From Collective Mind to Communication

"Collective mind" is introduced as a set of simple intelligent units (say, neurons, or interacting agents) that can communicate by exchanging information without explicit global control. Incomplete information is compensated for by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute to dynamical formalism of the agents' evolution, and in particular, to the reflective chains of their nested abstract images of the selves and nonselves. The proposed model consists of the system of stochastic differential equations in the Langevin form to represent motor dynamics, and the corresponding Fokker-Planck equation to represent mental dynamics. The main departure of this model from newtonian and statistical physics is due to feedback from the mental to the motor dynamics, which makes the Fokker-Planck equation nonlinear. Interpretations of this model from mathematical, physical, biological, and psychological viewpoints are discussed. The model is illustrated by the dynamics of a dialog.

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Publication Details

Journal
Complex Systems
Published
2024-09-19
DOI
https://doi.org/10.25088/complexsystems.14.4.335
Citations
4
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
0.00

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article

From Collective Mind to Communication

Michail Zak
4 citations
Complex Systems
Opinion Dynamics and Social Influence
article

From Collective Mind to Communication

Michail Zak
article en
4 citations

Abstract

No abstract available for this paper.

Complex SystemsVol. 14(4)
Jet Propulsion Laboratory (US)
University of Southern California, California Institute of Technology, Jet Propulsion Laboratory
Reduced inequalities
Openalex Percentile: Top 100%
Opinion Dynamics and Social Influence
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From Collective Mind to Communication — Michail Zak · Complex Systems (2024) | TGRS Research Map | TGRS