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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:547560 |
| Date | January 2012 |
| Creators | Di Prodi, Paolo |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/2869/ |
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