A Better Violinist Will Not Fix the Orchestra
How relationships shape the behavior of complex systems
We are taught early that understanding something means taking it apart. When a machine stops working, we inspect its components to find the fault. We often approach an organization in much the same way, examining individual performance or looking for a process that needs fixing.
Often, this works. A worn belt can explain an engine failure, just as a missing skill can explain why a task goes badly. Studying components and how they work together has helped us solve countless problems.
The difficulty begins when we assume that the explanation must always be found at that level. A group of accomplished musicians can give an uneven performance because they fail to coordinate. Recruiting a better violinist may leave the problem untouched. We need to understand what happens between the players as well as what each can do.
In such cases, learning more about each individual may get us only so far. We need to examine the interactions through which their efforts produce a shared result.
What taking things apart can tell us
Studying a system through its components is often described as reductionism. Its usefulness can encourage a stronger assumption: that enough detail about the components will provide every explanation we need. That assumption deserves scrutiny.
Of course, an engineer already looks beyond a parts catalog, tracing how forces travel through a structure and how one component affects another. The question is which interactions an explanation needs to include, and what we lose by leaving others out.
My friend and former colleague Fabio Montagnino and I occasionally spar over ideas, bringing his background in physics into conversation with mine in management scholarship. Reading an earlier draft of this essay, he pointed out that simple systems can also be highly organized. That observation sharpened my question: when does studying the parts separately obscure the behavior we want to explain?
A clock can be highly organized and still lend itself to being understood one mechanism at a time. What matters is how much the rest of the system affects the behavior of the part we are studying. If that behavior changes with its surroundings, studying the part in isolation may give us a misleading picture.
Physicist P. W. Anderson addressed this problem in his 1972 essay More Is Different. Knowing fundamental laws, he argued, does not mean we can reconstruct the behavior of complex systems from them. New levels of organization require their own concepts and explanations. Physics itself gives us reasons to question the sufficiency of explanations based on individual components.
The parts in their setting
In biology, a gene’s sequence is only part of what we need to know. Through gene regulation, cells control when a gene is expressed and in what amount. This helps them perform different functions and respond to changes in their environment. The sequence alone does not explain how that regulation works.
Studying a gene or protein in isolation can reveal an essential mechanism. We then need to establish how it operates within the living organism. Findings become misleading if we assume they apply unchanged under conditions we excluded from the study.
Consider two hospitals with similarly capable staff. For this thought experiment, assume that their resources and patient populations are also comparable. Yet one coordinates care reliably while the other repeatedly encounters delays.
Suppose that, in the second hospital, a test result reaches the ward after the team responsible for acting on it has finished its shift. Responsibility passes informally. The incoming team assumes someone else has followed up. Each person may be competent, yet the handover repeatedly fails.
Staff qualifications tell us little about the cause of the failure. Following the test result through the hospital would reveal where responsibility becomes unclear. Giving someone explicit responsibility for following up might improve care without changing the staff.
Calling this a problem of “culture” or “the system” would leave the explanation unfinished. We need to show how the arrangement contributes to the delay. Otherwise, we have merely exchanged one vague diagnosis for another.
A useful map leaves things out
The familiar observation that the map is not the territory helps here, provided we ask what a map is for.
Get Nicos Rossides’s stories in your inbox
Join Medium for free to get updates from this writer.
A transport map leaves out most of the geography above ground. Its usefulness depends on preserving the connections a traveler needs. A complete list of stations would be far less helpful in planning a journey. Simplification can improve understanding when it preserves the features relevant to our question. A useful explanation, like a useful map, helps us see what would otherwise be difficult to follow.
An organizational chart, for example, may accurately show reporting lines while leaving out the colleagues people actually turn to for advice. To understand how a difficult decision gets made, we may need to follow those conversations. More detailed job descriptions would not tell us what we need to know.
When an explanation falls short, it is worth asking what kind of information is missing before collecting more of the same.
When measurement enters the loop
People can also respond to the measures used to assess their work, changing the very behavior we are trying to understand.
Imagine a customer-support team evaluated primarily on cases closed per day. Staff might begin prioritizing easy cases or closing difficult ones before the customer’s problem is resolved. The reported number improves. Some customers then return, creating additional work.
Managers see the new figures and adjust targets or staffing. Employees respond to those decisions, changing what the next set of figures will show. This creates a feedback loop in which measurement helps shape subsequent behavior.
Feedback can also help a team correct a problem. Colleagues might gradually develop a way of sharing difficult cases as they learn which approaches work. When patterns of coordination develop through such interactions without anyone designing the overall arrangement, we describe this as self-organization. A feedback loop alone does not establish that this has happened.
To understand a measure’s effects, we have to follow how people use it. The same formula might help one team identify problems while encouraging another to hide them, depending on how managers respond to the results.
Expectations that help shape events
A market forecast can influence behavior in a similar way. Suppose a respected analyst predicts a downturn. Investors who believe the forecast may sell, contributing to the decline they anticipate. Others may regard the fall as a buying opportunity and counteract it. The effect depends on how participants interpret and respond to the information.
This feedback between people’s understanding of a situation and the events their actions help produce is often called reflexivity. It complicates prediction because publishing a forecast may change the conditions on which the forecast was based.
Understanding this process still requires close analysis. We need to examine how expectations influence trading, and how price changes then affect expectations. Studying the transactions and following the wider feedback loop are complementary ways of investigating the same problem.
What organization explains
What counts as an adequate explanation depends partly on the question we are asking.
In the hospital, a staff member’s account might explain why they did not act on a particular test result. To explain why results repeatedly go unattended, we would need to examine the handover arrangements. Both explanations can be valid, but they answer different questions.
This raises a philosophical question. If changing the handover arrangements changes what happens to patients, should we treat organization itself as a cause? At the very least, the arrangements belong in our explanation because they shape what people know and who is expected to act.
Whether this explanation could ultimately be restated entirely in terms of individual actions, or even physical processes, is a further question. Our examples do not settle that debate. They show why understanding the arrangements matters to the problem we are trying to solve.
For managers, the immediate task is to investigate how the work gets done. A recurring failure may call for a change in how responsibilities pass between teams. A disappointing response to a new target may require us to reconsider what it encourages people to do. These possibilities become visible when we look beyond individual performance.
Studying the parts remains essential, but we need to return them to their setting to understand how they work together. The hospital’s unattended test result gives us a place to start. Following its journey may explain more than another assessment of the people who handled it.
Dr. Nicos Rossides is a CEO, educator, and author whose work spans management, education, and artificial intelligence. He is Co-Founder and Chairman of the Advisory Board of listening247, a global insights firm, and Professor of Management at Minjiang University’s International Digital Economy College. His Routledge titles include Engaging the Workforce (2022), Employee Engagement in Startups (2025), and AI-Powered Insight (2026, with Michalis A. Michael).
