computer science

StochSD

StochSD is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand StochSD rather than just read about it. In short: 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 hand…

Key takeaways

StochSD belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect StochSD to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of StochSD from memory before moving on to harder problems.

Reference excerpt

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 a macro (CSS) model in StochSD to 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. This old consistency problem was stepwise solved between 2000 and 2010. This property is denoted Full Potential CSS modelling, see below.

Education and self-studies To support the use in education and self-studies, the StochSD package as well as course 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. StochSD is downloaded in many countries from all continents, and can also be run directly in a web browser (of which there is no statistics). It is described, compared and discussed, demonstrated in educational videos, and also referred to in various languages.

Full potential CSS modelling The Full Potential CSS concept is an extension of classical Continuous System Simulation, which provides the rules to make the results from macro-modelling consistent with those from micro-modelling. Briefly, in addition to modelling and simulating continuous flows between stocks represented by ‘real numbers’, StochSD can also handle transitions of discrete entities by integer numbers. But in contrast to including individual entities into a CSS model, StochSD preserves the aggregated 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 StochSD the construction and simulation of e.g. queuing models or combined discrete and continuous models are done in a straightforward way (see example below). The Full Potential concept also includes rules for how a stage has to be expanded into a structure of stocks and flows in order to reproduce a specific sojourn-time distribution, 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 model 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

… excerpt ends here. Continue reading the full article.

Illustrations

StochSD: 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.) — 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.)

Worked examples

Example 1 — a first encounter with StochSD

Start with the simplest possible case. Write down what StochSD claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to StochSD before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about StochSD ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of StochSD

In research: StochSD appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses StochSD in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: StochSD is common in secondary-school and first-year university syllabi. It links to neighbouring topics Free simulation software, Mathematical optimization software, Simulation programming languages, so understanding it makes those chapters shorter.
In everyday life: Look for StochSD outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study StochSD in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what StochSD means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain StochSD out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is StochSD in simple terms?

Why does StochSD matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study StochSD?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on StochSD.