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Information as a concept has many meanings, from everyday usage to technical settings. The concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. In its most restricted technical meaning, information is an ordered sequence of symbols.
The English word was apparently derived from the Latin accusative form (informationem) of the nominative (informatio): this noun is in its turn derived from the verb "informare" (to inform) in the sense of "to give form to the mind", "to discipline", "instruct", "teach": "Men so wise should go and inform their kings." (1330) Inform itself comes (via French) from the Latin verb informare, to give form to, to form an idea of. Furthermore, Latin itself already contained the word informatio meaning concept or idea, but the extent to which this may have influenced the development of the word information in English is unclear.
As a final note, the ancient Greek word for form was "μορφή" (morf -> morphe, Morph) and also είδος eidos (kind, idea, shape, set), the latter word was famously used in a technical philosophical sense by Plato (and later Aristotle) to denote the ideal identity or essence of something (see Theory of forms). "Eidos" can also be associated with thought, proposition or even concept.
As a message
Information is a term with many meanings depending on context, but is as a rule closely related to such concepts as meaning, knowledge, instruction, communication, representation, and mental stimulus. Simply stated, information is a message received and understood. In terms of data, it can be defined as a collection of facts from which conclusions may be drawn. There are many other aspects of information since it is the knowledge acquired through study or experience or instruction. But overall, information is the result of processing, manipulating and organizing data in a way that adds to the knowledge of the person receiving it.
Information is the state of a system of interest. Message is the information materialized.
Information is a quality of a message from a sender to one or more receivers. Information is always about something (size of a parameter, occurrence of an event, value, ethics, etc). Viewed in this manner, information does not have to be accurate; it may be a truth or a lie, or just the sound of a falling tree. Even a disruptive noise used to inhibit the flow of communication and create misunderstanding would in this view be a form of information. However, generally speaking, if the amount of information in the received message increases, the message is more accurate.
This model assumes there is a definite sender and at least one receiver. Many refinements of the model assume the existence of a common language understood by the sender and at least one of the receivers. An important variation identifies information as that which would be communicated by a message if it were sent from a sender to a receiver capable of understanding the message. In another variation, it is not required that the sender be capable of understanding the message, or even cognizant that there is a message, making information something that can be extracted from an environment, e.g., through observation, reading or measurement.
Communication theory provides a numerical measure of the uncertainty of an outcome. For example, we can say that "the signal contained thousands of bits of information". Communication theory tends to use the concept of information entropy, generally attributed to Claude Shannon, see below.
Another form of information is Fisher information, a concept of R.A. Fisher. This is used in application of statistics to estimation theory and to science in general. Fisher information is thought of as the amount of information that a message carries about an unobservable parameter. It can be computed from knowledge of the likelihood function defining the system. For example, with a normal likelihood function, the Fisher information is the reciprocal of the variance of the law. In the absence of knowledge of the likelihood law, the Fisher information may be computed from normally distributed score data as the reciprocal of their second moment.
Even though information and data are often used interchangeably, they are actually very different. Data is a set of unrelated information, and as such is of no use until it is properly evaluated. Upon evaluation, once there is some significant relation between data, and they show some relevance, then they are converted into information. Now this same data can be used for different purposes. Thus, till the data convey some information, they are not useful and therefore not information.
Measuring information entropy
The view of information as a message came into prominence with the publication in 1948 of an influential paper by Claude Shannon, "A Mathematical Theory of Communication." This thesis provides the foundations of information theory and endows the word information not only with a technical meaning but also a measure. If the sending device is equally likely to send any one of a set of N messages, then the preferred measure of "the information produced when one message is chosen from the set" is the base two logarithm of (This measure is called self-information). In this paper, Shannon continues:
The choice of a logarithmic base corresponds to the choice of a unit for measuring information. If the base 2 is used the resulting units may be called binary digits, or more briefly bits, a word suggested by J. W. Tukey. A device with two stable positions, such as a relay or a flip-flop circuit, can store one bit of information. N such devices can store N bits…
A complementary way of measuring information is provided by algorithmic information theory. In brief, this measures the information content of a list of symbols based on how predictable they are, or more specifically how easy it is to compute the list through a program: the information content of a sequence is the number of bits of the shortest program that computes it. The sequence below would have a very low algorithmic information measurement since it is a very predictable pattern, and as the pattern continues the measurement would not change. Shannon information would give the same information measurement for each symbol, since they are statistically random, and each new symbol would increase the measurement.
It is important to recognize the limitations of traditional information theory and algorithmic information theory from the perspective of human meaning. For example, when referring to the meaning content of a message Shannon noted “Frequently the messages have meaning… these semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages” (emphasis in original).
In information theory signals are part of a process, not a substance; they do something, they do not contain any specific meaning. Combining algorithmic information theory and information theory we can conclude that the most random signal contains the most information as it can be interpreted in any way and cannot be compressed.
Michael Reddy noted that "'signals' of the mathematical theory are 'patterns that can be exchanged'. There is no message contained in the signal, the signals convey the ability to select from a set of possible messages." In information theory "the system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design".
As sensory input
Often information is viewed as a type of input to an organism or designed device. Inputs are of two kinds. Some inputs are important to the function of the organism (for example, food) or device (energy) by themselves. In his book Sensory Ecology, Dusenbery called these causal inputs. Other inputs (information) are important only because they are associated with causal inputs and can be used to predict the occurrence of a causal input at a later time (and perhaps another place). Some information is important because of association with other information but eventually there must be a connection to a causal input. In practice, information is usually carried by weak stimuli that must be detected by specialized sensory systems and amplified by energy inputs before they can be functional to the organism or device. For example, light is often a causal input to plants but provides information to animals. The colored light reflected from a flower is too weak to do much photosynthetic work but the visual system of the bee detects it and the bee's nervous system uses the information to guide the bee to the flower, where the bee often finds nectar or pollen, which are causal inputs, serving a nutritional function.
Information is any type of sensory input. When an organism with a nervous system receives an input, it transforms the input into an electrical signal. This is regarded information by some. The idea of representation is still relevant, but in a slightly different manner. That is, while abstract painting does not represent anything concretely, when the viewer sees the painting, it is nevertheless transformed into electrical signals that create a representation of the painting. Defined this way, information does not have to be related to truth, communication, or representation of an object. Entertainment in general is not intended to be informative. Music, the performing arts, amusement parks, works of fiction and so on are thus forms of information in this sense, but they are not necessarily forms of information according to some definitions given above. Consider another example: food supplies both nutrition and taste for those who eat it. If information is equated to sensory input, then nutrition is not information but taste is.
As an influence which leads to a transformation
Information is any type of pattern that influences the formation or transformation of other patterns. In this sense, there is no need for a conscious mind to perceive, much less appreciate, the pattern. Consider, for example, DNA. The sequence of nucleotides is a pattern that influences the formation and development of an organism without any need for a conscious mind. Systems theory at times seems to refer to information in this sense, assuming information does not necessarily involve any conscious mind, and patterns circulating (due to feedback) in the system can be called information. In other words, it can be said that information in this sense is something potentially perceived as representation, though not created or presented for that purpose.
If, however, the premise of "influence" implies that information has been perceived by a conscious mind and also interpreted by it, the specific context associated with this interpretation may cause the transformation of the information into knowledge. Complex definitions of both "information" and "knowledge" make such semantic and logical analysis difficult, but the condition of "transformation" is an important point in the study of information as it relates to knowledge, especially in the business discipline of knowledge management. In this practice, tools and processes are used to assist a knowledge worker in performing research and making decisions, including steps such as:
- reviewing information in order to effectively derive value and meaning
- referencing metadata if any is available
- establishing a relevant context, often selecting from many possible contexts
- deriving new knowledge from the information
- making decisions or recommendations from the resulting knowledge.
Stewart (2001) argues that the transformation of information into knowledge is a critical one, lying at the core of value creation and competitive advantage for the modern enterprise.
When Marshall McLuhan speaks of media and their effects on human cultures, he refers to the structure of artifacts that in turn shape our behaviors and mindsets. Also, pheromones are often said to be "information" in this sense.
Records are a specialized form of information. Essentially, records are information produced consciously or as by-products of business activities or transactions and retained because of their value. Primarily their value is as evidence of the activities of the organization but they may also be retained for their informational value. Sound records management ensures that the integrity of records is preserved for as long as they are required.
The international standard on records management, ISO 15489, defines records as "information created, received, and maintained as evidence and information by an organization or person, in pursuance of legal obligations or in the transaction of business". The International Committee on Archives (ICA) Committee on electronic records defined a record as, "a specific piece of recorded information generated, collected or received in the initiation, conduct or completion of an activity and that comprises sufficient content, context and structure to provide proof or evidence of that activity".
Records may be maintained to retain corporate memory of the organization or to meet legal, fiscal or accountability requirements imposed on the organization. Willis (2005) expressed the view that sound management of business records and information delivered "…six key requirements for good corporate governance…transparency; accountability; due process; compliance; meeting statutory and common law requirements; and security of personal and corporate information."
Information and semiotics
Beynon-Davies  explains the multi-faceted concept of information in terms of signs and signal-sign systems. Signs themselves can be considered in terms of four inter-dependent levels, layers or branches of semiotics: pragmatics, semantics, syntax, and empirics. These four layers serve to connect the social world on the one hand with the physical or technical world on the other.
Pragmatics is concerned with the purpose of communication. Pragmatics links the issue of signs with that of intention. The focus of pragmatics is on the intentions of human agents underlying communicative behaviour. In other words, intentions link language to action.
Semantics is concerned with the meaning of a message conveyed in a communicative act. Semantics considers the content of communication. Semantics is the study of the meaning of signs - the association between signs and behaviour. Semantics can be considered as the study of the link between symbols and their referents or concepts; particularly the way in which signs relate to human behaviour.
Syntax is concerned with the formalism used to represent a message. Syntax as an area studies the form of communication in terms of the logic and grammar of sign systems. Syntax is devoted to the study of the form rather than the content of signs and sign-systems.
Empirics is the study of the signals used to carry a message; the physical characteristics of the medium of communication. Empirics is devoted to the study of communication channels and their characteristics, e.g., sound, light, electronic transmission etc.
Nielsen (2008) discusses the relationship between semiotics and information in relation to dictionaries. The concept of lexicographic information costs is introduced and refers to the efforts users of dictionaries need to make in order to, first, find the data sought and, secondly, understand the data so that they can generate information.
Communication normally exists within the context of some social situation. The social situation sets the context for the intentions conveyed (pragmatics) and the form in which communication takes place. In a communicative situation intentions are expressed through messages which comprise collections of inter-related signs taken from a language which is mutually understood by the agents involved in the communication. Mutual understanding implies that agents involved understand the chosen language in terms of its agreed syntax (syntactics) and semantics. The sender codes the message in the language and sends the message as signals along some communication channel (empirics). The chosen communication channel will have inherent properties which determine outcomes such as the speed with which communication can take place and over what distance.
More recently Shu-Kun Lin proposed a simple definition of information: Information is the amount of the data after data compression.
- The Bell System Technical Journal, Vol. 27, p. 379, (July 1948).
- Beynon-Davies P. (2002). Information Systems: an introduction to informatics in Organisations. Palgrave, Basingstoke, UK. ISBN 0-333-96390-3
- Alan Liu (2004). The Laws of Cool: Knowledge Work and the Culture of Information, University of Chicago Press
- Bekenstein, Jacob D. (2003, August). Information in the holographic universe. Scientific American.
- Shu-Kun Lin (2008). 'Gibbs Paradox and the Concepts of Information, Symmetry, Similarity and Their Relationship', Entropy, 10 (1), 1-5. Available online at Entropy journal website.
- Luciano Floridi, (2005). 'Is Information Meaningful Data?', Philosophy and Phenomenological Research, 70 (2), pp. 351 – 370. Available online at Oxford University
- Luciano Floridi, (2005). 'Semantic Conceptions of Information', The Stanford Encyclopedia of Philosophy (Winter 2005 Edition), Edward N. Zalta (ed.). Available online at Stanford University
- Sandro Nielsen: 'The Effect of Lexicographical Information Costs on Dictionary Making and Use', Lexikos 18/2008, 170-189.
- Stewart, Thomas, (2001). Wealth of Knowledge. Doubleday, New York, NY, 379 p.
- Young, Paul. The Nature of Information (1987). Greenwood Publishing Group, Westport, Ct. ISBN 0-275-92698-2.
- Semantic Conceptions of Information Review by Luciano Floridi for the Stanford Encyclopedia of Philosophy
- Principia Cybernetica entry on negentropy
- Fisher Information, a New Paradigm for Science: Introduction, Uncertainty principles, Wave equations, Ideas of Escher, Kant, Plato and Wheeler. This essay is continually revised in the light of ongoing research.
- How Much Information? 2003 an attempt to estimate how much new information is created each year (study was produced by faculty and students at the School of Information Management and Systems at the University of California at Berkeley.)
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