Trial and error

Last updated March 11, 2023

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

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

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

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

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

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

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]

Related Research Articles

Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object and a desired output value. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way. This statistical quality of an algorithm is measured through the so-called generalization error.

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.

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Edward Lee Thorndike was an American psychologist who spent nearly his entire career at Teachers College, Columbia University. His work on comparative psychology and the learning process led to the theory of connectionism and helped lay the scientific foundation for educational psychology. He also worked on solving industrial problems, such as employee exams and testing. He was a member of the board of the Psychological Corporation and served as president of the American Psychological Association in 1912. A Review of General Psychology survey, published in 2002, ranked Thorndike as the ninth-most cited psychologist of the 20th century. Edward Thorndike had a powerful impact on reinforcement theory and behavior analysis, providing the basic framework for empirical laws in behavior psychology with his law of effect. Through his contributions to the behavioral psychology field came his major impacts on education, where the law of effect has great influence in the classroom.

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Portia labiata is a jumping spider found in Sri Lanka, India, southern China, Burma (Myanmar), Malaysia, Singapore, Java, Sumatra and the Philippines. In this medium-sized jumping spider, the front part is orange-brown and the back part is brownish. The conspicuous main eyes provide vision more acute than a cat's during the day and 10 times more acute than a dragonfly's, and this is essential in P. labiata′s navigation, hunting and mating.

The following outline is provided as an overview of and topical guide to thought (thinking):

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Portia africana is a jumping spider found in Angola, Cameroon, the Central African Republic, Gabon, Ghana, the Ivory Coast, Sierra Leone, Zaire and Zambia. Its conspicuous main eyes provide vision more acute than a cat's during the day and 10 times more acute than a dragonfly's, and this is essential in P. africana′s navigation, hunting and mating.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

References

↑ 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.