Operations Research, INFORMS, vol. 62(5), pages 1028-1039, October.
    6. Michael Armstrong, 2011. "A verification study of the stochastic salvo combat model," Annals of Operations Research, Springer, vol. 186(1), pages 23-38, June.
    7. Donghyun Kim & Hyungil Moon & Donghyun Park & Hayong Shin, 2017. "An efficient approximate solution for stochastic Lanchester models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1470-1481, November.
    8. Chen Wang & Vicki M. Bier, 2016. "Quantifying Adversary Capabilities to Inform Defensive Resource Allocation," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 756-775, April.
    9. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    10. Chad W. Seagren & Donald P. Gaver & Patricia A. Jacobs, 2019. "A stochastic air combat logistics decision model for Blue versus Red opposition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 663-674, December.
    11. Hans Liwång, 2020. "The interconnectedness between efforts to reduce the risk related to accidents and attacks - naval examples," Journal of Transportation Security, Springer, vol. 13(3), pages 245-272, December.
    12. Claire Walton & Panos Lambrianides & Isaac Kaminer & Johannes Royset & Qi Gong, 2018. "Optimal motion planning in rapid‐fire combat situations with attacker uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(2), pages 101-119, March.
    13. Thomas W. Lucas & John E. McGunnigle, 2003. "When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(3), pages 197-217, April.
    14. Anelí Bongers & José L. Torres, 2021. "A bottleneck combat model: an application to the Battle of Thermopylae," Operational Research, Springer, vol. 21(4), pages 2859-2877, December.
    15. Michael J. Armstrong, 2004. "Effects of lethality in naval combat models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(1), pages 28-43, February.
    16. Michael J. Armstrong & Steven E. Sodergren, 2015. "Refighting Pickett's Charge: Mathematical Modeling of the Civil War Battlefield," Social Science Quarterly, Southwestern Social Science Association, vol. 96(4), pages 1153-1168, December.
    17. Michael J. Armstrong, 2005. "A Stochastic Salvo Model for Naval Surface Combat," Operations Research, INFORMS, vol. 53(5), pages 830-841, October.

    More about this item

    Keywords

    Stochastic salvo combat model; Battle of Midway; Monte Carlo simulation; Counterfactual analysis;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mal:wpaper:2017-1. 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: Ascension Andina (email available below). General contact details of provider: https://edirc.repec.org/data/dtmales.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.