Effectively Communicating With Group Decision Support Systems Using Information Theory

Effectively Communicating With Group Decision Support Systems Using Information Theory

Jamie S. Switzer (Colorado State University, USA) and Ralph V. Switzer (Colorado State University, USA)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch184

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As originally articulated, information theory is actually a mathematical theory grounded in the field of electrical engineering designed to evaluate the performance of communication systems (Mahowald, Fedorenko, Piantadosi, & Gibson, 2013; Ziemer & Tranter, 2002). Claude Shannon was an electrical engineer and mathematician working at Bell Laboratories in the 1940’s. In 1948 he published his landmark paper, “A Mathematical Theory of Communication” (Shannon, 1948). This seminal work was concerned with the transmission and storage of information (Lafrance, 1990), and provided “the analytical tools to evaluate the amount of information contained in message signals, and to compare the performance of actual systems” (Carne, 1999, p. 172). According to Gallager (2001), Shannon’s theory established a conceptual basis for modern digital communications, particularly with regard to data compression, data encryption, and data correction (Gappmair, 1999).

Information theory has been applied to many disparate disciplines, such as computer science, statistics, cybernetics, physiology, psychology, library science, biology, physics, economics, music, and art (Asadi, 2015; Dahling, 1962; DeFleur & Larsen, 1987; Overstreet, 1984; Pierce, 1980). In the field of communication, information theory has been applied to such areas as speech, linguistics, forensics, broadcasting, journalism, and even animal communication as studied by animal scientists (McCowan, Doyle, & Hanser, 2002; Scott-Phillips, 2015; Stephens, Barrett, & Mahometa, 2013; Watt & Krull, 1974).

There is some disagreement among scholars as to the applicability of information theory to the field of communication, in the non-technical, non-engineering sense (Cherry, 1957; Devlin, 1999; Weaver & Weaver, 1965; Young, 1987). Shannon himself initially cautioned against applying his theory to human communication (Haken & Portugali, 2015, p. 33; Rogers & Valente, 1993). But Shannon, in conjunction with Warren Weaver, did express some “optimism as to its wider applicability” (Warner, 2001, p. 24). In subsequent years other communication scholars have also explored more general applications of information theory (Pierce, 1980; Rogers & Valente, 1993). Indeed, Shannon’s simple yet elegant model of communication systems forms the basis of many introductory communication courses.

Key Terms in this Chapter

Communication: Human interaction with others to deliver information.

Entrophy: The many different ways a message can be constructed depending on the circumstance.

Group Decision Support Systems: Technology that facilitates people working interactively and dynamically with collective group data.

Noise: Interference that occurs in the transmission of a message.

Computer-Mediated Communication: Human interaction using technological devices.

Redundancy: Conveying the same information more than once in a message so the interpretation of the message is clear.

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