Abstract
To understand the operation of the informal social sphere in human or artificial societies, we need to be able to identify their existing behavioural conventions (institutions). This includes the contextualisation of seemingly objective facts with subjective assessments, especially when attempting to capture their meaning in the context of the analysed society. An example for this is numeric information that abstractly expresses attributes such as wealth, but only gains meaning in its societal context. In this work we present a conceptual approach that combines clustering techniques and Interval Type-2 Fuzzy Sets to extract structural information from aggregated subjective micro-level observations. A central objective, beyond the aggregation of information, is to facilitate the analysis on multiple levels of social organisation. We introduce the proposed mechanism and discuss its application potential.
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Notes
- 1.
For example, ‘low wealth’ could comprise the interval 0 to 50 ([0, 50]), medium wealth [50, 100], and anything above 100 considered as ‘high wealth’. The membership of boundary values (e.g. 50) with specific sets is of lower certainty than interval centres values (e.g. 75).
- 2.
Grouped boxes indicate system components along with elementary processing steps, whereas individual boxes with italicised labels signify processing artefacts such as inputs (e.g. intervals) and outputs (e.g. membership functions). Dashed boxes indicate the optional nature of the operation (e.g. statistical correction).
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Frantz, C.K., Savarimuthu, B.T.R., Purvis, M.K., Nowostawski, M. (2016). Generalising Social Structure Using Interval Type-2 Fuzzy Sets. In: Baldoni, M., Chopra, A., Son, T., Hirayama, K., Torroni, P. (eds) PRIMA 2016: Principles and Practice of Multi-Agent Systems. PRIMA 2016. Lecture Notes in Computer Science(), vol 9862. Springer, Cham. https://doi.org/10.1007/978-3-319-44832-9_22
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