Calculus of concepts
Another editor has reviewed this page's proposed deletion, endorses the proposal to delete, and adds:
If you remove the {{proposed deletion/dated}} tag above, please also remove this {{Proposed deletion endorsed}} tag. |
The calculus of concepts is an abstract language and theory, which was developed to simplify the reasons behind effective messaging when delivered to a specific target or set of targets.[citation needed] The theory aims to maximize the likelihood of desired outcomes, by using messaging elements and techniques while analyzing the delivery mechanisms in certain scenarios.[1][verification needed] The reduction of uncertainty, but not its elimination, is often cost effective and practical.
Empowered by the internet of things (IoT) the framework looks at numerous device such as smart phones, tablets, laptops, hand held gaming devices, GPS devices, automobile Event Data Recorders and other electronic devices as remote sensors capable of providing data channels.
By using elements, the theory discovers underlying key concepts and their relations to better understand how messages can by used to elicit desired behaviors through mental model heuristics and biases.[2] The framework does not serve up spam to potential consumers; it is a new paradigm for effective messaging. The nature of the data produced and consumed by devices in the IoT naturally lends itself to location-based awareness.[3]
Just-in-time and real-time broadcasting of key messages gives the framework an extra dimension, putting it at the forefront of behavioral methodologies. Broadcasting can take place across a number of platforms, text, photographic, video, audio or even direct human contact.
The use of anchoring-and-adjustment, framing and representativeness heuristics provides fertile grounds for “re-wiring” the decision making processes to include either positive or mitigating mental models of a given concept or set of related concepts.[4] The “re-wiring” will often produce results that have a significant impact on later decisions and behaviors on the target audience. The framework analyses key factors that influence the effectiveness of messaging mechanisms and how differing approaches can lead to entirely different results.[5]
Background
The calculus of concepts framework has been practically implemented utilizing a combination of Naive Bayes classification[6][not in citation given] and Support Vector Machines (SVM) algorithms to actively identify the key components of a messaging campaign and its effectiveness. The effectiveness of a communications campaign is often measured by numerous results including reach, frequency and duration.[7][not in citation given]
The training data set for the model implementation utilized the potential messages and delivery mechanisms with Actors, Actions, Objects, Contexts and Indicia as a few examples.
Each concept within the framework is treated by the practical implementation as either an independent or dependent variable (as applicable) and therefore may have a meaningful effect on the outcome of any communication. As with any machine-learning tool the Calculus of Concepts model implementation inputs can be either nominal or ordinal and depending on the particular case.
References
- ↑ Cialdini, Robert B. (2007). Influence : the psychology of persuasion (Rev. ed.). New York: Collins. ISBN 978-0061241895. Search this book on
- ↑ Klien, Gary (2011). Streetlights and Shadows: Searching for the Keys to Adaptive Decision Making. MIT Press. ISBN 978-0262516723. Search this book on
- ↑ Associati, Casaleggio. "The Evolution of Internet of Things" (PDF). Casaleggio.it. Retrieved 30 April 2013.
- ↑ Lopes, Lola L. "The Rhetoric of Irrationality". Theory & Psychology. 1 (1). doi:10.1177/0959354391011005.
- ↑ Holiday, Ryan (2012). Trust Me, I'm Lying: Confessions of a Media Manipulator. Portfolio Hardcover. ISBN 978-1591845539. Search this book on
- ↑ Zhang, Harry. "The Optimality of Naive Bayes" (PDF). University of New Brunswick. Retrieved 30 April 2013.
- ↑ Cortes, Corinna; Vapnik, Vladimir (September 1995). "Support-Vector Networks". Machine Learning. 20 (3): 273–297. doi:10.1023/A:1022627411411.
Further reading
- Uwe Mönnich (December 1983). "Toward a Calculus of Concepts as a Semantical Metalanguage". Meaning, Use, and Interpretation of Language (pp.342-360). De Gruyter. doi:10.1515/9783110852820.342. Search this book on
- The process of communication: an introduction to theory and practice David Kenneth Berlo (1960)
- The Image: A Guide to Pseudo-Events in America Boorstin, Daniel J (1992)
- Social Engineering: The Art of Human Hacking Hadnagy, Christopher (2010)
- Sway: The Irresistible Pull of Irrational Behavior Brafman, Ori, Brafman, Rom (2009)
This article "Calculus of concepts" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Calculus of concepts. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
