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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

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Cost functions in economic complexity.

Alessandro Bellina1, Paolo Buttà2, Vito D P Servedio3

  • 1Sapienza University of Rome, Sony Computer Science Laboratories, Centro Ricerche Enrico Fermi, Piazza del Viminale, 1, I-00184 Rome, Italy; , -Rome, Joint Initiative CREF-SONY, Centro Ricerche Enrico Fermi, Via Panisperna 89/A, 00184 Rome, Italy; and Physics Department, P.le A. Moro, 5, I-00185 Rome, Italy.

Physical Review. E
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

This study reinterprets economic complexity algorithms (ECI, EFC) as optimization problems, enhancing their theoretical foundation and computational efficiency. New methods accelerate convergence and identify network vulnerabilities in economic systems.

Related Experiment Videos

Last Updated: May 5, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

11.6K

Area of Science:

  • Network Science
  • Economic Systems Analysis
  • Optimization Theory

Background:

  • Economic complexity algorithms like ECI and EFC reveal hidden capabilities in economies.
  • Existing algorithms lack a unified theoretical framework and efficient computational methods.

Purpose of the Study:

  • To reformulate ECI and EFC as optimization problems.
  • To establish theoretical foundations and improve computational efficiency.
  • To extend applicability to diverse network structures.

Main Methods:

  • Reformulating ECI and EFC using cost functions and optimization.
  • Deriving a novel cost function for EFC, clarifying regularization.
  • Establishing the existence and uniqueness of EFC solutions.
  • Developing a gradient-based update rule for accelerated convergence.

Main Results:

  • ECI computation linked to network Laplacian eigenvectors.
  • Novel cost function for EFC derived, with theoretical guarantees.
  • Gradient-based update rule significantly accelerates convergence.
  • Linkwise energy identifies critical and vulnerable network regions.

Conclusions:

  • Optimization-based reformulation unifies economic complexity with spectral theory and network science.
  • New methods offer computational advantages and practical applications.
  • Energetic framework enhances analysis of economic trade networks.