Approaching parallel computing to simulating population dynamics in demography

Abstract

Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.

Hybrid models as transdisciplinary research enablers

“…Examples include the use of forecasting with DES ( Harper et al, 2017 ), optimal packing problem with ABS ( Mustafee and Bischoff, 2013 ), optimal coverage problem with ABS ( Karatas and Onggo, 2019 ), use of Soft Systems Methodology and Cognitive Mapping (both Soft OR) with DES ( Tako and Kotiadis, 2015 ; Pessôa et al, 2015 ). There are also HM studies that have incorporated techniques from disciplines such as Applied Computing, for example, DES and grid/Cloud computing ( Mustafee and Taylor, 2009 ; Taylor et al, 2018 ), ABS-DES with distributed simulation ( Anagnostou and Taylor, 2017 ), ABS with parallel computing ( Montañola-Sales et al, 2016 ). From the perspective of our research community, exploration of the extant knowledge in disciplines such as Engineering, Computer Science, Arts and Humanities, allow the identification of established research philosophies, methods, techniques and tools, which could be deployed in conjunction with computer simulation in one or more stages of an M&S study.…”

How can social–ecological system models simulate the emergence of social–ecological crises?

“…Ecological: Mechanistic modelling of honeybee populations based on individual-, colony-and population-level processes (Becher et al, 2018) Ecological: Databases of species traits such as the 'TRY' Plant Trait Database including dispersal traits (Kattge et al, 2011) Social-ecological: Agent-based modelling of huntergatherer strategies and environmental resources/ prey species in spatially explicit environment (Janssen & Hill, 2014 Social-ecological: Use of invasive species monitoring data to disentangle human-mediated and natural dispersal processes (Horvitz et al, 2017) Demographic change Social: Parallelised agent-based modelling of human population dynamics based on key processes (Montañola-Sales et al, 2016) Social: Global demographic databases (United Nations Statistics Division, 2019) Ecological: Stochastic population modelling of emperor penguin responses to climate change (Jenouvrier et al, 2009) Ecological: Bayesian modelling to extend species demography data coverage to under-studied species (Kindsvater et al, 2018) Social-ecological: Agent-based modelling of demographic change in indigenous hunting communities and their prey species (Iwamura et al, 2014) Social-ecological: Long-term data records covering changes in social and ecological communities as, e.g., road network develops in Amazon (Klarenberg et al, 2019) Institutional & governance interventions Social: Agent-based modelling of individual and institutional activities in land system (Holzhauer et al, 2019) Global/regional databases of policies and impacts relating to, for example, environment or climate (New Climate Institute, 2019;OECD, 2019) Ecological: Multi-model framework to identify pathways and policies to reverse biodiversity loss trends (Leclère et al, 2020) Social-ecological: Economic-environmental modelling to explore effects of different policies on land use and biodiversity (Bryan et al, 2016), and network modelling of the effects of social institutions on ecological conditions, for example, of coral reefs (Barnes et al, 2019) social-ecological models and datasets suggest that they are feasible (Table 2). If this approach is successfully developed and applied, it could have a number of other benefits.…”