IDEAS home Printed from https://ideas.repec.org/a/kap/expeco/v25y2022i1d10.1007_s10683-021-09714-x.html
   My bibliography  Save this article

Network defense and behavioral biases: an experimental study

Author

Listed:
  • Daniel Woods

    (Purdue University)

  • Mustafa Abdallah

    (Purdue University)

  • Saurabh Bagchi

    (Purdue University)

  • Shreyas Sundaram

    (Purdue University)

  • Timothy Cason

    (Purdue University)

Abstract

How do people distribute defenses over a directed network attack graph, where they must defend a critical node? This question is of interest to computer scientists, information technology and security professionals. Decision-makers are often subject to behavioral biases that cause them to make sub-optimal defense decisions, which can prove especially costly if the critical node is an essential infrastructure. We posit that non-linear probability weighting is one bias that may lead to sub-optimal decision-making in this environment, and provide an experimental test. We find support for this conjecture, and also identify other empirically important forms of biases such as naive diversification and preferences over the spatial timing of the revelation of an overall successful defense. The latter preference is related to the concept of anticipatory feelings induced by the timing of the resolution of uncertainty.

Suggested Citation

  • Daniel Woods & Mustafa Abdallah & Saurabh Bagchi & Shreyas Sundaram & Timothy Cason, 2022. "Network defense and behavioral biases: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 254-286, February.
  • Handle: RePEc:kap:expeco:v:25:y:2022:i:1:d:10.1007_s10683-021-09714-x
    DOI: 10.1007/s10683-021-09714-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10683-021-09714-x
    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/s10683-021-09714-x?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bloch, Francis & Dutta, Bhaskar & Dziubiński, Marcin, 2020. "A game of hide and seek in networks," Journal of Economic Theory, Elsevier, vol. 190(C).
    2. Subhasish M Chowdhury & Dan Kovenock & David Rojo Arjona & Nathaniel T Wilcox, 2021. "Focality and Asymmetry in Multi-Battle Contests," The Economic Journal, Royal Economic Society, vol. 131(636), pages 1593-1619.
    3. Dan Kovenock & Brian Roberson & Roman M. Sheremeta, 2019. "The attack and defense of weakest-link networks," Public Choice, Springer, vol. 179(3), pages 175-194, June.
    4. Subhasish Chowdhury & Dan Kovenock & Roman Sheremeta, 2013. "An experimental investigation of Colonel Blotto games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 52(3), pages 833-861, April.
    5. Deck, Cary & Sheremeta, Roman, 2012. "Fight or Flight?," MPRA Paper 52130, University Library of Munich, Germany.
    6. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    7. Mohammad E. Nikoofal & Jun Zhuang, 2012. "Robust Allocation of a Defensive Budget Considering an Attacker's Private Information," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 930-943, May.
    8. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," NBER Working Papers 25072, National Bureau of Economic Research, Inc.
    9. Vicki Bier & Santiago Oliveros & Larry Samuelson, 2007. "Choosing What to Protect: Strategic Defensive Allocation against an Unknown Attacker," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 9(4), pages 563-587, August.
    10. Thomas Epper & Helga Fehr-Duda, 2012. "The missing link: unifying risk taking and time discounting," ECON - Working Papers 096, Department of Economics - University of Zurich, revised Oct 2018.
    11. M.‐Elisabeth Paté‐Cornell & Marshall Kuypers & Matthew Smith & Philip Keller, 2018. "Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies," Risk Analysis, John Wiley & Sons, vol. 38(2), pages 226-241, February.
    12. Dan Kovenock & Brian Roberson, 2018. "The Optimal Defense Of Networks Of Targets," Economic Inquiry, Western Economic Association International, vol. 56(4), pages 2195-2211, October.
    13. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    14. Andrew Caplin & John Leahy, 2001. "Psychological Expected Utility Theory and Anticipatory Feelings," The Quarterly Journal of Economics, Oxford University Press, vol. 116(1), pages 55-79.
    15. Sanjeev Goyal & Adrien Vigier, 2014. "Attack, Defence, and Contagion in Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1518-1542.
    16. Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    17. Syngjoo Choi & Jeongbin Kim & Eungik Lee & Jungmin Lee, 2022. "Probability Weighting and Cognitive Ability," Management Science, INFORMS, vol. 68(7), pages 5201-5215, July.
    18. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    19. Callen, Mike & Isaqzadeh, Mohammad & Long, James D. & Sprenger, Charles, 2014. "Violence and risk preference: experimental evidence from Afghanistan," LSE Research Online Documents on Economics 102932, London School of Economics and Political Science, LSE Library.
    20. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    21. Sheremeta, Roman, 2018. "The Attack and Defense Games," MPRA Paper 95747, University Library of Munich, Germany.
    22. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    23. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    24. Michael Callen & Mohammad Isaqzadeh & James D. Long & Charles Sprenger, 2014. "Violence and Risk Preference: Experimental Evidence from Afghanistan," American Economic Review, American Economic Association, vol. 104(1), pages 123-148, January.
    25. Tomomi Tanaka & Colin F. Camerer & Quang Nguyen, 2010. "Risk and Time Preferences: Linking Experimental and Household Survey Data from Vietnam," American Economic Review, American Economic Association, vol. 100(1), pages 557-571, March.
    26. Acemoglu, Daron & Malekian, Azarakhsh & Ozdaglar, Asu, 2016. "Network security and contagion," Journal of Economic Theory, Elsevier, vol. 166(C), pages 536-585.
    27. Andrew Caplin & John Leahy, 2004. "The supply of information by a concerned expert," Economic Journal, Royal Economic Society, vol. 114(497), pages 487-505, July.
    28. Wu, Di & Xiao, Hui & Peng, Rui, 2018. "Object defense with preventive strike and false targets," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 76-80.
    29. Curley, Shawn P. & Yates, J. Frank & Abrams, Richard A., 1986. "Psychological sources of ambiguity avoidance," Organizational Behavior and Human Decision Processes, Elsevier, vol. 38(2), pages 230-256, October.
    30. Fehr-Duda, Helga & Epper, Thomas & Bruhin, Adrian & Schubert, Renate, 2011. "Risk and rationality: The effects of mood and decision rules on probability weighting," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 14-24, April.
    31. Chen, Daniel L. & Schonger, Martin & Wickens, Chris, 2016. "oTree—An open-source platform for laboratory, online, and field experiments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 88-97.
    32. McBride, Michael & Hewitt, David, 2013. "The enemy you can’t see: An investigation of the disruption of dark networks," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 32-50.
    33. Richard H. Thaler & Shlomo Benartzi, 2001. "Naive Diversification Strategies in Defined Contribution Saving Plans," American Economic Review, American Economic Association, vol. 91(1), pages 79-98, March.
    34. Kosfeld Michael, 2004. "Economic Networks in the Laboratory: A Survey," Review of Network Economics, De Gruyter, vol. 3(1), pages 1-23, March.
    35. Frechette, Guillaume R. & Schotter, Andrew (ed.), 2015. "Handbook of Experimental Economic Methodology," OUP Catalogue, Oxford University Press, number 9780195328325.
    36. Dziubiński, Marcin Konrad & Goyal, Sanjeev, 2017. "How do you defend a network?," Theoretical Economics, Econometric Society, vol. 12(1), January.
    37. Quang Nguyen & Colin Camerer & Tomomi Tanaka, 2010. "Risk and Time Preferences Linking Experimental and Household Data from Vietnam," Post-Print halshs-00547090, HAL.
    38. Adrian Bruhin & Helga Fehr-Duda & Thomas Epper, 2010. "Risk and Rationality: Uncovering Heterogeneity in Probability Distortion," Econometrica, Econometric Society, vol. 78(4), pages 1375-1412, July.
    39. repec:oup:restud:v:81:y:2014:i:4:p:1518-1542. is not listed on IDEAS
    40. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    41. Goeree, Jacob K. & Holt, Charles A. & Palfrey, Thomas R., 2003. "Risk averse behavior in generalized matching pennies games," Games and Economic Behavior, Elsevier, vol. 45(1), pages 97-113, October.
    42. Loewenstein, George, 1987. "Anticipation and the Valuation of Delayed Consumption," Economic Journal, Royal Economic Society, vol. 97(387), pages 666-684, September.
    43. Djawadi, Behnud Mir & Endres, Angelika & Hoyer, Britta & Recker, Sonja, 2019. "Network formation and disruption - An experiment are equilibrium networks too complex?," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 708-734.
    44. Britta Hoyer & Stephanie Rosenkranz, 2018. "Determinants of Equilibrium Selection in Network Formation: An Experiment," Games, MDPI, vol. 9(4), pages 1-25, November.
    45. Dziubiński, Marcin & Goyal, Sanjeev, 2013. "Network design and defence," Games and Economic Behavior, Elsevier, vol. 79(C), pages 30-43.
    46. Helga Fehr-Duda & Manuele Gennaro & Renate Schubert, 2006. "Gender, Financial Risk, and Probability Weights," Theory and Decision, Springer, vol. 60(2), pages 283-313, May.
    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. Dan Kovenock & Brian Roberson & Roman M. Sheremeta, 2019. "The attack and defense of weakest-link networks," Public Choice, Springer, vol. 179(3), pages 175-194, June.
    2. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    3. Vincent Laferrière & David Staubli & Christian Thöni, 2023. "Explaining Excess Entry in Winner-Take-All Markets," Management Science, INFORMS, vol. 69(2), pages 1050-1069, February.
    4. Anthony Newell, 2020. "Is your heart weighing down your prospects? Interoception, risk literacy and prospect theory," QuBE Working Papers 058, QUT Business School.
    5. Tamás Csermely & Alexander Rabas, 2016. "How to reveal people’s preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 107-136, December.
    6. Ola Andersson & Håkan J. Holm & Jean-Robert Tyran & Erik Wengström, 2020. "Robust inference in risk elicitation tasks," Journal of Risk and Uncertainty, Springer, vol. 61(3), pages 195-209, December.
    7. Mary Riddel, 2012. "Comparing risk preferences over financial and environmental lotteries," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 135-157, October.
    8. Bruhin, Adrian & Santos-Pinto, Luís & Staubli, David, 2018. "How do beliefs about skill affect risky decisions?," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 350-371.
    9. Thomas Epper & Helga Fehr-Duda & Adrian Bruhin, 2011. "Viewing the future through a warped lens: Why uncertainty generates hyperbolic discounting," Journal of Risk and Uncertainty, Springer, vol. 43(3), pages 169-203, December.
    10. Heutel, Garth, 2019. "Prospect theory and energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 236-254.
    11. Dan Kovenock & Brian Roberson, 2018. "The Optimal Defense Of Networks Of Targets," Economic Inquiry, Western Economic Association International, vol. 56(4), pages 2195-2211, October.
    12. Kairies-Schwarz, Nadja & Kokot, Johanna & Vomhof, Markus & Weßling, Jens, 2017. "Health insurance choice and risk preferences under cumulative prospect theory – an experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 374-397.
    13. Holden , Stein T. & Tilahun , Mesfin, 2019. "The Devil is in the Details: Risk Preferences, Choice List Design, and Measurement Error," CLTS Working Papers 3/19, Norwegian University of Life Sciences, Centre for Land Tenure Studies, revised 16 Oct 2019.
    14. Lucy F. Ackert & Richard Deaves & Jennifer Miele & Quang Nguyen, 2020. "Are Time Preference and Risk Preference Associated with Cognitive Intelligence and Emotional Intelligence?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(2), pages 136-156, April.
    15. Maren Baars & Michael Goedde‐Menke, 2022. "Ignorance illusion in decisions under risk: The impact of perceived expertise on probability weighting," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(1), pages 35-62, March.
    16. Anna Conte & Peter G Moffatt & Mary Riddel, 2019. "The Multivariate Random Preference Estimatorfor Switching Multiple Price List Data," University of East Anglia School of Economics Working Paper Series 2019-04, School of Economics, University of East Anglia, Norwich, UK..
    17. Andreas C. Drichoutis & Rodolfo M. Nayga, 2022. "On the stability of risk and time preferences amid the COVID-19 pandemic," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 759-794, June.
    18. Anna Conte & Peter G. Moffatt & Mary Riddel, 2015. "Heterogeneity in risk attitudes across domains: A bivariate random preference approach," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS) 15-10, School of Economics, University of East Anglia, Norwich, UK..
    19. Anwesha Bandyopadhyay & Lutfunnahar Begum & Philip J. Grossman, 2021. "Gender differences in the stability of risk attitudes," Journal of Risk and Uncertainty, Springer, vol. 63(2), pages 169-201, October.
    20. Freudenreich, Hanna & Musshoff, Oliver, 2022. "Experience of losses and aversion to uncertainty - experimental evidence from farmers in Mexico," Ecological Economics, Elsevier, vol. 195(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:kap:expeco:v:25:y:2022:i:1:d:10.1007_s10683-021-09714-x. 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.