StochSD

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StochSD

Developer(s)	Leif, Erik and Magnus Gustafsson
Initial release	June 16, 2017 (2017-06-16)
Stable release	2022.01.02 / January 2, 2022 (2022-01-02)
Repository	github.com/stochsd/stochsd
Written in	Javascript
Engine
Operating system	Windows, macOS, Linux
Type	System Dynamics software
License	Affero General Public License
Website	https://stochsd.sourceforge.io

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StochSD (Stochastic System Dynamics) is a free, open-source Continuous System Simulation (CSS) package intended for small and medium-sized models in education, self-studies and research. Technically, StochSD is based on the Insight Maker engine with its DE-solver, function library, error checker, macro facility, etc., while the design, graphical user’s interface, construction elements, result presentation, file handling, link checking, etc. are different. Also, tools for sensitivity analysis, and optimisation with or without constraints are included. In particular, StochSD includes features for stochastic modelling, post-analysis of multiple simulations, and presentation of the results in statistical form.

The design and development of StochSD were done during 2017-2022 with support from Uppsala University, Karolinska Institute, and the Swedish University of Agricultural Sciences.

StochSD was designed to fulfil the two purposes:

• To provide an open-source CSS language based on the System Dynamics philosophy, where a system is described in terms of stocks (compartments) and flows, and where pedagogic aspects, ease of use and understanding are prioritised.
• To enable Full Potential CSS modelling and simulation. This means that a macro (CSS) model in StochSD can produce results that are fully consistent (i.e.,contradiction-free) with those from a micro (Discrete Event Simulation (DES) or Agent-Based Simulation) model of a well-defined system under study.

Education and self-studies

To support the use in education and self-studies, the StochSD package as well as material to enable courses in classical CSS and Full Potential CSS modelling and simulation are provided at the StochSD Website. This course material contains lectures, laboratory exercises, instructive models, and StochSD manuals, which mainly are based on material developed at Uppsala University and the Swedish University of Agricultural Sciences for courses in modelling and simulation.

Full Potential CSS modelling

The Full Potential CSS concept is an extension of classical Continuous System Simulation that provides the rules so that the results from macro-modelling can become consistent with those from micro-modelling Here, in addition to modelling and simulating continuous flows between stocks represented by ‘real numbers’, it can also handle transitions of discrete entities by integer numbers. But in contrast to including individual entities into a CSS model, StochSD preserves the lumped macro approach for discrete entities by transferring integer number of entities (e.g., arrivals, accidents, deaths) during a time-step. However, such transitions may happen irregularly over time, so stochasticity often plays a crucial role in their modelling. Therefore, StochSD contains powerful random functions to model uncertainties of different kinds, as well as devices to collect statistics during a simulation and from multiple replications of the same stochastic model. In particular, StochSD enables the construction and simulation in a straightforward way of e.g. queuing models as well as macro models consistent with combined discrete and continuous models.

The Full Potential concept also includes rules about how a stage has to be expanded to a structure of stocks and flows in order to reproduce a specific sojourn time distribution (if not exponential), how attributes are to be handled, and where and how different types of uncertainty (structural, transition, initial value, parameter, and signal uncertainties) should be implemented.

Example: Continuous vs. Combined discrete and continuous models

A continuous prey-predator model and a combined model with continuous prey (e.g. X = Grass) and discrete predators (e.g. Y = Sheep), as well as a replication of the combined model are shown below.

Continuous model

${\begin{array}{lll}X(t+DT)=X(t)+DT\cdot (F1-F2-F3)&{\text{(CONTINUOUS GRASS)}}\\F1=a\cdot X&{\text{(Breeding)}}\\F2=b\cdot X\cdot Y&{\text{(Preyed upon)}}\\F3=c\cdot X\cdot X&{\text{(Internal competition)}}\\Y(t+DT)=Y(t)+DT\cdot (F4-F5)&{\text{(CONTINUOUS SHEEP)}}\\F4=d\cdot X\cdot Y&{\text{(Births)}}\\F5=e\cdot Y&{\text{(Deaths)}}\\\end{array}}$

Now assume that the births and deaths of the sheep are discrete random events with the expected rate d⋅X⋅Y and e⋅Y, respectively, then the number of events per time interval is Poisson distributed. The StochSD function PoFlow(expected_value) handles this by drawing a random number for each DT. The modification to obtain a combined model is shown in red, below.

Combined discrete and continuous model

${\begin{array}{lll}X(t+DT)=X(t)+DT\cdot (F1-F2-F3)&{\text{(CONTINUOUS GRASS)}}\\F1=a\cdot X&{\text{(Breeding)}}\\F2=b\cdot X\cdot Y&{\text{(Preyed upon)}}\\F3=c\cdot X\cdot X&{\text{(Internal competition)}}\\Y(t+DT)=Y(t)+DT\cdot (F4-F5)&{\text{(DISCRETE SHEEP)}}\\F4=\color {red}PoFlow(\color {black}d\cdot X\cdot Y\color {red})\color {black}&{\text{(Discrete births)}}\\F5=\color {red}PoFlow(\color {black}e\cdot Y\color {red})\color {black}&{\text{(Discrete deaths)}}\\\end{array}}$

In the replication shown, the discrete predators became extinct at around 235 time units (e.g., months). The continuous prey then increases logistically to an equilibrium without further stochastic variations.

The combined prey-predator model with continuous prey and discrete predators, and a replication of this model. (Note that the colouring of a primitive is preserved in the time plot.)

Comparing a continuous prey-predator model with the combined model reveals that the continuous model, starting at the equilibrium state (as in this example), will only produce two straight horizontal lines. Starting the continuous model outside the equilibrium state will make it approach the equilibrium for both species without lasting variations. Further, a phenomenon such as extinction cannot occur for a continuous model.

Creating the combined model, shown above, is straightforward. The traditional alternative of constructing a combined continuous and discrete model, with its mixture of disparate DES and CSS concepts, synchronisation of two different time-handling methods, and requiring a special combined simulation language is not an attractive option for a macro study.

External links

• Official website

This article "StochSD" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:StochSD. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

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