### Linear Vs. Circular Models of Communication

A Mathematical Theory of Communication

• Developed by Claude Shannon at Bell Telephone Labs and Warren Weaver at NSF in 1948-9
• Describes the process of message transmission and reception in a communication system such as telephony
• “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. 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. 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.” Shannon 1948
• “Relative to the broad subject of communication, there seem to be problems at three levels. Thus it seems reasonable to ask, serially:
• LEVEL A. How accurately can the symbols of communication be transmitted? (The technical problem.)
• LEVEL B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem.)
• LEVEL C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem.)” Weaver 1949
• Shannon had no intent to model human communication, but Weaver did.

• Shannon and Weaver's model
• Information (defined as “the reduction of uncertainty” by Berger)
• Not to be confused with meaning
• Amount of information is defined to be logarithm (base 2) of the number of available choices of message
• Specified in terms of a value known as entropy (randomness, the degree of disorder or uncertainty in a system)
• Entropy is calculated from the number of different symbols used in the communications and their relative probability of occurrence
• Message
• Has meaning, but semantic aspects of communication are irrelevant to the engineering problem
• Information source
• Produces a message or sequence of messages to be communicated to the receiving terminal
• Because entropy of information is probabilistic, the process of selection exercised by the information source is crucial to the statistical analyses included in the formal definition of Shannon and Weaver's theory
• Transmitter
• Operates on the message in some way to produce a signal (form in which a message is physically sent to the recipient such as sound waves, radio waves, variation in electrical current dependent on time and space) suitable for transmission over the channel
• Converts/encodes the message to a format suitable for transmission
• Channel
• The medium used to transmit the signal from transmitter to receiver (cables, air, paper, route)
• Noise source
• Entity that introduces something to the signal not intended by the information source
• Combination of transmitted signal and noise
• Performs the inverse operation of that done by the transmitter, reconstructing the message from the signal
• Converts/decodes the message
• Destination
• The person (or thing) for whom the message is intended

Types of Communication Systems

“We may roughly classify communication systems into three main categories: discrete, continuous and mixed. By a discrete system we will mean one in which both the message and the signal are a sequence of discrete symbols. A typical case is telegraphy where the message is a sequence of letters and the signal a sequence of dots, dashes and spaces. A continuous system is one in which the message and signal are both treated as continuous functions, e.g., radio or television. A mixed system is one in which both discrete and continuous variables appear, e.g., PCM (pulse code modulation) transmission of speech.”

The Necessity of Redundancy

“An approximation to the ideal would have the property that if the signal is altered in a reasonable way by the noise, the original can still be recovered. In other words the alteration will not in general bring it closer to another reasonable signal than the original. This is accomplished at the cost of a certain amount of redundancy in the coding. The redundancy must be introduced in the proper way to combat the particular noise structure involved. However, any redundancy in the source will usually help if it is utilized at the receiving point. In particular, if the source already has a certain redundancy and no attempt is made to eliminate it in matching to the channel, this redundancy will help combat noise. For example, in a noiseless telegraph channel one could save about 50% in time by proper encoding of the messages. This is not done and most of the redundancy of English remains in the channel symbols. This has the advantage, however, of allowing considerable noise in the channel. A sizable fraction of the letters can be received incorrectly and still reconstructed by the context. In fact this is probably not a bad approximation to the ideal in many cases, since the statistical structure of English is rather involved and the reasonable English sequences are not too far (in the sense required for the theorem) from a random selection.

Linear model of communication

• Sender -> message (encoding) -> channel (medium and noise) -> message (decoding) -> receiver
• General model of human communication based on Shannon and Weaver’s theory
• Functional, goal/task oriented, rules-based approach (not intuitive)
• Implies that communication is unilateral, or that turns are taken (alternating between being “sender” or “receiver”)
• Linear and sequential; so, time and the necessity of feedback (between sender and receiver) become important
• Ignores context, relationship between sender/receiver, previous knowledge and experiences of the sender and receiver, message content, and treats a communication act as a singular event isolated in time
• Applied to computer-to-computer communication
• Data sharing, and machine language (coded/decoded, compiled/ decompiled) (statelessness of the web)
• Applied to human-computer/computer-human communication
• SQL (Structured Query Language) and programming (command driven, sequential, coded/decoded, names are arbitrary, flow charts)
• Search engines, web browsers, Microsoft Office, operating systems, websites
• Systems analysis and design, interface design (must build in feedback mechanisms)(input/output, garbage in/garbage out)
• Error message generation, agents/bots, help features (feedback)
• Human Computer Interaction, Human Factors, Information Architecture, Usability Studies, Accessibility Studies are performed to counteract this
• To a limited degree, users can customize or personalize the interface
• Applied to CMC
• Communication software is built based on this theory (e-mail, chats, instant messengers, MUD’s, the standard “user” model, etc.)
• CMC research usually takes a functional approach emphasizing the importance of time, medium, and feedback/cues (CMC in the Handbook of Interpersonal Communication is in the “Processes and Functions” section); ignores the development of relationships over time; ignores the cultural, social, emotional, semiotic, and paradigmatic (expectational) factors already existing in the “senders and receivers” garnished from previous life experiences
• People are starting to use Wiki’s, Blog’s, participatory websites, and collaborative websites to counteract this (participants can change, add to, subtract from website content as they’d like: Who is a sender? Who is a receiver? The emphasis is on content, relationships, and community.)
• General implications of the linear model of communication
• People who create computer-mediated communication systems adopt (are indoctrinated in) the linear model of communication. Because of this, it is assumed that “users” will be able to fill in the blanks as regards to meaning (the semantics problem) and pragmatics (the effectiveness problem: how to use it, behavioral outcomes). This is not necessarily the case
• The linear model is not intuitive/hermeneutic (being based on Shannon’s mathematical theory; mathematics is not intuitive dealing in probabilities that may or may not adequately reflect actual phenomena; in that the linear model does not adequately reflect how actual human communication takes place and the importance that meaning, context, and relationships play), and results in the creation of systems that are not intuitive. Computer-mediated communication systems are never used in the way that they were designed to be used.
• Communication scholars need to take a more active role in informing computer-mediated communication system designers on how human communication actually works, and on how to improve computer-mediated communication tools