Random stimulus

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This is a class of creativity techniques that explore randomization. Most of their names start with word 'random': random word, random heuristic, random picture, random sound, etc. In each random creativity technique user is presented with a random stimulus and explores her associations that has a potential to bring novel ideas. The power of random stimulus is that it can lead you to explore some useful associations that could never be explored intentionally.

Random Word technique is the simplest technique of this class where a randomly picked word is used to generate new associations. By getting a random word and thinking how you can use it to solve your problem you are practically guaranteed to attack the problem from a different direction from that you would normally. (Ray, 1989)

Low-tech implementations of the random word technique often use a pile of index cards and shuffling as a mechanism of randomization. For example, "The oblique strategies" created by Brian Eno and Peter Schmidt in 1975 is a set of 100 cards, each of which is a suggestion of a course of action or thinking to assist in creative situations, where standard logical solutions don't produce a desired result. High-tech implementations of these techniques use computers, random number generators, and availabiliy of internet resources to extend the potential of this technique. Simple random techniques are classified by modality of association (Verbal, Visual, Audial, Kinesthetic). Multi-modal techniques combine different modalities, e.g. random article, website, or video (Kosorukoff, 2000). 'Random article' link is an example of this kind of technique implemented by MediaWiki software. The best tool to explore a random website creativity technique is Stumbleupon.

Evolutionary-computation model of creativity (Goldberg, 1989; Kosorukoff, 2000; Goldberg, 2002) views random stimulus creativity techniques as mutation operators. Each such operator has some potential to bring a relatively small and beneficial change (innovation). Success of this process can be characterized by the innovation rate (Goldberg, 1989). In this context, it is the share of random stimuli that were useful among all presented. The innovation rate depends on the distribution from which the random stimuli are sampled. Improving innovation rate is an important research problem in human-based evolutionary computation.

[edit] References

Michael Ray, Rochelle Myers (1989). Creativity in business, Doubleday, NY.
David Goldberg (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley
Alex Kosorukoff (2000), Human-based genetic algorithm, link
David Goldberg (2002), The design of innovation: Lessons from and for Competent Genetic Algorithms, Springer