Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory

Di Prodi, Paolo (2012) Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory. PhD thesis, University of Glasgow.

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This thesis develops a theoretical framework for the generation of artificial societies. In particular
it shows how sub-systems emerge when the agents are able to learn and have the ability
to communicate.
This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated
originally by the German sociologist Luhmann, with concepts of Shannon's information
theory applied to adaptive learning agents.
Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational
modelling paradigm involving the modelling of phenomena as dynamical systems of
interacting agents. The thesis in particular, investigates the functions and properties necessary
to reproduce the paradigm of society by using the mentioned ABM approach.
Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems
can then be composed by agents with a reduced behavioural complexity. For example in
society there are people who produce goods and other who distribute them.
Both the behaviour and communication is learned by the agent and not imposed. The simulated
task is to collect food, keep it and eat it until sated. Every agent communicates its energy state
to the neighbouring agents. This results in two subsystems whereas agents in the first collect
food and in the latter steal food from others. The ratio between the number of agents that
belongs to the first system and to the second system, depends on the number of food resources.
Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise
by reducing the amount of sensory information or, equivalently, reducing the complexity of the
perceived environment from the agent's perspective. Shannon's information theorem is used
to assess the performance of the simulated learning agents. A practical measure, based on the
concept of Shannon's information
ow, is developed and applied to adaptive controllers which
use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning.
The behavioural complexity is measured with a novel information measure, called Predictive
Performance, which is able to measure at a subjective level how good an agent is performing
a task. This is then used to quantify the social division of tasks in a social group of honest,
cooperative food foraging, communicating agents.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Multi agent systems, social systems, self organizing systems, autonomous social systems, information theory for adaptive agents, adaptive learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Computer software
Q Science > Q Science (General)
Colleges/Schools: College of Science and Engineering > School of Engineering
Supervisor's Name: Porr, Dr. Bernd
Date of Award: 2012
Depositing User: Mr Paolo Di Prodi
Unique ID: glathesis:2012-2869
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 28 Feb 2012
Last Modified: 10 Dec 2012 14:01

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