Many of today’s hardest scientific problems share an uncomfortable feature: they do not stay inside disciplinary boundaries. A virus changes public health, which changes behavior, which changes economies. Carbon emissions alter climate, which reshapes ecosystems, migration, and politics. A new technology modifies not only machines but institutions and attention.
This is the kind of interconnectedness that Ludwig von Bertalanffy tried to make scientifically legible. Beginning in the mid-20th century, the Austrian biologist developed general systems theory as a framework for studying organized wholes rather than isolated parts. Its central claim was not that “everything is connected” in a vague or mystical sense. It was that many systems—organisms, ecosystems, organizations, and technologies—share structural principles that cannot be understood by reducing them to components alone.
From parts to relationships
Classical scientific analysis often succeeds by separating a phenomenon into smaller units. That approach remains indispensable. But Bertalanffy argued that living systems are not merely collections of parts. Their behavior depends on relationships, flows, feedback, and organization.
An organism maintains itself by exchanging matter and energy with its environment. It is therefore an open system, not a sealed mechanism. Its stability is dynamic: cells are continually replaced, chemical gradients are maintained, and information is processed while the overall organization persists. Bertalanffy called attention to this apparent paradox—continuous change producing durable form.
The same logic appears at other scales. A forest is more than its species list; its structure emerges from interactions among organisms, soil, water, and climate. An organization is more than its employees; routines, incentives, communication, and hierarchy shape what the group can do. In each case, properties arise from the arrangement and interaction of parts.
Why the framework is relevant now
Modern research has supplied tools Bertalanffy did not possess: network analysis, computational modeling, high-throughput biology, and large-scale simulations. These methods have made it possible to trace dependencies across genes, species, markets, infrastructure, and social networks.
They have also validated a warning at the heart of systems thinking: interventions can produce effects far from their original target. A policy that improves one measure may worsen another. Removing a predator can reorganize an entire food web. Changing one biological pathway can trigger compensatory responses elsewhere. In complex systems, cause and effect are often distributed across time and scale.
This does not mean systems are unknowable, or that every event is equally connected to every other. It means explanations must specify which connections matter, how strong they are, and under what conditions feedback changes the outcome. Contemporary systems biology and Earth-system science increasingly treat these questions as central rather than optional.
The unresolved problem: prediction
Systems theory is powerful at revealing dependence, but connection does not automatically deliver prediction. Complex systems can be sensitive to initial conditions, reorganize as they evolve, and generate genuinely emergent behavior. A model may accurately reproduce broad patterns while failing to predict a particular turning point.
That limitation is especially consequential as researchers build “digital twins” of organs, cities, and the planet. The ambition is to anticipate cascading failures and identify interventions before they become crises. The challenge is deciding which details must be represented, which can be ignored, and how models should incorporate human adaptation.
Bertalanffy’s enduring contribution is therefore less a single theory than a discipline of attention. Before asking what a part does, systems science asks what relations sustain the whole—and what happens when those relations change.
