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Related Experiment Video

Updated: Jun 24, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Randomized shortest-path problems: two related models.

Marco Saerens1, Youssef Achbany, François Fouss

  • 1Information Systems Unit and Machine Learning Group, Université catholique de Louvain, Louvain-la Neuve B-1348, Belgium. marco.saerens@uclouvain.be

Neural Computation
|March 28, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a randomized shortest-path (RSP) algorithm to balance routing costs and network exploration. The method optimizes transition probabilities for Markov chains, ensuring predictable randomness and efficient routing.

Related Experiment Videos

Last Updated: Jun 24, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Operations Research
  • Network Science
  • Statistical Physics

Background:

  • Traditional shortest-path algorithms use deterministic routing.
  • Deterministic routing can lead to predictability and congestion.
  • There's a need for routing policies that balance cost minimization with controlled randomness.

Purpose of the Study:

  • To develop a framework for the randomized shortest-path (RSP) problem.
  • To minimize expected routing costs while maintaining a target network entropy level.
  • To explore the connection between RSP and existing transportation models.

Main Methods:

  • Formulated the RSP problem using finite Markov chains and transition probabilities.
  • Derived necessary conditions for optimal randomized policies, iterating them via Bellman's value iteration.
  • Revisited Akamatsu's model using a sum-over-paths statistical physics formalism.

Main Results:

  • Developed an iterative method to compute optimal randomized policies.
  • Demonstrated equivalence between the first RSP model and Akamatsu's model under specific entropy constraints.
  • Showcased a more computationally efficient method for Akamatsu's model using linear systems.

Conclusions:

  • The proposed RSP framework effectively balances routing cost and exploration.
  • The statistical physics approach offers an elegant and unified method for analysis.
  • The second model provides a computationally efficient and sound solution for randomized routing.