Efficient parallel simulation of spatially-explicit agent-based epidemiological models

2016 ◽  
Vol 93-94 ◽  
pp. 102-119 ◽  
Author(s):  
Dhananjai M. Rao
Author(s):  
Joshua M. Epstein

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.


2020 ◽  
Vol 28 (5) ◽  
pp. 377-397 ◽  
Author(s):  
Kaarel Sikk ◽  
Geoffrey Caruso

The behavioural ecological approach to anthropology states that the density and distribution of resources determines optimal patterns of resource use and also sets its constraints to grouping, mobility and settlement choice. Central place foraging (CPF) models have been used for analyzing foraging behaviours of hunter-gatherers and drawing a causal link from the volume of available resources in the environment to the mobility decisions of hunter-gatherers. In this study, we propose a spatially explicit agent-based CPF model. We explore its potential for explaining the formation of settlement patterns and test its robustness to the configuration of space. Building on a model assuming homogeneous energy distributions, we had to add several new parameters and an adaptation mechanism for foragers to predict the length of their stay, together with a heterogeneous environment configuration. The validation of the model shows that the spatially explicit CPF is generally robust to spatial configuration of energy resources. The total volume of energy has a significant effect on constraining sedentism as predicted by aspatial model and thus can be used on different environmental conditions. Still the spatial autocorrelation of resource distribution has a linear effect on optimal mobility decisions and needs to be considered in predictive models. The effect on settlement location choice is not substantial and is more determined by other characteristics of settlement location. This limits the CPF models in analyzing settlement pattern formation processes.


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