The ASCII codes for the word "Wikipedia" represented in binary, the numeral system most commonly used for encoding computer information.

Information, in its most restricted technical sense, is a sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals. Information is any kind of event that affects the state of a dynamic system. Conceptually, information is the message (utterance or expression) being conveyed. This concept has numerous other meanings in different contexts.^[1] Moreover, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, representation, and especially entropy.

Etymology[link]

The English word was apparently derived from the Latin stem (information-) 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 informer) from the Latin verb informare, to give form, 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 not clear.

The ancient Greek word for form was μορφή (morphe; cf. morph) and also εἶδος (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.

Information theory approach[link]

From the stance of information theory, information is taken as a sequence of symbols from an alphabet, say an input alphabet χ, and an output alphabet ϒ. Information processing consists of an input-output function that maps any input sequence from χ into an output sequence from ϒ. The mapping may be probabilistic or determinate. It may have memory or be memoryless.^[2]

As sensory input[link]

Often information is viewed as a type of input to an organism or system. Inputs are of two kinds. Some inputs are important to the function of the organism (for example, food) or system (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 system. 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.

As representation and complexity[link]

The cognitive scientist and applied mathematician Ronaldo Vigo argues that information is a relative concept that involves at least two related entities in order to make quantitative sense. These are: any dimensionally defined category of objects S, and any of its subsets R. R, in essence, is a representation of S, or, in other words, carries or conveys representational (and hence, conceptual) information about S. Vigo then defines the amount of information that R conveys about S as the rate of change in the complexity of S whenever the objects in R are removed from S. Under "Vigo information", pattern, invariance, complexity, representation, and information—five fundamental constructs of universal science—are unified under a novel mathematical framework.^[3] Among other things, the framework aims to overcome the limitations of Shannon-Weaver information when attempting to characterize and measure subjective information.

As an influence which leads to a transformation[link]

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.^{[citation needed]} 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. For example, Gregory Bateson defines "information" as a "difference that makes a difference".

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.

The Danish Dictionary of Information Terms^[4] argues that information only provides an answer to a posed question. Whether the answer provides knowledge depends on the informed person. So a generalized definition of the concept should be: "Information" = An answer to a specific question".

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.

As a property in physics[link]

In 2003 J. D. Bekenstein claimed there is a growing trend in physics to define the physical world as being made of information itself (and thus information is defined in this way) (see Digital physics). Information has a well-defined meaning in physics. Examples of this include the phenomenon of quantum entanglement where particles can interact without reference to their separation or the speed of light. Information itself cannot travel faster than light even if the information is transmitted indirectly. This could lead to the fact that all attempts at physically observing a particle with an "entangled" relationship to another are slowed down, even though the particles are not connected in any other way other than by the information they carry.

Another link is demonstrated by the Maxwell's demon thought experiment. In this experiment, a direct relationship between information and another physical property, entropy, is demonstrated. A consequence is that it is impossible to destroy information without increasing the entropy of a system; in practical terms this often means generating heat. Another, more philosophical outcome is that information could be thought of as interchangeable with energy. Thus, in the study of logic gates, the theoretical lower bound of thermal energy released by an AND gate is higher than for the NOT gate (because information is destroyed in an AND gate and simply converted in a NOT gate). Physical information is of particular importance in the theory of quantum computers.

Technologically mediated information[link]

It is estimated that the world's technological capacity to store information grew from 2.6 (optimally compressed) exabytes in 1986, which is the informational equivalent to less than one 730-MB CD-ROM per person in 1986 (539 MB per person), to 295 (optimally compressed) exabytes in 2007.^[5] This is the informational equivalent of almost 61 CD-ROM per person in 2007.^[6]

The world’s combined technological capacity to receive information through one-way broadcast networks was the informational equivalent of 174 newspapers per person per day in 2007.^[5]

The world's combined effective capacity to exchange information through two-way telecommunication networks was the informational equivalent of 6 newspapers per person per day in 2007.^[6]

As records[link]

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[link]

Beynon-Davies^[7][8] 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 the context within which signs are used. The focus of pragmatics is on the intentions of living agents underlying communicative behaviour. In other words, pragmatics 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.

See also[link]

References[link]

Further reading[link]

• 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.
• Gleick, James (2011). The Information: A History, a Theory, a Flood. Pantheon, New York, NY.
• 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 PhilSci Archive
• 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.

External links[link]

Look up information in Wiktionary, the free dictionary.

Wikiquote has a collection of quotations related to: Information

• 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)
• (Danish) Informationsordbogen.dk The Danish Dictionary of Information Terms / Informationsordbogen

Vlatko Vedral is a Serbian born (and naturalised British Citizen) physicist and Professor of Physics at the University of Oxford and CQT (Centre for Quantum Technologies) at the National University of Singapore and a Fellow of Wolfson College. He is known for his research on the theory of Entanglement and Quantum Information Theory. As of 2010 he has published over 150 research papers in quantum mechanics and quantum information and was awarded the Royal Society Wolfson Research Merit Award in 2007. He has held a Professorship at Leeds, visiting professorships in Vienna and Singapore (NUS) and at Perimeter Institute in Canada. As of 2010, there were over 7,500 citations to Vlatko Vedral's research papers. He is the author of several books, including Decoding Reality.

Vlatko Vedral
Born	Serbia
Residence	United Kingdom  Singapore
Nationality	British  Serbian
Fields	Physics
Institutions	Oxford University National University of Singapore
Alma mater	Imperial College London
Known for	Quantum Entanglement

Education and career[link]

After completing secondary education at Matematička gimnazija, he received his B.S. and then a Ph.D. degree at Imperial College in 1998. He then was elected Elsag-Bailey post-doctoral research fellow in Oxford. He then held a research fellowship at Merton College Oxford before returning to Imperial College as the Governor’s lecturer to start a quantum information science research group, a position he held from 2000-2004. Before returning to Oxford, he was Centenary Professor of Quantum Information Science at Leeds University 2004-2009. He currently holds a joint appointment as a Professor of Physics and the University of Oxford and CQT (Centre for Quantum Technologies) at the National University of Singapore. He was elected Fellow of Wolfson College in 2009.

Books[link]

• Modern Foundations of Quantum Optics (Imperial College Press, 2005)
• Introduction to Quantum Information Science (Oxford University Press, 2006)
• Introductory Quantum Physics and Relativity (Imperial College Press, 2010)

Awards[link]

• Recipient of Marko V. Jaric Award, 2011. https://wiki.physics.udel.edu/ssd/Recipients_of_Marko_V._Jaric_Award
• Royal Society Wolfson Research Merit Award, 2007.
• The World Scientific Physics Research Medal, 2009. http://www.physics.nus.edu.sg/~phyips/awards2009.html

See also[link]

• Clarendon Laboratory
• Centre for Quantum Technologies
• Decoding Reality
• Quantum discord

External links[link]

• Vlatko Vedral Homepage

References[link]

^{[1] [2] [3] [4]}

Claude Elwood Shannon (April 30, 1916 – February 24, 2001) was an American mathematician, electronic engineer, and cryptographer known as "the father of information theory".^[1][2]

Shannon is famous for having founded information theory with one landmark paper published in 1948. But he is also credited with founding both digital computer and digital circuit design theory in 1937, when, as a 21-year-old master's student at MIT, he wrote a thesis demonstrating that electrical application of Boolean algebra could construct and resolve any logical, numerical relationship. It has been claimed that this was the most important master's thesis of all time.^[3] Shannon contributed to the field of cryptanalysis during World War II and afterwards, including basic work on code breaking.

Biography[link]

Shannon was born in Petoskey, Michigan. His father, Claude Sr (1862–1934), a descendant of early New Jersey settlers, was a self-made businessman and for a while, Judge of Probate. His mother, Mabel Wolf Shannon (1890–1945), daughter of German immigrants, was a language teacher and for a number of years principal of Gaylord High School, Michigan. The first 16 years of Shannon's life were spent in Gaylord, Michigan, where he attended public school, graduating from Gaylord High School in 1932. Shannon showed an inclination towards mechanical things. His best subjects were science and mathematics, and at home he constructed such devices as models of planes, a radio-controlled model boat and a wireless telegraph system to a friend's house half a mile away. While growing up, he worked as a messenger for Western Union. His childhood hero was Thomas Edison, who he later learned was a distant cousin. Both were descendants of John Ogden, a colonial leader and an ancestor of many distinguished people.^[4][5]

Boolean theory[link]

In 1932 he entered the University of Michigan, where he took a course that introduced him to the works of George Boole. He graduated in 1936 with two bachelor's degrees, one in electrical engineering and one in mathematics. Later he began his graduate studies at the Massachusetts Institute of Technology (MIT), where he worked on Vannevar Bush's differential analyzer, an analog computer.^[6]

While studying the complicated ad hoc circuits of the differential analyzer, Shannon saw that Boole's concepts could be used to great utility. A paper drawn from his 1937 master's thesis, A Symbolic Analysis of Relay and Switching Circuits,^[7] was published in the 1938 issue of the Transactions of the American Institute of Electrical Engineers. It also earned Shannon the Alfred Noble American Institute of American Engineers Award in 1940. Howard Gardner, of Harvard University, called Shannon's thesis "possibly the most important, and also the most famous, master's thesis of the century."

Victor Shestakov, at Moscow State University, had proposed a theory of electric switches based on Boolean logic earlier than Shannon, in 1935, but the first publication of Shestakov's result took place in 1941, after the publication of Shannon's thesis.

In this work, Shannon proved that Boolean algebra and binary arithmetic could be used to simplify the arrangement of the electromechanical relays then used in telephone routing switches, then expanded the concept and also proved that it should be possible to use arrangements of relays to solve Boolean algebra problems. Exploiting this property of electrical switches to do logic is the basic concept that underlies all electronic digital computers. Shannon's work became the foundation of practical digital circuit design when it became widely known among the electrical engineering community during and after World War II. The theoretical rigor of Shannon's work completely replaced the ad hoc methods that had previously prevailed.

Vannevar Bush suggested that Shannon, flush with this success, work on his dissertation at Cold Spring Harbor Laboratory, funded by the Carnegie Institution headed by Bush, to develop similar mathematical relationships for Mendelian genetics, which resulted in Shannon's 1940 PhD thesis at MIT, An Algebra for Theoretical Genetics.^[8]

In 1940, Shannon became a National Research Fellow at the Institute for Advanced Study in Princeton, New Jersey. At Princeton, Shannon had the opportunity to discuss his ideas with influential scientists and mathematicians such as Hermann Weyl and John von Neumann, and even had the occasional encounter with Albert Einstein. Shannon worked freely across disciplines, and began to shape the ideas that would become information theory.^[9]

Wartime research[link]

Shannon then joined Bell Labs to work on fire-control systems and cryptography during World War II, under a contract with section D-2 (Control Systems section) of the National Defense Research Committee (NDRC).

He met his wife Betty when she was a numerical analyst at Bell Labs. They married in 1949.^[10]

For two months early in 1943, Shannon came into contact with the leading British cryptanalyst and mathematician Alan Turing. Turing had been posted to Washington to share with the US Navy's cryptanalytic service the methods used by the British Government Code and Cypher School at Bletchley Park to break the ciphers used by the German U-boats in the North Atlantic.^[11] He was also interested in the encipherment of speech and to this end spent time at Bell Labs. Shannon and Turing met at teatime in the cafeteria.^[11] Turing showed Shannon his seminal 1936 paper that defined what is now known as the "Universal Turing machine"^[12][13] which impressed him, as many of its ideas were complementary to his own.

In 1945, as the war was coming to an end, the NDRC was issuing a summary of technical reports as a last step prior to its eventual closing down. Inside the volume on fire control a special essay titled Data Smoothing and Prediction in Fire-Control Systems, coauthored by Shannon, Ralph Beebe Blackman, and Hendrik Wade Bode, formally treated the problem of smoothing the data in fire-control by analogy with "the problem of separating a signal from interfering noise in communications systems."^[14] In other words it modeled the problem in terms of data and signal processing and thus heralded the coming of the information age.

His work on cryptography was even more closely related to his later publications on communication theory.^[15] At the close of the war, he prepared a classified memorandum for Bell Telephone Labs entitled "A Mathematical Theory of Cryptography," dated September, 1945. A declassified version of this paper was subsequently published in 1949 as "Communication Theory of Secrecy Systems" in the Bell System Technical Journal. This paper incorporated many of the concepts and mathematical formulations that also appeared in his A Mathematical Theory of Communication. Shannon said that his wartime insights into communication theory and cryptography developed simultaneously and "they were so close together you couldn’t separate them".^[16] In a footnote near the beginning of the classified report, Shannon announced his intention to "develop these results ... in a forthcoming memorandum on the transmission of information."^[17]

While at Bell Labs, he proved that the one-time pad is unbreakable in his World War II research that was later published in October 1949. He also proved that any unbreakable system must have essentially the same characteristics as the one-time pad: the key must be truly random, as large as the plaintext, never reused in whole or part, and kept secret.^[18]

Postwar contributions[link]

In 1948 the promised memorandum appeared as "A Mathematical Theory of Communication", an article in two parts in the July and October issues of the Bell System Technical Journal. This work focuses on the problem of how best to encode the information a sender wants to transmit. In this fundamental work he used tools in probability theory, developed by Norbert Wiener, which were in their nascent stages of being applied to communication theory at that time. Shannon developed information entropy as a measure for the uncertainty in a message while essentially inventing the field of information theory.

The book, co-authored with Warren Weaver, The Mathematical Theory of Communication, reprints Shannon's 1948 article and Weaver's popularization of it, which is accessible to the non-specialist. Shannon's concepts were also popularized, subject to his own proofreading, in John Robinson Pierce's Symbols, Signals, and Noise.

Information theory's fundamental contribution to natural language processing and computational linguistics was further established in 1951, in his article "Prediction and Entropy of Printed English", showing upper and lower bounds of entropy on the statistics of English - giving a statistical foundation to language analysis. In addition, he proved that treating whitespace as the 27th letter of the alphabet actually lowers uncertainty in written language, providing a clear quantifiable link between cultural practice and probabilistic cognition.

Another notable paper published in 1949 is "Communication Theory of Secrecy Systems", a declassified version of his wartime work on the mathematical theory of cryptography, in which he proved that all theoretically unbreakable ciphers must have the same requirements as the one-time pad. He is also credited with the introduction of sampling theory, which is concerned with representing a continuous-time signal from a (uniform) discrete set of samples. This theory was essential in enabling telecommunications to move from analog to digital transmissions systems in the 1960s and later.

He returned to MIT to hold an endowed chair in 1956.

Hobbies and inventions[link]

Outside of his academic pursuits, Shannon was interested in juggling, unicycling, and chess. He also invented many devices, including rocket-powered flying discs, a motorized pogo stick, and a flame-throwing trumpet for a science exhibition^{[citation needed]}. One of his more humorous devices was a box kept on his desk called the "Ultimate Machine", based on an idea by Marvin Minsky. Otherwise featureless, the box possessed a single switch on its side. When the switch was flipped, the lid of the box opened and a mechanical hand reached out, flipped off the switch, then retracted back inside the box. Renewed interest in the "Ultimate Machine" has emerged on YouTube and Thingiverse. In addition he built a device that could solve the Rubik's Cube puzzle.^[4]

He is also considered the co-inventor of the first wearable computer along with Edward O. Thorp.^[19] The device was used to improve the odds when playing roulette.

Legacy and tributes[link]

Shannon came to MIT in 1956 to join its faculty and to conduct work in the Research Laboratory of Electronics (RLE). He continued to serve on the MIT faculty until 1978. To commemorate his achievements, there were celebrations of his work in 2001, and there are currently six statues of Shannon sculpted by Eugene L. Daub: one at the University of Michigan; one at MIT in the Laboratory for Information and Decision Systems; one in Gaylord, Michigan; one at the University of California, San Diego; one at Bell Labs; and another at AT&T Shannon Labs.^[20] After the breakup of the Bell system, the part of Bell Labs that remained with AT&T was named Shannon Labs in his honor.

According to Neil Sloane, an AT&T Fellow who co-edited Shannon's large collection of papers in 1993, the perspective introduced by Shannon's communication theory (now called information theory) is the foundation of the digital revolution, and every device containing a microprocessor or microcontroller is a conceptual descendant of Shannon's 1948 publication:^[21] "He's one of the great men of the century. Without him, none of the things we know today would exist. The whole digital revolution started with him."^[22]

Shannon developed Alzheimer's disease, and spent his last few years in a Massachusetts nursing home. He was survived by his wife, Mary Elizabeth Moore Shannon; a son, Andrew Moore Shannon; a daughter, Margarita Shannon; a sister, Catherine S. Kay; and two granddaughters.^[10][23]

Shannon was oblivious to the marvels of the digital revolution because his mind was ravaged by Alzheimer's disease. His wife mentioned in his obituary that had it not been for Alzheimer's "he would have been bemused" by it all.^[22]

Other work[link]

Shannon's mouse[link]

Theseus, created in 1950, was a magnetic mouse controlled by a relay circuit that enabled it to move around a maze of 25 squares. Its dimensions were the same as an average mouse.^[2] The maze configuration was flexible and it could be modified at will.^[2] The mouse was designed to search through the corridors until it found the target. Having travelled through the maze, the mouse would then be placed anywhere it had been before and because of its prior experience it could go directly to the target. If placed in unfamiliar territory, it was programmed to search until it reached a known location and then it would proceed to the target, adding the new knowledge to its memory thus learning.^[2] Shannon's mouse appears to have been the first artificial learning device of its kind.^[2]

Shannon's computer chess program[link]

In 1950 Shannon published a paper on computer chess entitled Programming a Computer for Playing Chess. It describes how a machine or computer could be made to play a reasonable game of chess. His process for having the computer decide on which move to make is a minimax procedure, based on an evaluation function of a given chess position. Shannon gave a rough example of an evaluation function in which the value of the black position was subtracted from that of the white position. Material was counted according to the usual relative chess piece relative value (1 point for a pawn, 3 points for a knight or bishop, 5 points for a rook, and 9 points for a queen).^[24] He considered some positional factors, subtracting ½ point for each doubled pawns, backward pawn, and isolated pawn. Another positional factor in the evaluation function was mobility, adding 0.1 point for each legal move available. Finally, he considered checkmate to be the capture of the king, and gave the king the artificial value of 200 points. Quoting from the paper:

The coefficients .5 and .1 are merely the writer's rough estimate. Furthermore, there are many other terms that should be included. The formula is given only for illustrative purposes. Checkmate has been artificially included here by giving the king the large value 200 (anything greater than the maximum of all other terms would do).

The evaluation function is clearly for illustrative purposes, as Shannon stated. For example, according to the function, pawns that are doubled as well as isolated would have no value at all, which is clearly unrealistic.

The Las Vegas connection: information theory and its applications to game theory[link]

Shannon and his wife Betty also used to go on weekends to Las Vegas with M.I.T. mathematician Ed Thorp,^[25] and made very successful forays in blackjack using game theory type methods co-developed with fellow Bell Labs associate, physicist John L. Kelly Jr. based on principles of information theory.^[26] They made a fortune, as detailed in the book Fortune's Formula by William Poundstone and corroborated by the writings of Elwyn Berlekamp,^[27] Kelly's research assistant in 1960 and 1962.^[3] Shannon and Thorp also applied the same theory, later known as the Kelly criterion, to the stock market with even better results.^[28] Over the decades, Kelly's scientific formula has become a part of mainstream investment theory^[29] and the most prominent users, well-known and successful billionaire investors Warren Buffett,^[30][31] Bill Gross^[32], and Jim Simons use Kelly methods. Warren Buffett met Thorp the first time in 1968. It's said that Buffett uses a form of the Kelly criterion in deciding how much money to put into various holdings. Also Elwyn Berlekamp had applied the same logical algorithm for Axcom Trading Advisors, an alternative investment management company, that he had founded. Berlekamp's company was acquired by Jim Simons and his Renaissance Technologies Corp hedge fund in 1992, whereafter its investment instruments were either subsumed into (or essentially renamed as) Renaissance's flagship Medallion Fund. But as Kelly's original paper demonstrates, the criterion is only valid when the investment or "game" is played many times over, with the same probability of winning or losing each time, and the same payout ratio.^[33]

The theory was also exploited by the famous MIT Blackjack Team, which was a group of students and ex-students from the Massachusetts Institute of Technology, Harvard Business School, Harvard University, and other leading colleges who used card-counting techniques and other sophisticated strategies to beat casinos at blackjack worldwide. The team and its successors operated successfully from 1979 through the beginning of the 21st century. Many other blackjack teams have been formed around the world with the goal of beating the casinos.

Claude Shannon's card count techniques were explained in Bringing Down the House, the best-selling book published in 2003 about the MIT Blackjack Team by Ben Mezrich. In 2008, the book was adapted into a drama film titled 21.

Shannon's maxim[link]

Shannon formulated a version of Kerckhoffs' principle as "the enemy knows the system". In this form it is known as "Shannon's maxim".

Awards and honors list[link]

See also[link]

Shannon–Fano coding Shannon–Hartley theorem Nyquist–Shannon sampling theorem Noisy channel coding theorem Rate distortion theory Information theory Channel Capacity Confusion and diffusion

Systems science portal One-time pad Shannon switching game Shannon number Claude E. Shannon Award Shannon index Shannon's source coding theorem Information entropy Shannon's expansion

References[link]