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Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization
This paper presents an agent-based complex system simulation of settlement structure change using methods derived from entropy maximization modeling. The approach is applied to model the movement of people and goods in urban settings to study how settlement size hierarchy develops. While entropy max...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer US
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750773/ https://www.ncbi.nlm.nih.gov/pubmed/29368754 http://dx.doi.org/10.1007/s10816-014-9219-6 |
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author | Altaweel, Mark |
author_facet | Altaweel, Mark |
author_sort | Altaweel, Mark |
collection | PubMed |
description | This paper presents an agent-based complex system simulation of settlement structure change using methods derived from entropy maximization modeling. The approach is applied to model the movement of people and goods in urban settings to study how settlement size hierarchy develops. While entropy maximization is well known for assessing settlement structure change over different spatiotemporal settings, approaches have rarely attempted to develop and apply this methodology to understand how individual and household decisions may affect settlement size distributions. A new method developed in this paper allows individual decision-makers to chose where to settle based on social-environmental factors, evaluate settlements based on geography and relative benefits, while retaining concepts derived from entropy maximization with settlement size affected by movement ability and site attractiveness feedbacks. To demonstrate the applicability of the theoretical and methodological approach, case study settlement patterns from the Middle Bronze (MBA) and Iron Ages (IA) in the Iraqi North Jazirah Survey (NJS) are used. Results indicate clear differences in settlement factors and household choices in simulations that lead to settlement size hierarchies comparable to the two evaluated periods. Conflict and socio-political cohesion, both their presence and absence, are suggested to have major roles in affecting the observed settlement hierarchy. More broadly, the model is made applicable for different empirically based settings, while being generalized to incorporate data uncertainty, making the model useful for understanding urbanism from top-down and bottom-up perspectives. |
format | Online Article Text |
id | pubmed-5750773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-57507732018-01-22 Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization Altaweel, Mark J Archaeol Method Theory Article This paper presents an agent-based complex system simulation of settlement structure change using methods derived from entropy maximization modeling. The approach is applied to model the movement of people and goods in urban settings to study how settlement size hierarchy develops. While entropy maximization is well known for assessing settlement structure change over different spatiotemporal settings, approaches have rarely attempted to develop and apply this methodology to understand how individual and household decisions may affect settlement size distributions. A new method developed in this paper allows individual decision-makers to chose where to settle based on social-environmental factors, evaluate settlements based on geography and relative benefits, while retaining concepts derived from entropy maximization with settlement size affected by movement ability and site attractiveness feedbacks. To demonstrate the applicability of the theoretical and methodological approach, case study settlement patterns from the Middle Bronze (MBA) and Iron Ages (IA) in the Iraqi North Jazirah Survey (NJS) are used. Results indicate clear differences in settlement factors and household choices in simulations that lead to settlement size hierarchies comparable to the two evaluated periods. Conflict and socio-political cohesion, both their presence and absence, are suggested to have major roles in affecting the observed settlement hierarchy. More broadly, the model is made applicable for different empirically based settings, while being generalized to incorporate data uncertainty, making the model useful for understanding urbanism from top-down and bottom-up perspectives. Springer US 2014-09-16 2015 /pmc/articles/PMC5750773/ /pubmed/29368754 http://dx.doi.org/10.1007/s10816-014-9219-6 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Article Altaweel, Mark Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title | Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title_full | Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title_fullStr | Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title_full_unstemmed | Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title_short | Settlement Dynamics and Hierarchy from Agent Decision-Making: a Method Derived from Entropy Maximization |
title_sort | settlement dynamics and hierarchy from agent decision-making: a method derived from entropy maximization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750773/ https://www.ncbi.nlm.nih.gov/pubmed/29368754 http://dx.doi.org/10.1007/s10816-014-9219-6 |
work_keys_str_mv | AT altaweelmark settlementdynamicsandhierarchyfromagentdecisionmakingamethodderivedfromentropymaximization |