Abstract
As cyber threats continue to evolve, there is growing recognition that effective security requires a deeper scientific understanding of the complex drivers of human judgment and decision-making. The emerging field of neuroeconomics, combining economics, psychology, and neuroscience, provides new theoretical frameworks, measurement techniques, and models to elucidate the neural foundations underpinning human motivations and behaviors critical to cyber contexts. This paper reviews key neuroeconomic concepts including dual-process thinking, prospect theory, and social decision neuroscience and highlights their potential for generating new insights and interventions to strengthen cybersecurity. Ethical considerations surrounding neuroeconomic monitoring and potential manipulations also demand careful governance. Overall, neuroeconomic research promises to advance cybersecurity practices and policies by grounding them in realistic neural models of human cognition, emotions, and social dynamics. Careful interdisciplinary collaboration will be key to validating applications and avoiding pitfalls as neuroeconomic tools and theories are integrated into both cybersecurity scholarship and practice. This neuro-cognitive approach represents a compelling frontier with immense opportunities to transform cybersecurity through enhanced appreciation of the human dimension.
Keywords
Neuroeconomics, Cybersecurity, Decision-making, Human factors, Cognitive neuroscience,Metrics
References
- Bokhari, S.H., Wahab, A., Malik, H., Ahmad, J., & Lee, M. (2020). Deep neural network model based on eeg for cybersecurity insider threats detection. Scientific Reports, 10(1), 1-10.
- Borst, J.P., Taatgen, N.A., & Van Rijn, H. (2010). The problem state: A cognitive bottleneck in multitasking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(2), 363-382. https://psycnet.apa.org/doi/10.1037/a0018106
- Brinton Anderson, B., Vance, A., Kirwan, C.B., Eargle, D., & Jenkins, J.L. (2016). How users perceive and respond to security messages: A NeuroIS research agenda and empirical study. European Journal of Information Systems, 25(4), 364-390. https://doi.org/10.1057/ejis.2015.21
- Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. Journal of Economic Literature, 43(1), 9-64. https://doi.org/10.1257/0022051053737843
- Chen, J.Q. (2019). A Strategic Decision-Making Framework in Cyberspace. IGI Global, 12. https://doi.org/10.1037/14322-003
- CISCO. (2008). Data leakage worldwide: The high cost of insider threats.
- Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J.J., Ding, M., & Poeppel, D. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology, 27(9), 1375-1380. https://doi.org/10.1016/j.cub.2017.04.002
- Fehr, E., & Rangel, A. (2011). Neuroeconomic foundations of economic choice—recent advances. Journal of Economic Perspectives, 25(4), 3-30. https://doi.org/10.1257/jep.25.4.3
- Fox, C.R., & Poldrack, R.A. (2009). Prospect theory and the brain. Neuroeconomics, 145-173. https://doi.org/10.1016/B978-0-12-374176-9.00011-7
- Glimcher, P.W., & Fehr, E. (2013). Neuroeconomics: Decision making and the brain. Academic Press.
- Grayot, J.D. (2020). Dual process theories in behavioral economics and neuroeconomics: A critical review. Review of Philosophy and Psychology, 11(1), 105-136. https://doi.org/10.1007/s13164-019-00446-9
- Greene, J.D., & Paxton, J.M. (2009). Patterns of neural activity associated with honest and dishonest moral decisions. Proceedings of the National Academy of Sciences, 106(30), 12506-12511. https://doi.org/10.1073/pnas.0900152106
- Hayashi, Y., & Tahmasbi, N. (2020). Decision-making process underlying bystanders’ helping cyberbullying victims: A behavioral economic analysis of role of social discounting. Computers in human behavior, 104, 106157. https://doi.org/10.1016/j.chb.2019.106157
- Hinson, J.M., Jameson, T.L., & Whitney, P. (2003). Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(2), 298-306. https://psycnet.apa.org/doi/10.1037/0278-7393.29.2.298
- Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C.F. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310(5754), 1680-1683. https://doi.org/10.1126/science.1115327
- Huettel, S.A., Stowe, C.J., Gordon, E.M., Warner, B.T., & Platt, M.L. (2006). Neural signatures of economic preferences for risk and ambiguity. Neuron, 49(5), 765-775. https://doi.org/10.1016/j.neuron.2006.01.024
- IBM. (2019). IBM security report: Cost of a data breach report 2019. Computer Fraud & Security, 2019(8). https://doi.org/10.1016/S1361-3723(19)30081-8
- Jones, O.D., Wagner, A.D., Faigman, D.L., & Raichle, M.E. (2013). Neuroscientists in court. Nature Reviews Neuroscience, 14(10), 730-736. https://doi.org/10.1038/nrn3585
- Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449-1475. https://doi.org/10.1257/000282803322655392
- Kahneman, D., & Egan, P. (2011). Thinking, fast and slow. Farrar, Straus and Giroux, 1.
- Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk Handbook of the Fundamentals of Financial Decision Making, 99-127. https://doi.org/10.1142/9789814417358_0006
- Klucharev, V., Hytönen, K., Rijpkema, M., Smidts, A., & Fernández, G. (2009). Reinforcement learning signal predicts social conformity. Neuron, 61(1), 140-151. https://doi.org/10.1016/j.neuron.2008.11.027
- Kostyuk, N., & Wayne, C. (2021). The microfoundations of state cybersecurity: Cyber risk perceptions and the mass public. Journal of Global Security Studies, 6(2), ogz077. https://doi.org/10.1093/jogss/ogz077
- Krajbich, I., Oud, B., & Fehr, E. (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. American Economic Review, 104(5), 501-506. https://doi.org/10.1257/aer.104.5.501
- Kritika (2024). A review on harmonizing psychological factors into cyber space. International Journal of Scientific Research in Network Security and Communication, 12(2), 11-18
- Kritika, M. (2024). A comprehensive study on navigating neuroethics in Cyberspace. AI and Ethics, 1-8. https://doi.org/10.1007/s43681-024-00486-7
- Kritika. (2023). Demystifying Cyber Crimes. IGI Global.
- Kusev, P., Purser, H., Heilman, R., Cooke, A.J., Van Schaik, P., Baranova, V., Martin, R., & Ayton, P. (2017). Understanding risky behavior: The influence of cognitive, emotional and hormonal factors on decision-making under risk. Frontiers in Psychology, 8, 102. https://doi.org/10.3389/fpsyg.2017.00102
- Lerner, J.S., Li, Y., Valdesolo, P., & Kassam, K.S. (2015). Emotion and decision making. Annual Review of Psychology, 66, 799-823. https://doi.org/10.1146/annurev-psych-010213-115043
- Levin, I.P., McElroy, T., Gaeth, G.J., Hedgcock, W., & Denburg, N.L. (2014). Behavioral and neuroscience methods for studying neuroeconomic processes: What we can learn from framing effects. American Psychological Association, 43–69. https://psycnet.apa.org/doi/10.1037/14322-003
- Margittai, Z., Nave, G., van Wingerden, M., Joëls, M., Schwabe, L., & Kalenscher, T. (2018). Glucocorticoids dissociably modulate prefrontal and hippocampal presynaptic terminals after acute stress. Cerebral Cortex, 28(3), 985-996. https://doi.org/10.1093/cercor/bhx008
- Markovych, I. (2021). Neuroeconomics as a synthesis of economics, psychology and neurobiology.
- McClure, S.M., Laibson, D.I., Loewenstein, G., & Cohen, J.D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503-507. https://doi.org/10.1126/science.1100907
- Moody, G.D., Siponen, M., & Pahnila, S. (2018). Toward a unified model of information security policy compliance. MIS Quarterly, 42(1), 285-311. https://doi.org/10.25300/MISQ/2018/13853
- Mrazek, M.D., Franklin, M.S., Phillips, D.T., Baird, B., & Schooler, J.W. (2013). Mindfulness training improves working memory capacity and GRE performance while reducing mind wandering. Psychological Science, 24(5), 776-781. https://doi.org/10.1177/0956797612459659
- Mueller, S.T., Piper, B.J., Geerken, A.R., Dixon, K.L., Kroliczak, G., Olsen, R.K., & Alsip, C.L. (2010). Sensitivity and specificity of the Impulsive-Premeditated Aggression Scale (IPAS) for classifying impulsive and premeditated aggression. Personality and Individual Differences, 48(3), 279-284. https://doi.org/10.1016/j.paid.2009.10.014
- Nurse, J.R., Buckley, O., Legg, P.A., Goldsmith, M., Creese, S., Wright, G.R., & Whitty, M. (2014). Understanding insider threat: A framework for characterising attacks. IEEE Security and Privacy Workshops, 214-228. https://doi.org/10.1109/SPW.2014.38
- Nurse, J.R., Buckley, O., Legg, P.A., Goldsmith, M., Creese, S., Wright, G.R., & Whitty, M. (2014). Understanding insider threat: A framework for characterising attacks. IEEE Security and Privacy Workshops, IEEE, USA. https://doi.org/10.1109/SPW.2014.38
- Preston, S.D., Buchanan, T.W., Stansfield, R.B., & Bechara, A. (2007). Effects of anticipatory stress on decision making in a gambling task. Behavioral Neuroscience, 121(2), 257-263. https://psycnet.apa.org/doi/10.1037/0735-7044.121.2.257
- Proctor, R.W., & Chen, J. (2015). The role of human factors/ergonomics in the science of security: decision making and action selection in cyberspace. Human factors, 57(5), 721-727. https://doi.org/10.1177/0018720815585906
- Rao, R.P., Parigi, P., Glimcher, P., & Ryan, J. (2018). Biologically inspired strategies for defending against cyberattacks: Resource constraints, strategic reasoning and deception. Cognitive Research: Principles and Implications, 3(1), 1-29.
- Riedl, R., & Javor, A. (2012). The biology of trust: Integrating evidence from genetics, endocrinology, and functional brain imaging. Journal of Neuroscience, Psychology, and Economics, 5(2), 63-91. https://psycnet.apa.org/doi/10.1037/a0026318
- Serra, D. (2021). Decision-making: from neuroscience to neuroeconomics—an overview. Theory and Decision, 91(1), 1-80. https://doi.org/10.1007/s11238-021-09830-3
- Takahashi, T. (2009). Theoretical frameworks for neuroeconomics of intertemporal choice. Journal of Neuroscience, Psychology, and Economics, 2(2), 75. https://psycnet.apa.org/doi/10.1037/a0015463
- Tennison, M.N., & Moreno, J.D. (2012). Neuroscience, ethics, and national security: The state of the art. PLoS Biology, 10(3), e1001289. https://doi.org/10.1371/journal.pbio.1001289
- Teper, R., Zhong, M., & Inzlicht, M. (2015). How emotions shape moral behavior: Some answers (and questions) for the field of moral psychology. Social and Personality Psychology Compass, 9(1), 1-14. https://doi.org/10.1111/spc3.12154
- Thaler, R.H., & Sunstein, C.R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
- Tom, S.M., Fox, C.R., Trepel, C., & Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-518. https://doi.org/10.1126/science.1134239
- Vance, A., Jenkins, J.L., Anderson, B., Bjornn, D.K., & Kirwan, C.B. (2020). Tuning out security warnings: Management Information Systems Research Center, University of Minnesota, MIS Quarterly, 44(2), 355-380.
- Wittmann, B.C., Daw, N.D., Seymour, B., & Dolan, R.J. (2008). Striatal activity underlies novelty-based choice in humans. Neuron, 58(6), 967-973. https://doi.org/10.1016/j.neuron.2008.04.027
- Yang, N., Singh, T., & Johnston, A. (2020). a replication study of user motivation in protecting information security using protection motivation theory and self-determination theory. AIS Transactions on Replication Research, 6(1), 10.
- Yu, R. (2016). Stress potentiates decision biases: A stress induced deliberation-to-intuition (SIDI) model. Neurobiology of Stress, 3, 83-95. https://doi.org/10.1016/j.ynstr.2015.12.006