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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Social Network Limits Language Complexity.

Matthew Lou-Magnuson¹, Luca Onnis¹

¹Linguistics and Multilingual Studies, Nanyang Technological University.

Cognitive Science

|September 29, 2018

Summary

This summary is machine-generated.

Languages spoken in smaller communities tend to have more complex grammar structures. This study uses an agent-based model to explore how social network properties influence language evolution and complexity.

Keywords:

Agent-based model Grammaticalization Language change Language complexity Language evolution Social network

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Area of Science:

Linguistics
Computational Social Science
Evolutionary Biology

Background:

Natural languages exhibit varying degrees of nested compositional structure.
Historically, languages from smaller communities show deeper compositional embedding than those from larger ones.
This linguistic observation is supported by statistical analyses of language structures.

Purpose of the Study:

To mechanistically examine the link between community size and linguistic complexity.
To propose and analyze an agent-based model of language evolution.
To identify key socio-cultural factors driving grammatical complexity.

Main Methods:

Developed an agent-based model simulating language transfer and change.
Incorporated cultural evolutionary principles into the model.
Analyzed the impact of social network properties on linguistic structure.

Main Results:

Identified social network transitivity as a critical parameter for compositional structure evolution.
Found that hierarchical patterning in scale-free distributions acts as an inhibitor to complexity.
The model provides a mechanistic explanation for observed linguistic patterns.

Conclusions:

Social network structure significantly influences the evolution of grammatical complexity.
Transitivity in social networks promotes deeper linguistic embedding.
Understanding these evolutionary dynamics offers insights into language diversity.