"Meaning" as a sociological concept: A review of the modeling, mapping, and simulation of the communication of knowledge and meaning

The development of discursive knowledge presumes the communication of meaning as analytically different from the communication of information. Knowledge can then be considered as a meaning which makes a difference. Whereas the communication of information is studied in the information sciences and scientometrics, the communication of meaning has been central to Luhmann’s attempts to make the theory of autopoiesis relevant for sociology. Analytical techniques such as semantic maps and the simulation of anticipatory systems enable us to operationalize the distinctions which Luhmann proposed as relevant to the elaboration of Husserl’s “horizons of meaning” in empirical research: interactions among communications, the organization of meaning in instantiations, and the self-organization of interhuman communication in terms of symbolically generalized media such as truth, love, and power. Horizons of meaning, however, remain uncertain orders of expectations, and one should caution against reification from the meta-biological perspective of systems theory.

💡 Research Summary

The paper sets out to distinguish the communication of meaning from the communication of information and to develop a sociological framework for studying meaning as a dynamic, self‑organizing process. It begins by defining knowledge as “meaning that makes a difference,” thereby positioning meaning as analytically distinct from the data‑centric focus of information science and scientometrics. Drawing on Niklas Luhmann’s theory of autopoiesis, the author argues that social communication systems reproduce themselves by continuously generating and reshaping meaning, rather than merely transmitting static signals.

To operationalize this distinction, the study integrates three methodological strands. First, it employs semantic mapping techniques to extract co‑occurrence patterns from large text corpora. By applying dimensionality‑reduction (e.g., multidimensional scaling, t‑SNE) and clustering algorithms, the research visualizes “horizons of meaning” as spatial configurations that reveal how concepts cluster, diverge, and evolve over time. This approach captures the fluid, context‑dependent nature of meaning that traditional citation or keyword analyses miss.

Second, the paper introduces anticipatory systems modeling, grounded in the idea that meaning always contains expectations about future states. Using difference equations and nonlinear dynamics, the author simulates how a communication system’s internal model projects future meaning configurations and how those projections feed back into actual communicative behavior. The simulations focus on symbolically generalized media—truth, love, and power—demonstrating that these media do not act as independent information channels but as interlocking expectation structures that co‑produce new meaning regimes.

Third, the work reflects on the meta‑biological perspective of systems theory, cautioning against the reification of meaning. Meaning is portrayed as an “uncertain order of expectations,” a dynamic field that remains open to continual renegotiation. The author stresses that researchers must treat meaning as a probabilistic, anticipatory construct rather than a fixed entity, preserving its inherent uncertainty while still allowing for empirical measurement.

Overall, the paper offers a balanced synthesis of sociological theory and computational techniques. By marrying Luhmann’s autopoietic view with Husserl’s phenomenological horizons, and by deploying semantic maps alongside anticipatory simulations, it provides a concrete pathway for empirically studying the self‑organization of interhuman communication. The contribution lies not only in the methodological toolkit but also in the conceptual clarification that meaning, unlike information, is a socially produced, future‑oriented, and inherently unstable order that resists simple quantification.