Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Continuous-Time Distributed Learning for Collective Wisdom Maximization

Baković, Luka LU ; Como, Giacomo LU ; Fagnani, Fabio ; Proskurnikov, Anton and Tegling, Emma LU orcid (2025) 64th IEEE Conference on Decision and Control, CDC 2025 p.4281-4286
Abstract

Motivated by the well established idea that collective wisdom is greater than that of an individual, we propose a novel learning dynamics as a sort of companion to the Abelson model of opinion dynamics. Agents are assumed to make independent guesses about the true state of the world after which they engage in opinion exchange leading to consensus. We investigate the problem of finding the optimal parameters for this exchange, e.g. those that minimize the variance of the consensus value. Specifically, the parameter we examine is susceptibility to opinion change. We propose a dynamics for distributed learning of the optimal parameters and analytically show that it converges for all relevant initial conditions by linking to well... (More)

Motivated by the well established idea that collective wisdom is greater than that of an individual, we propose a novel learning dynamics as a sort of companion to the Abelson model of opinion dynamics. Agents are assumed to make independent guesses about the true state of the world after which they engage in opinion exchange leading to consensus. We investigate the problem of finding the optimal parameters for this exchange, e.g. those that minimize the variance of the consensus value. Specifically, the parameter we examine is susceptibility to opinion change. We propose a dynamics for distributed learning of the optimal parameters and analytically show that it converges for all relevant initial conditions by linking to well established results from consensus theory. Lastly, a numerical example provides intuition on both system behavior and our proof methods.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2025 IEEE 64th Conference on Decision and Control, CDC 2025
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
64th IEEE Conference on Decision and Control, CDC 2025
conference location
Rio de Janeiro, Brazil
conference dates
2025-12-09 - 2025-12-12
external identifiers
  • scopus:105031908896
ISBN
9798331526276
DOI
10.1109/CDC57313.2025.11312017
language
English
LU publication?
yes
id
39adfed8-60e5-4283-8b56-009daa45136f
date added to LUP
2026-04-20 12:38:19
date last changed
2026-04-20 12:39:12
@inproceedings{39adfed8-60e5-4283-8b56-009daa45136f,
  abstract     = {{<p>Motivated by the well established idea that collective wisdom is greater than that of an individual, we propose a novel learning dynamics as a sort of companion to the Abelson model of opinion dynamics. Agents are assumed to make independent guesses about the true state of the world after which they engage in opinion exchange leading to consensus. We investigate the problem of finding the optimal parameters for this exchange, e.g. those that minimize the variance of the consensus value. Specifically, the parameter we examine is susceptibility to opinion change. We propose a dynamics for distributed learning of the optimal parameters and analytically show that it converges for all relevant initial conditions by linking to well established results from consensus theory. Lastly, a numerical example provides intuition on both system behavior and our proof methods.</p>}},
  author       = {{Baković, Luka and Como, Giacomo and Fagnani, Fabio and Proskurnikov, Anton and Tegling, Emma}},
  booktitle    = {{2025 IEEE 64th Conference on Decision and Control, CDC 2025}},
  isbn         = {{9798331526276}},
  language     = {{eng}},
  pages        = {{4281--4286}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Continuous-Time Distributed Learning for Collective Wisdom Maximization}},
  url          = {{http://dx.doi.org/10.1109/CDC57313.2025.11312017}},
  doi          = {{10.1109/CDC57313.2025.11312017}},
  year         = {{2025}},
}