Decision-Making Mechanism under Economic Management Risk Based on Event-related Potential Neuroimaging Technique

Abstract

This paper aims to gain more insights into risk-based decision-making of the brain. For this purpose, the author decided to adopt such methods as electroencephalography (EEG) experiment, literature review and hypothesis testing. Based on the neuroscience for decision-making, the mechanism of management risk-based decision-making was investigated under different economic risks. Then, the "economic management risk-based decision-making" hypothesis was presented in light of the mathematical model for risk-based decision-making and the neuroscience for decision-making. After that, the author prepared the detailed design of an experiment and conducted preexperiments. The high-time resolution event-related potential (ERP) technique was adopted to capture the signal processing procedure and neural action pattern in the brain in different risk scenarios. Through the analysis of the experimental data, it is discovered that the brain's decision-making under economic management risk demonstrates the decision-maker's adaptive choice. The research findings provide valuable reference for the decision-making of enterprise managers.

A Decision Model for Public Economic Environment Monitoring and Management Decision Using Improved Ant Colony Algorithm

“…Recent developments in the disciplines of environmental quality assessment, weather forecasting, economic management, and teaching process evaluation have given rise to a type of fuzzy mathematics technique known as the decision model. However, with continued use in complex systems like the economy and society, problems' complexity, and unpredictability as well as the ambiguity of human thought continue to grow, making it challenging for individuals to evaluate them with objectivity and come to judgments [ 4 ]. Any economic management decision-making issue, regardless of how straightforward or difficult, must meet the following four sorts of criteria: technologically sophisticated, economically rational, implementable, and policy-permitting.…”

Human brain and financial behavior: a neurofinance perspective

“…Brain structures can be studied with various neuroimaging techniques. The methods to measure the brain activity are diverse and involve neuroimaging techniques such as magnetoencephalography (MEG) (Beugré, 2009; Braeutigam, 2005; Goto et al , 2017; Kenning and Plassmann, 2005; Rooij and Orden, 2011; Solnais et al , 2013), fMRI (Coricelli and Nagel, 2009; Harlé and Sanfey, 2012; Lusk et al , 2015; Marota et al , 2000), positron emission tomography (PET) (Charron et al , 2008a; Zak, 2018), electroencephalography (EEG) (Charron et al , 2008a, 2008b; da Rocha et al , 2013; Goto et al , 2017; Kasabov, 2014; Sundararajan et al , 2017; Takahashi, 2012; Zhang, 2018), computed tomography (CT) scans (Krueger et al , 2009). These methods are a way to explore the neural underpinnings of financial decision making.…”

Machine learning‐based classifying of risk‐takers and risk‐aversive individuals using resting‐state EEG data: A pilot feasibility study

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