46 The What and Why of Categories
The What and Why of Categories
Categories are equivalence classes, sets or groups of things or abstract entities that we treat the same. This does not mean that every instance of a category is identical, only that from some perspective, or for some purpose, we are treating them as equivalent based on what they have in common. When we consider something as a member of a category, we are making choices about which of its properties or roles we are focusing on and which ones we are ignoring. We do this automatically and unconsciously most of the time, but we can also do it in an explicit and self-aware way. When we create categories with conscious effort, we often say that we are creating a model, or just modeling. You should be familiar with the idea that a model is a set of simplified descriptions or a physical representation that removes some complexity to emphasize some features or characteristics and to de-emphasize others.
When we encounter objects or situations, recognizing them as members of a category helps us know how to interact with them. For example, when we enter an unfamiliar building we might need to open or pass through an entryway that we recognize as a door. We might never have seen that particular door before, but it has properties and affordances that we know that all doors have; it has a doorknob or a handle; it allows access to a larger space; it opens and closes. By mentally assigning this particular door to the “doors” category we distinguish it from “windows,” a category that also contains objects that sometimes have handles and that open and close, but which we do not normally pass through to enter another space. Categorization judgments are therefore not just about what is included in a class, but also about what is excluded from a class. Nevertheless, the category boundaries are not sharp; a “Dutch door” is divided horizontally in half so that the bottom can be closed like a door while the top can stay open like a window.
Categories are cognitive and linguistic models for applying prior knowledge; creating and using categories are essential human activities. Categories enable us to relate things to each other in terms of similarity and dissimilarity and are involved whenever we perceive, communicate, analyze, predict, or classify. Without categories, we would perceive the world as an unorganized blur of things with no understandable or memorable relation to each other. Every wall-entry we encounter would be new to us, and we would have to discover its properties and supported interactions as though we had never before encountered a door. Of course, we still often need to identify something as a particular instance, but categories enable us to understand how it is equivalent to other instances. We can interchangeably relate to something as specific as “the wooden door to the main conference room” or more generally as “any door.”
All human languages and cultures divide up the world into categories. How and why this takes place has long been debated by philosophers, psychologists and anthropologists. One explanation for this differentiation is that people recognize structure in the world, and then create categories of things that “go together” or are somehow similar. An alternative view says that human minds make sense of the world by imposing structure on it, and that what goes together or seems similar is the outcome rather than a cause of categorization. Bulmer framed the contrast in a memorable way by asking which came first, the chicken (the objective facts of nature) or the egghead (the role of the human intellect).
A secondary and more specialized debate going on for the last few decades among linguists, cognitive scientists, and computer scientists concerns the extent to which the cognitive mechanisms involved in category formation are specialized for that purpose rather than more general learning processes.
Even before they can talk, children behave in ways that suggest they have formed categories based on shape, color, and other properties they can directly perceive in physical objects.
People almost effortlessly learn tens of thousands of categories embodied in the culture and language in which they grow up. People also rely on their own experiences, preferences, and goals to adapt these cultural categories or create entirely individual ones that they use to organize resources that they personally arrange. Later on, through situational training and formal education, people learn to apply systematic and logical thinking processes so that they can create and understand categories in engineering, logistics, transport, science, law, business, and other institutional contexts.
These three contexts of cultural, individual, and institutional categorization share some core ideas but they emphasize different processes and purposes for creating categories, so they are a useful distinction. Cultural categorization can be understood as a natural human cognitive ability that serves as a foundation for both informal and formal organizing systems. Individual categorization tends to grow spontaneously out of our personal activities. Institutional categorization responds to the need for formal coordination and cooperation within and between companies, governments, and other goal-oriented enterprises.
In contrast to these three categorization contexts in which categories are created by people, computational categories are created by computer programs for information retrieval, machine learning, predictive analytics, and other applications. Computational categories are similar to those created by people in some ways but differ substantially in other ways.
Cultural categories are the archetypical form of categories upon which individual and institutional categories are usually based. Cultural categories tend to describe our everyday experiences of the world and our accumulated cultural knowledge. Such categories describe objects, events, settings, internal experiences, physical orientation, relationships between entities, and many other aspects of human experience. Cultural categories are learned primarily, with little explicit instruction, through normal exposure of children with their caregivers; they are associated with language acquisition and language use within particular cultural contexts.
Two thousand years ago Plato wrote that living species could be identified by “carving nature at its joints,” the natural boundaries or discontinuities between types of things where the differences are the largest or most salient. Plato’s metaphor is intuitively appealing because we can easily come up with examples of perceptible properties or behaviors of physical things that go together that make some ways of categorizing them seem more natural than others.
Natural languages rely heavily on nouns to talk about categories of things because it is useful to have a shorthand way of referring to a set of properties that co-occur in predictable ways. For example, in English (borrowed from Portuguese) we have a word for “banana” because a particular curved shape, greenish-yellow or yellow color, and a convenient size tend to co-occur in a familiar edible object, so it became useful to give it a name. The word “banana” brings together this configuration of highly interrelated perceptions into a unified concept so we do not have to refer to bananas by listing their properties.
Languages differ a great deal in the words they contain and also in more fundamental ways that they require speakers or writers to attend to details about the world or aspects of experience that another language allows them to ignore. This idea is often described as linguistic relativity. (See the sidebar, Linguistic Relativity.)
For example, speakers of the Australian aboriginal language, Guugu Yimithirr, do not use concepts of left and right, but rather use cardinal directions. Where in English we might say to a person facing north, “Take a step to your left,” they would use their term for west. If the person faced south, we would change our instruction to “right,” but they would still use their term for west. Imagine how difficult it would be for a speaker of Guugu Yimithirr and a speaker of English to collaborate in organizing a storage room or a closet.
It is not controversial to notice that different cultures and language communities have different experiences and activities that give them contrasting knowledge about particular domains. No one would doubt that university undergraduates in Chicago would think differently about animals than inhabitants of Guatemalan rain forests, or even that different types of “tree experts” (taxonomists, landscape workers, foresters, and tree maintenance personnel) would categorize trees differently.
On the other hand, despite the wide variation in the climates, environments, and cultures that produce them, at a high level “folk taxonomies” that describe natural phenomena are surprisingly consistent around the world. Half a century ago the sociologists Emile Durkheim and Marcel Mauss observed that the language and structure of folk taxonomies mirrors that of human family relationships (e.g., different types of trees might be “siblings,” but animals would be part of another family entirely). They suggested that framing the world in terms of familiar human relationships allowed people to understand it more easily.
Anthropologist Brent Berlin, a more recent researcher, concurs with Durkheim and Mauss’s observation that kinship relations and folk taxonomies are related, but argues that humans patterned their family structures after the natural world, not the other way around.
Individual categories are created in an organizing system to satisfy the ad hoc requirements that arise from a person’s unique experiences, preferences, and resource collections. Unlike cultural categories, which usually develop slowly and last a long time, individual categories are created by intentional activity, in response to a specific situation, or to solve an emerging organizational challenge. As a consequence, the categories in individual organizing systems generally have short lifetimes and rarely outlive the person who created them.
Individual categories draw from cultural categories but differ in two important ways. First, individual categories sometimes have an imaginative or metaphorical basis that is meaningful to the person who created them but which might distort or misinterpret cultural categories. Second, individual categories are often specialized or synthesized versions of cultural categories that capture particular experiences or personal history. For example, a person who has lived in China and Mexico, or lived with people from those places, might have highly individualized categories for foods they like and dislike that incorporate characteristics of both Chinese and Mexican cuisine.
Individual categories in organizing systems also reflect the idiosyncratic set of household goods, music, books, website bookmarks, or other resources that a person might have collected over time. The organizing systems for financial records, personal papers, or email messages often use highly specialized categories that are shaped by specific tasks to be performed, relationships with other people, events of personal history, and other highly individualized considerations. Put another way, individual categories are used to organize resource collections that are likely not representative samples of all resources of the type being collected. If everyone had the same collection of music, books, clothes, or toys the world would be a boring place.
Traditionally, individual categorization systems were usually not visible to, or shared with, others, whereas, this has become an increasingly common situation for people using web-based organizing system for pictures, music, or other personal resources. On websites like the popular Flickr, Instagram, and YouTube sites for photos and videos, people typically use existing cultural categories to tag their content as well as individual ones that they invent.
In contrast to cultural categories that are created and used implicitly, and to individual categories that are used by people acting alone, institutional categories are created and used explicitly, and most often by many people in coordination with each other. Institutional categories are most often created in abstract and information-intensive domains where unambiguous and precise categories are needed to regulate and systematize activity, to enable information sharing and reuse, and to reduce transaction costs. Furthermore, instead of describing the world as it is, institutional categories are usually defined to change or control the world by imposing semantic models that are more formal and arbitrary than those in cultural categories. Laws, regulations, and standards often specify institutional categories, along with decision rules for assigning resources to new categories, and behavior rules that prescribe how people must interact with them. The rigorous definition of institutional categories enables classification: the systematic assignment of resources to categories in an organizing system.
Creating institutional categories by more systematic processes than cultural or individual categories does not ensure that they will be used in systematic and rational ways, because the reasoning and rationale behind institutional categories might be unknown to, or ignored by, the people who use them. Likewise, this way of creating categories does not prevent them from being biased. Indeed, the goal of institutional categories is often to impose or incentivize biases in interpretation or behavior. There is no better example of this than the practice of gerrymandering, designing the boundaries of election districts to give one political party or ethnic group an advantage.(See the sidebar, Gerrymandering in Illinois.)
Institutional categorization stands apart from individual categorization primarily because it invariably requires significant efforts to reconcile mismatches between existing individual categories, where those categories embody useful working or contextual knowledge that is lost in the move to a formal institutional system.
Institutional categorization efforts must also overcome the vagueness and inconsistency of cultural categories because the former must often conform to stricter logical standards to support inference and meet legal requirements. Furthermore, institutional categorization is usually a process that must be accounted for in a budget and staffing plans. While some kinds of institutional categories can be devised or discovered by computational processes, most of them are created through the collaboration of many individuals, typically from various parts of an organization or from different firms. For example, with the gerrymandering case we just discussed, it is important to emphasize that the inputs to these programs and the decisions about districting are controlled by people, which is why the districts are institutional categories; the programs are simply tools that make the process more efficient. 
The different business or technical perspectives of the participants are often the essential ingredients in developing robust categories that can meet carefully identified requirements. And as requirements change over time, institutional categories must often change as well, implying version control, compliance testing, and other formal maintenance and governance processes.
Some institutional categories that initially had narrow or focused applicability have found their way into more popular use and are now considered cultural categories. A good example is the periodic table in chemistry, which Mendeleev developed in 1869 as a new system of categories for the chemical elements. The periodic table proved essential to scientists in understanding their properties and in predicting undiscovered ones. Today the periodic table is taught in elementary schools, and many things other than elements are commonly arranged using a graphical structure that resembles the periodic table of elements in chemistry, including sci-fi films and movies, desserts, and superheroes.
A “Categorization Continuum”
As we have seen, the concepts of cultural, individual, and institutional categorization usefully distinguish the primary processes and purposes when people create categories. However, these three kinds of categories can fuse, clash, and recombine with each other. Rather than viewing them as having precise boundaries, we might view them as regions on a continuum of categorization activities and methods.
Consider a few different perspectives on categorizing animals as an example. Scientific institutions categorize animals according to explicit, principled classification systems, such as the Linnaean taxonomy that assigns animals to a phylum, class, order, family, genus and species. Cultural categorization practices cannot be adequately described in terms of a master taxonomy, and are more fluid, converging with principled taxonomies sometimes, and diverging at other times. While human beings are classified within the animal kingdom in biological classification systems, people are usually not considered animals in most cultural contexts. Sometimes a scientific designation for human beings, homo sapiens is even applied to human beings in cultural contexts, since the genus-species taxonomic designation has influenced cultural conceptions of people and (other) animals over the years.
Animals are also often culturally categorized as pets or non-pets. The category “pets” commonly includes dogs, cats, and fish. A pet cat might be categorized at multiple levels that incorporate individual, cultural, and institutional perspectives on categorization—as an “animal” (cultural/institutional), as a “mammal” (institutional), as a “domestic short-hair” (institutional) as a “cat” (cultural), and as a “troublemaker” or a “favorite” (individual), among other possibilities, in addition to being identified individually by one or more pet names. Furthermore, not everyone experiences pets as just dogs, cats and fish. Some people have relatively unusual pets, like pigs. For individuals who have pet pigs or who know people with pet pigs, “pigs” may be included in the “pets” category. If enough people have pet pigs, eventually “pigs” could be included in mainstream culture’s pet category.
Categorization skewed toward cultural perspectives incorporate relatively traditional categories, such as those learned implicitly from social interactions, like mainstream understandings of what kinds of animals are “pets,” while categorization skewed toward institutional perspectives emphasizes explicit, formal categories, like the categories employed in biological classification systems.
Computational categories are created by computer programs when the number of resources, or when the number of descriptions or observations associated with each resource, are so large that people cannot think about them effectively. Computational categories are created for information retrieval, predictive analytics, and other applications where information scale or speed requirements are critical. The resulting categories are similar to those created by people in some ways but differ substantially in other ways.
The simplest kind of computational categories can be created using descriptive statistics (see “Organizing With Descriptive Statistics”). Descriptive statistics do not identify the categories they create by giving them familiar cultural or institutional labels. Instead, they create implicit categories of items according to how much they differ from the most typical or frequent ones. For example, in any dataset where the values follow the normal distribution, statistics of central tendency and dispersion serve as standard reference measures for any observation. These statistics identify categories of items that are very different or statistically unlikely outliers, which could be signals of measurement errors, poorly calibrated equipment, employees who are inadequately trained or committing fraud, or other problems. The “Six Sigma” methodology for process improvement and quality control rests on this idea that careful and consistent collection of statistics can make any measurable operation better.
Many text processing methods and applications use simple statistics to categorize words by their frequency in a language, in a collection of documents, or in individual documents, and these categories are exploited in many information retrieval applications (see “Interactions Based on Instance Properties” and “Interactions Based on Collection Properties”).
Categories that people create and label also can be used more explicitly in computational algorithms and applications. In particular, a program that can assign an item or instance to one or more existing categories is called a classifier. The subfield of computer science known as machine learning is home to numerous techniques for creating classifiers by training them with already correctly categorized examples. This training is called supervised learning; it is supervised because it starts with instances labeled by category, and it involves learning because over time the classifier improves its performance by adjusting the weights for features that distinguish the categories. But strictly speaking, supervised learning techniques do not learn the categories; they implement and apply categories that they inherit or are given to them. We will further discuss the computational implementation of categories created by people in “Implementing Categories”.
In contrast, many computational techniques in machine learning can analyze a collection of resources to discover statistical regularities or correlations among the items, creating a set of categories without any labeled training data. This is called unsupervised learning or statistical pattern recognition. As we pointed out in “Cultural Categories”, we learn most of our cultural categories without any explicit instruction about them, so it is not surprising that computational models of categorization developed by cognitive scientists often employ unsupervised statistical learning methods.
Many computational categories are like individual categories because they are tied to specific collections of resources or data and are designed to satisfy narrow goals. The individual categories you use to organize your email inbox or the files on your computer reflect your specific interests, activities, and personal network and are surely different than those of anyone else. Similarly, your credit card company analyzes your specific transactions to create computational categories of “likely good” and “likely fraudulent” that are different for every cardholder.
This focused scope is obvious when we consider how we might describe a computational category. “Fraudulent transaction for cardholder 4264123456780123” is not lexicalized with a one-word label as familiar cultural categories are. “Door” and “window” have broad scopes that are not tied to a single purpose. Put another way, the “door” and “window” cultural categories are highly reusable, as are institutional categories like those used to collect economic or health data that can be analyzed for many different purposes. The definitions of “door” and “window” might be a little fuzzy, but institutional categories are more precisely defined, often by law or regulation. Examples are the North American Industry Classification System(NAICS) from the US Census Bureau and the United Nations Standard Products and Services Code(UNSPC).
A final contrast between categories created by people and those created computationally is that the former can almost always be inspected and reasoned about by other people, but only some of the latter can. A computational model that categorizes loan applicants as good or poor credit risks probably uses properties like age, income, home address, and marital status, so that a banker can understand and explain a credit decision. However, many other computational categories, especially those that created by clustering and deep learning techniques, are inseparable from the mathematical model that learned to use them, and as a result are uninterpretable by people.
A machine learning algorithm for classifying objects in images creates a complex multi-layer neural network whose features have no clear relationship to the categories, and this network has no other use. Put another way, machine learning programs are very general because they can be employed in any domain with high dimensional data, but what they learn cannot be applied in any other domain.
Cognitive science mostly focuses on the automatic and unconscious mechanisms for creating and using categories. This disciplinary perspective emphasizes the activation of category knowledge for the purpose of making inferences and “going beyond the information given,” to use Bruner’s classic phrase (Bruner 1957). In contrast, the discipline of organizing focuses on the explicit and self-aware mechanisms for creating and using categories because by definition, organizing systems serve intentional and often highly explicit purposes. Organizing systems facilitate inferences about the resources they contain, but the more constrained purposes for which resources are described and arranged makes inference a secondary goal.
Cognitive science is also highly focused on understanding and creating computational models of the mechanisms for creating and using categories. These models blend data-driven or bottom-up processing with knowledge-driven or top-down processing to simulate the time course and results of categorization at both fine-grained scales (as in word or object recognition) and over developmental time frames (as in how children learn categories). The discipline of organizing can learn from these models about the types of properties and principles that organizing systems use, but these computational models are not a primary concern to us in this book.
However, even the way this debate has been framed is a bit controversial. Bulmer’s chicken, the “categories are in the world” position, has been described as empirical, environment-driven, bottom-up, or objectivist, and these are not synonymous. Likewise, the “egghead” position that “categories are in the mind” has been called rational, constructive, top-down, experiential, and embodied—and they are also not synonyms. See (Bulmer 1970). See also (Lakoff 1990), (Malt 1995).
Is there a “universal grammar” or a “language faculty” that imposes strong constraints on human language and cognition? (Chomsky 1965) and (Jackendoff 1996) think so. Such proposals imply cognitive representations in which categories are explicit structures in memory with associated instances and properties. In contrast, generalized learning theories model category formation as the adjustment of the patterns and weighting of connections in neural processing networks that are not specialized for language in any way. Computational simulations of semantic networks can reproduce the experimental and behavioral results about language acquisition and semantic judgments that have been used as evidence for explicit category representations without needing anything like them. (Rogers and McClelland 2008) thoroughly review the explicit category models and then show how relatively simple learning models can do without them.
The debates about human category formation also extend to issues of how children learn categories and categorization methods. Most psychologists argue that category learning starts with general learning mechanisms that are very perceptually based, but they do not agree whether to characterize these changes as “stages” or as phases in a more complex dynamical system. Over time more specific learning techniques evolve that focus on correlations among perceptual properties (things with wings tend to have feathers), correlations among properties and roles (things with eyes tend to eat), and ultimately correlations among roles (things that eat tend to sleep). See (Smith and Thelen 2003).
These three contexts were proposed by (Glushko, Maglio, Matlock, and Barsalou 2008), who pointed out that cognitive science has focused on cultural categorization and largely ignored individual and institutional contexts. They argue that taking a broader view of categorization highlights dimensions on which it varies that are not apparent when only cultural categories are considered. For example, institutional categories are usually designed and maintained using prescriptive methods that have no analogues with cultural categories. There is a difference between institutional categories created for people, and categories created in institutions by computers in the predictive analytics, data mining sense.
This quote comes from Plato’s Phaedrus dialogue, written around 370 BCE. Contemporary philosophers and cognitive scientists commonly invoke it in discussions about whether “natural kinds” exist. . For example, see (Campbell, O’Rourke, and Slater 2011), and (Hutchins 2010), (Atran 1987), and others have argued that the existence of perceptual discontinuities is not sufficient to account for category formation. Instead, people assume that members of a biological category must have an essence of co-occurring properties and these guide people to focus on the salient differences, thereby creating categories. Property clusters enable inferences about causality, which then builds a framework on which additional categories can be created and refined. For example, if “having wings” and “flying” are co-occurring properties that suggest a “bird” category, wings are then inferred as the causal basis of flying, and wings become more salient.
Pronouns, adjectives, verbs, adverbs, prepositions, conjunctions, particles, and numerals and other “parts of speech” are also grammatical categories, but nouns carry most of the semantic weight.
In contrast, the set of possible interactions with even a simple object like a banana is very large. We can pick, peel, slice, smash, eat, or throw a banana, so instead of capturing this complexity in the meaning of banana it gets parceled into the verbs that can act on the banana noun. Doing so requires languages to use verbs to capture a broader and more abstract type of meaning that is determined by the nouns with which they are combined. Familiar verbs like “set,” “put,” and “get” have dozens of different senses as a result because they go with so many different nouns. We set fires and we set tables, but fires and tables have little in common. The intangible character of verbs and the complexity of multiple meanings make it easier to focus instead on their associated nouns, which are often physical resources, and create organizing systems that emphasize the latter rather than the former. We create organizing systems that focus on verbs when we are categorizing actions, behaviors, or services where the resources that are involved are less visible or less directly involved in the supported interactions.
Many languages have a system of grammatical gender in which all nouns must be identified as masculine or feminine using definite articles (el and la in Spanish, le and la in French, and so on) and corresponding pronouns. Languages also contrast in how they describe time, spatial relationships, and in which things are treated as countable objects (one ox, two oxen) as opposed to substances or mass nouns that do not have distinct singular and plural forms (like water or dirt). (Deutscher 2011) carefully reviews and discredits the strong Whorfian view and makes the case for a more nuanced perspective on linguistic relativity. He also reviews much of Lera Boroditsky’s important work in this area. George Lakoff’s book with the title Women, Fire, and Dangerous Things (Lakoff 1990) provocatively points out differences in gender rules among languages; in an aboriginal language called Dyirbal many dangerous things, including fire have feminine gender, meanwhile “fire” is masculine in Spanish (el feugo) and French (le feu).
This analysis comes from (Haviland 1998). More recently, Lera Boroditsky has done many interesting studies and experiments about linguistic relativity. See (Boroditksy 2003) for an academic summary and (Boroditsky 2010, 2011) for more popular treatments.
This was ultimately reflected in complex mythological systems, such as Greek mythology, where genealogical relationships between gods represented category relationships among the phenomena with which they were associated. As human knowledge grew and the taxonomies became more comprehensive and complex, Durkheim and Mauss argued, they lay the groundwork for scientific classifications and shed their mythological roots. (Durkheim 1963).
The personal archives of people who turn out to be famous or important are the exception that proves this rule. In that case, the individual’s organizing system and its categories are preserved along with their contents.
The typical syntactic constraint that tags are delimited by white space encourages the creation of new categories by combining existing category names using concatenation and camel case conventions; photos that could be categorized as “Berkeley” and “Student” are sometimes tagged as “BerkeleyStudent.” Similar generative processes for creating individual category names are used with Twitter “hashtags” where tweets about events are often categorized with an ad hoc tag that combines an event name and a year identifier like “#NBAFinals16.”
Consider how the cultural category of “killing a person” is refined by the legal system to distinguish manslaughter and different degrees of murder based on the amount of intentionality and planning involved (e.g., first and second degree murder) and the roles of people involved with the killing (accessory). In general, the purpose of laws is to replace coarse judgments of categorization based on overall similarity of facts with rule-based categorization based on specific dimensions or properties.
The word was invented in 1812 in a newspaper article critical of Massachusetts governor Elbridge Gerry, who oversaw the creation of biased electoral districts. One such district was so contorted in shape, it was said to look like a salamander, and thus was called a Gerrymander. The practice remains widespread, but nowadays sophisticated computer programs can select voters on any number of characteristics and create boundaries that either “pack” them into a single district to concentrate their voting power or “crack” them into multiple districts to dilute it.
The particularities or idiosyncrasies of individual categorization systems sometimes capture user expertise and knowledge that is not represented in the institutional categories that replace them. Many of the readers of this book are information professionals whose technological competence is central to their work and which helps them to be creative. But for a great many other people, information technology has enabled the routinization of work in offices, assembly lines, and in other jobs where new institutionalized job categories have “downskilled” or “deskilled” the nature of work, destroying competence and engendering a great deal of resistance from the affected workers.
Similar technical concerns arise in within-company and multi-company standardization efforts, but the competitive and potentially anti-competitive character of the latter imposes greater complexity by introducing considerations of business strategy and politics. Credible standards-making in multi-company contexts depends on an explicit and transparent process for gathering and prioritizing requirements, negotiating specifications that satisfy them, and ensuring conformant implementations—without at any point giving any participating firm an advantage. See the OASIS Technical Committee Process for an example (
https://www.oasis-open.org/policies-guidelines/tc-process) and (Rosenthal et al. 2004) for an analysis of best practices.
Unfortunately, in this transition from science to popular culture, many of these so-called periodic tables are just ad hoc collections that ignore the essential idea that the rows and columns capture explanatory principles about resource properties that vary in a periodic manner. A notable exception is Andrew Plotkin's Periodic Table of Dessert. See (Suehle 2012) and Plotkin's table at (Periodic Table of Dessert).
The Corporate Average Fuel Economy(CAFE) standards have been developed by the United States National Highway Traffic Safety Administration (
http://www.nhtsa.gov/fuel-economy) since 1975. For a careful and critical assessment of CAFE, including the politics of categorization for vehicles like the PT Cruiser, see the 2002 report from the Committee on the Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards, National Research Council.