From Wikipedia, the free encyclopedia
Trial and error is a fundamental method of problem-solving[1] characterized by repeated, varied attempts which are continued until success,[2] or until the practicer stops trying.
According to W.H. Thorpe, the term was devised by C. Lloyd Morgan (1852–1936) after trying out similar phrases «trial and failure» and «trial and practice».[3] Under Morgan’s Canon, animal behaviour should be explained in the simplest possible way. Where behavior seems to imply higher mental processes, it might be explained by trial-and-error learning. An example is a skillful way in which his terrier Tony opened the garden gate, easily misunderstood as an insightful act by someone seeing the final behavior. Lloyd Morgan, however, had watched and recorded the series of approximations by which the dog had gradually learned the response, and could demonstrate that no insight was required to explain it.
Edward Lee Thorndike was the initiator of the theory of trial and error learning based on the findings he showed how to manage a trial-and-error experiment in the laboratory. In his famous experiment, a cat was placed in a series of puzzle boxes in order to study the law of effect in learning.[4] He plotted to learn curves which recorded the timing for each trial. Thorndike’s key observation was that learning was promoted by positive results, which was later refined and extended by B. F. Skinner’s operant conditioning.
Trial and error is also a method of problem solving, repair, tuning, or obtaining knowledge. In the field of computer science, the method is called generate and test (Brute force). In elementary algebra, when solving equations, it is guess and check.
This approach can be seen as one of the two basic approaches to problem-solving, contrasted with an approach using insight and theory. However, there are intermediate methods which for example, use theory to guide the method, an approach known as guided empiricism.
This way of thinking has become a mainstay of Karl Popper’s critical rationalism.
Methodology[edit]
The trial and error approach is used most successfully with simple problems and in games, and it is often the last resort when no apparent rule applies. This does not mean that the approach is inherently careless, for an individual can be methodical in manipulating the variables in an attempt to sort through possibilities that could result in success. Nevertheless, this method is often used by people who have little knowledge in the problem area. The trial-and-error approach has been studied from its natural computational point of view [5]
Simplest applications[edit]
Ashby (1960, section 11/5) offers three simple strategies for dealing with the same basic exercise-problem, which have very different efficiencies. Suppose a collection of 1000 on/off switches have to be set to a particular combination by random-based testing, where each test is expected to take one second. [This is also discussed in Traill (1978–2006, section C1.2]. The strategies are:
- the perfectionist all-or-nothing method, with no attempt at holding partial successes. This would be expected to take more than 10^301 seconds, [i.e., 2^1000 seconds, or 3·5×(10^291) centuries]
- a serial-test of switches, holding on to the partial successes (assuming that these are manifest), which would take 500 seconds on average
- parallel-but-individual testing of all switches simultaneously, which would take only one second
Note the tacit assumption here that no intelligence or insight is brought to bear on the problem. However, the existence of different available strategies allows us to consider a separate («superior») domain of processing — a «meta-level» above the mechanics of switch handling — where the various available strategies can be randomly chosen. Once again this is «trial and error», but of a different type.
Hierarchies[edit]
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 (1978–2006) suggests that this Ashby-hierarchy probably coincides with Piaget’s well-known theory of developmental stages. [This work also discusses Ashby’s 1000-switch example; see §C1.2]. After all, it is part of Piagetian doctrine that children learn first by 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».
Application[edit]
Traill (2008, espec. Table «S» on p.31) follows Jerne and Popper in seeing this strategy as probably underlying all knowledge-gathering systems — at least in their initial phase.
Four such systems are identified:
- Natural selection which «educates» the DNA of the species,
- The brain of the individual (just discussed);
- The «brain» of society-as-such (including the publicly held body of science); and
- The adaptive immune system.
Features[edit]
Trial and error has a number of features:
- solution-oriented: trial and error makes no attempt to discover why a solution works, merely that it is a solution.
- problem-specific: trial and error makes no attempt to generalize a solution to other problems.
- non-optimal: trial and error is generally an attempt to find a solution, not all solutions, and not the best solution.
- needs little knowledge: trials and error can proceed where there is little or no knowledge of the subject.
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[edit]
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 using a technique called structure–activity relationship. (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 polymer technology to find new polymer types or families.
Trial and error is also commonly seen in player responses to video games — when faced with an obstacle or boss, players often form a number of strategies to surpass the obstacle or defeat the boss, with each strategy being carried out before the player either succeeds or quits the game.
Sports teams also make use of trial and error to qualify for and/or progress through the playoffs and win the championship, attempting different strategies, plays, lineups and formations in hopes of defeating each and every opponent along the way to victory. This is especially crucial in playoff series in which multiple wins are required to advance, where a team that loses a game will have the opportunity to try new tactics to find a way to win, if they are not eliminated yet.
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 can be considered as a form of trial and error.[6] 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.
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.
Jumping spiders of the genus Portia use trial and error to find new tactics against unfamiliar prey or in unusual situations, and remember the new tactics.[7] Tests show that Portia fimbriata and Portia labiata can use trial and error in an artificial environment, where the spider’s objective is to cross a miniature lagoon that is too wide for a simple jump, and must either jump then swim or only swim.[8][9]
See also[edit]
- Ariadne’s thread (logic)
- Brute-force attack
- Brute-force search
- Dictionary attack
- Empiricism
- Genetic algorithm
- Learning curve
- Margin of error
- Regula falsi
- Voodoo programming
References[edit]
- ^ Campbell, Donald T. (November 1960). «Blind variation and selective retention in creative thoughts as in other knowledge processes». Psychological Review. 67 (6): 380–400. doi:10.1037/h0040373. PMID 13690223.
- ^ Concise Oxford Dictionary p1489
- ^ Thorpe W.H. The origins and rise of ethology. Hutchinson, London & Praeger, New York. p26. ISBN 978-0-03-053251-1
- ^ Thorndike E.L. 1898. Animal intelligence: an experimental study of the association processes in animals. Psychological Monographs #8.
- ^ X. Bei, N. Chen, S. Zhang, On the Complexity of Trial and Error, STOC 2013
- ^ Wright, Serwall (1932). «The roles of mutation, inbreeding, crossbreeding and selection in evolution» (PDF). Proceedings of the Sixth International Congress on Genetics. Volume 1. Number 6: 365. Retrieved 17 March 2014.
- ^ Harland, D.P. & Jackson, R.R. (2000). ««Eight-legged cats» and how they see — a review of recent research on jumping spiders (Araneae: Salticidae)» (PDF). Cimbebasia. 16: 231–240. Archived from the original (PDF) on 28 September 2006. Retrieved 5 May 2011.
- ^ Jackson, Robert R.; Fiona R. Cross; Chris M. Carter (2006). «Geographic Variation in a Spider’s Ability to Solve a Confinement Problem by Trial and Error». International Journal of Comparative Psychology. 19 (3): 282–296. doi:10.46867/IJCP.2006.19.03.06. Retrieved 8 June 2011.
- ^ Jackson, Robert R.; Chris M. Carter; Michael S. Tarsitano (2001). «Trial-and-error solving of a confinement problem by a jumping spider, Portia fimbriata«. Behaviour. Leiden: Koninklijke Brill. 138 (10): 1215–1234. doi:10.1163/15685390152822184. ISSN 0005-7959. JSTOR 4535886.
Further reading[edit]
- Ashby, W. R. (1960: Second Edition). Design for a Brain. Chapman & Hall: London.
- Traill, R.R. (1978–2006). Molecular explanation for intelligence…, Brunel University Thesis, HDL.handle.net
- Traill, R.R. (2008). Thinking by Molecule, Synapse, or both? — From Piaget’s Schema, to the Selecting/Editing of ncRNA. Ondwelle: Melbourne. Ondwelle.com — or French version Ondwelle.com.
- Zippelius, R. (1991). Die experimentierende Methode im Recht (Trial and error in Jurisprudence), Academy of Science, Mainz, ISBN 3-515-05901-6
Метод проб и ошибок (в просторечии также: метод (научного) тыка) — является врождённым методом мышления человека. Также этот метод называют методом перебора вариантов.
В 1898 году описан Э. Торндайком как форма научения, основанная на закреплении случайно совершённых двигательных и мыслительных актов, за счет которых была решена значимая для животного задача. В следующих пробах время, которое затрачивается животным на решение аналогичных задач в аналогичных условиях, постепенно, хотя и не линейно, уменьшается, до тех пор, пока не приобретает форму мгновенного решения. Последующий анализ метода проб и ошибок показал, что он не является полностью хаотическим и нецелесообразным, а интегрирует в себе прошлый опыт и новые условия для решения задачи.
Достоинства и недостатки
Если рассматривать абсолютно случайный перебор вариантов, то можно сделать следующие выводы:
Достоинства метода:
- Этому методу не надо учиться.
- Методическая простота решения.
- Удовлетворительно решаются простые задачи (не более 10 проб и ошибок).
Недостатки метода:
- Плохо решаются задачи средней сложности (более 20—30 проб и ошибок) и практически не решаются сложные задачи (более 1000 проб и ошибок).
- Нет приёмов решения.
- Нет алгоритма мышления, мы не управляем процессом думанья. Идет почти хаотичный перебор вариантов.
- Неизвестно, когда будет решение и будет ли вообще.
- Отсутствуют критерии оценки силы решения, поэтому неясно, когда прекращать думать. А вдруг в следующее мгновение придет гениальное решение?
- Требуются большие затраты времени и волевых усилий при решении трудных задач.
- Иногда ошибаться нельзя ИЛИ этот метод не подходит (не будет человек резать на бомбе провода наугад).
Считается, что для метода проб и ошибок выполняется правило — «первое пришедшее в голову решение — слабое». Объясняют этот феномен тем, что человек старается поскорее освободиться от неприятной неопределённости и делает то, что пришло в голову первым.
Метод проб и ошибок лежит в основе принятия решений участниками рынка в условиях совершенной конкуренции, что является одной из главных причин постоянных кризисов.
ТРИЗ
МПиО — аббревиатура, обозначающая метод проб и ошибок. Часто встречается в текстах, так или иначе связанных с Теорией решения изобретательских задач. В ТРИЗ метод проб и ошибок рассматривается как эталон неэффективности. Для оценки какого-либо другого эвристического метода его сравнивают с МПиО. Так как МПиО — это метод перебора вариантов, то можно количественно определить число вариантов при использовании МПиО и сравнить с ним какой-либо другой эвристический метод. Такое математическое исследование предполагает, что количество необходимых вариантов обратно пропорционально эффективности метода и прямо пропорционально времени нахождения решения при его использовании. Однако точные количественные и статистические исследования проводятся редко. В ТРИЗ ограничиваются приблизительной количественной оценкой эффективности по уровням изобретательских задач (Ю. П. Саламатов).
Как точные, так и приблизительные количественные сравнения с МПиО возможны при допущении полной случайности перебора вариантов при использовании МПиО. В рамках ТРИЗ такая точка зрения служит обоснованием неэффективности МПиО. Однако с другой стороны выбор вариантов не может быть полностью произвольным. Он ограничен предыдущим опытом, инерцией мышления, стереотипами и гештальтами. На этом ставится акцент в рамках другой теории — метасистематике. В рамках метасистематики основным недостатком МПиО считается фактическая неслучайность перебора вариантов.
См. также
- Решение задач
- Теория решения изобретательских задач
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Trial and error, or trial by error or try an error, is a general method of problem solving, fixing things, or for obtaining knowledge.
«Learning doesn’t happen from failure itself but rather from analyzing the failure, making a change, and then trying again.»[1]
In the field of computer science, the method is called generate and test. In elementary algebra, when solving equations, it is «guess and check».
This approach can be seen as one of the two basic approaches to problem solving and is contrasted with an approach using insight and theory.
Process
Bricolage —
In trial and error, one selects a possible answer, applies it to the problem and, if it is not successful, selects (or generates) another possibility that is subsequently tried. The process ends when a possibility yields a solution.
In some versions of trial and error, the option that is a priori viewed as the most likely one should be tried first, followed by the next most likely, and so on until a solution is found, or all the options are exhausted. In other versions, options are simply tried at random.
Methodology
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
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. [This is also discussed in Traill (1978/2006, section C1.2]. The strategies are:
- the perfectionist all-or-nothing method, with no attempt at holding partial successes. This would be expected to take more than 10^301 seconds, [i.e. 2^1000 seconds, or 3·5×(10^291) centuries!];
- a serial-test of switches, holding on to the partial successes (assuming that these are manifest) would take 500 seconds; while
- a parallel-but-individual testing of all switches simultaneously would take only one second.
Note the tacit assumption here that no intelligence or insight is brought to bear on the problem. However, the existence of different available strategies allows us to consider a separate («superior») domain of processing — a «meta-level» above the mechanics of switch handling — where the various available strategies can be randomly chosen. Once again this is «trial and error», but of a different type. This leads us to:
Trial-and-error Hierarchies
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 (1978/2006) suggests that this Ashby-hierarchy probably coincides with Piaget’s well-known theory of developmental stages. [This work also discusses Ashby’s 1000-switch example; see §C1.2]. 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?
Traill (2008, espec. Table «S» on p.31) follows Jerne and Popper in seeing this strategy as probably underlying all knowledge-gathering systems — at least in their initial phase.
Four such systems are identified:
- Darwinian evolution which «educates» the DNA of the species!
- The brain of the individual (just discussed);
- The «brain» of society-as-such (including the publicly-held body of science); and
- The immune system.
An ambiguity: Can we have «intention» during a «trial»
In the Ashby-and-Cybernetics tradition, the word «trial» usually implies random-or-arbitrary, without any deliberate choice.
However amongst non-cyberneticians, «trial» will often imply a deliberate subjective act by some adult human agent; (e.g. in a court-room, or laboratory). So that has sometimes led to confusion.
Of course the situation becomes even more confusing if one accepts Ashby’s hierarchical explanation of intelligence, and its implied ability to be deliberate and to creatively design — all based ultimately on non-deliberate actions! The lesson here seems to be that one must simply be careful to clarify the meaning of one’s own words, and indeed the words of others. [Incidentally it seems that consciousness is not an essential ingredient for intelligence as discussed above.]
Features
Trial and error has a number of features:
- solution-oriented: trial and error makes no attempt to discover why a solution works, merely that it is a solution.
- problem-specific: trial and error makes no attempt to generalise a solution to other problems.
- non-optimal: trial and error is generally an attempt to find a solution, not all solutions, and not the best solution.
- needs little knowledge: trials and error can proceed where there is little or no knowledge of the subject.
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
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 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.
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.
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
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.
References
- Ashby, W. R. (1960: Second Edition). Design for a Brain. Chapman & Hall: London.
- Traill, R.R. (1978/2006). Molecular explanation for intelligence…, Brunel University Thesis, HDL.handle.net
- Traill, R.R. (2008). Thinking by Molecule, Synapse, or both? — From Piaget’s Schema, to the Selecting/Editing of ncRNA. Ondwelle: Melbourne. Ondwelle.com — or French version Ondwelle.com.
See also
- Brute force attack
- Brute-force search
- Empiricism
References
- ↑ Coding Horror: Fail Early, Fail Often
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Trial and error, or trial by error, is a general method of problem solving for obtaining knowledge, both propositional knowledge and know-how. In the field of computer science, the method is called generate and test. In elementary algebra, when solving equations, it is «guess and check».
This approach can be seen as one of the two basic approaches to problem solving and is contrasted with an approach using insight and theory.
Process
Bricolage -In trial and error, one selects a possible answer, applies it to the problem and, if it is not successful, selects (or generates) another possibility that is subsequently tried. The process ends when a possibility yields a solution.
In some versions of trial and error, the option that is a priori viewed as the most likely one should be tried first, followed by the next most likely, and so on until a solution is found, or all the options are exhausted. In other versions, options are simply tried at random.
Methodology
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.
Features
Trial and error has a number of features:
* solution-oriented: trial and error makes no attempt to discover «why» a solution works, merely that it «is» a solution.
* problem-specific: trial and error makes no attempt to generalise a solution to other problems.
* non-optimal: trial and error is an attempt to find «a» solution, not «all» solutions, and not the «best» solution.
* needs little knowledge: trials and error can proceed where there is little or no knowledge of the subject.
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.
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 as the error. Thus after a long time ‘knowledge’ of well-adapted genomes accumulates simply by virtue of them being «able» to reproduce.
Bogosort can be viewed as a trial and error approach to sorting a list.
In mathematics, the method of trial and error can be used to solve formulae — it is a slower, less precise method than algebra, but is easier to understand.
ee also
* Brute force attack
* Brute-force search
* Empiricism
Notes
Wikimedia Foundation.
2010.
На основании Вашего запроса эти примеры могут содержать грубую лексику.
На основании Вашего запроса эти примеры могут содержать разговорную лексику.
методом проб и ошибок
метода проб и ошибок
Because of this, dermatologists often use a trial-and-error approach to finding the most appropriate treatment for their patient.
Из-за этого, дерматологи часто используют метод проб и ошибок, когда ищут наиболее подходящее лечение своего пациента.
But this trial-and-error approach has also resulted in tragedy, indicating that adult experiences with a drug aren’t always a reliable predictor of how children will react.
Но этот метод проб и ошибок также приводил к трагедиям, доказывая, что взрослый опыт применения лекарственных средств не всегда служит надежным показателем детской реакции.
Therefore, researchers are requested to find the best material for sodium ion battery among vast number of candidates by trial-and-error approach.
Поэтому исследователи попытались найти подходящий материал для натрий-ионного аккумулятора среди огромного числа кандидатов методом проб и ошибок.
Effective treatment often requires a trial-and-error approach that takes time.
Through a trial-and-error approach, he developed a reliable method for helping others to overcome their own dyslexia.
Методом проб и ошибок он разработал надежный способ, позволяющий помогать другим преодолевать дислексию.
This allows to minimize all costs and completely avoid incorrect system solutions of the existing trial-and-error approach.
Это позволяет минимизировать все затраты и полностью избежать неверных системных решений существующего подхода «методом проб и ошибок».
However, «metamaterial design has relied on extensive experimentation and a trial-and-error approach,» Bessa says.
«Однако дизайн метаматериалов основывался на обширных экспериментах и методе проб и ошибок», — говорит Бесса.
Americans tend toward a trial-and-error approach: they do something, fail, learn from this, acquire theories and try again.
Американцы тяготеют к методу проб и ошибок: они что-то делают, терпят неудачу, извлекают уроки, усваивают теорию и делают новую попытку.
Davis shared his ideas with others, discovering to his surprise that most of his artist friends were also dyslexic, and through a trial-and-error approach developed a reliable method for helping others to overcome their own dyslexia.
Дейвис поделился своими соображениями с другими, к своему удивлению узнавая, что большинство его друзей-художников также были дислектиками, и методом проб и ошибок он разработал надежный способ оказания помощи другим в преодолении дислексии.
If I could change one thing about Finnish working life it would be… creating a more open culture to talk about failures and the importance of a trial-and-error approach.
Если бы я могла изменить что-то одно в финской рабочей жизни, это было бы… создание более открытой культуры, чтобы говорить о неудачах и важности метода проб и ошибок.
But don’t worry, all you need to do now is try Fix 1, which is kind of a trial-and-error approach but can also be a real fix.
Но не волнуйтесь, все, что вам нужно сделать сейчас, это попробовать Исправить 1, который является методом проб и ошибок, но также может быть реальным исправлением.
To make these breakthroughs organizations such as Google DeepMind and OpenAI have employed reinforcement learning, which sees the system learn technologies using a trial-and-error approach over the course of a huge number of games.
Чтобы сделать эти прорывы организаций, таких как Google DeepMind и OpenAI используют обучение с подкреплением, который видит систему изучить технологии, с помощью проб и ошибок подход в течение огромного количества игр.
The Duda, Hart & Stork (2001) text provide a simple example which nicely illustrates the process, but the feasibility of such an unguided trial-and-error approach for more substantial problems is dubious.
Текст статьи Дауда, Харта и Сторка даёт простой пример, который хорошо иллюстрирует процесс, но выполнимость такого неуправляемого подхода проб и ошибок для более крупных задач сомнительна.
This trial-and-error approach significantly increases the design costs and time to market.
Такой метод проб и ошибок существенно увеличивает стоимость разработки и время продвижения на рынок.
The Davis theory came out of the trial-and-error approach, as a way to explain why the Davis methods work.
Теория Дэвиса возникла вследствии метода проб и ошибок, как способ объяснения того, почему его методы работают.
For the last few years, most firms jumped on the social media bandwagon, but unfortunately the trial-and-error approach used by most has produced only mediocre results.
Многие компании стали последователями моды на социальные сети, но, к сожалению, метод проб и ошибок, используемый большинством из них, привел только к посредственным результатам.
«By finding the scales or the combination of scales that matter most for a given application, we can establish which surface finishing techniques will achieve the best results, reducing the need for a costly and time-consuming trial-and-error approach.»
«Найдя весы или комбинацию весов, которые наиболее важны для конкретного применения, мы можем установить, какие методы отделки поверхности дадут наилучшие результаты, уменьшив потребность в дорогостоящем и пробном подходе, основанном на методах проб и ошибок».
Результатов: 17. Точных совпадений: 17. Затраченное время: 26 мс
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The Use of Trial and Error To Solve Problems
Some complex problems can be solved by a technique that is called trial and error. Trial and error is typically good for problems where you have multiple chances to get the correct solution. However, this is not a good technique for problems that don’t give you multiple chances to find a solution.
An example of situations where you wouldn’t want to use trial and error are diffusing a bomb or performing an operation on a patient. In these situations, making an error can lead to disaster. Trial and error is used best when it is applied to situations that give your large amounts of time and safety to come up with a solution.
In addition to this, trial and error is also a great way to gain knowledge. Basically, a person that uses the trial and error method will try to a method to see if it is a good solution. If it is not a good solution, they try another option. If the method works, the person using it has acquired the correct solution to a problem. However, there are some situations where there are too many options, and it is not feasible for a person to go through all of them to find out which one works the best. In this event, a person will want to use the option that has the best possible chances of succeeding. If this doesn’t work, they can try the next best option until they find a good solution.
There are a number of important factors that makes trial and error a good tool to use for solving problems. The purpose of trial and error is not to find out why a problem was solved. It is primarily used to solve the problem. While this may be good in some fields, it may not work so well in others. For example, while trial and error may be excellent in finding solutions to mechanical or engineering problems, it may not be good for certain fields which ask «why» a solution works. Trial and error is primarily good for fields where the solution is the most important factor. This is often the case in math courses which are taught in high school or college.
Most math teachers place an emphasis on using trial and error to find a solution to problems, and many of them don’t spend a whole lot of time explaining «why» a solution works. One reason for this is because most math teachers have time constraints. However, some students who take advanced math classes in college may learn more about why certain solutions work. Another good aspect of the trial and error method is that it does not try to use a solution as a way of solving more than one problem. Trial and error is primarily used to find a single solution to a single problem.
Trial and error is not a method of finding the best solution, nor is it a method of finding all solutions. It is a problem solving technique that is simply used to find a solution. One of the most powerful advantages to this technique is that it does not require you to have a lot of knowledge. However, it may require you to have large amounts of patience. Trial and error is typically used to discover new drugs, and it also plays an important role in the scientific method as well. Some also feel that organic evolution is a form of trial and error, because random mutations will occur until they are successful.
Trial and error is also an excellent tool for inventors. The inventor will first imagine a device they would like to invent, and then they may go through the trial and error process to find the best ways of inventing the device. While trial and error is an extremely powerful tool that can be used to solve problems, it also has some weaknesses. I mentioned this briefly in the first paragraph. Trial and error is inefficient in situations where making an error can lead to serious injury or death. A good example of this would be the attempts that were made by aircraft engineers to break the sound barrier. Though they succeeded, many pilots died because they would use the trial and error process. In the situation, and error would often lead to the plane crashing.
Author Description
Editorial Team at Exforsys is a team of IT Consulting and Training team led by Chandra Vennapoosa.
Learning begins when the organism faces a new and difficult situation – a problem. Most learning organism counters errors, and with repeated trials, errors reduce. The phenomenon is called Trial and Error Learning in a simple sense.
Trial and Error Learning is only one of many theories of learning in Behavioral Psychology. Some other forms of learning include
Insight Learning
Latent Learning
Observational Learning
The first miniature Trial and Error learning system of the method was provided by Thorndike’s research on Animal Intelligence in 1898. This form of learning falls under S-R learning theory and also known as Connectionism.
Classic Experiment on Trial and Error Learning
Thorndike placed a hungry cat inside a puzzle box, and a plate of fish was kept outside the box. It was impossible for the cat to get to the plate, unless it could open the door and get out. Thorndike had arranged the puzzle box such that, the cat either had to pull a loop or press a lever in order to open the door.
Initially, the cat moved randomly inside the box; biting and clawing at the bars, thrusting its paws and trying to squeeze out of the box. After several minutes of trying these ineffective responses, the cat accidentally pulled the loop. Having hit the correct response, the cat managed to get out and it was awarded with a small piece of fish.
The cat was placed inside the box again. This time around, the cat took less time to pull the loop. The exercise was continued repeatedly. It was seen that as the number of trials increased, the time taken to pull the loop decreased. As the response latency decreased, the cat finally learned the trick; it then pulled the loop as soon as it was put in the box and managed to get out.
The term “Trial and Error Learning” was then introduced as the number of trials resulted in decreased number of errors.
Basic Conditions for Trial and Error Learning
Drive is an essential factor that triggers the various conditions for this phenomenon. If we look at the experiment above, hunger was the cat’s drive which stimulated it into trying out various responses until it finally learned the trick. Drive motivates to learn and make organism active to learn.
Blockade/barrier in satisfaction of drive
Trial and Errors occur only when there is barrier or blockade in between hunger and food. In the above experiment, satisfaction of hunger was only possible through consumption of food, but there was barrier in achieving the food. The barrier was the problem which needed to be solved in order to receive food. The attempts to solve the problem led to trial and error activities.
Random Activities
When the solution is not present beforehand, organism tends to act in random manner in its attempt to solve the problem. It’s purely because of the lack of knowledge.
Accidental Success
The first time an organism gets something right after repeated trials is always accidental, hence the term, accidental success. For instance, the first time the cat managed to pull the lever was accidental success.
Selection of Right Response
Accidental success is not the ultimate solution to any problem. The organism keeps trying out its previously tried out random attempts until it stumbles upon the right response. With repeated trial, the solution can be isolated.
Fixation of the Right Response
It’s the final stage of learning. The organism having identified the right response stays fixed on it, which lets the organism to act immediately when presented with the same situation. For instance, the cat managed to pull the loop immediately once it had, say, mastered the trick, or reached the final stage of Trial and Error Learning.
Laws of Trial and Error Learning
Law of Exercise
Law of exercise is a fundamental basis of trial and error learning since organism learns as a result of repeated trials/practice/exercise. The law can be subdivided into two parts:
Law of Use: If any action is repeated by an organism in certain condition, learning occurs.
Law of Disuse: If the action is not repeated by an organism, there will be no learning.
Law of Effect
Simply put, law of effect states that satisfaction lead to the repetition of the action. Reward causes satisfaction. Thorndike stated that satisfying state of affairs is a key to learning, defining it as
One which the animal does nothing to avoid, often doing such things as to attain and preserve it.
If the reward satisfies, the exercise then strengthens the connection, and the connection is weakened when the exercise leads to undesirable outcomes. The reward must satisfy, and the greater reward has greater effect on learning.
Law of Readiness
Readiness is the function of motivation, which is guided more by law of reward. The law states that when conduction cells are prepared for particular action, this will lead to satisfaction.
One of the biggest challenges for new entrepreneurs (and even many experienced entrepreneurs) is overcoming their fear of failure. Obviously no one wants to fail, no matter the stakes, but could there in fact be a right way to fail? One that lessens the fear and increases the learning?
Beneficial failure, the kind that teaches you along the way, requires a trial-and-error approach, where you make small, increasingly informed bets until you’ve learned enough to seize a larger opportunity. If you move forward with this mindset, and understand that failing early, cheaply, and often can actually mitigate risks when you’re ready to move on to bigger opportunities, you’re off to the races.
This kind of trial-and-error approach tackles the fear of failure head on, and makes much more sense than making big bets with your (and other people’s, if you have investors) time, money and reputation and simply hoping for the best.
Playing the Lottery and Playing Poker
It often seems that entrepreneurship is almost solely about entrepreneurial lottery winners — diving into the sexiest new industry, betting big, and finding billions of dollars and instant fame.
Playing the lottery is for suckers. Sure, occasionally there’s a big winner, but the odds are way against you, even before you start. There are alternative ways to make a wager, though. That’s why thinking about entrepreneurship as more of a poker game than a lottery may be a helpful way to reframe your approach to risks.
In poker, you don’t begin with a large, high-risk bet; you start with a small ante. If you get a poor hand, you fold and wait for another chance. Most importantly, over time you learn to anticipate the moves of your competitors and see opportunities that you wouldn’t have before. Play long enough, and you’ll take home a big pot and minimize the risk for yourself.
Poker is all about the skillful use of trial and error: betting more on better hands, folding when the odds are against you, and paying just enough to see the next card. This process of staging risks through trial and error is actually proven to be mathematically superior.
Play the game of entrepreneurship long enough, with enough attention to developing your skills and sizing up the competitors, and you’ll get a low-risk chance to capitalize on an extraordinary responsibility. You might lose a few small hands getting there, but it’s critical to think in terms of the long-game.
Failing Early, Cheaply and Often — With an End in Mind
Whether you’re afraid of failure or not, there is a simple, systematic way to begin thinking about and implementing the trial-and-error approach. The refrain you should become familiar with is fail early, cheaply, and often.
- Early: You want to disprove bad ideas and opportunities as soon as possible. The longer you stretch out something that’s just not working, the more you run the risk of trying to confirm a wrong solution rather than figuring out the right one. Design and stage your trials so that you can easily recognize when to scrap one for the next. That way, the next time something fails, it will be for a different reason and one that gets you closer to the right answer. Take the biggest unknown risks early on; otherwise, you’ll put in a lot of work in only to find later that it might not be worth it after all.
- Cheaply: Don’t own when you can rent. Don’t rent when you can borrow. Don’t move to an office park if you can do it out of your garage. Trial and error is about staging and scaling. Start small. Start lean. Start cheap. And then adjust accordingly. Bet big only when the odds are way in your favor, like building a plant only after you have a loyal following of customers.
- Often: The faster and more often you fail, the faster you’ll find an answer — or find that there isn’t an answer, and you need to look elsewhere. Run multiple experiments. Test wacky ideas (they often turn out to be right!). Over time, with this mindset, you’ll learn to discover with 80 percent accuracy whether something’s working — with only 20 percent of the effort.
It’s important to remember, though, to do all of these things with the bigger picture outcome in mind. Without establishing goals at the outset, there is no focus to trial and error, and nothing to measure it against. And be sure to admit when you fail, both to yourself and your team. Don’t let ego get in the way of a great business.