Why is analogy important

Knowing these types is extremely important, as identifying the relationships between the two parts of an analogy is key to finding the missing information that follows. Below are the eleven analogy types with which any student taking an exam should be familiar:. After familiarizing oneself with the types of analogies, students should seek to be able to identify the type of analogy with which they are faced as they begin to practice analogical reasoning.

By knowing the type of analogy you are looking at, it is easier to deduce the answer that will represent a similar relationship. Recognizing the type is the juncture at which solid analytical reasoning begins, and training oneself to think in a logical, deductive manner is key to acing tests of analogies; gaining mastery in these exercises pours over into all areas of life, and in approaching comprehension in many areas both academic and otherwise.

A good vocabulary will also enable one to approach any test of analogies with greater ease and success. Reading and reviewing vocabulary lists are two ways, and studying Greek and Latin word roots is an incredibly efficient way of becoming knowledgeable of myriad words at the same time!

Alphabet Analogy: In this type of analogy, two groups of letters related to each other in same way, are given. Each letter of the 1st group is moved 14 steps forward to obtain the corresponding letter of the 2nd group. A similar relationship will exist between the 3rd and 4th groups. In questions based on analogy, a particular relationship is given and another similar relationship has to be identified from the alternatives provided.

The candidates are asked to identify and point out relationships, similarities or differences, and dissimilarities in a series or between groups of numbers. The act of grouping various objects on the basis of their common characteristics is known as classification.

In other words, a classification analogy is where there is a comparison between terms as per which group they belong to.

A metaphor is often poetically saying something is something else. An analogy is saying something is like something else to make some sort of an explanatory point. You can use metaphors and similes when creating an analogy. An analogy is simply a comparison of two things that are usually thought of as being different, but are similar in some way. They are written in a specific format such as apple : fruit :: carrot : vegetable. An analogy is the comparison of two pairs of words that have the same relationship.

An analogy is way of comparison: it is when one idea, concept, or thing is compared to something else that is significantly different from the first.

The purpose of an analogy is to better explain or expand the idea or concept by comparing it to something else that may be familiar to it and to the reader. Try your hand at this example. Begin typing your search term above and press enter to search.

Second and more importantly, we need to look not just at the construction of analogy mappings but at the ways in which individual analogical arguments are debated in fields such as mathematics, physics, philosophy and the law. These high-level debates require reasoning that bears little resemblance to the computational processes of ACME or Copycat.

There is, accordingly, room for both computational and traditional philosophical models of analogical reasoning. Aristotle and Mill, whose approach is echoed in textbook discussions, suggest counting similarities. In each of these approaches, the problem is twofold: overall similarity is not a reliable guide to plausibility, and it fails to explain the plausibility of any analogical argument.

The fundamental idea is that a good analogical argument must satisfy two conditions:. Prior Association. There must be a clear connection, in the source domain, between the known similarities the positive analogy and the further similarity that is projected to hold in the target domain the hypothetical analogy.

This relationship determines which features of the source are critical to the analogical inference. Potential for Generalization. There must be reason to think that the same kind of connection could obtain in the target domain. More pointedly: there must be no critical disanalogy between the domains.

The first order of business is to make the prior association explicit. The standards of explicitness vary depending on the nature of this association causal relation, mathematical proof, functional relationship, and so forth.

The two general principles are fleshed out via a set of subordinate models that allow us to identify critical features and hence critical disanalogies. To see how this works, consider Example 7 Rectangles and boxes. In this analogical argument, the source domain is two-dimensional geometry: we know that of all rectangles with a fixed perimeter, the square has maximum area.

The target domain is three-dimensional geometry: by analogy, we conjecture that of all boxes with a fixed surface area, the cube has maximum volume.

This argument should be evaluated not by counting similarities, looking to pre-theoretic resemblances between rectangles and boxes, or constructing connectionist representations of the domains and computing a systematicity score for possible mappings.

Instead, we should begin with a precise articulation of the prior association in the source domain, which amounts to a specific proof for the result about rectangles. We should then identify, relative to that proof, the critical features of the source domain: namely, the concepts and assumptions used in the proof. Finally, we should assess the potential for generalization: whether, in the three-dimensional setting, those critical features are known to lack analogues in the target domain.

The articulation model is meant to reflect the conversations that can and do take place between an advocate and a critic of an analogical argument. But even if we agree with Norton on this point, we might still be interested in having an account that gives us guidelines for evaluating analogical arguments. According to Norton, each analogical argument is warranted by local facts that must be investigated and justified empirically. Second, there are additional factual properties of the target system which, when taken together with the uniformity, warrant the analogical inference.

Through his newly invented telescope, Galileo observed points of light on the moon ahead of the advancing edge of sunlight. Noting that the same thing happens on earth when sunlight strikes the mountains, he concluded that there must be mountains on the moon and even provided a reasonable estimate of their height.

In this example, Norton tells us, the the fact of the analogy is that shadows and other optical phenomena are generated in the same way on the earth and on the moon; the additional fact about the target is the existence of points of light ahead of the advancing edge of sunlight on the moon. The fact of the analogy is a local uniformity that powers the inference.

That happens with explanatory analogies such as Example 5 the Acoustical Analogy , and mathematical analogies such as Example 7 Rectangles and Boxes.

This approach has been adopted by philosophers of archaeology, evolutionary biology and other historical sciences Wylie and Chapman ; Currie ; Currie ; Currie For example, Currie explores in detail the use of ethnographic analogy Example 13 between shamanastic motifs used by the contemporary San people and similar motifs in ancient rock art, found both among ancestors of the San direct historical analogy and in European rock art indirect historical analogy.

Analogical arguments support the hypothesis that in each of these cultures, rock art symbolizes hallucinogenic experiences. Currie examines criteria that focus on assumptions about stability of cultural traits and environment-culture relationships. Currie , and Wylie Wylie and Chapman also stress the importance of robustness reasoning that combines analogical arguments of moderate strength with other forms of evidence to yield strong conclusions. Practice-based approaches can thus yield specific guidelines unlikely to be matched by any general theory of analogical reasoning.

One caveat is worth mentioning. Field-specific criteria for ethnographic analogy are elicited against a background of decades of methodological controversy Wylie and Chapman Critics and defenders of ethnographic analogy have appealed to general models of scientific method e. To advance the methodological debate, practice-based approaches must either make connections to these general models or explain why the lack of any such connection is unproblematic. Close attention to analogical arguments in practice can also provide valuable challenges to general ideas about analogical inference.

Both Hesse and Bartha reject the idea that a purely formal analogy, with no physical significance, can support a plausible analogical inference in physics. Complex analogies between classical statistical mechanics CSM and quantum field theory QFT have played a crucial role in the development and application of renormalization group RG methods in both theories Example Fraser notes substantial physical disanalogies between CSM and QFT, and concludes that the reasoning is based entirely on formal analogies.

What philosophical basis can be provided for reasoning by analogy? What justification can be given for the claim that analogical arguments deliver plausible conclusions? There have been several ideas for answering this question. Any attempt to provide a general justification for analogical reasoning faces a basic dilemma.

The demands of generality require a high-level formulation of the problem and hence an abstract characterization of analogical arguments, such as schema 4. On the other hand, as noted previously, many analogical arguments that conform to schema 4 are bad arguments. So a general justification of analogical reasoning cannot provide support for all arguments that conform to 4 , on pain of proving too much.

The problem of justification is linked to the problem of characterizing good analogical arguments. This difficulty afflicts some of the strategies described in this section. Analogical reasoning may be cast in a deductive mold. If successful, this strategy neatly solves the problem of justification. A valid deductive argument is as good as it gets. On this analysis, an analogical argument between source domain S and target T begins with the assumption of positive analogy P S and P T , as well as the additional information Q S.

Provided we can treat that intermediate generalization as an independent premise, we have a deductively valid argument. Notice, though, that the existence of the generalization renders the analogy irrelevant. We can derive Q T from the generalization and P T , without any knowledge of the source domain. Some recent analyses follow Aristotle in treating analogical arguments as reliant upon extra sometimes tacit premises, typically drawn from background knowledge, that convert the inference into a deductively valid argument——but without making the source domain irrelevant.

Davies and Russell introduce a version that relies upon what they call determination rules Russell ; Davies and Russell ; Davies Suppose that Q and P 1 , …, P m are variables, and we have background knowledge that the value of Q is determined by the values of P 1 , …, P m. More generally, the form of a determination rule is. If we assume such a rule as part of our background knowledge, then an analogical argument with conclusion Q T is deductively valid.

Only by combining the rule with information about the source domain can we derive the value of Q T. Provided two cars are indistinguishable on each of these points, they will have the same value.

Weitzenfeld proposes a variant of this approach, advancing the slightly more general thesis that analogical arguments are deductive arguments with a missing enthymematic premise that amounts to a determination rule. Do determination rules give us a solution to the problem of providing a justification for analogical arguments?

In general: no. Analogies are commonly applied to problems such as Example 8 morphine and meperidine , where we are not even aware of all relevant factors, let alone in possession of a determination rule.

Medical researchers conduct drug tests on animals without knowing all attributes that might be relevant to the effects of the drug.

Indeed, one of the main objectives of such testing is to guard against reactions unanticipated by theory. For cases such as animal testing, neither option seems realistic. Recasting analogy as a deductive argument may help to bring out background assumptions, but it makes little headway with the problem of justification.

That problem re-appears as the need to state and establish the plausibility of a determination rule, and that is at least as difficult as justifying the original analogical argument. Some philosophers have attempted to portray, and justify, analogical reasoning in terms of some well-understood inductive argument pattern.

There have been three moderately popular versions of this strategy. The first treats analogical reasoning as generalization from a single case. The second treats it as a kind of sampling argument. The third recognizes the argument from analogy as a distinctive form, but treats past successes as evidence for future success.

Can such a simple analysis of analogical arguments succeed? A single instance can sometimes lead to a justified generalization. Even if we accept that there are such cases, the objection to understanding all analogical arguments as single-case induction is obvious: the view is simply too restrictive. Most analogical arguments will not meet the requisite conditions. We may not know that we are dealing with a natural kind or Aristotelian nature when we make the analogical argument.

We may not know which properties are essential. Interpreting the argument from analogy as single-case induction is also counter-productive in another way. The simplistic analysis does nothing to advance the search for criteria that help us to distinguish between relevant and irrelevant similarities, and hence between good and bad analogical arguments.

On the sampling conception of analogical arguments, acknowledged similarities between two domains are treated as statistically relevant evidence for further similarities. His only restriction has to do with sample size: we must be relatively knowledgeable about both A and B. Mill saw no difficulty in using analogical reasoning to infer characteristics of newly discovered species of plants or animals, given our extensive knowledge of botany and zoology. The sampling argument is presented in more explicit mathematical form by Harrod If the majority of known properties that belong to S also belong to T , then we should expect most other properties of S to belong to T , for it is unlikely that we would have come to know just the common properties.

How are we to count similarities and differences? The ratio of shared to total known properties varies dramatically according to how we do this. A second serious difficulty is the problem of bias : we cannot justify the assumption that the sample of known features is random. By contrast, the presentation of an analogical argument is always partisan.

Bias enters into the initial representation of similarities and differences: an advocate of the argument will highlight similarities, while a critic will play up differences. The paradigm of repeated selection from an urn seems totally inappropriate. Additional variations of the sampling approach have been developed e. Section 3. Liston offers a possible response: physicists are entitled to use Pythagorean analogies on the basis of induction from their past success:.

Setting aside familiar worries about arguments from success, the real problem here is to determine what counts as a similar strategy. In essence, that amounts to isolating the features of successful Pythagorean analogies.

An a priori approach traces the validity of a pattern of analogical reasoning, or of a particular analogical argument, to some broad and fundamental principle. Three such approaches will be outlined here.

The first is due to Keynes Keynes appeals to his famous Principle of the Limitation of Independent Variety, which he articulates as follows:. Armed with this Principle and some additional assumptions, Keynes is able to show that in cases where there is no negative analogy , knowledge of the positive analogy increases the logical probability of the conclusion. If there is a non-trivial negative analogy, however, then the probability of the conclusion remains unchanged, as was pointed out by Hesse In her , she proposes what she calls the Clustering Postulate : the assumption that our epistemic probability function has a built-in bias towards generalization.

The objections to such postulates of uniformity are well-known see Salmon , but even if we waive them, her argument fails. In simplified form, they require the existence of non-trivial positive analogy and no known critical disanalogy. There are two modalities here. Bartha argues that satisfaction of the criteria of the articulation model is sufficient to establish the modality in the antecedent, i.

He further suggests that prima facie plausibility provides a reasonable reading of the modality in the consequent, i. The argument is vulnerable to two sorts of concerns. First, there are questions about the interpretation of the symmetry principle. Second, there is a residual worry that this justification, like all the others, proves too much. The articulation model may be too vague or too permissive. Arguably, the most promising available defense of analogical reasoning may be found in its application to case law see Precedent and Analogy in Legal Reasoning.

Judicial decisions are based on the verdicts and reasoning that have governed relevantly similar cases, according to the doctrine of stare decisis Levi ; Llewellyn ; Cross and Harris ; Sunstein In practice, of course, the situation is extremely complex. No two cases are identical. The ratio must be understood in the context of the facts of the original case, and there is considerable room for debate about its generality and its applicability to future cases.

If a consensus emerges that a past case was wrongly decided, later judgments will distinguish it from new cases, effectively restricting the scope of the ratio to the original case. The practice of following precedent can be justified by two main practical considerations.

First, and above all, the practice is conservative : it provides a relatively stable basis for replicable decisions. People need to be able to predict the actions of the courts and formulate plans accordingly. Stare decisis serves as a check against arbitrary judicial decisions.

Second, the practice is still reasonably progressive : it allows for the gradual evolution of the law. Careful judges distinguish bad decisions; new values and a new consensus can emerge in a series of decisions over time.

In theory, then, stare decisis strikes a healthy balance between conservative and progressive social values. This justification is pragmatic. It pre-supposes a common set of social values, and links the use of analogical reasoning to optimal promotion of those values.

Notice also that justification occurs at the level of the practice in general; individual analogical arguments sometimes go astray. A full examination of the nature and foundations for stare decisis is beyond the scope of this entry, but it is worth asking the question: might it be possible to generalize the justification for stare decisis?

Is a parallel pragmatic justification available for analogical arguments in general? Bartha offers a preliminary attempt to provide such a justification by shifting from social values to epistemic values.

The general idea is that reasoning by analogy is especially well suited to the attainment of a common set of epistemic goals or values. In simple terms, analogical reasoning—when it conforms to certain criteria—achieves an excellent perhaps optimal balance between the competing demands of stability and innovation.

It supports both conservative epistemic values, such as simplicity and coherence with existing belief, and progressive epistemic values, such as fruitfulness and theoretical unification McMullin provides a classic list. As emphasized earlier, analogical reasoning takes in a great deal more than analogical arguments. In this section, we examine two broad contexts in which analogical reasoning is important. The first, still closely linked to analogical arguments, is the confirmation of scientific hypotheses.

Confirmation may also signify the logical relationship of inductive support that obtains between a hypothesis H and a proposition E that expresses the relevant evidence. Can analogical arguments play a role, either in the process or in the logical relationship? Arguably yes to both , but this role has to be delineated carefully, and several obstacles remain in the way of a clear account.

The second context is conceptual and theoretical development in cutting-edge scientific research. Analogies are used to suggest possible extensions of theoretical concepts and ideas. The reasoning is linked to considerations of plausibility, but there is no straightforward analysis in terms of analogical arguments. How is analogical reasoning related to the confirmation of scientific hypotheses?

The examples and philosophical discussion from earlier sections suggest that a good analogical argument can indeed provide support for a hypothesis. But there are good reasons to doubt the claim that analogies provide actual confirmation. In the first place, there is a logical difficulty.

To appreciate this, let us concentrate on confirmation as a relationship between propositions. Christensen offers a helpful general characterization:. Some propositions seem to help make it rational to believe other propositions. When our current confidence in E helps make rational our current confidence in H , we say that E confirms H. A Bayesian agent starts with an assignment of subjective probabilities to a class of propositions.

Confirmation is understood as a three-place relation:. To confirm H is to raise its conditional probability, relative to K. For Bayesians, here is the logical difficulty: it seems that an analogical argument cannot provide confirmation.

In the first place, it is not clear that we can encapsulate the information contained in an analogical argument in a single proposition, E. Second, even if we can formulate a proposition E that expresses that information, it is typically not appropriate to treat it as evidence because the information contained in E is already part of the background, K.

This is a version of the problem of old evidence; see confirmation. Again, the definition of confirmation in terms of Bayesian conditionalization seems inapplicable. Here we face a second difficulty, once again most easily stated within a Bayesian framework. Van Fraassen has a well-known objection to any belief-updating rule other than conditionalization.

This objection applies to any rule that allows us to boost credences when there is no new evidence. Adopting any such rule would lead us to acknowledge as fair a system of bets that foreseeably leads to certain loss. There appear to be at least three routes to avoiding these difficulties and finding a role for analogical arguments within Bayesian epistemology. First, there is what we might call minimal Bayesianism.

If analogical reasoning is directed primarily towards prior probability assignments, it can provide inductive support while remaining formally distinct from confirmation, avoiding the logical difficulties noted above. This approach is minimally Bayesian because it provides nothing more than an entry point into the Bayesian apparatus, and it only applies to novel hypotheses.

An orthodox Bayesian, such as de Finetti de Finetti and Savage , de Finetti , might have no problem in allowing that analogies play this role. The second approach is liberal Bayesianism : we can change our prior probabilities in a non-rule-based fashion. Something along these lines is needed if analogical arguments are supposed to shift opinion about an already existing hypothesis without any new evidence. As Hawthorne notes, some Bayesians simply accept that both initial assignments and ongoing revision of prior probabilities based on plausibility arguments can be rational, but.

In other words, by not stating any rules for this type of probability revision, we avoid the difficulties noted by van Fraassen. This approach admits analogical reasoning into the Bayesian tent, but acknowledges a dark corner of the tent in which rationality operates without any clear rules.

Recently, a third approach has attracted interest: analogue confirmation or confirmation via analogue simulation. As described in Dardashti et al.

Our key idea is that, in certain circumstances, predictions concerning inaccessible phenomena can be confirmed via an analogue simulation in a different system. Unlike real black holes, some of these analogues can be and indeed have been implemented and studied in the lab.

Given the exact formal analogy between our models for these systems and our models of black holes, and certain important additional assumptions, Dardashti et al. For instance, the observation of phenomena analogous to Hawking radiation in the analogue systems would provide confirmation for the existence of Hawking radiation in black holes. In a second paper Dardashti et al. The appeal of a clearly articulated mechanism for analogue confirmation is obvious.

It would provide a tool for exploring confirmation of inaccessible phenomena not just in cosmology, but also in historical sciences such as archaeology and evolutionary biology, and in areas of medical science where ethical constraints rule out experiments on human subjects. According to Reading Rockets, as students grow older, they are challenged to do more and more with the information they have learned and stored in their brains.

This strategy requires higher-order thinking skills HOTS. According to TeacherVision , analogies have proven to be effective learning tools for reinforcing thinking skills and conceptual understanding. Analogies require students to develop useful learning strategies that help them understand the relationship between words and how they fit together. Research shows using analogies in the classroom helps students understand a lesson more easily as teachers form connections between the new topic and what has already been taught.

When it comes to differentiating classroom curriculum for advanced learners, practicing analogies is a powerful tool that not only challenges them but encourages them to think creatively. It teaches creative and critical thinking skills and presents a challenge that advanced learners enjoy. This interactive competition can enhance the standards for students and prepare them for future academic achievement.

The views expressed herein represent the opinions of the author and not necessarily the National Association for Gifted Children. Skip to main content. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Not a Member?