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Neuroscience Applications in Financial Markets: A Practitioner's Perspective

Neuroscience Applications in Financial Markets: A Practitioner's Perspective

Alessia Falsarone
Copyright: © 2018 |Pages: 21
ISBN13: 9781522554783|ISBN10: 1522554785|EISBN13: 9781522554790
DOI: 10.4018/978-1-5225-5478-3.ch024
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MLA

Falsarone, Alessia. "Neuroscience Applications in Financial Markets: A Practitioner's Perspective." Applications of Neuroscience: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2018, pp. 477-497. https://doi.org/10.4018/978-1-5225-5478-3.ch024

APA

Falsarone, A. (2018). Neuroscience Applications in Financial Markets: A Practitioner's Perspective. In I. Management Association (Ed.), Applications of Neuroscience: Breakthroughs in Research and Practice (pp. 477-497). IGI Global. https://doi.org/10.4018/978-1-5225-5478-3.ch024

Chicago

Falsarone, Alessia. "Neuroscience Applications in Financial Markets: A Practitioner's Perspective." In Applications of Neuroscience: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 477-497. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5478-3.ch024

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Abstract

The 2007-2008 Global Financial Crisis provided strong evidence of social dynamics in global financial markets which challenge basic rationality of independent decision makers when choices invoke short term trade-offs as opposed to sustainable long term socio-economic value creation and its ethical implications. Individual market participants are drawn into a Darwinian review of today's decision making environment in which they are called to make choices under systemic uncertainty. Sophisticated visualization tools from fMRI to PET scans allow for brain functions and decision making behaviors to be analyzed in an evolutionary context. From the theory of bounded rationality to its modern critiques by behavioralists, this chapter investigates how social learning may be at its best right at this moment, when ambiguity and known unknowns are extensive and expected utility frameworks are being replaced by local optima findings and “good enough” solutions.

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