1 What Is a Model?: From Compression to Construal
Scientific models are often thought of as simplified representations—“maps” or “pictures”—of reality, tools that help us navigate complexity by reducing it to manageable form. But within a relational ontology grounded in Systemic Functional Linguistics (SFL) and informed by Edelman’s Theory of Neuronal Group Selection (TNGS), models can be understood far more profoundly: not as static mirrors, but as dynamic instances of meaning potential that both compress and construe the unfolding coherence of phenomena.
Compression as Coherence
At their core, models are compressions of relational processes and fields of unfolding. Just as particles emerge as compressed patterns within continuous fields, models condense vast webs of interaction and variation into structured, accessible forms. This compression is not arbitrary but is shaped by coherence: the patterned relations that hold together phenomena across dimensions of space, time, and causality. Models extract and amplify these coherences, enabling observers to grasp and work with them.
Construal as Meaning-Making
But compression alone is not modelling. For a model to function as a semiotic system—one that is meaningful and usable—it must be construed by conscious agents within communities of practice. This construal draws on value systems, purpose, and shared conventions to interpret the compressed patterns as meaningful configurations, whether numerical, visual, conceptual, or linguistic.
Models as Semiotic Instances
This perspective reframes models from static “pictures” to semiotic instances: dynamic, interpretable construals arising from material coherence but transcending mere physicality. Models are not simply “out there” but are enacted through the interaction of observer, community, and the phenomena under study. They instantiate meaning potentials shaped by cultural, cognitive, and methodological systems.
Implications
Understanding models as compressed and construed relational processes invites a new epistemology: one that foregrounds the role of the observer, the semiotic system, and the collective meaning potential from which models emerge. It also opens paths to explore how models evolve, how they relate across domains, and how they mediate the unfolding of scientific knowledge.
2 The Model in Practice: Interactions, Limits, and the Ecology of Knowledge
Building on our understanding of models as compressions and semiotic construals, we now turn to the practical dimensions of modelling in science and knowledge-making. How do models operate within fields of interaction? What are their limits? And how do they participate in the broader ecology of knowledge?
Models as Interactional Processes
Models are not isolated artefacts; they emerge, evolve, and function through ongoing interactions among observers, instruments, data, and phenomena. Each iteration—whether a mathematical formula, a conceptual framework, or a computational simulation—is shaped by this relational interplay. Models adapt to new observations, refine predictions, and respond to challenges, reflecting the dynamic and situated nature of knowledge.
Limits and Boundary Conditions
Every model embodies constraints—boundary conditions that define its domain of applicability and the assumptions it carries. These limits are essential: they acknowledge that models compress complex realities and that no model can capture every detail. Recognising these boundaries prevents the conflation of second-order semiotic reality (the model) with first-order material reality (the processes being modelled), and invites continual critical engagement and revision.
The Ecology of Models
Models coexist within an ecology of knowledge, interacting with other models, theories, and practices across disciplines. This ecology is not hierarchical but networked, with models influencing and transforming one another. Interdisciplinary dialogues reveal complementarities and tensions, highlighting how models mediate meaning across contexts.
The Role of Meaning and Value
As semiotic construals, models also carry meaning potentials that extend beyond empirical fit. They embody values, priorities, and interpretive frameworks that influence how phenomena are understood and acted upon. Awareness of these dimensions enriches the practice of modelling, situating it within human purposes and cultural contexts.
Towards Reflexive Modelling
Informed by a relational ontology, reflexive modelling acknowledges the mutual shaping of models and observers. It encourages openness to alternative perspectives, iterative refinement, and the embracing of complexity without succumbing to reductionism.
3 Compression and Coherence: Modelling as Meaning-Making
Having explored models as relational construals and situated practices, we now turn to the underlying dynamics that allow models to function at all: compression and coherence. In the relational ontology we are developing, these are not just technical or cognitive processes — they are meaning-making activities, unfolding within and across fields of potential.
Compression: From Process to Pattern
To model is to compress unfolding phenomena — to abstract patterns from complex processes. This is not simplification for its own sake, but a necessary condition of intelligibility. Just as language compresses experience into meaning, models compress relational unfoldings into selective representations. A model, then, is not a mirror of reality, but an enactment of coherence within constraint.
Compression does not negate complexity; it manages it. By selecting what differences make a difference, models allow us to interact meaningfully with the world — to anticipate, to question, to interpret. But every act of compression implies exclusions: unmodelled variables, unacknowledged assumptions, unseen interactions.
Coherence: Holding Meaning Together
If compression makes a model functionally possible, coherence makes it meaningful. A model must hold together across its internal structure and its external deployments. It must cohere with other models, with empirical observations, and with the broader systems of knowledge in which it operates.
Coherence is not reducible to consistency or predictive success. In a relational ontology, coherence is the resonance of a model within a field of meaning — its capacity to stabilise intelligibility across instances. A model coheres when it enables understanding, links phenomena, and supports purposeful action, even if it is partial or provisional.
The Model as Semiotic Instance
From this perspective, each model is an instance of meaning — not a derivation from reality, but an actualisation of meaning potential in a particular relational configuration. It is a semiotic act, grounded in material processes but structured by symbolic systems. This holds whether the model is a graph, a mathematical expression, a verbal explanation, or a simulation: all are instances of construal.
This view also dissolves the divide between scientific and everyday models. The child’s mental model of gravity, the engineer’s stress diagram, and the physicist’s field equations are all compressions of potential into instance, meaningful because they resonate within their contexts.
A Modelling Ethic
If models are acts of meaning, they carry responsibility. We must attend not only to how well a model works, but also to what it foregrounds, what it hides, whom it serves, and how it might evolve. Modelling, then, is not just a methodological activity — it is an ethical and ontological one.
Reflective Coda — Modelling as Construal, Relation, and Responsibility
Throughout this trilogy, we have re-examined scientific modelling through the lens of relational ontology: not as a search for ultimate reality, but as a patterned unfolding of meaning. Models, in this view, do not depict things-in-themselves but instantiate relational coherences — selective construals of experience within specific fields of potential.
We began by reframing models not as mirrors of reality, but as relational construals: semiotic instances that emerge from the activation of social and cognitive potentials. These construals are not arbitrary. They compress patterned regularities across processes, stabilising meaning within a shared context of interpretation.
We then examined the situated practices through which models are produced and refined — not as neutral activities, but as forms of social semiosis shaped by tools, traditions, constraints, and purposes. The scientist does not merely extract truth from the world but configures meaningful relations within it. Modelling, like all meaning-making, is a material and symbolic process.
Finally, we turned to compression and coherence as fundamental operations in modelling. Compression renders complexity tractable; coherence holds meaning together across time, context, and application. Modelling is thus always perspectival: it selects, relates, omits, and reframes. Its power lies not in its completeness, but in its meaningful partiality.
This relational approach does not weaken the epistemic power of science — it situates it. By understanding models as semiotic acts within unfolding systems, we gain a clearer view of both their capacity and their limits. We can ask not just whether a model works, but how and why it means what it does, for whom, and with what consequences.
The implications are both theoretical and ethical. To model is to construe. And to construe is to take a stance within a world of unfolding relations.
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