Duality-based Mode Operations and Pyramid Multilayer Mapping for Rhetorical Modes

TL;DR

This paper introduces duality-based operations and a pyramid multilayer mapping framework to expand and quantify rhetorical modes, enabling more dynamic and measurable discourse systems in AI and linguistic research.

Contribution

It proposes novel duality-based mode operations and a hierarchical mapping framework to enhance and quantify rhetorical diversity and complexity in computational models.

Findings

Quantified expressive diversity using Shannon entropy.

Identified the Marginal Rhetorical Bit (MRB) for measuring growth.

Hierarchical selection reduces choice uncertainty.

Abstract

Rhetorical modes are useful in both academic and non-academic writing, and can be subjects to be studied within linguistic research and computational modeling. Establishing a conceptual bridge among these domains could enable each to benefit from the others. This paper proposes duality-based mode operations (split-unite, forward-backward, expansion-reduction and orthogonal dualities) to expand the set of rhetorical modes, introducing generated modes like combination and generalization, thereby enhancing epistemic diversity across multiple applications. It further presents a pyramid multilayer mapping framework (e.g., three layers from the rhetorical model layer, to cognitive layer, and to epistemic layers) that reduces the resulting cognitive complexity. The degrees of expressive diversity and complexity reduction are quantified through binomial combinatorics and Shannon entropy analysis. A Marginal Rhetorical Bit (MRB) is identified, permitting the definition of a rhetorical-scalable parameter that measures expressive growth speed in bits per stage. A direct entropy measure shows that hierarchical selection over smaller subsets markedly reduces choice uncertainty compared with flat selection across all modes. These considerations appear to transform static and non-measurable rhetorical taxonomies into more dynamic and more measurable systems for discourse design. From this work, it would be possible to identify a pathway for future AI systems to operate not only on language tokens but on layered rhetorical reasoning structures, bridging linguistic, pedagogical, academic, and computational research