Enhance Decision-Making With Control Systems

This article is part of our exclusive series of career advice in partnership with the IEEE technology and engineering management company.

Much of engineering is decision -making. Engineers make decisions concerning product design, programs management, technological road cards, research guidelines, leadership of technical teams, etc.

As a former president of the IEEE Control Systems Society and now the elected president 2026 of the IEEE Technology and Engineering Management Society, as well as to occupy management positions in the industry and the academic world, I have thought a lot about the links between control systems and technology management.

The safe and reliable performance of spaces and spaces, cars and trucks, houses and buildings, chemical factories and manufacturing facilities, communication and financial networks, and many other complex systems is based on automation and control systems. But, as I discuss here, the concepts of control engineering are also relevant for human decision -making in technology management.

Whether in engineering or management, the uncertainties are omnipresent. In the case of this last field, we can never be sure of the innovation processes, market projections and personalities and capacities of people. Indeed, uncertainties may seem so overwhelming that some may be tempted to make a decision by turning a piece.

But most decisions are not randomly made, and engineering control offers information on managerial decision -making in uncertainty.

Mental models and uncertainty

We are counting on mental models – our knowledge, our beliefs, our hypotheses, our experiences, observations and reasoning. But the models of any variety are not reality. They are at best precise approximations, and they are completely wrong at worst. It is essential that all decision -makers recognize the differences between their mental models and their reality, then act to reduce inadequacy.

Let me draw an analogy from the control engineering. To develop a control system for an airplane, for example, mathematical models – not mental variety – are developed from the plane cell. For digital accuracy, models require “sufficient excitation”, which means providing a variety of entries, such as deviations from flight control surfaces and measure how the plane reacts.

Based on this data, required precision models can be created and integrated into the design of the flight controller. The data must be rich enough for relevant signals to be able to exceed the unrelevant noise.

Decisions are rarely one And-Done business. Living a team, managing a project, allocating resources and undertaking a design all requires regular interactions with others, initial decisions adjusted regularly over time.

The same goes for mental models for human decision -making. Monitoring daily normal operations of an organization or project would probably not provide information from a signal / noise ratio high enough for mental models to be reliably put.

Instead, tasks and special situations can help achieve the goal. For example, a manager could give a difficult task to a member of the team mainly to improve the mental model of the employee manager, rather than responding to an urgent organizational need. The improved mental model can help the leader determine the best role for the employee when a real real situation occurs.

Whatever effort, mental models will never be perfect. There will always be uncertainty. So a crucial lesson for decision -makers to keep in mind is that all you know, you only think you know. Resist the temptation to believe that you really know the truth.

As a decision maker, the objects of your mental models include your organization, other stakeholders and the external environment. But they also include your self-model model. You must have a clear understanding of your own capacities, preferences and circumstances. The examples include your workload, the pace you work best, your flexibility in light of other priorities and what motivates you. And, of course, you should appreciate that your personal models are also uncertain.

People often don’t know themselves as well as they think. Be honest with yourself and ask for the comments of colleagues and trusted friends. Do not react defensively; Listen to the comments, then think. This can strengthen your understanding of yourself.

Dynamics and decision -making

Sometimes the effects of a decision are not immediately apparent. It can take days or even years to make it happen. In the meantime, observations can provide an indication of the effects, but they could also be wrong. In the theory of control, for example, we teach the concept of opposite response, where the initial response to a decision is the opposite of the final effect.

A simple example is what happens to the benefits of a company if it considerably increases its research and development investment. For the next quarters, the profits will probably be lower due to R&D expenses. Once the new products are deployed, profitability will probably increase.

A manager who does not recognize the temporary trend of the opposite response and reduces R&D resources can get worse rather than improve questions by sacrificing the long -term vitality of the company. Such short -see decisions occur too often.

Decisions are rarely one And-Done business. Living a team, managing a project, allocating resources and undertaking a design all requires regular interactions with others, initial decisions adjusted regularly over time.

These dynamics must be taken into account in complex decision -making situations. The adjustments are based on the monitoring of the activity, thus closing the feedback loop.

Delays can be particularly difficult to manage. As indicated, the decisions taken about projects and processes take the time to have an impact. Delays can result from various sources, including communication problems, new policies, endowment problems, supply times and report processes.

To be an effective decision maker, your mental model should include delays. The complications resulting from unforeseen setbacks in the feedback processes are well known, both in control engineering and systems engineering. The ability to anticipate delays – and, as far as possible, to reduce them – is a precious skill for decision -makers.

Connection of points

The interconnections between the concepts of mental models, uncertainty, dynamics and feedback are deep and fascinating. There are many ideas they offer for decision -making.

An example is the compromise of robust-performance in control engineering. The compromise refers to the fact that the highest performance levels cannot be affected while being robust during periods of strong uncertainty. This insight is the basis of the “free lunch” theorem in optimization, which means that no decision -making approach can be optimal in all situations.

When the levels of uncertainty increase a discrepancy between a mental model and reality, the presence of noisy data or external disturbances, decision -making should be less aggressive. Instead, you must respond by making progressive changes and pending feedback signals. To paraphrase, the more uncertain the situation, the more you have to cover yourself.