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Generative science

Generative science is a biology 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 Generative science rather than just read about it. In short: Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena".

Generative science — main illustration
Generative science — illustration

Key takeaways

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

Reference excerpt

Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena". By modelling such interactions, it can suggest that properties exist in the system that had not been noticed in the real world situation. An example field of study is how unintended consequences arise in social processes. Generative sciences often explore natural phenomena at several levels of organization. Self-organizing natural systems are a central subject, studied both theoretically and by simulation experiments. The study of complex systems in general has been grouped under the heading of "general systems theory", particularly by Ludwig von Bertalanffy, Anatol Rapoport, Ralph Gerard, and Kenneth Boulding.

Scientific and philosophical origins

The development of computers and automata theory laid a technical foundation for the growth of the generative sciences. For example:

Cellular automata are mathematical representations of simple entities interacting under deterministic rules to manifest complex behaviours. They can be used to model emergent processes of the physical universe, neural cognitive processes and social behavior. Conway's Game of Life is a zero-player game based on cellular automata, meaning that the only input is in setting the initial conditions, and the game is to see how the system evolves. In 1996 Joshua M. Epstein and Robert Axtell wrote the book Growing Artificial Societies which proposes a set of automaton rules and a system called Sugarscape which models a population dependent on resources (called sugar). Artificial neural networks attempt to solve problems in the same way that the human brain would, although they are still several orders of magnitude less complex than the human brain and closer to the computing power of a worm. Advances in the understanding of the human brain often stimulate new patterns in neural networks. One of the most influential advances in the generative sciences as related to cognitive science came from Noam Chomsky's (1957) development of generative grammar, which separated language generation from semantic content, and thereby revealed important questions about human language. It was also in the early 1950s that psychologists at the MIT including Kurt Lewin, Jacob Levy Moreno and Fritz Heider laid the foundations for group dynamics research which later developed into social network analysis.

See also Generative systems – Technologies that can produce change driven by audiences

References

External links http://www.swarthmore.edu/socsci/tburke1/artsoc.html Archived 2005-04-09 at the Wayback Machine (Artificial Societies, Virtual Worlds and the Shared Problems and Possibilities of Emergence) http://jasss.soc.surrey.ac.uk/JASSS.html (The Journal of Artificial Societies and Social Simulation)

Illustrations

Generative science: Interaction between a few simple rules and parameters can produce endless, seemingly unpredictable complexity.
Interaction between a few simple rules and parameters can produce endless, seemingly unpredictable complexity.
Generative science: Turbulence in the tip vortex from an airplane wing. Studies of the critical point beyond which a system creates turbulence were important for chaos theory, analyzed for example by the Soviet physicist Lev Landau who developed the Landau-Hopf theory of turbulence. David Ruelle and Floris Takens later predicted, against Landau, that fluid turbulence could develop through a strange attractor, a main concept of chaos theory.
Turbulence in the tip vortex from an airplane wing. Studies of the critical point beyond which a system creates turbulence were important for chaos theory, analyzed for example by the Soviet physicist Lev Landau who developed the Landau-Hopf theory of turbulence. David Ruelle and Floris Takens later predicted, against Landau, that fluid turbulence could develop through a strange attractor, a main concept of chaos theory.
Generative science: Computer simulation of the branching architecture of the dendrites of pyramidal neurons.[5]
Computer simulation of the branching architecture of the dendrites of pyramidal neurons.[5]
Generative science: The natural phenomenon of herd behaviour as in a flock of birds can be modelled artificially using simple rules in individual units, with swarm intelligence rather than any centralized control.
The natural phenomenon of herd behaviour as in a flock of birds can be modelled artificially using simple rules in individual units, with swarm intelligence rather than any centralized control.

Worked examples

Example 1 — a first encounter with Generative science

Start with the simplest possible case. Write down what Generative science 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 Generative science 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 Generative science 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 Generative science

In research
Generative science 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 Generative science 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
Generative science is common in secondary-school and first-year university syllabi. It links to neighbouring topics Systems theory, so understanding it makes those chapters shorter.
In everyday life
Look for Generative science 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 Generative science in 20 minutes

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

Frequently asked questions

What is Generative science in simple terms?

Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phe…

Why does Generative science 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 Generative science?

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 Generative science.

Tags

  • Systems theory

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