Value, Representation, Information and Communication
<|reference_start|>Value, Representation, Information and Communication: A new analytic framework is first formalized via the usage of the Monadology (Leibniz 1898), to expand the understanding of Zermelo-Fraenkel-choice set theory (ZFC) and Von Neumann-Bernays-Godel set theory (NBG). Implicitly, the framework levels value, representation and information separately. Given the fact that there exists a coincidental equivalence between Von Neumann universe and originally-formalized motivation in ZFC, this work hypothesizes the essential of ordered values for one monand, to carry out efficient communication with the rest. This work then focuses on the relationship among values, representation and information (and suggests potential methods for quantitative analysis). First, this framework generalizes the definition of values and representations from "Indexes approximate Values" principle by (Peng 2023) via surreal numbers (Knuth 1974). Second, credited to surreal numbers, this work recursively connects representations and information via subsets of sets. Therefore, the definition to metric space(s) is naturally formed by representations, and quantitative methods (e.g., Hausdorff Distance) can be applied for quantitative analysis among (sub)sets. Third, this framework conjectures that: as long as the metric space is (or can be formed as) complete, the existence tests can be performed via Cauchy Sequence (or its generalized methods). This work finally revisits the communication theory, and suggests new perspectives from the new analytic framework. Particularly, this work hypothesizes a (quantitative) relationship between values and representation, and conjectures that: the optimal construction of representations exists, and it can be derived as the core value of one monad via Cauchy Inequality (or its generalized methods).<|reference_end|>
@article{peng2024value,,
    title={Value, Representation, Information and Communication},
    author={Xiangjun Peng},
    journal={arXiv preprint arXiv:2404.00404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00404},
    primaryClass={cs.IT math.IT}
}