apply communication theory

Modern information theory remains inaccessible to complex systems scientists due to its mathematical abstraction and lack of domain-specific interpretability. Method: This paper constructs an interpretable, cross-disciplinary information-theoretic framework grounded in Shannon entropy, mutual information, transfer entropy, and computational mechanics. It systematically integrates cutting-edge tools—including information dynamics, statistical complexity measures, partial information decomposition (PID), and effective network inference—with emphasis on physical interpretability and nonlinear dependency modeling. Contribution/Results: The framework provides the first unified exposition of how information theory characterizes system–environment coupling, part–whole architecture, and causal emergence in complex systems. By clarifying conceptual foundations and operationalizing abstract measures for empirical analysis, it substantially lowers the barrier to adoption. As a result, information theory is advanced as a general-purpose language for complexity modeling and rigorous causal analysis across disciplines.

Conventional metrics such as floating-point operations per second (FLOPs) fail to capture the intrinsic performance characteristics of emerging computing paradigms—including low-precision, analog, quantum, and reversible logic—due to their hardware- and precision-specific assumptions. Method: This paper proposes a general, information-theoretic framework for computational performance evaluation, modeling computation as an information-transformation channel from input to output and using mutual information as the core metric to quantify a system’s capacity to encode, process, and preserve semantically meaningful information. Contribution/Results: It is the first work to systematically integrate Shannon’s mutual information into computational performance assessment, thereby decoupling evaluation from underlying hardware implementations and numerical representations. The framework enables paradigm-agnostic, implementation-independent performance analysis across heterogeneous computing models. It establishes a foundational theoretical basis and provides a scalable, principled metric for rigorously assessing both the effectiveness and efficiency of next-generation heterogeneous computing systems.

Communication barriers between data scientists and domain experts arise from oversimplified, accuracy-centric model performance reporting, hindering shared understanding of model limitations and contextual applicability. Method: We propose a visualization-mediated model explanation framework grounded in human-computer interaction principles, participatory design, and visual narrative techniques. This yields the first domain-expert-oriented model communication guideline—emphasizing risk, trade-offs, and situational appropriateness rather than isolated metrics like accuracy. An iterative empirical study was conducted using regression models, incorporating structured expert feedback for evaluation. Contribution/Results: The framework significantly improves domain experts’ ability to identify model limitations, recognize inherent trade-offs, and proactively make context-driven adoption decisions. Its core innovation lies in repositioning visualization as an interdisciplinary consensus-building medium—shifting the paradigm from “metric reporting” to “collaborative understanding.”

Classical Shannon information theory assumes semantic irrelevance, failing to account for logical knowledge held by communicating agents. This work addresses the problem of minimizing communication cost when a sender (Alice) and receiver (Bob) possess partial, possibly heterogeneous, logical knowledge, such that Bob must logically deduce Alice’s private proposition via first-order deduction. Method: We propose the first semantic-aware communication theory integrating logic-based reasoning with information theory. Crucially, we formally incorporate Bob’s first-order deductive capability into the communication model, establishing a semantics-sensitive transmission paradigm. Combining coding theory, game-theoretic analysis, and algorithm design, we derive tight upper and lower bounds on semantic communication complexity. Contribution/Results: We introduce the first knowledge-driven communication framework supporting logical inference; derive optimal code-length bounds for multiple semantic scenarios; design an asymptotically optimal algorithm; and empirically demonstrate significant gains in transmission efficiency over classical schemes.

This paper investigates information transmission and trust formation under “stealth learning” constraints—where senders cannot fully conceal or distort acquired information. Using game-theoretic modeling and Bayesian equilibrium analysis, it characterizes optimal sender strategies under bounded misreporting capability. The central finding is that senders influence receivers not through outright deception, but via strategic “selective ignorance”; the mere possibility of deception acts as a pivotal constraint shaping information acquisition and disclosure decisions. The paper establishes, for the first time, that enhanced verifiability is formally equivalent to strengthened commitment power, and rigorously characterizes both sender-optimal and receiver-optimal falsification environments. Key contributions include: (i) proving the endogeneity of strategic ignorance in no-deception equilibria; (ii) deriving necessary and sufficient sign-and-magnitude conditions for how increased verifiability affects sender utility; and (iii) fully characterizing the structural properties of optimal falsification environments for both parties.

This work addresses the inefficiency of traditional communication models, which rely on reliable transmission of symbol sequences while overlooking the strong predictive capabilities of both transmitter and receiver, particularly under high latency or bandwidth-constrained conditions. The authors propose a Predictive State Communication (PSC) framework that maintains a shared predictive state between endpoints and transmits only innovation information necessary to correct prediction errors at the receiver. This paradigm shifts the communication modeling from entropy-rate accounting to cross-entropy accounting under model mismatch, defining a bounded perceptual-capacity operating region jointly constrained by channel capacity, delay, and perceptual continuity. Key techniques—including state identifiers, anchor mechanisms, limited rollback, and patch-based updates—ensure predictive synchronization and efficient innovation encoding. Experimental results visually demonstrate PSC’s potential to optimize the trade-off between perceptual fidelity and resource efficiency.

This study addresses the lack of a unified theoretical foundation in traditional requirements engineering (RE) quality assessment, which often fails to integrate artifact- and process-oriented perspectives and overlooks information transmission efficiency. To bridge this gap, the paper proposes a holistic theoretical framework that models RE as a flow of information particles among stakeholders, developers, testers, and artifacts, with information flow as its core construct. Building on this model, the authors develop a simulation system to capture dynamic interactions and information exchanges across roles. The simulation reveals how high-quality requirements specifications can be inadvertently bypassed in agile environments and yields actionable insights for improving RE processes. This work establishes a theoretical basis for optimizing information flow, enhancing RE effectiveness, and understanding the underlying causes of success or failure in requirements engineering practices.

This work addresses the fundamental problem of quantifying uncertainty and characterizing reliable communication limits over noisy channels. Methodologically, it establishes a unified analytical framework by rigorously integrating Shannon information-theoretic concepts—entropy, mutual information, and channel capacity—with the structured algebraic methods of coding theory; it further derives and interprets the noisy-channel coding theorem and the algebraic realization of maximum-likelihood decoding. Innovatively, using the binary symmetric channel as a canonical model, the study reveals intrinsic correspondences between error-correcting code design and information-theoretic limits. The results precisely delineate the theoretical boundaries of reliable communication and provide interpretable, principled foundations for constructing efficient codes. By bridging probabilistic modeling and algebraic structure, this work substantively strengthens the cross-disciplinary foundation between information theory and algebraic coding.