Abstract
Modelling managed resource systems can involve the integration of multiple software modules into a single codebase. These modules are often written by non-software specialists, using heterogeneous terminologies and modelling approaches. One approach to model integration is to use a central structure to which each external module connects. This common interface acts as an agreed mode of communication for all contributors. We propose the Python Network Simulation (Pynsim) Framework, an open-source library for building simulation models of networked systems. Pynsim's central structure is a network, but it also supports non-physical entities like organisational hierarchies. We present two case studies using Pynsim which demonstrate how its use can lead to flexible and maintainable simulation models. First is a multi-agent model simulating the hydrologic and human components of Jordan's water system. The second uses a multi-objective evolutionary algorithm to identify the best locations for new run-of-river power plants in Switzerland.
Highlights
IntroductionThe use of simulation to model managed environmental systems is well established (Buytaert et al, 2012; Loucks et al, 2005; Robinson, 2014; Sanchez, 2007; Thorp and Bronson, 2013), as is the need to integrate models from multiple disciplines in order to represent the complexities and interdependencies of environmental systems (Burroughs, 2007; Castilla-Rho et al, 2015; Knapen et al, 2013)
For the Jordan Water Project, Python Network Simulation (Pynsim) provided a common framework for the development of a complex integrated model, serving as a foundation to standardize model conceptualization and development among a group of researchers from a variety of disciplines, each approaching environmental modelling from a unique disciplinary and methodological perspective
The Jordan Water Project is an example of this situation
Summary
The use of simulation to model managed environmental systems is well established (Buytaert et al, 2012; Loucks et al, 2005; Robinson, 2014; Sanchez, 2007; Thorp and Bronson, 2013), as is the need to integrate models from multiple disciplines in order to represent the complexities and interdependencies of environmental systems (Burroughs, 2007; Castilla-Rho et al, 2015; Knapen et al, 2013). Agent-based modelling is an established methodology to resource modelling (Castilla-Rho et al, 2015; Kelly et al, 2013; Tesfatsion et al, 2017), and several toolkits and languages are available (Hiebeler et al, 1994; Luke et al, 2003; Collier et al, 2003; Tisue and Wilensky, 2004). Multi-Agent Based Simulation (MABS) is a widely used technique, with several examples of cross-disciplinary model integration (Ghazi et al, 2014; Bosse et al, 2013; Daloglu et al, 2014)
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