Table of Contents

Risk Related Brain Regions Detected with 3D Image FPCA

Ying Chen, National University of Singapore (NUS)
Wolfgang K. Härdle, Humboldt University of Berlin - Institute for Statistics and Econometrics, Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)
Qiang He, National University of Singapore (NUS) - Department of Statistics and Applied Probability
Piotr Majer, Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Commentary on Neuro-Economic Modeling

Jyoti Satpathy, Defence Research & Development Organization (DRDO)
Sasmita Mishra, Kalinga Institute of Industrial Technology (KIIT University) - School of Management


NEUROECONOMICS eJOURNAL

"Risk Related Brain Regions Detected with 3D Image FPCA" Free Download

YING CHEN, National University of Singapore (NUS)
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WOLFGANG K. HÄRDLE, Humboldt University of Berlin - Institute for Statistics and Econometrics, Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)
Email:
QIANG HE, National University of Singapore (NUS) - Department of Statistics and Applied Probability
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PIOTR MAJER, Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)
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Risk attitude and perception is reflected in brain reactions during RPID experiments. Given the fMRI data, an important research question is how to detect risk related regions and to investigate the relation between risk preferences and brain activity. Conventional methods are often insensitive to or misrepresent the original spatial patterns and interdependence of the fMRI data. In order to cope with this fact we propose a 3D Image Functional Principal Component Analysis (3D Image FPCA) method that directly converts the brain signals to fundamental spatial common factors and subject-specific temporal factor loadings via proper orthogonal decomposition. Simulation study and real data analysis show that the 3D Image FPCA method improves the quality of spatial representations and guarantees the contiguity of risk related regions. The selected regions provide signature scores and carry explanatory power for subjects' risk attitudes. For in-sample analysis, the 3D Image method perfectly classifies both strongly and weakly risk averse subjects. In out-of-sample, it achieves 73-88% overall accuracy, with 90-100% rate for strongly risk averse subjects, and 49-71% for weakly risk averse subjects.

"Commentary on Neuro-Economic Modeling" 

JYOTI SATPATHY, Defence Research & Development Organization (DRDO)
Email:
SASMITA MISHRA, Kalinga Institute of Industrial Technology (KIIT University) - School of Management
Email:

Traditional interpretation has relied on revealed preferences and (sometimes) on verbal reports to understand desires of individuals and predict actions. Neuropsycho-economics adds a third method; direct observation of underlying mechanisms leading to choices. Neuroeconomic Model makes one extra step. It uses evidence on brain activity to build models that explain and predict observed behaviors. The advantages are; first, bounded rationality can be modeled in a number of ways. This provides precise guidelines vis-à-vis constraints that should be imposed in decision-making processes. Second, by explicitly modeling strategic interaction between distinct brain systems, it is possible to provide micro foundations for some aspects of preferences traditionally considered exogenous. Theoretical brain models provide new testable implications about functionality of brain systems and their relative importance in different aspects of choice process. Preference-based approach poses axioms on (non-observable) tastes, while choice-based approach defines properties of (observable) choices. As long as rational paradigm holds, consistent choices correspond to rational preferences. If focus is on choices and renounces to draw inferences about preferences, models are bound to be limited to situations in which all possible choices are observed. If, instead, researcher draws inferences about preferences from observation of choices, modeling becomes speculative. This paper proposes is to use not only information on choices but on brain mechanisms that lead to those choices.

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About this eJournal

This eJournal distributes working and accepted paper abstracts focused on research where economic outcomes are the product of many individual decisions, constrained by scarcity, and equilibrium forces that simultaneously shape a person's social networks and the institutionally defined rules of the game. Decisions are made by computations in the brain which produce action-choices that directly affect the homeostatic wellbeing of the individual and choices that indirectly change wellbeing by changing an individual's future constraints, the scope of their social networks, and their message sending rights within the institutions they participate. Neuroeconomics broadly speaking is interested in the study of these computations and the resulting choices they produce. This includes experiments that attempt to understand the mechanisms of neuronal computations that produce action-choices, theories which predict how neuronal computations in socio-economic environments produce decisions, outcomes and wellbeing, and policy which use our understanding of neuoroeconomic behavior to either build or defend better solutions to societal problems.

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Neuroeconomics eJournal

ANDREW W. LO
Harris & Harris Group Professor, Massachusetts Institute of Technology (MIT) - Sloan School of Management, Principal Investigator, Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL), National Bureau of Economic Research (NBER)

P. READ MONTAGUE
Professor, Baylor University - Department of Neuroscience

VERNON L. SMITH
Professor of Economics and Law, Chapman University - Economic Science Institute, Chapman University School of Law