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*Date: [http://www.wikipedia.org/wiki/19th_Century 1806]

*Date: [https://www.wikipedia.org/wiki/19th_Century 1806]

*a finding out of the best way to reach a desired result or a correct solution by trying out one or more ways or means and by noting and eliminating [[error]]s or [[cause]]s of failure; also : the trying of one [[thing]] or another until something succeeds

*a finding out of the best way to reach a desired result or a correct solution by trying out one or more ways or means and by noting and eliminating [[error]]s or [[cause]]s of failure; also : the trying of one [[thing]] or another until something succeeds

This approach is more successful with simple [[problems]] and in [[games]], and is often resorted to when no apparent rule applies. This does not mean that the approach need be careless, for an [[individual]] can be methodical in manipulating the [[variables]] in an attempt to sort through [[possibilities]] that may result in success. Nevertheless, this [[method]] is often used by people who have little [[knowledge]] in the problem area.

This approach is more successful with simple [[problems]] and in [[games]], and is often resorted to when no apparent rule applies. This does not mean that the approach need be careless, for an [[individual]] can be methodical in manipulating the [[variables]] in an attempt to sort through [[possibilities]] that may result in success. Nevertheless, this [[method]] is often used by people who have little [[knowledge]] in the problem area.

==Simplest applications==

==Simplest applications==

[http://en.wikipedia.org/wiki/William_Ross_Ashby Ashby] (1960, section 11/5) offers three simple [[strategies]] for dealing with the same basic exercise-problem; and they have very [[different]] efficiencies: Suppose there are 1000 on/off switches which have to be set to a particular combination by [[random]]-based testing, each test to take one second. The strategies are:

[https://en.wikipedia.org/wiki/William_Ross_Ashby Ashby] (1960, section 11/5) offers three simple [[strategies]] for dealing with the same basic exercise-problem; and they have very [[different]] efficiencies: Suppose there are 1000 on/off switches which have to be set to a particular combination by [[random]]-based testing, each test to take one second. The strategies are:

* the perfectionist all-or-nothing [[method]], with no attempt at holding partial successes. (Slowest)

* the perfectionist all-or-nothing [[method]], with no attempt at holding partial successes. (Slowest)

Ashby's [[book]] develops this "meta-level" [[idea]], and extends it into a whole recursive [[sequence]] of [[levels]], successively above each other in a systematic [[hierarchy]]. On this basis he [[argues]] that human [[intelligence]] emerges from such [[organization]]: relying heavily on trial-and-error (at least initially at each new [[stage]]), but emerging with what we would call "intelligence" at the end of it all. Thus presumably the topmost level of the hierarchy (at any stage) will still depend on simple trial-and-error.

Ashby's [[book]] develops this "meta-level" [[idea]], and extends it into a whole recursive [[sequence]] of [[levels]], successively above each other in a systematic [[hierarchy]]. On this basis he [[argues]] that human [[intelligence]] emerges from such [[organization]]: relying heavily on trial-and-error (at least initially at each new [[stage]]), but emerging with what we would call "intelligence" at the end of it all. Thus presumably the topmost level of the hierarchy (at any stage) will still depend on simple trial-and-error.

Traill suggests that this Ashby-hierarchy probably coincides with [http://en.wikipedia.org/wiki/Jean_Piaget Piaget]'s well-known [[theory]] of developmental stages. After all, it is part of Piagetian doctrine that [[children]] learn by first actively doing in a more-or-less [[random]] way, and then hopefully learn from the consequences — which all has a certain resemblance to Ashby's random "trial-and-error".

Traill suggests that this Ashby-hierarchy probably coincides with [https://en.wikipedia.org/wiki/Jean_Piaget Piaget]'s well-known [[theory]] of developmental stages. After all, it is part of Piagetian doctrine that [[children]] learn by first actively doing in a more-or-less [[random]] way, and then hopefully learn from the consequences — which all has a certain resemblance to Ashby's random "trial-and-error".

==The basic strategy in many fields?==

==The basic strategy in many fields?==

Traill follows [http://en.wikipedia.org/wiki/Niels_Kaj_Jerne Jerne] and [http://en.wikipedia.org/wiki/Karl_Popper Popper] in seeing this [[strategy]] as probably underlying all [[knowledge]]-gathering [[systems]] — at least in their initial [[phase]].

Traill follows [https://en.wikipedia.org/wiki/Niels_Kaj_Jerne Jerne] and [https://en.wikipedia.org/wiki/Karl_Popper Popper] in seeing this [[strategy]] as probably underlying all [[knowledge]]-gathering [[systems]] — at least in their initial [[phase]].

Four such systems are identified:

Four such systems are identified:

* [http://en.wikipedia.org/wiki/Darwinian_evolution Darwinian evolution] which "educates" the [[DNA]] of the [[species]]!

* [https://en.wikipedia.org/wiki/Darwinian_evolution Darwinian evolution] which "educates" the [[DNA]] of the [[species]]!

* The [[brain]] of the [[individual]] (just discussed);

* The [[brain]] of the [[individual]] (just discussed);

* The "brain" of [[society]]-as-such (including the publicly-held body of [[science]]); and

* The "brain" of [[society]]-as-such (including the publicly-held body of [[science]]); and

* The [http://en.wikipedia.org/wiki/Immune_system immune system].

* The [https://en.wikipedia.org/wiki/Immune_system immune system].

==Features==

==Features==

Trial and error has a number of features:

Trial and error has a number of features:

It is possible to use trial and error to find all solutions or the best solution, when a testably [[finite]] number of possible solutions exist. To find all solutions, one simply makes a note and continues, rather than ending the [[process]], when a solution is found, until all solutions have been tried. To find the best solution, one finds all solutions by the [[method]] just described and then comparatively [[evaluates]] them based upon some predefined set of criteria, the [[existence]] of which is a condition for the possibility of finding a best solution. (Also, when only one solution can exist, as in assembling a jigsaw puzzle, then any solution found is the only solution and so is necessarily the best.)

It is possible to use trial and error to find all solutions or the best solution, when a testably [[finite]] number of possible solutions exist. To find all solutions, one simply makes a note and continues, rather than ending the [[process]], when a solution is found, until all solutions have been tried. To find the best solution, one finds all solutions by the [[method]] just described and then comparatively [[evaluates]] them based upon some predefined set of criteria, the [[existence]] of which is a condition for the possibility of finding a best solution. (Also, when only one solution can exist, as in assembling a jigsaw puzzle, then any solution found is the only solution and so is necessarily the best.)

==Examples==

==Examples==

Trial and error has [[traditionally]] been the main method of finding new drugs, such as antibiotics. [[Chemists]] simply try chemicals at [[random]] until they find one with the desired [[effect]]. In a more sophisticated version, chemists select a narrow range of chemicals it is thought may have some effect. (The latter case can be alternatively considered as a changing of the [[problem]] rather than of the solution [[strategy]]: instead of "What chemical will work well as an antibiotic?" the problem in the sophisticated approach is "Which, if any, of the chemicals in this narrow range will work well as an antibiotic?") The method is used widely in many [[disciplines]], such as [http://en.wikipedia.org/wiki/Polymer_technology polymer technology] to find new polymer types or families.

Trial and error has [[traditionally]] been the main method of finding new drugs, such as antibiotics. [[Chemists]] simply try chemicals at [[random]] until they find one with the desired [[effect]]. In a more sophisticated version, chemists select a narrow range of chemicals it is thought may have some effect. (The latter case can be alternatively considered as a changing of the [[problem]] rather than of the solution [[strategy]]: instead of "What chemical will work well as an antibiotic?" the problem in the sophisticated approach is "Which, if any, of the chemicals in this narrow range will work well as an antibiotic?") The method is used widely in many [[disciplines]], such as [https://en.wikipedia.org/wiki/Polymer_technology polymer technology] to find new polymer types or families.

The [[scientific method]] can be regarded as containing an element of trial and error in its formulation and testing of [[hypotheses]]. Also compare [[genetic]] [[algorithms]], simulated annealing and reinforcement learning - all varieties for search which apply the basic idea of trial and error.

The [[scientific method]] can be regarded as containing an element of trial and error in its formulation and testing of [[hypotheses]]. Also compare [[genetic]] [[algorithms]], simulated annealing and reinforcement learning - all varieties for search which apply the basic idea of trial and error.

[[Biological]] [[evolution]] is also a form of trial and error. [[Random]] [[mutations]] and sexual genetic variations can be viewed as trials and poor reproductive fitness, or lack of improved fitness, as the error. Thus after a long time '[[knowledge]]' of well-adapted genomes accumulates simply by [[virtue]] of them being able to [[reproduce]].

[[Biological]] [[evolution]] is also a form of trial and error. [[Random]] [[mutations]] and sexual genetic variations can be viewed as trials and poor reproductive fitness, or lack of improved fitness, as the error. Thus after a long time '[[knowledge]]' of well-adapted genomes accumulates simply by [[virtue]] of them being able to [[reproduce]].

[http://en.wikipedia.org/wiki/Bogosort Bogosort], a conceptual sorting [[algorithm]] (that is extremely inefficient and impractical), can be viewed as a trial and error approach to sorting a list. However, typical simple examples of bogosort do not track which orders of the list have been tried and may try the same order any number of times, which violates one of the basic principles of trial and error. Trial and error is actually more [[efficient]] and [[practical]] than bogosort; unlike bogosort, it is guaranteed to halt in finite time on a finite list, and might even be a reasonable way to sort extremely short lists under some conditions.

[https://en.wikipedia.org/wiki/Bogosort Bogosort], a conceptual sorting [[algorithm]] (that is extremely inefficient and impractical), can be viewed as a trial and error approach to sorting a list. However, typical simple examples of bogosort do not track which orders of the list have been tried and may try the same order any number of times, which violates one of the basic principles of trial and error. Trial and error is actually more [[efficient]] and [[practical]] than bogosort; unlike bogosort, it is guaranteed to halt in finite time on a finite list, and might even be a reasonable way to sort extremely short lists under some conditions.

==Issues with trial and error==

==Issues with trial and error==

Trial and error is usually a last resort for a particular [[problem]], as there are a number of problems with it. For one, trial and error is tedious and monotonous. Also, it is very time-consuming; chemical engineers must sift through millions of various [[potential]] chemicals before they find one that works. Fortunately, computers are best suited for trial and error; they do not succumb to the [[boredom]] that [[humans]] do, and can potentially do thousands of trial-and-error segments in the blink of an eye.

Trial and error is usually a last resort for a particular [[problem]], as there are a number of problems with it. For one, trial and error is tedious and monotonous. Also, it is very time-consuming; chemical engineers must sift through millions of various [[potential]] chemicals before they find one that works. Fortunately, computers are best suited for trial and error; they do not succumb to the [[boredom]] that [[humans]] do, and can potentially do thousands of trial-and-error segments in the blink of an eye.

[[Category: General Reference]]

[[Category: General Reference]]