Economic sustainability in relation to demographic decline of Celtic agglomerations in Central Europe: multiple-scenario approach

0464767 - ARÚ 2017 RIV CH eng C - Conference Paper (international conference)
Štekerová, K. - Danielisová, Alžběta
Economic sustainability in relation to demographic decline of Celtic agglomerations in Central Europe: multiple-scenario approach.
Simulating prehistoric and ancient worlds. Cham: Springer, 2016 - (Barceló, J.; Del Castillo, F.), s. 335-357. Computational social sciences. ISBN 978-3-319-31479-2. ISSN 2509-9574.
[Social Simulation Conference 2014 - Simulating the Past to Understand Human History. Barcelona (ES), 01.09.2014-05.09.2014]
R&D Projects: GA ČR GAP405/12/0926
Institutional support: RVO:67985912
Keywords : economic sustainability * Celtic agglomerations * multiple-scenario * social simulation
OECD category: Archaeology

We aim to verify hypotheses about the probable self-subsistence of oppida by means of models of the population dynamics and socio-economic behavior of one particular site, the Staré Hradisko oppidum in Bohemia. We intend to demonstrate the ability to move from a static data of fragmented nature (archaeological and environmental records) to dynamic modelling that incorporates feedback mechanisms and nonlinear responses to a wide range of input data. Our models, which are implemented in NetLogo, are based on domain knowledge and general demographical patterns of birth-rates, mortality, and agricultural practices: (1) the model of population dynamics generates data on synthetic population for four alternative depopulation scenarios, (2) the model of food production and land use is designed to enable experimenting with carrying capacity of the environment with respect to alternative exploitation scenarios, (3) the workforce model is used for studying allocation of working capacities during the harvest season which is understood to be one of "bottlenecks" of the agricultural year. The models show some of upper limits of the self-subsistence, i.e. the highest possible size of population in the location, the largest sustainable area of fields and the maximum workforce allocation possible to the agricultural production.
Permanent Link: http://hdl.handle.net/11104/0263545