Information dynamics in computational systems

Studies how information flow, increase, and explicitness emerge from computation relative to observers and subsystems, grounded in domain theory and order-theoretic formalism.

16 members. Each node is clickable.

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Drawn from 7 sources

The papers/notes whose extracted claims & findings make up this cluster.

Bridges (6)

Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.

Design principles for care-centered systems16 shared
Observer-relative computational information theory5 shared
Continuous vs discrete computability2 shared
Order theory in computer science1 shared
Christopher Alexander's 15 properties clustering1 shared
Black-box computation as autonomous manipulator1 shared

Claims (14)

Due to its elementary yet powerful formal theory, FCA can express other methods, and therefore has the potential to unify the methodology of data analysis.Ganter's assertion about FCA's foundational role and integrative potential in data analysis.
Agents and their interactions are intrinsic to studying information flow in computation, not peripheral to classical information theory.Central thesis: traditional static information theories fail to capture dynamic interaction necessary for understanding modern computing.
Combining visualization and statistical analysis enables deeper understanding of the fifteen properties and their interactions.
Computation seeping into all aspects of human life through black box apparatus acquires autonomous existence, manipulating behavior in favor of arbitrary games detached from concrete world.Flusser's critical diagnosis of technological domination; frames cybernetic diagrams as potential strategy for subversion and freedom.
Continuous dynamics inhabit real closed fields (complete, decidable); discrete arithmetic is incomplete and uncomputable.Mathematical argument that continuous biological computation operates in a richer formal domain; suggests computational advantages for consciousness-relevant processing.
Down-sets are more convenient than up-sets for many purposes in order theory.Observation that ↓ operator is easier to work with than ↑ due to order-preservation rather than order-reversal.
Information flow and increase must be understood relative to subsystems and their interaction with environment.
Information increase in computation arises from data reduction and making implicit information explicit, not logical increase.Author's proposed resolution to the information increase paradox: computation gains utility through extraction and filtering, not creation of logically new content.
Information increase in computation is observer-dependent, relative to the computational direction chosen.Extension of information dynamics: direction of information increase (e.g., forward multiplication vs. reverse factorization) depends on observer's goals.
Making information explicit—extracting normal forms from computation.
Order provides terminology, notation, and models across diverse areas of computer science including comparisons of size, information content, and definedness.
Powersets are too nice for pure set models; ordered set models are richer and can capture more computational behaviors.
The order relation ≤ on a poset P is determined entirely by the down-sets in P.
The page is physically finite but conceptually infinite through associative fieldsPhysical page limits are measurable, but the associative and referential possibilities within the text create endless branching potential.

Findings (2)

Continuous functions in Domain Theory reflect that computational processes have access only to finite information at each finite stage.Mathematical principle grounding the formal definition of continuity in domain theory to physical and epistemic constraints on computation.
Least Fixpoint Theorem: A continuous function f on an ω-cpo with least element has a unique least fixpoint definable as ⊔ f^n(⊥).Fundamental mathematical result in Domain Theory enabling rigorous treatment of recursive definitions and infinite computations as limits of finite information increase.