Analogy, or Analogical Reasoning, is a form of reasoning that uses similarities between different objects, situations, or problems to deepen understanding, develop explanations, or generate new solutions.
For example, the human circulatory system can provide inspiration for designing a material supply network within a factory. Blood circulation and industrial material distribution involve different objects and purposes. However, they share certain relationships, such as transporting substances from a supply source to multiple destinations and adjusting supply according to changes in demand.
By examining such common relationships, it may be possible to apply principles known in one field to problems in another.
Analogies are also widely used in everyday explanations and education. For example, comparing an electrical circuit to flowing water can make it easier to understand relationships among electric current, voltage, and resistance.
In science and engineering, comparisons between different phenomena have sometimes contributed to the development of new hypotheses and models.
However, discovering similarities between two objects does not establish that the properties or laws governing one object also apply to the other.
It is necessary to examine which characteristics are similar, whether those similarities are relevant to the problem, and what important differences exist between the objects.
The value of analogy does not lie simply in recognizing similarities between different objects. It lies in transforming relationships and mechanisms known in one domain into hypotheses that can be investigated in another.
The History and Theoretical Background of Analogy
Analogy is not a particular creative thinking technique invented in modern times. It is a fundamental form of human reasoning that has been used since antiquity.
Aristotle discussed reasoning based on similarities between different objects. Analogy subsequently became an important subject of investigation in philosophy, science, mathematics, law, education, and other disciplines.
In the philosophy of science, Mary Hesse examined the role of scientific models and analogies in her 1966 book Models and Analogies in Science.
Hesse emphasized the importance of distinguishing similarities, differences, and relationships that remain unknown between the objects being compared.
Cognitive science has investigated how people retrieve relevant examples from memory, identify correspondences between different situations, and generate new inferences through analogy.
Representative research includes the studies of analogical problem-solving by Mary Gick and Keith Holyoak, and Structure-Mapping Theory developed by Dedre Gentner.
Gentner proposed Structure-Mapping Theory in 1983 to explain why correspondence between relationships can be more important than similarity between the individual attributes of objects.
However, Structure-Mapping Theory is one cognitive scientific theory of analogical reasoning. It is not synonymous with analogy itself.
This page introduces analogy as a practical approach to thinking, while the detailed mechanisms of Structure-Mapping Theory are examined on a separate page.
The Basic Structure of Analogy
Analogical reasoning generally distinguishes between two domains.
The Source Domain is an object, situation, or example that is already relatively well understood.
The Target Domain is the object or problem we want to understand, solve, or explore.
For example, when using blood circulation as a reference for designing an industrial supply network, the circulatory system is the source domain and the industrial supply network is the target domain.
Possible correspondences include the following.
| Source Domain: Blood Circulation | Target Domain: Industrial Supply Network | Common Function or Relationship |
|---|---|---|
| Heart and circulatory driving mechanisms | Pumps and supply equipment | Drive the movement of substances |
| Blood vessels | Pipes and transportation routes | Transport substances |
| Organs and tissues | Production equipment and work locations | Receive necessary supplies |
| Regulation of blood flow | Adjustment of flow and delivery quantities | Respond to changes in demand |
| Maintenance of circulation and supply | Continuous replenishment and recovery | Maintain system operation |
This table does not imply that the two systems operate through identical mechanisms.
For example, blood circulation involves physiological regulatory processes, while industrial supply networks may use sensors, controllers, and logistics management systems.
The properties of the transported substances, network structures, operating conditions, and patterns of demand may also differ.
The important point is not to imitate the source domain directly, but to examine which functions and relationships might be transferable to the target domain.
Surface Similarity and Relational Similarity
An important distinction in analogical reasoning is the difference between surface similarity and similarity of relationships or mechanisms.
Surface similarity refers to directly observable characteristics such as shape, color, size, terminology, or material.
For example, aircraft wings and bird wings share certain visual characteristics and are both described as wings.
However, when examining how flight is achieved, similarity of shape alone is insufficient.
It is necessary to investigate lift generation, fluid dynamics, structural strength, flight control, and the relationships that make flight possible.
Conversely, objects that look entirely different may share similar relational structures.
For example, electrical circuits and fluid flow through pipes are visually very different.
However, under appropriate conditions, the relationship between electrical potential difference and electric current can be compared with the relationship between pressure difference and fluid flow rate.
For a resistive electrical circuit, Ohm’s law can be expressed as:
Voltage Difference = Electrical Resistance × Electric Current
For fluid flow under suitable conditions, an analogous relationship can be expressed as:
Pressure Difference = Hydraulic Resistance × Volumetric Flow Rate
This correspondence does not mean that electricity and fluids are the same substance.
It means that certain relationships used to describe their behavior have a similar mathematical form.
However, the simple proportional relationship may not hold under all fluid flow conditions, such as turbulent flow or changing fluid and channel properties.
This example illustrates that the usefulness of analogy depends more on the correspondence of relationships and their conditions of validity than on the external appearance of the objects.
Four Main Roles of Analogy
Analogy serves different purposes depending on how it is used.
| Role | Purpose | Example |
|---|---|---|
| Understanding and Explanation | Explain unfamiliar ideas through familiar examples | Compare electrical circuits with water flow |
| Problem-Solving | Apply a solution principle used in another problem | Transfer the idea of distributed transportation routes to another field |
| Hypothesis Generation | Develop new explanations from common relationships between domains | Use biological supply or repair mechanisms as references for engineering hypotheses |
| Model Development | Describe different phenomena through common structures or mathematical relationships | Use correspondences between electrical and hydraulic systems |
These activities are related, but they should be distinguished.
In particular, using analogy to explain something already known differs from using analogy to explore possibilities that have not yet been established.
In explanatory analogies, the primary objective is to support understanding.
When generating new technological or business hypotheses, it becomes necessary to investigate relationships across domains more carefully and examine whether they can be applied under actual conditions.
Analogical Problem-Solving — Research on the Radiation Problem
A representative study demonstrating how analogies contribute to problem-solving is “Analogical Problem Solving” by Mary Gick and Keith Holyoak, published in 1980.
Their research used Karl Duncker’s radiation problem and a structurally similar military story.
In the radiation problem, the objective is to destroy a tumor using radiation.
However, applying sufficiently strong radiation from a single direction would also damage healthy tissue along the path. Weak radiation would not be sufficient to destroy the tumor.
One possible solution is to apply weaker radiation from multiple directions so that its effects converge at the tumor.
The research presented a military story involving a comparable relational structure.
In the story, an army needs to capture a fortress. Sending the entire army along a single route would create serious difficulties.
However, dividing the army into smaller groups and having them approach the fortress simultaneously along different routes provides an alternative solution.
The correspondences can be represented as follows.
| Military Problem | Radiation Problem | Common Relationship |
|---|---|---|
| Fortress | Tumor | Central target to be attacked or treated |
| Army | Radiation | Means of acting upon the target |
| Concentrating forces along one route | Applying strong radiation from one direction | Creates undesirable effects along the route |
| Dividing forces among multiple routes | Applying weaker radiation from multiple directions | Reduces undesirable effects along individual routes |
| Simultaneous convergence at the fortress | Convergence of radiation at the tumor | Produces the required effect at the target |
The important point is not that military operations are superficially similar to medicine.
It is that the objectives, obstacles, constraints, and solution mechanisms share corresponding relationships.
Gick and Holyoak’s research demonstrated that people may have difficulty using an example from another domain to solve a new problem, even when they have encountered the relevant example. Cues directing attention to the analogy can be important.
This suggests that knowing a useful example does not guarantee that it will be retrieved spontaneously when needed.
This is an example from cognitive psychology research on problem-solving, not a description of clinical radiation therapy procedures.
A Major Challenge Is Retrieving the Right Source
When discussing analogy, attention often focuses on comparing two examples and identifying what they have in common.
However, an important problem occurs before comparison begins.
Where do we find the example that should be used as the source of the analogy?
Suppose a factory wants to improve a system that distributes necessary substances to many processing locations.
An engineer familiar with manufacturing equipment and logistics may think of conventional piping or material handling systems.
However, the engineer may not immediately consider human blood circulation, plant transport tissues, urban water networks, or computer networks.
Even if these systems are familiar, their relevance to the current problem may not be recognized.
Research by Gentner, Kenneth Forbus, and others has investigated analogical retrieval, including the role of surface similarity as a cue for remembering examples and the role of relational similarity in evaluating their usefulness.
This distinction separates two activities:
Analogical Retrieval, which involves finding relevant examples from existing knowledge, and Analogical Mapping, which involves identifying correspondences between the retrieved source and the target.
Retrieval may be particularly challenging in cross-domain analogy.
Consequently, when using analogy as a creative thinking method, it is important to consider not only how examples should be compared but also how potential source examples can be discovered.
Near Analogies and Far Analogies
Analogies can be made between closely related domains or between very different domains.
These are sometimes described as Near Analogies and Far Analogies.
For example, applying a cooling technology from one type of automobile to another relatively similar vehicle may be considered a near analogy.
By contrast, using biological thermoregulation or plant water transport as inspiration for cooling or supply systems in industrial equipment may involve a more distant analogy.
Near analogies may be easier to apply because materials, environments, constraints, and operating conditions are relatively similar.
Far analogies may suggest less familiar solutions but can be more difficult to discover, interpret, or adapt to practical conditions.
However, an analogy from a more distant domain is not necessarily more creative or technically useful.
Nor does distance between fields necessarily imply a lack of similarity in functions or relationships.
Two technologies may belong to entirely different industrial categories while relying on common physical principles or control mechanisms.
The important consideration is not how surprising the source domain appears, but whether its relationships can provide useful insights into the target problem.
A Practical Approach to Analogical Reasoning
There is no single procedure that must be followed when using analogy.
However, when exploring technological hypotheses through knowledge from other domains, the following activities can provide a useful framework.
| Activity | Main Question | Possible Outcome |
|---|---|---|
| 1. Characterize the problem | What must be achieved, and what prevents it? | Functional description of the problem |
| 2. Search for source domains | Are similar functions or relationships found elsewhere? | Candidate source examples |
| 3. Identify correspondences | Which elements perform corresponding roles? | Mapping of relationships |
| 4. Examine differences and constraints | Which conditions differ, and what might prevent transfer? | Applicability conditions and limitations |
| 5. Generate hypotheses | Which principles from the source might be useful? | Application or technological hypotheses |
| 6. Examine feasibility | What evidence would be needed to evaluate the hypotheses? | Research, testing, and experimental questions |
This table is one possible way to organize analogical exploration.
It is not necessary to follow the activities in a fixed sequence.
Finding an interesting source may change how the original problem is understood. Testing an initial hypothesis may also lead to the discovery of a different source domain.
Furthermore, analogy should not be reduced to selecting one similar example and immediately drawing a conclusion.
Comparing multiple potential sources can be valuable.
Practical Example — Developing Self-Healing Systems from Biological Circulation
Consider an engineering self-healing system.
Such a system might supply repair materials to locations where structural materials or equipment components have been damaged.
For a single repair event, it may be possible to use microcapsules containing repair substances.
However, if the same location experiences repeated damage, a system that releases its stored repair material only once may be insufficient.
This leads to the following question:
“Rather than relying on a one-time local reserve, is there a mechanism capable of repeatedly supplying substances wherever they are needed?”
Possible sources of analogy include the human circulatory system and plant transport tissues.
Extracting Common Functions
For example, several functions can be identified in biological circulation.
| Function in a Circulatory System | Possible Correspondence in an Engineering Self-Healing System |
|---|---|
| Supply substances | Deliver substances necessary for repair |
| Transport substances to multiple locations | Reach areas where damage may occur |
| Regulate supply | Adjust delivery according to the location and extent of damage |
| Use connected transportation pathways | Use internal channels or supply networks |
| Maintain the supply function | Enable repeated replenishment |
These correspondences may suggest a technological hypothesis involving the delivery of repair substances through internal channels rather than relying exclusively on materials stored locally.
Examining the Differences
However, biological circulation and engineered self-healing systems differ in important respects.
Biological circulation involves many physiological functions beyond material transport.
In an engineered system, incorporating internal channels may reduce the mechanical strength of the material.
If a repair substance solidifies, it may block the supply pathways.
Storage, replenishment, transportation, chemical reactions, durability, and damage detection must also be considered.
It would therefore be inappropriate to conclude immediately that a system resembling blood circulation will necessarily support repeated repair.
Formulating a Hypothesis
One possible technological hypothesis is:
“If repair substances can be replenished through supply channels incorporated into a material, the system may support repeated repair in situations where one-time local storage is insufficient.”
Evaluating this hypothesis would require investigation of channel fabrication, structural strength, clogging, damage detection, and the reaction conditions of repair materials.
In this example, biological circulation does not prove that the proposed engineering system is feasible.
Rather, analogy provides a starting point for developing a hypothesis that can be investigated experimentally.
Research on self-healing materials with vascular or channel-based delivery mechanisms already exists. This example illustrates analogical reasoning rather than claiming the discovery of an entirely new invention.
Using Multiple Analogies for the Same Problem
It is important not to become overly committed to a single source analogy.
For example, the problem of repeated material repair could be explored using several different reference systems.
| Candidate Source | Relevant Function or Relationship | Possible Direction |
|---|---|---|
| Blood Circulation | Continuous material supply to multiple destinations | Design of supply pathways |
| Plant Vascular Systems | Branched transport structures | Distributed supply pathways |
| Urban Water Networks | Network operations and pressure management | Large-scale supply control |
| Computer Networks | Route selection and alternative paths following failures | Redundancy in supply pathways |
| Logistics Systems | Supply and replenishment according to demand | Management of repair substances |
This comparison explores ideas from different domains.
However, computer network routing cannot simply be applied directly to a physical fluid network.
Communication networks transmit information, whereas fluid networks transport material substances.
Any transferred principle must therefore be examined in relation to the physical mechanisms, operating conditions, and constraints of the target.
Comparing multiple analogies can help shift attention away from imitating a particular example and toward more general functions and principles.
What Should Be Compared in an Analogy?
When comparing examples from different domains, similarity in appearance or terminology is rarely sufficient.
Depending on the problem, the following perspectives may be useful.
| Perspective | What to Examine |
|---|---|
| Function | What does the system accomplish? |
| Components | What elements constitute the system? |
| Relationships | How do the elements interact? |
| Inputs and Outputs | What enters the system, and what does it produce? |
| Mechanisms | Which physical, chemical, or informational processes are involved? |
| Control | How does the system respond to changing conditions? |
| Constraints | Under what conditions does the system function or fail? |
| Scale and Time | How do size, speed, and duration differ? |
| Feasibility | Are the necessary technologies and resources available in the target domain? |
For example, when applying a biological function to an industrial product, similarity of function alone may be insufficient.
A biological system may achieve a function through self-organization or metabolism.
Reproducing a comparable function in an engineered material or device may require an entirely different implementation mechanism.
It is important to distinguish the function shared by two systems from the mechanisms through which each system realizes that function.
Limitation 1 — Being Misled by Surface Similarity
A common problem in analogical reasoning is assuming that important relationships are the same because objects look alike or share similar terminology.
For example, a device may have a wiring structure that resembles a biological nervous system.
This does not establish that the device has the same information-processing capabilities as a nervous system.
Similarly, describing artificial intelligence as “learning” does not mean that its learning processes are identical to human experiential learning.
Similarity in terminology or metaphor does not establish similarity of mechanisms.
When evaluating analogies, it is necessary to identify the specific attributes and relationships involved and avoid overvaluing similarities that are irrelevant to the problem.
Limitation 2 — Properties Do Not Automatically Transfer Between Domains
Even when two objects are similar in several respects, other properties may differ substantially.
For example, blood circulation and industrial supply networks may share certain functions, but the physiological regulation involved in circulation cannot necessarily be transferred to industrial equipment.
This is a fundamental limitation when inferring new properties of a target from a source.
In the philosophy of science, analogical reasoning has been examined through distinctions between known similarities, known differences, and relationships that remain uncertain.
Differences are especially important when they affect the conditions required for the proposed relationship to hold.
Rather than merely accumulating similarities, it is necessary to investigate differences that might invalidate the proposed conclusion.
Limitation 3 — The Choice of Source Can Bias Thinking
When the first analogy that comes to mind seems particularly attractive, thinking may become restricted by that analogy.
For example, comparing an industrial supply system with blood circulation may encourage designers to focus mainly on piping and pumps.
However, alternative approaches might involve mobile transportation devices, localized production, storage and release mechanisms, or wireless energy transfer.
The first analogy selected does not represent every possible solution.
It may therefore be useful to compare multiple sources and consider approaches that do not rely on analogy at all.
Limitation 4 — Different Conditions of Applicability
Even when similar mechanisms exist in different domains, conditions involving materials, scale, time, temperature, pressure, cost, and safety may differ.
For example, applying a biological microstructure to an industrial material may introduce problems involving durability or mass production, even if the microscopic geometry can be reproduced.
A mechanism that works in a laboratory experiment may also fail when implemented in a much larger production system.
In physical analogies, changes in dimensions or operating conditions can alter which phenomena dominate the system’s behavior.
Consequently, visual or functional similarity does not justify assuming that the same principle will operate under different conditions.
Practical feasibility requires investigation and testing based on the specific technological conditions.
Limitation 5 — A Useful Explanation Is Not Evidence
Analogies are frequently used to explain difficult subjects.
For example, comparing business management to navigating a ship can help explain direction, external conditions, decision-making, and coordination.
However, similarity between a company and a ship does not establish that methods effective for navigation will also be effective for managing a business.
Helping someone understand an explanation is different from proving that its underlying claim is correct.
Furthermore, the analogy that is easiest to explain may not be the most appropriate analogy.
When using analogies in education or communication, it is helpful to clarify what the analogy is intended to explain and where the correspondence ends.
Analogy and Abduction
Analogy and Abduction can both contribute to hypothesis generation, but they are different forms of reasoning.
Abduction involves developing hypotheses that could explain observed phenomena.
For example, when a manufacturing process suddenly experiences an increased defect rate, an engineer might propose the hypothesis that equipment temperature control has changed.
Analogy involves comparing a situation with another example or domain.
For instance, an engineer might examine similar defects in another manufacturing process or temperature control problems in different equipment to identify possible explanations.
In this case, analogy provides candidate explanations that can contribute to abductive hypothesis formation.
| Perspective | Analogy | Abduction |
|---|---|---|
| Starting Point | Comparison between different objects or situations | An observation requiring explanation |
| Main Operation | Identify corresponding relationships | Generate hypotheses that could explain the observation |
| Possible Outcome | New relationships, applications, or explanatory candidates | Explanatory hypotheses |
| Main Limitation | Similarity does not guarantee valid inference | Plausible explanations are not necessarily correct |
These forms of reasoning are complementary rather than competing.
A hypothesis generated through analogy and abduction may then be used to derive testable predictions through deduction.
Those predictions can subsequently be evaluated using observations and experiments.
Analogy and Structure-Mapping Theory
Analogical reasoning depends on identifying correspondences between different objects or situations.
However, explaining how these correspondences are established requires a theory of cognitive processing.
Structure-Mapping Theory is one of the leading approaches to this problem.
In Gentner’s 1983 work, correspondence between relationships is emphasized over similarity between individual attributes.
The theory also emphasizes systematic relational structures rather than isolated connections.
For example, two systems may share the general function of transporting substances.
However, a richer analogy becomes possible when multiple relationships among sources, transportation pathways, destinations, and supply regulation correspond systematically.
Relationships established in the source may also suggest candidate inferences concerning the target.
Nevertheless, Structure-Mapping Theory is not the only theory of analogy.
Evaluating analogies also involves physical and semantic constraints, the intended purpose of the analogy, and other considerations.
The rules of structural correspondence and their computational implementation are examined in greater detail on the separate page devoted to Structure-Mapping Theory.
Analogy, Synectics, and TRIZ
Synectics is a creative thinking approach that explicitly incorporates analogy.
Developed by William J. J. Gordon and colleagues, Synectics emphasizes using analogies to change how problems are understood and encourage alternative ideas.
Its commonly described forms include Direct Analogy, Personal Analogy, Symbolic Analogy, and Fantasy Analogy.
TRIZ also provides methods for identifying recurring contradictions and solution principles across technological problems.
TRIZ is not synonymous with analogy, but it is related to analogical reasoning through its use of solution principles from previous technical problems in new situations.
Analogy can therefore function as an independent approach to thinking while also serving as a basic cognitive operation within other creative methods.
Analogy, Concept Maps, and Knowledge Graphs
Identifying relationships between different objects is central to analogical reasoning.
Concept Maps and Knowledge Graphs can provide ways of representing these relationships.
For example, a technical knowledge representation might contain propositions such as:
“Material M provides Function F.”
“Function F may help solve Problem P.”
If relationships from different domains are described in comparable forms, it may be possible to investigate their structural correspondences.
However, similarity between relationships depicted in two Concept Maps does not guarantee that the same technological principles apply.
Likewise, comparing relationships stored in a Knowledge Graph does not establish that a new technological hypothesis is feasible.
Relationships automatically extracted from large document collections may contain errors or omit important conditions.
Consequently, when using concept relationships to support analogy, it is desirable to retain information about sources, conditions, meanings, and verification status.
Analogy and Machine-Learned Similarity
Machine learning may help identify potential sources of analogy within large information collections.
For example, documents can be converted into embedding vectors and retrieved according to semantic similarity.
Methods such as Growing Neural Gas (GNG) can also organize large numbers of documents or examples into groups based on their characteristics, helping reveal distributions and clusters within the information.
However, discovering similarity through machine learning is different from establishing an analogy.
For example, two technical documents may use similar terminology and therefore appear close together in an embedding space.
Yet the technologies described may perform different functions or rely on different mechanisms.
Conversely, documents from different industries using very different terminology may describe systems with similar functions or relational structures.
Cross-domain analogy therefore requires examining functions, relationships, and mechanisms rather than relying exclusively on general semantic similarity.
One possible division of responsibilities is shown below.
| Processing Method | Main Role |
|---|---|
| Embedding-Based Search | Retrieve broadly related documents and candidate examples |
| GNG-Based Classification | Organize large information collections into conceptual groups |
| LLM-Based Extraction | Extract functions, mechanisms, and constraints |
| Relationship Mapping | Compare functional and relational structures between source and target |
| Hypothesis Generation | Propose potentially transferable principles |
| Verification | Investigate technological conditions and supporting evidence |
This is one possible architecture for computational support of analogical reasoning.
Proximity in a machine-learned representation does not automatically establish structural correspondence.
The effectiveness of methods such as GNG for identifying distant analogical sources also depends on how the information is represented and retrieved.
AI-Assisted Analogical Reasoning
LLMs and other AI technologies can support analogical reasoning by suggesting connections between knowledge from different domains.
For example, an AI system might be given the following question:
“Explore mechanisms from engineering, biology, logistics, information networks, and other fields that can repeatedly supply substances wherever they are needed, rather than relying on one-time local storage.”
This question describes a required function or mechanism rather than specifying the name of a particular product or technology.
Consequently, it may lead to examples from domains that would not readily occur to someone using conventional keyword searches.
However, AI-generated analogies require careful evaluation.
For example, it is relatively easy to state that blood circulation and logistics networks are similar.
It is much more difficult to determine accurately what can be transferred between them and under which conditions.
LLMs may also generate plausible but incorrect relationships between source and target domains.
Recent research has reported that LLMs can perform well on certain analogical reasoning tasks while still making errors in complex relational mapping and transfer between unfamiliar situations.
It is therefore important to distinguish generating candidate analogies from evaluating their validity.
Representing AI-Generated Analogies
For example, the following information could be retained for each candidate analogy.
| Field | Description |
|---|---|
| Target Problem | The problem to be solved and the required function |
| Source Domain | A potentially relevant example |
| Common Relationships | Corresponding functions, structures, or mechanisms |
| Differences | Differences in materials, environments, scale, or constraints |
| Application Hypothesis | How a principle from the source might be applied |
| Established Evidence | Sources confirming the mechanism in the source domain |
| Unverified Questions | Information required to assess applicability to the target |
| Verification Method | Experiments, prototypes, research, simulations, etc. |
Organizing information in this way can help transform a collection of interesting analogies into concrete research questions.
It also makes it possible to distinguish existing examples from new application hypotheses generated by AI.
Does Analogy Guarantee New Inventions?
Analogy can provide valuable starting points for developing new technologies and applications.
However, ideas generated through analogy are not necessarily novel or feasible.
A technology suggested through an analogy may already have been developed in another field.
A principle may be promising but require materials or equipment that are not currently available.
A technically feasible solution may also be impractical because of cost, safety, regulatory, maintenance, or other constraints.
When developing an analogical idea into an R&D theme, at least four questions should be considered.
First, is the proposed technology or principle already known?
Second, will the mechanism that operates in the source domain also function under the conditions of the target domain?
Third, does the proposed application offer advantages over existing alternatives?
Fourth, what investigations or experiments would be necessary to evaluate the hypothesis?
Analogical idea generation and technological feasibility assessment are separate activities.
The elegance or creativity of an analogy alone is not sufficient evidence of technological value.
Analogy in ThinkNavi
ThinkNavi treats Analogy as a fundamental approach to thinking that connects familiar knowledge or examples with different objects and problems.
Analogy is related not only to creative thinking methods such as Brainstorming, Synectics, and TRIZ, but also to problem understanding, hypothesis formation, and the construction of scientific models.
For ThinkNavi Free, a possible implementation would provide worksheets for manually recording target problems, candidate source domains, common functions, differences, and application hypotheses.
Potential LLM-assisted capabilities include discovering candidate sources, extracting functions and relationships, mapping correspondences across domains, and generating application hypotheses.
For systems such as ConceptMiner R&D, which explore opportunities across large collections of academic papers, patents, and technical information, supporting the discovery of source examples from unfamiliar domains becomes especially important.
Rather than restricting exploration to existing technological terminology or industrial classifications, a problem could be described in terms of the functions, characteristics, and mechanisms required.
This description could then be used to investigate related information across different fields.
Another possible approach would combine concept groups generated through machine learning with LLM-based examination of specific relationships and applicability conditions.
However, discovering similar information is different from identifying a meaningful analogy.
Identifying a meaningful analogy is also different from establishing that a new technology will actually work.
These are distinct stages requiring different forms of analysis and verification.
The capabilities described above represent possible directions for future development and do not imply that all are currently available.
The essential purpose of analogy is not simply to find something resembling an existing example.
It is to abstract relationships and functions from different objects and examine whether those relationships can provide useful insights into another problem.
By investigating differences as well as similarities, and by examining the conditions under which a mechanism operates, an initial idea can be developed into a testable hypothesis.
Analogy is not merely a method for repeating what we already know. It is a way of looking beyond familiar domains and discovering new relationships between different bodies of knowledge. However, whether those relationships actually hold must be established through further investigation.
Related Topics
- Structure-Mapping Theory | What Corresponds in an Analogy?
- Synectics | Discovering Solutions Across Domains Through Analogy
- Brainstorming — Generating More Ideas by Suspending Judgment
- SCAMPER | Generating New Ideas Through Seven Perspectives
- TRIZ | A Systematic Approach to Inventive Problem Solving Through Technical Contradictions
- Concept Formation | How Categories Are Made
- sConcept Maps — Representing Relationships Between Concepts and Understanding Knowledge Structures
- The KJ Method | Building Meaningful Structures from Fragmented Information
- Induction, Deduction, and Abduction — Discovering Patterns, Drawing Conclusions, and Generating New Hypothese
- The Scientific Method and Falsifiability — Generating Hypotheses and Testing Them Critically
- Problem Structuring — Breaking Down Problems, Understanding Relationships, and Finding Paths to Solutions
- Design Thinking — From Human-Centered Problem Discovery to Ideation, Prototyping, and Testing
- Systems Thinking | Interdependence and Change Over Time
- Logical Thinking — Building Sound Arguments, Communicating Reasoning, and Achieving Mutual Understanding
- Critical Thinking | Examining Claims and Evidence
- Morphological Analysis | Explore Combinations
- Concept Exploration
References and Further Reading
- Stanford Encyclopedia of Philosophy — Analogy and Analogical Reasoning
- Mary B. Hesse, Models and Analogies in Science, University of Notre Dame Press, 1966.
- Dedre Gentner, “Structure-Mapping: A Theoretical Framework for Analogy,” Cognitive Science, 7(2), 1983, pp. 155–170.
- Mary L. Gick and Keith J. Holyoak, “Analogical Problem Solving,” Cognitive Psychology, 12(3), 1980, pp. 306–355.
- Mary L. Gick and Keith J. Holyoak, “Schema Induction and Analogical Transfer,” Cognitive Psychology, 15(1), 1983, pp. 1–38.
- Dedre Gentner and Arthur B. Markman, “Structural Alignment During Similarity Comparisons,” Cognitive Psychology, 25(4), 1993, pp. 431–467.
- Brian Falkenhainer, Kenneth D. Forbus, and Dedre Gentner, “The Structure-Mapping Engine: Algorithm and Examples,” Artificial Intelligence, 41(1), 1989, pp. 1–63.
- Kenneth D. Forbus, Dedre Gentner, and Keith Law, “MAC/FAC: A Model of Similarity-Based Retrieval,” Cognitive Science, 19(2), 1995, pp. 141–205.
- Keith J. Holyoak and Paul Thagard, Mental Leaps: Analogy in Creative Thought, MIT Press, 1995.
- Douglas Hofstadter and Emmanuel Sander, Surfaces and Essences: Analogy as the Fuel and Fire of Thinking, Basic Books, 2013.
- William J. J. Gordon, Synectics: The Development of Creative Capacity, Harper, 1961.
- Mayukh Das and Wolf-Tilo Balke, “From Prototypical to Relational: How LLMs Navigate Complex Analogies,” Proceedings of the 18th International Natural Language Generation Conference, 2025.
- Sam Musker et al., “LLMs as Models for Analogical Reasoning,” Journal of Memory and Language, 145, 2025, 104676.