Fuzzy Logic for Social Simulation Using NetLogo

Authored by Luis R Izquierdo, Doina Olaru, Segismundo S Izquierdo, Sharon Purchase, Geoffrey N Soutar

Date Published: 2015

Sponsors: Spanish Ministry of Science and Innovation (MICINN) Australian Research Council (ARC)

Platforms: NetLogo

Model Documentation: Other Narrative

Model Code URLs: https://github.com/luis-r-izquierdo/netlogo-fuzzy-logic-extension/releases

Abstract

Fuzzy Logic is a framework particularly useful to formalise and deal with imprecise concepts and statements expressed in natural language. This paper has three related aims. First, it aims to provide a short introduction to the basics of Fuzzy Logic within the context of social simulation. Secondly, it presents a well-documented NetLogo extension that facilitates the use of Fuzzy Logic within NetLogo. Finally, by providing a concrete example, it shows how researchers can use the Fuzzy Logic extension to build agent-based models in which individual agents hold their own fuzzy concepts and use their own fuzzy rules, which may also change over time. We argue that Fuzzy Logic and the tools provided here can be useful in Social Simulation in different ways. For example, they can assist in the process of analysing the robustness of a certain social theory expressed in natural language to different specifications of the imprecise concepts that the theory may contain (such as e.g. ``wealthy{''}, ``poor{''} or ``disadvantaged{''}). They can also facilitate the exploration of the effect that heterogeneity in concept interpretations may have in a society (i.e. the significance of the fact that different people may have different interpretations of the same concept). Thus, this paper and the tools included in it can make the endeavour of translating social theories into computer programs easier and also more rigorous.

Tags

Uncertainty time-series systems Integration Differential-calculus Linguistic-synthesis Forecasting enrollments Diffusion-model Sets Controllers