A Novel Approach for Designing a Cognitive Sugarscape Cellular Society Using An Extended Moren Network

Authored by Masumeh Maleki, Nasim Nourafza, Saeed Setayeshi

Date Published: 2016

DOI: 10.1080/10798587.2015.1090720

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

`Artificial life' is a term used to describe man-made systems that have been designed to behave in ways that simulate the behavior of natural living systems. Agent-based modeling of social processes is called `artificial society.' The Sugarscape model is used to model, interpret, and organize social, political, and economic processes in an artificial society. In science, cognition refers to a whole series of mental processes, including attention, memory, language comprehension and production, and learning. The aim of this study was to develop a cognitive Sugarscape model. A Sugarscape model was designed, and the agents were placed in the environment randomly; the parameters of the model, such as sugar level and metabolism, were assigned to the agent randomly. In the present study, learning was applied in the Sugarscape model by the Moren algorithm and compared with the learner Sugarscape model that used the Boltzmann learning algorithm in previous studies. The results of the comparison showed that the number of agents in the sugar peaks in the convergent time of the cognitive Sugarscape model exceeded those in the learner Sugarscape model that used the Boltzmann learning algorithm.

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