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Title: Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge

Abstract

The dynamical principle for a population of interacting individuals with mutual pairwise knowledge, presented by the author in a previous paper for the case of constant knowledge, is extended to include the possibility that the knowledge is time-dependent. Several mechanisms are presented by which the mutual knowledge, represented by a matrix K, can be altered, leading to dynamical equations for K(t). The author presents various examples of the transient and long time asymptotic behavior of K(t) for populations of relatively isolated individuals interacting infrequently in local binary collisions. Among the effects observed in the numerical experiments are knowledge diffusion, learning transients, and fluctuating equilibria. This approach will be most appropriate to small populations of complex individuals such as simple animals, robots, computer networks, agent-mediated traffic, simple ecosystems, and games. Evidence of metastable states and intermittent switching leads them to envision a spectroscopy associated with such transitions that is independent of the specific physical individuals and the population. Such spectra may serve as good lumped descriptors of the collective emergent behavior of large classes of populations in which mutual knowledge is an important part of the dynamics.

Authors:
Publication Date:
Research Org.:
Sandia National Labs., Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Research, Washington, DC (United States)
OSTI Identifier:
495734
Report Number(s):
SAND-95-8505-Pt.2
ON: DE97007011; TRN: AHC29715%%53
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: Jul 1995
Country of Publication:
United States
Language:
English
Subject:
66 PHYSICS; POPULATION DYNAMICS; THEORETICAL DATA; LEARNING; EXPERT SYSTEMS; ARTIFICIAL INTELLIGENCE; ANIMALS; BEHAVIOR; ROBOTS; TRAFFIC CONTROL

Citation Formats

Schmieder, R.W.. Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge. United States: N. p., 1995. Web. doi:10.2172/495734.
Schmieder, R.W.. Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge. United States. doi:10.2172/495734.
Schmieder, R.W.. Sat . "Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge". United States. doi:10.2172/495734. https://www.osti.gov/servlets/purl/495734.
@article{osti_495734,
title = {Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge},
author = {Schmieder, R.W.},
abstractNote = {The dynamical principle for a population of interacting individuals with mutual pairwise knowledge, presented by the author in a previous paper for the case of constant knowledge, is extended to include the possibility that the knowledge is time-dependent. Several mechanisms are presented by which the mutual knowledge, represented by a matrix K, can be altered, leading to dynamical equations for K(t). The author presents various examples of the transient and long time asymptotic behavior of K(t) for populations of relatively isolated individuals interacting infrequently in local binary collisions. Among the effects observed in the numerical experiments are knowledge diffusion, learning transients, and fluctuating equilibria. This approach will be most appropriate to small populations of complex individuals such as simple animals, robots, computer networks, agent-mediated traffic, simple ecosystems, and games. Evidence of metastable states and intermittent switching leads them to envision a spectroscopy associated with such transitions that is independent of the specific physical individuals and the population. Such spectra may serve as good lumped descriptors of the collective emergent behavior of large classes of populations in which mutual knowledge is an important part of the dynamics.},
doi = {10.2172/495734},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 1995},
month = {Sat Jul 01 00:00:00 EDT 1995}
}

Technical Report:

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