Subset (3)

documents · 289k rows

Split (1)

train · 289k rows

chunk_id stringlengths 3 8	chunk stringlengths 1 1k
9791_63	Reducibility Susan Haack once claimed that a many-valued logic requires neither intermediate terms between true and false, nor a rejection of bivalence. Her suggestion was, that the intermediate terms (i.e. the gradations of truth) can always be restated as conditional if-then statements, and by implication, that fuzzy logic is fully reducible to binary true-or-false logic. This interpretation is disputed (it assumes that the knowledge already exists to fit the intermediate terms to a logical sequence), but even if it was correct, assigning a number to the applicability of a statement is often enormously more efficient than a long string of if-then statements that would have the same intended meaning. That point is obviously of great importance to computer programmers, educators and administrators seeking to code a process, activity, message or operation as simply as possible, according to logically consistent rules.
9791_64	Quantification It may be wonderful to have access to an unlimited number of distinctions to define what one means, but not all scholars would agree that any concept is equal to, or reducible to, a mathematical set. Some phenomena are difficult or impossible to quantify and count, in particular if they lack discrete boundaries (for example, clouds).
9791_65	Formalization Qualities may not be fully reducible to quantities – if there are no qualities, it may become impossible to say what the numbers are numbers of, or what they refer to, except that they refer to other numbers or numerical expressions such as algebraic equations. A measure requires a counting unit defined by a category, but the definition of that category is essentially qualitative; a language which is used to communicate data is difficult to operate, without any qualitative distinctions and categories. We may, for example, transmit a text in binary code, but the binary code does not tell us directly what the text intends. It has to be translated, decoded or converted first, before it becomes comprehensible.
9791_66	In creating a formalization or formal specification of a concept, for example for the purpose of measurement, administrative procedure or programming, part of the meaning of the concept may be changed or lost. For example, if we deliberately program an event according to a concept, it might kill off the spontaneity, spirit, authenticity and motivational pattern which is ordinarily associated with that type of event.
9791_67	Quantification is not an unproblematic process. To quantify a phenomenon, we may have to introduce special assumptions and definitions which disregard part of the phenomenon in its totality. The economist John Maynard Keynes concluded that formalization "runs the risk of leaving behind the subjectmatter we are interested in" and "also runs the risk of increasing rather than decreasing the muddle." Friedrich Hayek stated that “it is certainly not scientific to insist on measurement where you don't know what your measurements mean. There are cases where measurements are not relevant.” The Hayekian big data guru Viktor Mayer-Schönberger states that "A system based on money and price solved a problem of too much information and not enough processing power, but in the process of distilling information down to price, many details get lost."
9791_68	Michael Polanyi stated that "the process of formalizing all knowledge to the exclusion of any tacit knowing is self-defeating", since to mathematize a concept we need to be able to identify it in the first instance without mathematization.
9791_69	Measurement Programmers, statisticians or logicians are concerned in their work with the main operational or technical significance of a concept which is specifiable in objective, quantifiable terms. They are not primarily concerned with all kinds of imaginative frameworks associated with the concept, or with those aspects of the concept which seem to have no particular functional purpose – however entertaining they might be. However, some of the qualitative characteristics of the concept may not be quantifiable or measurable at all, at least not directly. The temptation exists to ignore them, or try to infer them from data results.
9791_70	If, for example, we want to count the number of trees in a forest area with any precision, we have to define what counts as one tree, and perhaps distinguish them from saplings, split trees, dead trees, fallen trees etc. Soon enough it becomes apparent that the quantification of trees involves a degree of abstraction – we decide to disregard some timber, dead or alive, from the population of trees, in order to count those trees that conform to our chosen concept of a tree. We operate in fact with an abstract concept of what a tree is, which diverges to some extent from the true diversity of trees there are.
9791_71	Even so, there may be some trees, of which it is not very clear, whether they should be counted as a tree, or not; a certain amount of "fuzziness" in the concept of a tree may therefore remain. The implication is, that the seemingly "exact" number offered for the total quantity of trees in the forest may be much less exact than one might think - it is probably more an estimate or indication of magnitude, rather than an exact description. Yet - and this is the point - the imprecise measure can be very useful and sufficient for all intended purposes. It is tempting to think, that if something can be measured, it must exist, and that if we cannot measure it, it does not exist. Neither might be true. Researchers try to measure such things as intelligence or gross domestic product, without much scientific agreement about what these things actually are, how they exist, and what the correct measures might be.
9791_72	When one wants to count and quantify distinct objects using numbers, one needs to be able to distinguish between those separate objects, but if this is difficult or impossible, then, although this may not invalidate a quantitative procedure as such, quantification is not really possible in practice; at best, we may be able to assume or infer indirectly a certain distribution of quantities that must be there. In this sense, scientists often use proxy variables to substitute as measures for variables which are known (or thought) to be there, but which themselves cannot be observed or measured directly. Vague or fuzzy The exact relationship between vagueness and fuzziness is disputed.
9791_73	Philosophy Philosophers often regard fuzziness as a particular kind of vagueness, and consider that "no specific assignment of semantic values to vague predicates, not even a fuzzy one, can fully satisfy our conception of what the extensions of vague predicates are like". Surveying recent literature on how to characterize vagueness, Matti Eklund states that appeal to lack of sharp boundaries, borderline cases and “sorites-susceptible" predicates are the three informal characterizations of vagueness which are most common in the literature.
9791_74	Zadeh's argument However, Lotfi A. Zadeh claimed that "vagueness connotes insufficient specificity, whereas fuzziness connotes unsharpness of class boundaries". Thus, he argued, a sentence like "I will be back in a few minutes" is fuzzy but not vague, whereas a sentence such as "I will be back sometime", is fuzzy and vague. His suggestion was that fuzziness and vagueness are logically quite different qualities, rather than fuzziness being a type or subcategory of vagueness. Zadeh claimed that "inappropriate use of the term 'vague' is still a common practice in the literature of philosophy". Ethics In the scholarly inquiry about ethics and meta-ethics, vague or fuzzy concepts and borderline cases are standard topics of controversy. Central to ethics are theories of "value", what is "good" or "bad" for people and why that is, and the idea of "rule following" as a condition for moral integrity, consistency and non-arbitrary behaviour.
9791_75	Yet, if human valuations or moral rules are only vague or fuzzy, then they may not be able to orient or guide behaviour. It may become impossible to operationalize rules. Evaluations may not permit definite moral judgements, in that case. Hence, clarifying fuzzy moral notions is usually considered to be critical for the ethical endeavour as a whole. Excessive precision Nevertheless, Scott Soames has made the case that vagueness or fuzziness can be valuable to rule-makers, because "their use of it is valuable to the people to whom rules are addressed". It may be more practical and effective to allow for some leeway (and personal responsibility) in the interpretation of how a rule should be applied - bearing in mind the overall purpose which the rule intends to achieve.
9791_76	If a rule or procedure is stipulated too exactly, it can sometimes have a result which is contrary to the aim which it was intended to help achieve. For example, "The Children and Young Persons Act could have specified a precise age below which a child may not be left unsupervised. But doing so would have incurred quite substantial forms of arbitrariness (for various reasons, and particularly because of the different capacities of children of the same age)".
9791_77	Rule conflict A related sort of problem is, that if the application of a legal concept is pursued too exactly and rigorously, it may have consequences that cause a serious conflict with another legal concept. This is not necessarily a matter of bad law-making. When a law is made, it may not be possible to anticipate all the cases and events to which it will apply later (even if 95% of possible cases are predictable). The longer a law is in force, the more likely it is, that people will run into problems with it, that were not foreseen when the law was made. So, the further implications of one rule may conflict with another rule. "Common sense" might not be able to resolve things. In that scenario, too much precision can get in the way of justice. Very likely a special court ruling wil have to set a norm. The general problem for jurists is, whether "the arbitrariness resulting from precision is worse than the arbitrariness resulting from the application of a vague standard".
9791_78	Mathematics The definitional disputes about fuzziness remain unresolved so far, mainly because, as anthropologists and psychologists have documented, different languages (or symbol systems) that have been created by people to signal meanings suggest different ontologies. Put simply: it is not merely that describing "what is there" involves symbolic representations of some kind. How distinctions are drawn, influences perceptions of "what is there", and vice versa, perceptions of "what is there" influence how distinctions are drawn. This is an important reason why, as Alfred Korzybski noted, people frequently confuse the symbolic representation of reality, conveyed by languages and signs, with reality itself.
9791_79	Fuzziness implies, that there exists a potentially infinite number of truth values between complete truth and complete falsehood. If that is the case, it creates the foundational issue of what, in the case, can justify or prove the existence of the categorical absolutes which are assumed by logical or quantitative inference. If there is an infinite number of shades of grey, how do we know what is totally black and white, and how could we identify that? Tegmark To illustrate the ontological issues, cosmologist Max Tegmark argues boldly that the universe consists of math: "If you accept the idea that both space itself, and all the stuff in space, have no properties at all except mathematical properties," then the idea that everything is mathematical "starts to sound a little bit less insane."
9791_80	Tegmark moves from the epistemic claim that mathematics is the only known symbol system which can in principle express absolutely everything, to the methodological claim that everything is reducible to mathematical relationships, and then to the ontological claim, that ultimately everything that exists is mathematical (the mathematical universe hypothesis). The argument is then reversed, so that because everything is mathematical in reality, mathematics is necessarily the ultimate universal symbol system. The main criticisms of Tegmark's approach are that (1) the steps in this argument do not necessarily follow, (2) no conclusive proof or test is possible for the claim that such an exhaustive mathematical expression or reduction is feasible, and (3) it may be that a complete reduction to mathematics cannot be accomplished, without at least partly altering, negating or deleting a non-mathematical significance of phenomena, experienced perhaps as qualia.
9791_81	Zalta In his meta-mathematical metaphysics, Edward N. Zalta has claimed that for every set of properties of a concrete object, there always exists exactly one abstract object that encodes exactly that set of properties and no others - a foundational assumption or axiom for his ontology of abstract objects By implication, for every fuzzy object there exists always at least one defuzzified concept which encodes it exactly. It is a modern interpretation of Plato's metaphysics of knowledge, which expresses confidence in the ability of science to conceptualize the world exactly.
9791_82	Platonism The Platonic-style interpretation was critiqued by Hartry H. Field. Mark Balaguer argues that we do not really know whether mind-independent abstract objects exist or not; so far, we cannot prove whether Platonic realism is definitely true or false. Defending a cognitive realism, Scott Soames argues that the reason why this unsolvable conundrum has persisted, is because the ultimate constitution of the meaning of concepts and propositions was misconceived. Traditionally, it was thought that concepts can be truly representational, because ultimately they are related to intrinsically representational Platonic complexes of universals and particulars. However, once concepts and propositions are regarded as cognitive-event types, it is possible to claim that they are able to be representational, because they are constitutively related to intrinsically representational cognitive acts in the real world. As another philosopher put it,
9791_83	Along these lines, it could be argued that reality, and the human cognition of reality, will inevitably contain some fuzzy characteristics, which can be represented only by concepts which are themselves fuzzy to some or other extent. Social science and the media The idea of fuzzy concepts has also been applied in the philosophical, sociological and linguistic analysis of human behaviour.
9791_84	Sociology and linguistics In a 1973 paper, George Lakoff analyzed hedges in the interpretation of the meaning of categories. Charles Ragin and others have applied the idea to sociological analysis. For example, fuzzy set qualitative comparative analysis ("fsQCA") has been used by German researchers to study problems posed by ethnic diversity in Latin America. In New Zealand, Taiwan, Iran, Malaysia, the European Union and Croatia, economists have used fuzzy concepts to model and measure the underground economy of their country. Kofi Kissi Dompere applied methods of fuzzy decision, approximate reasoning, negotiation games and fuzzy mathematics to analyze the role of money, information and resources in a "political economy of rent-seeking", viewed as a game played between powerful corporations and the government.
9791_85	Thomas Kron uses fuzzy logic to model sociological theory. On the one hand, he has presented an integral action-theoretical model with the help of fuzzy logic. With Lars Winter he works on the extension of the system theory of Niklas Luhmann by means of the "Kosko-Cube". Furthermore, he has explained transnational terrorism and other contemporary phenomena with the help of fuzzy logic, e.g. uncertainty, hybridity, violence and culture.
9791_86	A concept may be deliberately created by sociologists as an ideal type to understand something imaginatively, without any strong claim that it is a "true and complete description" or a "true and complete reflection" of whatever is being conceptualized. In a more general sociological or journalistic sense, a "fuzzy concept" has come to mean a concept which is meaningful but inexact, implying that it does not exhaustively or completely define the meaning of the phenomenon to which it refers – often because it is too abstract. In this context, it is said that fuzzy concepts "lack clarity and are difficult to test or operationalize". To specify the relevant meaning more precisely, additional distinctions, conditions and/or qualifiers would be required.
9791_87	A few examples can illustrate this kind of usage: a handbook of sociology states that "The theory of interaction rituals contains some gaps that need to be filled and some fuzzy concepts that need to be differentiated." The idea is, that if finer distinctions are introduced, then the fuzziness or vagueness would be eliminated. a book on youth culture describes ethnicity as "a fuzzy concept that overlaps at times with concepts of race, minority, nationality and tribe". In this case, part of the fuzziness consists in the inability to distinguish precisely between a concept and a different, but closely related concept.
9791_88	a book on sociological theory argues that the Critical Theory of domination faces the problem that "reality itself has become a rather meaningless, fuzzy concept." The suggestion here is, that the variations in how theoretical concepts are applied have become so large, that the concepts could mean all kinds of things, and therefore are crucially vague (with the implication, that they are not useful any longer for that very reason). A history book states: "Sodomy was a vague and fuzzy concept in medieval and early modern Europe, and was often associated with a variety of supposedly related moral and criminal offenses, including heresy, witchcraft, sedition, and treason. St Thomas Aquinas... categorized sodomy with an assortment of sexual behaviours "from which generation [i.e. procreation] cannot follow". In this case, because a concept is defined by what it excludes, it remains somewhat vague what items of activity it would specifically include.
9791_89	Mass media The main reason why the term "fuzzy concept" is now often used in describing human behaviour, is that human interaction has many characteristics which are difficult to quantify and measure precisely (although we know that they have magnitudes and proportions), among other things because they are interactive and reflexive (the observers and the observed mutually influence the meaning of events). Those human characteristics can be usefully expressed only in an approximate way (see reflexivity (social theory)). Newspaper stories frequently contain fuzzy concepts, which are readily understood and used, even although they are far from exact. Thus, many of the meanings which people ordinarily use to negotiate their way through life in reality turn out to be "fuzzy concepts". While people often do need to be exact about some things (e.g. money or time), many areas of their lives involve expressions which are far from exact.
9791_90	Sometimes the term is also used in a pejorative sense. For example, a New York Times journalist wrote that Prince Sihanouk "seems unable to differentiate between friends and enemies, a disturbing trait since it suggests that he stands for nothing beyond the fuzzy concept of peace and prosperity in Cambodia". Applied social science The use of fuzzy logic in the social sciences and humanities has remained limited until recently. Lotfi A. Zadeh said in a 1994 interview that: Two decades later, after a digital information explosion due to the growing use of the internet and mobile phones worldwide, fuzzy concepts and fuzzy logic are being widely applied in big data analysis of social, commercial and psychological phenomena. Many sociometric and psychometric indicators are based partly on fuzzy concepts and fuzzy variables.
9791_91	Jaakko Hintikka once claimed that "the logic of natural language we are in effect already using can serve as a "fuzzy logic" better than its trade name variant without any additional assumptions or constructions." That might help to explain why fuzzy logic has not been used much to formalize concepts in the "soft" social sciences. Lotfi A. Zadeh rejected such an interpretation, on the ground that in many human endeavours as well as technologies it is highly important to define more exactly "to what extent" something is applicable or true, when it is known that its applicability can vary to some important extent among large populations. Reasoning which accepts and uses fuzzy concepts can be shown to be perfectly valid with the aid of fuzzy logic, because the degrees of applicability of a concept can be more precisely and efficiently defined with the aid of numerical notation.
9791_92	Another possible explanation for the traditional lack of use of fuzzy logic by social scientists is simply that, beyond basic statistical analysis (using programs such as SPSS and Excel) the mathematical knowledge of social scientists is often rather limited; they may not know how to formalize and code a fuzzy concept using the conventions of fuzzy logic. The standard software packages used provide only a limited capacity to analyze fuzzy data sets, if at all, and considerable skills are required. Yet Jaakko Hintikka may be correct, in the sense that it can be much more efficient to use natural language to denote a complex idea, than to formalize it in logical terms. The quest for formalization might introduce much more complexity, which is not wanted, and which detracts from communicating the relevant issue. Some concepts used in social science may be impossible to formalize exactly, even though they are quite useful and people understand their appropriate application quite well.
9791_93	Uncertainty Fuzzy concepts can generate uncertainty because they are imprecise (especially if they refer to a process in motion, or a process of transformation where something is "in the process of turning into something else"). In that case, they do not provide a clear orientation for action or decision-making ("what does X really mean, intend or imply?"); reducing fuzziness, perhaps by applying fuzzy logic, might generate more certainty. Relevance However, this is not necessarily always so. A concept, even although it is not fuzzy at all, and even though it is very exact, could equally well fail to capture the meaning of something adequately. That is, a concept can be very precise and exact, but not – or insufficiently – applicable or relevant in the situation to which it refers. In this sense, a definition can be "very precise", but "miss the point" altogether.
9791_94	Security A fuzzy concept may indeed provide more security, because it provides a meaning for something when an exact concept is unavailable – which is better than not being able to denote it at all. A concept such as God, although not easily definable, for instance can provide security to the believer.
9791_95	Observer effect In physics, the observer effect and Heisenberg's uncertainty principle indicate that there is a physical limit to the amount of precision that is knowable, with regard to the movements of subatomic particles and waves. That is, features of physical reality exist, where we can know that they vary in magnitude, but of which we can never know or predict exactly how big or small the variations are. This insight suggests that, in some areas of our experience of the physical world, fuzziness is inevitable and can never be totally removed. Since the physical universe itself is incredibly large and diverse, it is not easy to imagine it, grasp it or describe it without using fuzzy concepts.
9791_96	Language Ordinary language, which uses symbolic conventions and associations which are often not logical, inherently contains many fuzzy concepts – "knowing what you mean" in this case depends partly on knowing the context (or being familiar with the way in which a term is normally used, or what it is associated with). This can be easily verified for instance by consulting a dictionary, a thesaurus or an encyclopedia which show the multiple meanings of words, or by observing the behaviours involved in ordinary relationships which rely on mutually understood meanings (see also Imprecise language). Bertrand Russell regarded ordinary language (in contrast to logic) as intrinsically vague.
9791_97	Implicature To communicate, receive or convey a message, an individual somehow has to bridge his own intended meaning and the meanings which are understood by others, i.e., the message has to be conveyed in a way that it will be socially understood, preferably in the intended manner. Thus, people might state: "you have to say it in a way that I understand". Even if the message is clear and precise, it may nevertheless not be received in the way it was intended. Bridging meanings may be done instinctively, habitually or unconsciously, but it usually involves a choice of terms, assumptions or symbols whose meanings are not completely fixed, but which depend among other things on how the receivers of the message respond to it, or the context. In this sense, meaning is often "negotiated" or "interactive" (or, more cynically, manipulated). This gives rise to many fuzzy concepts.
9791_98	The semantic challenge of conveying meanings to an audience was explored in detail, and analyzed logically, by the British philosopher Paul Grice - using, among other things, the concept of implicature. Implicature refers to what is suggested by a message to the recipient, without being either explicitly expressed or logically entailed by its content. The suggestion could be very clear to the recipient (perhaps a sort of code), but it could also be vague or fuzzy.
9791_99	Paradoxes Even using ordinary set theory and binary logic to reason something out, logicians have discovered that it is possible to generate statements which are logically speaking not completely true or imply a paradox, even although in other respects they conform to logical rules (see Russell's paradox). David Hilbert concluded that the existence of such logical paradoxes tells us "that we must develop a meta-mathematical analysis of the notions of proof and of the axiomatic method; their importance is methodological as well as epistemological". Psychology Various different aspects of human experience commonly generate concepts with fuzzy characteristics.
9791_100	Human vs. computer The formation of fuzzy concepts is partly due to the fact that the human brain does not operate like a computer (see also Chinese room). While ordinary computers use strict binary logic gates, the brain does not; i.e., it is capable of making all kinds of neural associations according to all kinds of ordering principles (or fairly chaotically) in associative patterns which are not logical but nevertheless meaningful. For example, a work of art can be meaningful without being logical. A pattern can be regular, ordered and/or non-arbitrary, hence meaningful, without it being possible to describe it completely or exhaustively in formal-logical terms. Something can be meaningful although we cannot name it, or we might only be able to name it and nothing else.
9791_101	Human brains can also interpret the same phenomenon in several different but interacting frames of reference, at the same time, or in quick succession, without there necessarily being an explicit logical connection between the frames (see also framing effect).
9791_102	According to fuzzy-trace theory, partly inspired by Gestalt psychology, human intuition is a non-arbitrary, reasonable and rational process of cognition; it literally "makes sense" (see also: Problem of multiple generality). Learning In part, fuzzy concepts arise also because learning or the growth of understanding involves a transition from a vague awareness, which cannot orient behaviour greatly, to clearer insight, which can orient behaviour. At the first encounter with an idea, the sense of the idea may be rather hazy. When more experience with the idea has occurred, a clearer and more precise grasp of the idea results, as well as a better understanding of how and when to use the idea (or not).
9791_103	In his study of implicit learning, Arthur S. Reber affirms that there does not exist a very sharp boundary between the conscious and the unconscious, and "there are always going to be lots of fuzzy borderline cases of material that is marginally conscious and lots of elusive instances of functions and processes that seem to slip in and out of personal awareness". Thus, an inevitable component of fuzziness exists and persists in human consciousness, because of continual variation of gradations in awareness, along a continuum from the conscious, the preconscious, and the subconscious to the unconscious. The hypnotherapist Milton H. Erickson noted likewise that the conscious mind and the unconscious normally interact.
9791_104	Limits Some psychologists and logicians argue that fuzzy concepts are a necessary consequence of the reality that any kind of distinction we might like to draw has limits of application. At a certain level of generality, a distinction works fine. But if we pursued its application in a very exact and rigorous manner, or overextend its application, it appears that the distinction simply does not apply in some areas or contexts, or that we cannot fully specify how it should be drawn. An analogy might be, that zooming a telescope, camera, or microscope in and out, reveals that a pattern which is sharply focused at a certain distance becomes blurry at another distance, or disappears altogether.
9791_105	Complexity Faced with any large, complex and continually changing phenomenon, any short statement made about that phenomenon is likely to be "fuzzy", i.e., it is meaningful, but – strictly speaking – incorrect and imprecise. It will not really do full justice to the reality of what is happening with the phenomenon. A correct, precise statement would require a lot of elaborations and qualifiers. Nevertheless, the "fuzzy" description turns out to be a useful shorthand that saves a lot of time in communicating what is going on ("you know what I mean").
9791_106	Cognition In psychophysics, it was discovered that the perceptual distinctions we draw in the mind are often more definite than they are in the real world. Thus, the brain actually tends to "sharpen up" or "enhance" our perceptions of differences in the external world. Between black and white, we are able to detect only a limited number of shades of gray, or colour gradations (there are "detection thresholds"). Motion blur refers to the loss of detail when a person looks at a fast-moving object, or is moving fast while the eyes are focused on something stationary. In a movie reel, the human eye can detect a sequence of up to 10 or 12 still images per second. At around 18 to 26 frames per second, the brain will "see" the sequence of individual images as a moving scene.
9791_107	If there are more gradations and transitions in reality, than our conceptual or perceptual distinctions can capture, then it could be argued that how those distinctions will actually apply, must necessarily become vaguer at some point. Novelty In interacting with the external world, the human mind may often encounter new, or partly new phenomena or relationships which cannot (yet) be sharply defined given the background knowledge available, and by known distinctions, associations or generalizations.
9791_108	Chaos It also can be argued that fuzzy concepts are generated by a certain sort of lifestyle or way of working which evades definite distinctions, makes them impossible or inoperable, or which is in some way chaotic. To obtain concepts which are not fuzzy, it must be possible to test out their application in some way. But in the absence of any relevant clear distinctions, lacking an orderly environment, or when everything is "in a state of flux" or in transition, it may not be possible to do so, so that the amount of fuzziness increases. Everyday occurrence Fuzzy concepts often play a role in the creative process of forming new concepts to understand something. In the most primitive sense, this can be observed in infants who, through practical experience, learn to identify, distinguish and generalise the correct application of a concept, and relate it to other concepts.
9791_109	However, fuzzy concepts may also occur in scientific, journalistic, programming and philosophical activity, when a thinker is in the process of clarifying and defining a newly emerging concept which is based on distinctions which, for one reason or another, cannot (yet) be more exactly specified or validated. Fuzzy concepts are often used to denote complex phenomena, or to describe something which is developing and changing, which might involve shedding some old meanings and acquiring new ones.
9791_110	Areas In meteorology, where changes and effects of complex interactions in the atmosphere are studied, the weather reports often use fuzzy expressions indicating a broad trend, likelihood or level. The main reason is that the forecast can rarely be totally exact for any given location. In biology, protein complexes with multiple structural forms are called fuzzy complexes. The different conformations can result in different, even opposite functions. The conformational ensemble is modulated by the environmental conditions. Post-translational modifications or alternative splicing can also impact the ensemble and thereby the affinity or specificity of interactions. Genetic fuzzy systems use algorithms or genetic programming which simulate natural evolutionary processes, in order to understand their structures and parameters.
9791_111	In medical diagnosis, the assessment of what the symptoms of a patient are often cannot be very exactly specified, since there are many possible qualitative and quantitative gradations in severity, incidence or frequency that could occur. Different symptoms may also overlap to some extent. These gradations can be difficult to measure, it may cost a lot of time and money, and so the medical professionals might use approximate "fuzzy" categories in their judgement of a medical condition or a patient's condition. Although it may not be exact, the diagnosis is often useful enough for treatment purposes. Fuzzy logic is increasingly employed in diagnostic and medical equipment capable of measuring gradations of a condition.
9791_112	In information services, fuzzy concepts are frequently encountered because a customer or client asks a question about something which could be interpreted in different ways, or, a document is transmitted of a type or meaning which cannot be easily allocated to a known type or category, or to a known procedure. It might take considerable inquiry to "place" the information, or establish in what framework it should be understood.
9791_113	In phenomenology, which aims to study the structure of subjective experience without preconceptions, an important insight is that how someone experiences something can be influenced both by the influence of the thing being experienced itself, but also by how the person responds to it. Thus, the actual experience the person has, is shaped by an "interactive object-subject relationship". To describe this experience, fuzzy categories are often necessary, since it is often impossible to predict or describe with great exactitude what the interaction will be, and how it is experienced.
9791_114	In translation work, fuzzy concepts are analyzed for the purpose of good translation. A concept in one language may not have quite the same meaning or significance in another language, or it may not be feasible to translate it literally, or at all. Some languages have concepts which do not exist in another language, raising the problem of how one would most easily render their meaning. In computer-assisted translation, a technique called fuzzy matching is used to find the most likely translation of a piece of text, using previous translated texts as a basis.
9791_115	In hypnotherapy, fuzzy language is deliberately used for the purpose of trance induction. Hypnotic suggestions are often couched in a somewhat vague, general or ambiguous language requiring interpretation by the subject. The intention is to distract and shift the conscious awareness of the subject away from external reality to her own internal state. In response to the somewhat confusing signals she gets, the awareness of the subject spontaneously tends to withdraw inward, in search of understanding or escape.
9791_116	In business and economics, it was discovered that "we are guided less by a correct exact knowledge of our self-interest than by a socially learned, evolved, intuitive grasp derived from mental shortcuts (frames, reference points, envy, addiction, temptation, fairness)". Thus, economic preferences are often fuzzy preferences, a highly important point for suppliers of products and services. Fuzzy set empirical methodologies are increasingly used by economic analysts to analyze the extent to which members of a population belong to a specific market category, because that can make a big difference to business results.
9791_117	In sexology, sex and gender are conceptualized by gender pluralists as a spectrum or continuum, or a set of scaled characteristics. Thus, the idea that people are either heterosexual men, heterosexual women, gay, lesbian, bisexual or transsexual is far too simplistic; gender identity is a matter of degree, a graded concept, which for that very reason is a fuzzy concept with unsharp boundaries. For example, somebody who is "mainly" heterosexual, may occasionally have had non-heterosexual contacts, without this warranting a definite "bisexual" label. A great variety of sexual orientations are possible and can co-exist. In the course of history, typical male or female gender roles and gender characteristics can also gradually change, so that the extent to which they express "masculine" or "feminine" traits is, at any time, a matter of degree, i.e. fuzzy.
9791_118	In politics, it can be highly important and problematic how exactly a conceptual distinction is drawn, or indeed whether a distinction is drawn at all; distinctions used in administration may be deliberately sharpened, or kept fuzzy, due to some political motive or power relationship. Politicians may be deliberately vague about some things, and very clear and explicit about others; if there is information that proves their case, they become very precise, but if the information doesn't prove their case, they become vague or say nothing.
9791_119	In statistical research, it is an aim to measure the magnitudes of phenomena. For this purpose, phenomena have to be grouped and categorized, so that distinct and discrete counting units can be defined. It must be possible to allocate all observations to mutually exclusive categories, so that they are properly quantifiable. Survey observations do not spontaneously transform themselves into countable data; they have to be identified, categorized and classified in such a way, that identical observations can be grouped together, and that observations are not counted twice or more. A well-designed questionnaire ensures that the questions are interpreted in the same way by all respondents, and that the respondents are really able to answer them within the formats provided. Again, for this purpose, it is a requirement that the concepts being used are exactly and comprehensibly defined for all concerned, and not fuzzy. There could be a margin of measurement error, but the amount of error
9791_120	must be kept within tolerable limits, and preferably its magnitude should be known.
9791_121	In theology an attempt is made to define more precisely the meaning of spiritual concepts, which refer to how human beings construct the meaning of human existence, and, often, the relationship people have with a supernatural world. Many spiritual concepts and beliefs are fuzzy, to the extent that, although abstract, they often have a highly personalized meaning, or involve personal interpretation of a type that is not easy to define in a cut-and-dried way. A similar situation occurs in psychotherapy. The Dutch theologian Kees de Groot has explored the imprecise notion that psychotherapy is like an "implicit religion", defined as a "fuzzy concept" (it all depends on what one means by "psychotherapy" and "religion"). The philosopher of spirituality Ken Wilber argued that "nothing is 100% right or wrong", things merely "vary in their degree of incompleteness and dysfunction"; no one and nothing is 100% good or evil, each just varies "in their degree of ignorance and disconnection". This
9791_122	insight suggests, that all human valuations can be considered as graded concepts, where each qualitative judgement has at least implicitly a sense of quantitative proportion attached to it.
9791_123	In the legal system, it is essential that rules are interpreted and applied in a standard way, so that the same sorts of cases and the same sorts of circumstances are treated equally. Otherwise one would be accused of arbitrariness, which would not serve the interests of justice. Consequently, lawmakers aim to devise definitions and categories which are sufficiently precise, so that they are not open to different interpretations. For this purpose, it is critically important to remove fuzziness, and differences of interpretation are typically resolved through a court ruling based on evidence. Alternatively, some other procedure is devised which permits the correct distinction to be discovered and made.
9791_124	In administration, archiving and accounting, fuzziness problems in interpretation and boundary problems can arise, because it is not clear to what category exactly a case, item, document, transaction or piece of data belongs. In principle, each case, event or item must be allocated to the correct category in a procedure, but it may be, that it is difficult to make the appropriate or relevant distinctions.
9791_125	Generalities It could be argued that many concepts used fairly universally in daily life (e.g. "love", "God", "health", "social", "tolerance" etc.) are inherently or intrinsically fuzzy concepts, to the extent that their meaning can never be completely and exactly specified with logical operators or objective terms, and can have multiple interpretations, which are at least in part purely subjective. Yet despite this limitation, such concepts are not meaningless. People keep using the concepts, even if they are difficult to define precisely.
9791_126	Multiple meanings It may also be possible to specify one personal meaning for the concept, without however placing restrictions on a different use of the concept in other contexts (as when, for example, one says "this is what I mean by X" in contrast to other possible meanings). In ordinary speech, concepts may sometimes also be uttered purely randomly; for example a child may repeat the same idea in completely unrelated contexts, or an expletive term may be uttered arbitrarily. A feeling or sense is conveyed, without it being fully clear what it is about. Happiness may be an example of a word with variable meanings depending on context or timing.
9791_127	Ambiguities Fuzzy concepts can be used deliberately to create ambiguity and vagueness, as an evasive tactic, or to bridge what would otherwise be immediately recognized as a contradiction of terms. They might be used to indicate that there is definitely a connection between two things, without giving a complete specification of what the connection is, for some or other reason. This could be due to a failure or refusal to be more precise. But it could also be a prologue to a more exact formulation of a concept, or to a better understanding of it. Efficiency Fuzzy concepts can be used as a practical method to describe something of which a complete description would be an unmanageably large undertaking, or very time-consuming; thus, a simplified indication of what is at issue is regarded as sufficient, although it is not exact.
9791_128	Popper There is also such a thing as an "economy of distinctions", meaning that it is not helpful or efficient to use more detailed definitions than are really necessary for a given purpose. In this sense, Karl Popper rejected pedantry and commented that: The provision of "too many details" could be disorienting and confusing, instead of being enlightening, while a fuzzy term might be sufficient to provide an orientation. The reason for using fuzzy concepts can therefore be purely pragmatic, if it is not feasible or desirable (for practical purposes) to provide "all the details" about the meaning of a shared symbol or sign. Thus people might say "I realize this is not exact, but you know what I mean" – they assume practically that stating all the details is not required for the purpose of the communication.
9791_129	Fuzzy logic gambit Lotfi A. Zadeh picked up this point, and drew attention to a "major misunderstanding" about applying fuzzy logic. It is true that the basic aim of fuzzy logic is to make what is imprecise more precise. Yet in many cases, fuzzy logic is used paradoxically to "imprecisiate what is precise", meaning that there is a deliberate tolerance for imprecision for the sake of simplicity of procedure and economy of expression. In such uses, there is a tolerance for imprecision, because making ideas more precise would be unnecessary and costly, while "imprecisiation reduces cost and enhances tractability" (tractability means "being easy to manage or operationalize"). Zadeh calls this approach the "Fuzzy Logic Gambit" (a gambit means giving up something now, to achieve a better position later).
9791_130	In the Fuzzy Logic Gambit, "what is sacrificed is precision in [quantitative] value, but not precision in meaning", and more concretely, "imprecisiation in value is followed by precisiation in meaning". Zadeh cited as example Takeshi Yamakawa's programming for an inverted pendulum, where differential equations are replaced by fuzzy if-then rules in which words are used in place of numbers. Fuzzy vs. Boolean Common use of this sort of approach (combining words and numbers in programming), has led some logicians to regard fuzzy logic merely as an extension of Boolean logic (a two-valued logic or binary logic is simply replaced with a many-valued logic).
9791_131	However, Boolean concepts have a logical structure which differs from fuzzy concepts. An important feature in Boolean logic is, that an element of a set can also belong to any number of other sets; even so, the element either does, or does not belong to a set (or sets). By contrast, whether an element belongs to a fuzzy set is a matter of degree, and not always a definite yes-or-no question.
9791_132	All the same, the Greek mathematician Costas Drossos suggests in various papers that, using a "non-standard" mathematical approach, we could also construct fuzzy sets with Boolean characteristics and Boolean sets with fuzzy characteristics. This would imply, that in practice the boundary between fuzzy sets and Boolean sets is itself fuzzy, rather than absolute. For a simplified example, we might be able to state, that a concept X is definitely applicable to a finite set of phenomena, and definitely not applicable to all other phenomena. Yet, within the finite set of relevant items, X might be fully applicable to one subset of the included phenomena, while it is applicable only “to some varying extent or degree” to another subset of phenomena which are also included in the set. Following ordinary set theory, this generates logical problems, if e.g. overlapping subsets within sets are related to other overlapping subsets within other sets.
9791_133	Clarifying methods In mathematical logic, computer programming, philosophy and linguistics fuzzy concepts can be analyzed and defined more accurately or comprehensively, by describing or modelling the concepts using the terms of fuzzy logic or other substructural logics. More generally, clarification techniques can be used such as:
9791_134	1. Contextualizing the concept by defining the setting or situation in which the concept is used, or how it is used appropriately (context). 2. Identifying the intention, purpose, aim or goal associated with the concept (teleology and design). 3. Comparing and contrasting the concept with related ideas in the present or the past (comparative and comparative research). 4. Creating a model, likeness, analogy, metaphor, prototype or narrative which shows what the concept is about or how it is applied (isomorphism, simulation or successive approximation ). 5. Probing the assumptions on which a concept is based, or which are associated with its use (critical thought, tacit assumption). 6. Mapping or graphing the applications of the concept using some basic parameters, or using some diagrams or flow charts to understand the relationships between elements involved (visualization and concept map).
9791_135	7. Examining ‘’how likely’’ it is that the concept applies, statistically or intuitively (probability theory). 8. Specifying relevant conditions to which the concept applies, as a procedure (computer programming, formal concept analysis). 9. Concretizing the concept – finding specific examples, illustrations, details or cases to which it applies (exemplar, exemplification). 10. Reducing or restating fuzzy concepts in terms which are simpler or similar, and which are not fuzzy or less fuzzy (simplification, dimensionality reduction, plain language, KISS principle or concision). 11. Trying out a concept, by using it in interactions, practical work or in communication, and assessing the feedback to understand how the boundaries and distinctions of the concept are being drawn (trial and error or pilot experiment). 12. Engaging in a structured dialogue or repeated discussion, to exchange ideas about how to get specific about what it means and how to clear it up (scrum method).
9791_136	13. Allocating different applications of the concept to different but related sets (Boolean logic). 14. Identifying operational rules defining the use of the concept, which can be stated in a language and which cover all or most cases (material conditional). 15. Classifying, categorizing, grouping, or inventorizing all or most cases or uses to which the concept applies (taxonomy, cluster analysis and typology).
9791_137	16. Applying a meta-language which includes fuzzy concepts in a more inclusive categorical system which is not fuzzy (meta). 17. Creating a measure or scale of the degree to which the concept applies (metrology). 18. Examining the distribution patterns or distributional frequency of (possibly different) uses of the concept (statistics). 19. Specifying a series of logical operators or inferential system which captures all or most cases to which the concept applies (algorithm). 20. Relating the fuzzy concept to other concepts which are not fuzzy or less fuzzy, or simply by replacing the fuzzy concept altogether with another, alternative concept which is not fuzzy yet "works the same way" (proxy) 21. Engaging in meditation, or taking the proverbial "run around the block" to clarify the mind, and thus improve precision of thought about the definitional issue (self-care).
9791_138	In this way, we can obtain a more exact understanding of the meaning and use of a fuzzy concept, and possibly decrease the amount of fuzziness. It may not be possible to specify all the possible meanings or applications of a concept completely and exhaustively, but if it is possible to capture the majority of them, statistically or otherwise, this may be useful enough for practical purposes. Defuzzification A process of defuzzification is said to occur, when fuzzy concepts can be logically described in terms of fuzzy sets, or the relationships between fuzzy sets, which makes it possible to define variations in the meaning or applicability of concepts as quantities. Effectively, qualitative differences are in that case described more precisely as quantitative variations, or quantitative variability. Assigning a numerical value then denotes the magnitude of variation along a scale from zero to one.
9791_139	The difficulty that can occur in judging the fuzziness of a concept can be illustrated with the question "Is this one of those?". If it is not possible to clearly answer this question, that could be because "this" (the object) is itself fuzzy and evades definition, or because "one of those" (the concept of the object) is fuzzy and inadequately defined. Thus, the source of fuzziness may be in (1) the nature of the reality being dealt with, (2) the concepts used to interpret it, or (3) the way in which the two are being related by a person. It may be that the personal meanings which people attach to something are quite clear to the persons themselves, but that it is not possible to communicate those meanings to others except as fuzzy concepts. See also References
9791_140	External links James F. Brule, Fuzzy systems tutorial "Fuzzy Logic", Stanford Encyclopedia of Philosophy "Vagueness", Stanford Encyclopedia of Philosophy Calvin College Engineering Department, Getting Started with Fuzzy Logic 2009 Benjamin Franklin Medal Winner: Lotfi A. Zadeh Lin Shang, Lecture on fuzzy and rough sets, Nanjing University Rudolf Kruse and Christian Moewes on fuzzy set theory Concepts Dialectic Non-classical logic Iranian inventions Azerbaijani inventions
9792_0	Dimmer was the name under which New Zealand musician Shayne Carter (formerly of Straitjacket Fits, The DoubleHappys, and Bored Games) recorded and played music from 1994. It began as an umbrella name for jam sessions and short-lived band line-ups, then home recordings, then an ensemble with various members and guests. This evolution led to more settled four-piece rock band (especially from 2006–10, when only the bassist changed). At least 41 musicians have been acknowledged as playing a part in Dimmer over 18 years, with Carter the only permanent fixture. The last Dimmer recordings were made in 2009, with the band playing live shows through 2010. A short farewell tour announced the end of the band in 2012, and Carter began recording under his own name after that. Dimmer reunited for live shows in 2018.
9792_1	All four of Dimmer's albums were admired by critics, and all earned multiple New Zealand Music Award nominations. Non-album singles were released in 1995 and 1996, with debut album I Believe You Are A Star not following until 2001. In 2004 You've Got To Hear The Music was named New Zealand's Best Rock Album for the year, and Dimmer named Best Group. There My Dear saw Carter return to playing and recording with a live rock band in 2006, and return to the national album charts. Final album Degrees of Existence (2009) was recorded by the longest-lasting version of the band. 1994–96: Crystalator, Don't Make Me Buy Out Your Silence, and abandoned recording sessions Straitjacket Fits split in 1994, "brought low by the vagaries of the international music industry". Interviewed in 2012, Shayne Carter said that "I was completely over rock. The Dimmer thing was totally anti-rock and I became interested in not only the groove thing but doing quiet music as well."
9792_2	Carter moved back to Dunedin, later saying that he "dropped out, I suppose" and "wanted to get grounded after all my running around". While there, he began using the name Dimmer as "an umbrella thing...with me as the common denominator". The first Dimmer music came from jam sessions in Dunedin. Carter explained in 2012 that "I used the name Dimmer because I thought using your own name was really uncool." For a short while, a three-piece version of Dimmer coalesced, with Peter Jefferies (This Kind of Punishment, Nocturnal Projections) on drums and Lou Allison on bass. Carter and Jefferies had collaborated on singles before – "Randolph's Going Home" in 1986, and "Knocked Out Or Thereabouts" in 1992.
9792_3	I still wanted to rock when I formed Dimmer, because I needed to exorcise myself. I built a new set of songs with Lou and Peter. It's a thing with me to start from scratch with every new band. I have to contradict whatever I've just said and I also need to prove that I have somewhere left to go. – Shayne Carter, Dead People I Have Known, 2019 On 17 June 1994 this line-up debuted with an "abrasive and deliberately uncommercial" seven-song set at the Empire Hotel. Reviewer Grant McDougall said that "Dimmer's songs are all about dynamics, explosive and meteoric. They starkly show for the first time ever what Carter can actually do by himself without the restrictions of having to complement another guitarist." The band toured New Zealand in August that year. After August's tour, Allison moved back to the UK and Carter also broke with Peter Jefferies. "Peter was as talented as me...but I didn't want to share the table anymore."
9792_4	"Crystalator" (b/w "Dawn's Coming In") Dimmer's first official release, the 7" "Crystalator" single was recorded by Carter, Jefferies and Allison in 1994 and released in 1995 by Flying Nun (New Zealand) and Sub Pop (USA). I put down a song with Lou on the bass and Peter Jefferies on the drums...at Fish Street Studios. [...] Peter's beat was murderous and Lou played a single note. I was exploring a new minimalism, trying to boil music down to a quintessential truth. 'Crytalator' is one of my favourite songs, even if it's an instrumental. In it, I hear anger, lamenting, and a giant "Fuck you" to [Straitjacket Fits record labels] Mushroom, Arista, and anyone else who'd suppressed me." – Shayne Carter, Dead People I Have Known, 2019
9792_5	"Crystalator" is an instrumental track. The New Zealand Herald describes it as "mangled noise... that ratcheted up and let rip with a barbed guitar riff". Flying Nun founder Roger Sheppard said that the song "sounds rollickingly amazing in that strident 'here I am, listen to me' way that only an instrumental can communicate. Who needs words when a guitar can spit out these sorts of sounds." The b-side was "Dawn's Coming In", which Carter says is "strong as well, even though with its hushed restraint it was totally the opposite of 'Crystalator'." Both these tracks were also released on Flying Nun compilations. "Crystalator" appeared on Pop Eyed in 1996, and in 2005 "Dawn's Coming In" was included in Where in the World Is Wendy Broccoli? A collection of out of print Flying Nun singles 1981–1996 (along with "Knocked Out Or Thereabouts").
9792_6	An early version of the Dimmer song "Seed" appeared on Star Trackers, a cassette that was given away with issue 4 of Australian label Spunk Records' Spunkzine in winter 1995. It was credited to Shayne Carter as a solo artist. ("Seed" would later be rerecorded for I Believe You Are A Star.) Through the second half of the 1990s "[there was] the odd Dunedin solo gig but, for the most part, Shayne Carter disappeared from the public eye." Carter has called the period from 1995 "a lost weekend that actually lasted for six years". "Don't Make Me Buy Out Your Silence" After the dissolution of the "Crystalator" line-up, Carter continued jamming with Dunedin musicians. One short-lived line-up included bassist Chris Heazlewood (King Loser) and drummer Matt Middleton, who released what Carter caller "a series of near-genius cassettes" under the name Crude. Middleton soon moved to Melbourne.
9792_7	A four-piece line-up of Carter, Heazlewood, Cameron Bain (guitar) and Robbie Yeats (drums, The Dead C) began sessions that were intended to result in a debut Dimmer album. Carter called this "the baddest early version of Dimmer", but the sessions ended in what he described as "tears, soap operas, that kinda stuff" and resulted in only a short EP. "Don't Make Me Buy Out Your Silence" was the only official release from this phase of Dimmer. The song "Don't Make Me Buy Out Your Silence" was "partly inspired by Tricky" and written to capture a sense of paranoia. In 1996 it came out as a 7" vinyl release (with "Pacer" as a b-side), and as a three-track CD EP (which added "On the Road", a cover of "On the Road Again" by Canned Heat). This was the last Flying Nun release of Dimmer's, originally credited only to Carter. These credits changed ten years later when, as There My Dear bonus tracks, "Don't Make Me Buy Out Your Silence" acknowledged Bain and "Pacer" credited all band members.
9792_8	None of the early Dimmer line-ups lasted long. In Carter's telling, Chris Heazlewood "became a dick. He...wrote 'Ace of Spades' on his scrawny chest when he played at the Big Day Out. He told people it was okay he was in with Carter because he was going to 'fuck it up'. I wondered if he was jealous, or if he resented me... He left Dimmer by rubbishing me on the internet, which hurt because he was showboating at my expense. I didn't speak to him for years, even after he apologised." A video for "Don't Make Me Buy Out Your Silence" received NZ On Air funding and was directed by Steve Morrison. No more music was released by Dimmer until 1999's "Evolution" single. In 2007 all five tracks from the "Crystalator" and "Don't Make Me Buy Out Your Silence" singles were included as bonus tracks on an Australian release of 2006 album There My Dear.
9792_9	1997–2001: I Believe You Are A Star Carter moved to Auckland in 1997 and, inspired by "new music [including] avant-electronica and whatever else was fresh and non-mainstream", switched from playing rock music to producing tracks on Pro Tools. "After I put out the first album, there’s all this 'it doesn't sound like Straitjacket Fits'. Well, no, it doesn't. That's why I quit the band – because I didn't want to be doing that. [...It] actually took me five or six years to put together. That came on the back of the Straitjackets, and I think I was disillusioned with the whole music thing at the time. I wanted to figure out a lot of things in my head." - Shayne Carter, 2009
9792_10	Most of the writing and recording that eventually became Dimmer's first album ("[the song] "Smoke"...took about four years to write") took place at Carter's homes over a number of years, with drummer Gary Sullivan (JPSE, Chug, The Stereo Bus) the other main participant. At least one song, "Seed", predated Carter's move to Auckland, an early version of it having been one of the tracks recorded in the mainly-abandoned Dunedin sessions of 1995. Locations for Carter and Sullivan's sessions included the former Ponsonby Road premises of the store Beautiful Music, then later Norfolk Street, where Carter spent an advance from Sony Records to have either a shipping container or Portacom building (depending on which recollection of Carter's you trust) installed in his backyard by crane.
9792_11	In 1999 the first release from these sessions, "Evolution", came out as a CD single with "Sad Guy" and the Tryhard Remix of "Evolution" as b-sides. The song's video featured Carter's father playing an older version of Shayne. It was directed by Darryl Ward and funded by NZ On Air. It was two years before Dimmer's debut album, I Believe You Are A Star, which included "Evolution" and a reworked "Seed", was released in 2001. The writing and production of all but one track ("Sad Guy") are solely credited to Carter. Five other musicians (including Bic Runga) appear in what The Listener called "hardly essential cameos". The album had a seven-week run in the New Zealand album charts, starting at #17 and getting as high as #13. Videos were made for "Seed", "I Believe You Are a Star", and "Drop You Off". In 2018, I Believe You Are A Star was released on vinyl for the first time. At the time Carter said he still considered it the best album he'd ever made.
9792_12	Critical reception
9792_13	I Believe You Are A Star received high critical acclaim, including a 5-star review from the New Zealand Herald that called it "a dark wonder", "a great album", and "one to which all other New Zealand albums in 2001 will be compared". It was especially noted for its electronic feel, "introverted minimalism" and its contrast to the rock music Carter had made before. As reviewer Nick Bollinger put it in The Listener, "Carter could have ridden the momentum they [Straitjacket Fits] created by promptly launching another axe-wielding line-up. Instead he cleared the decks, and began a long process of finding, and then refining, a whole other concept. ... The computer is the primary compositional tool here. Harmonic figures circle repetitively, vocal lines are spare and dislocated in an electronic landscape. Like hip-hop, the music seems to be led by the rhythms." Gary Steel's review in Metro magazine called it "possibly one of the most original, daring, and outrageously well-defined pieces of
9792_14	musical art to have emanated from this country".
9792_15	Released seven years after the last Straitjacket Fits record, I Believe You Are A Star is described by music historian John Dix as "one of the great New Zealand 'comeback' albums", and by music critic Gary Steel (writing in 2016) as Carter's "masterpiece". At the 2002 New Zealand Music Awards Dimmer was nominated for Best Music Video (for "Seed") and Best Album Art. At the 2001 bNet NZ Music Awards the album won Best Rock Release and Carter was named Most Outstanding Musician, although that trophy was lost at the ceremony.
9792_16	2003–06: You've Got To Hear The Music and All Looks the Same at Night While working on the next Dimmer album, Carter listened to "heaps of Miles Davis and Thelonious Monk", which became a point of contention with his record label, Sony. In an interview with Pavement magazine, Carter said "I went up there [to Sony] one day and I got to raid the closets [...] I grabbed Thelonious Monk because they had a lot of Monk records. One of the people who is quite highly powered in that company was quite upset by the fact I was grabbing Thelonious Monk instead of Creed records because that's what I should be aiming for. To me, that pretty much summed up the whole opposites mentality." Sony dropped Dimmer before the second album, leaving Carter feeling "like a failure". He shifted to Festival Mushroom Records.
9792_17	Inspired by jazz musicians like Monk and Davis, Carter decided that on his next album he "wanted to hear mistakes and fumbly human stuff. I wanted it to sound like a bunch of tunes that people could sit around and clap their hands to". Not wanting this album to take as long as the first, he had written most of the songs "in a month last year [2003]". The release was delayed while Dimmer changed record companies. You've Got To Hear The Music was released in 2004. Stylistic differences with I Believe You Are a Star included instrumentation – Carter recorded himself on acoustic guitar and used "real drums on all the tracks" – and the number (and range) of players included. This album featured 19 musicians other than Carter, included backing vocals from Anika Moa and a returning Bic Runga, strings arranged by Graeme Downes, and (on "Getting What You Give") the Fat Freddy's Drop horn section. The album name came from a conversation Carter and Gary Sullivan had about The Third Man.
9792_18	Music videos were made for "Getting What You Give", "Come Here", and "Case". NZ On Screen calls "Case"'s video a "piece of stop motion cleverness" for which "at least 3,080 Polaroid photographs appear to have been taken". By this time, Dimmer had seen Carter collaborate with more than two dozen other musicians but he still described it as "essentially [...] a solo project. [...] It doesn't seem cool if it just has your name there. It seems cooler to have some sort of umbrella, something that makes it a bit more enigmatic." You've Got To Hear the Music was the last Dimmer recording project to fit this description.
9792_19	A live Dimmer performance broadcast on Radio New Zealand in 2004 featured Carter, Anika Moa (guitar/vocals), Willy Scott (drums), Ned Ngatae (guitar), Mike Hall (bass), Andy Morton (keyboards), and Heather Mansfield (glockenspiel). Mansfield was the only one not to have played on the album, although she would appear on 2006's There My Dear. They played songs from You've Got to Hear the Music and I Believe You Are a Star. You've Got To Hear The Music: Critical reception and awards Music critics met You've Got To Hear The Music positively. Common themes included positive comparisons to I Believe You Are A Star and praise for Carter continuing to produce styles of music different to his previous work.
9792_20	John Dix describes You've Got To Hear The Music as "another evolutionary step – as different to its predecessor as Dimmer is to Straitjacket Fits." In a four-star New Zealand Herald review Russell Baillie called the album "not quite as gripping or experimental as its predecessor", but said that "with its bent grooves and odd wiring, You've Got To Hear the Music is an album that stays intriguing on repeat listens." Writing for The Listener, Nick Bollinger declared the album Carter's "masterpiece". Compared to I Believe You Are A Star, he saw it as "more generous, both melodically and emotionally". He noted the influence of black American music, as well as Carter's increased mastery of electronic music production and return to "real" songwriting. In sum, Bollinger wrote, "Dimmer's second album has a depth and soul that others don't come near".
9792_21	Audioculture notes its "pronounced soul and groove influences", while in MZ Musician magazine, Jacob Connor noted that "Dimmer's electro-folk musings simmer to a laconic groove", and said that "a restrained elegance makes the music replayable". He concluded that the album is "a rewarding recording from a national treasure." At that year's New Zealand Music Awards it won Best Rock Album, and Dimmer was named Best Group (as well as being nominated for Album of the Year, Single of the Year ("Getting What You Give"), Best Cover Art, and Best Music Video). The album spent five weeks in the New Zealand top 40 album charts, peaking at #19, and earned Gold certification. All Looks the Same at Night compilation In 2006 a compilation of tracks selected from Dimmer's first two albums was released internationally by Rogue Records. All Looks the Same at Night included one disc of 13 songs, and one of seven music videos.

Subsets and Splits

No saved queries yet

Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.