Sugarscape

Key takeaways

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

Reference excerpt

Sugarscape is a model for artificially intelligent agent-based social simulation following some or all rules presented by Joshua M. Epstein & Robert Axtell in their book Growing Artificial Societies.

Origin Fundaments of Sugarscape models can be traced back to the University of Maryland where economist Thomas Schelling presented his paper titled Models of Segregation. Written in 1969, Schelling and the rest of the social environment modelling fraternity had their options limited by a lack of adequate computing power and an applicable programming mechanism to fully develop the potential of their model. John Conway's agent-based simulation "Game of Life" was enhanced and applied to Schelling's original idea by Joshua M. Epstein and Robert Axtell in their book Growing Artificial Societies. To demonstrate their findings on the field of agent-based simulation, a model was created and distributed with their book on CD-ROM. The concept of this model has come to be known as "the Sugarscape model". Since then, the name "Sugarscape" has been used for agent-based models using rules similar to those defined by Epstein & Axtell.

Principles All Sugarscape models include the agents (inhabitants), the environment (a two-dimensional grid) and the rules governing the interaction of the agents with each other and the environment. The original model presented by J. Epstein & R. Axtell (considered as the first large scale agent model) is based on a 51x51 cell grid, where every cell can contain different amounts of sugar (or spice). In every step agents look around, find the closest cell filled with sugar, move and metabolize. They can leave pollution, die, reproduce, inherit sources, transfer information, trade or borrow sugar, generate immunity or transmit diseases - depending on the specific scenario and variables defined at the set-up of the model. Sugar in simulation could be seen as a metaphor for resources in an artificial world through which the examiner can study the effects of social dynamics such as evolution, marital status and inheritance on populations. Exact simulation of the original rules provided by J. Epstein & R. Axtell in their book can be problematic and it is not always possible to recreate the same results as those presented in Growing Artificial Societies.

Model implementations The Sugarscape model has had several implementations, some of which are available as free and open source software.

Ascape An original implementation was developed in Ascape, Java software suitable for agent-based social simulation. The Sugarscape model remains part of the built-in library of models distributed with Ascape.

NetLogo NetLogo has been used to build Sugarscape models. Three Sugarscape scenarios are included in the NetLogo Models Library: "Immediate Growback", "Constant Growback" and "Wealth Distribution". Besides these three scenarios lies Iain Weaver's Sugarscape NetLogo model, which is part of the User Community Models Library. "It builds on Owen Densmore's NetLogo community model to encompass all rules discussed in Growing Artificial Societies with the exception of the combat rule (although trivial to include, it adds little value to the model)." The model is equipped with rich documentation including instructions for successful replication of the original Sugarscape rules.

SugarScape on steroids Due to the emergent nature of agent-based models (ABMs), it is critical that the population sizes in the simulations match the population sizes of the dynamic systems being modelled. However, the performance of contemporary agent simulation frameworks has been inadequate to handle such large population sizes and parallel computing frameworks designed to run on computing clusters has been limited by available bandwidth. As computing power increases with Moore's law, the size and complexity of simulation frameworks can be expected to increase. The team of R. M. D’Souza, M. Lysenko and K Rahmani from Michigan Technological University used a Sugarscape model to demonstrate the power of Graphics processing units (GPU) in ABM simulations with over 50 updates per second with agent populations exceeding 2 million.

Mathematica Another implementation can be found written in Mathematica.

MASON GMU's MASON project, available under the Academic Free License, also includes an implementation of Sugarscape.

References

External links NetLogo Models Library NetLogo: Immediate Growback NetLogo: Constant Growback NetLogo: Wealth Distribution NetLogo: Community Models Library NetLogo Community Models: Sugarscape Sugarscape in Mathematica Sugarscape in MASON

Worked examples

Example 1 — a first encounter with Sugarscape

Start with the simplest possible case. Write down what Sugarscape claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, 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 Sugarscape 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 Sugarscape 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 Sugarscape

In research: Sugarscape appears in biology 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 Sugarscape 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: Sugarscape is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial life, Cellular automaton rules, Self-organization, so understanding it makes those chapters shorter.
In everyday life: Look for Sugarscape 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.

How to study Sugarscape in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Sugarscape 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 Sugarscape out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Sugarscape in simple terms?

Sugarscape is a model for artificially intelligent agent-based social simulation following some or all rules presented by Joshua M. Epstein & Robert Axtell in their book Growing Artificial Societies.

Why does Sugarscape matter?

Because it connects several biology 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 Sugarscape?

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 Sugarscape.