What’s Conv Theory?

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Conversation theory is the general study of how knowledge is constructed through interactions between parties. The parties involved can be human or non-human, including artificial intelligence entities. Conversation theory seeks to define how two parties come to a mutual understanding of concepts or perform other cognitive work through interaction.

Scientists describe conversation theory as a cybernetic discipline. Cybernetics involves the study of structures of regulation or meaning. Similar disciplines are referred to as information theory or control theory.

A dialectical approach is another term often used for conversation theory. A dialectic is a structure based on two opposing principles, ideas, or structures. In this case, the parties involved in conversation theory understand dialectical opposites. As in the generic dialectical process, the two parties, initially in a kind of antithesis, come to a synthesis through interaction.

Part of conversation theory is the search for stable reference points in communications. This helps scientists identify how meaning is acquired through interactions. Those who study this communication of science could also create specific relationships between the parties. For example, a conversation theory model might include a relationship called a teacher-student relationship. Alternatively, the two parties involved could be referred to as a generator and a responder, or their actions described as a dialectic of production and assumption.

Conversation theory also covers different types of conversation. This includes natural language-based conversation, such as the participation of humans with each other, or the use of technical languages ​​such as an object-oriented language or a meta-language; this would include elements useful in computer programming and other types of technology work. Conversation theory students might also look at concepts such as libraries in their general study of how meaning occurs between parts.

Overall, conversation theory is useful for informing many types of technologies. This idea, pioneered by Gordon Pask, was useful, for example, in the concept of the “Turing Test” or “Turing Machine”, named after Alan Turing, in which a machine’s competence is measured by how well it can imitate the conversation of a human being. This kind of theory also works for building decision support software or other types of decision support tools. Further research will help scientists be able to implement more robust versions of artificial intelligence that could create breakthroughs in many fields and industries.

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