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Systems Thinking Wins Over Decision Makers

JD Solomon
Mar 9

Systems thinking helps you connect, inform, and build rapport with decision makers. JD Solomon Inc. provides practical solutions for project development and communication. — *Systems thinking helps you connect, inform, and build rapport with decision makers.*

Effective communication is rarely about isolated elements. Perfect wording, flawless visuals, or an eloquent speaker are not required. What matters most is the seamless interaction of multiple components working together. Systems thinking transforms communication from a disjointed process searching for perfection into a well-orchestrated system that produces confidence.

Understanding Communication as a System

A system is a collection of related parts that create an outcome greater than the sum of the individual components. In communication, these parts include structure, clarity, medium, feedback, and the context in which the message is delivered.

A car’s engine, wheels, and transmission must work in harmony to move forward. Communication elements must function together to achieve effectiveness.

You Don’t Have to Be the Most Attractive or Best Spoken

The key takeaway from systems thinking is that individual perfection is unnecessary. A car does not require an ideal tire pressure or every cylinder running at peak efficiency to function effectively. Likewise, in communication, one does not need to be the most articulate speaker or the most skilled writer.

What matters most is that all communication components work together.

The FINESSE Approach: A Systems Thinking Model for Communication

The FINESSE framework is an example of systems thinking applied to communication. FINESSE stands for Frame, Illustrate, Noise Reduction, Empathy, Structure, Synergy, and Ethics. Each of these components plays an individual role. However, the true power comes from their interaction.

Frame: Setting the context ensures the message is understood correctly.
Illustrate: Using visuals or examples makes abstract concepts concrete.
Noise Reduction: Filtering out unnecessary information prevents confusion.
Empathy: Understanding the audience’s needs fosters engagement.
Structure: Organizing information logically aids comprehension.
Synergy: Ensuring all elements complement one another enhances clarity.
Ethics: Communicating truthfully builds trust and credibility.

FINESSE helps professionals create messages that drive action by treating communication as a system.

FINESSE in Action

The FINESSE Fishbone (cause-and-effect) Diagram produces an easy mental model to help recall the necessary system components. The FINESSE Checklist provides a concise tool for developing and checking the communication. Both are available at the JD Solomon Inc website under the Resources tab.

The Input-Process-Output (IPO) Model in Communication

Another practical tool from systems thinking is the Input-Process-Output (IPO) model. This model breaks down communication into three critical aspects.

Inputs

Data, reports, expert insights, visuals, and audience insights are the basic raw materials for effective communication. High-quality inputs lead to more effective communication.

Processes

This aspect involves shaping inputs into a meaningful message. It includes selecting the right medium, structuring content, and refining delivery.

Outputs

The final message should align with the intended outcome. That’s true whether it’s a report, a presentation, or a conversation.

The IPO Model in Action

The pressure is on as you start to develop a presentation for the Board of a senior executive. Begin by defining what action the decision maker should take. Next, frame what inputs are in and which are out.

Systems Thinking in Action

Frame the problem and streamline the information using the IPO model (expressed as the F as Frame in FINESSE). Next, structure the delivery using the FINESSE Fishbone Diagram and the FINESSE Checklist. Make sure each bone of FINESSE is sufficiently addressed, but the perfection of each component is not required.

Winning over Decision Makers

The power of systems thinking lies in recognizing that effectiveness comes from integration, not perfection. Professionals can craft messages that are understood and drive action by focusing on how elements interact rather than individual components. Effective communication is produced by how all the parts work together.

Solomon, J. D. (2025, March 6). How to use systems thinking to win over decision makers. Communicating with FINESSE. https://communicatingwithfinesse.substack.com/p/how-to-use-systems-thinking-to-win

JD Solomon writes and consults on decision-making, reliability, risk, and communication for leaders and technical professionals. His work connects technical disciplines with human understanding to help people make better decisions and build stronger systems. Learn more at www.jdsolomonsolutions.com and www.communicatingwithfinesse.com.

Environmental Risk and Uncertainty: Eliminating Uncertainty

JD Solomon
Mar 4

Leaders need to know whether uncertainty is a knowledge gap (fixable) or a natural variability (not fixable). JD Solomon Inc. provides practical solutions for addressing environmental risk and uncertainty. — Leaders need to know whether uncertainty is a knowledge gap (fixable) or a natural variability (not fixable).

In large, complex systems, uncertainty is a technical reality that must be classified, measured, and managed. The most useful distinction is between epistemic uncertainty, which reflects limits in our knowledge, and aleatoric uncertainty, which reflects inherent variability in the world itself. Serious quantitative modeling depends on knowing the difference, because what can be reduced must be pursued and what cannot be reduced must be designed for. The uncertainty that cannot be eliminated must be effectively managed and communicated.

Epistemic Uncertainty

Epistemic uncertainty is defined as those uncertainties due to simplifying model assumptions, missing physical data, or our basic lack of knowledge. Some examples include the way we express inputs or relationships to describe natural phenomena, the inputs we chose to put in (or leave out), and certain types of numeric errors (such as those related to precision or significant figures).

Epistemic uncertainty is limited by our understanding of what we know (knowledge) and to the choices we make in applying the knowledge (judgement).

Aleatoric Uncertainty

Aleatoric uncertainty is defined as those uncertainties that are inherent to a problem or to an event that cannot be reduced by additional knowledge. Additional runs (trials) of an experiment or additional observations may help to narrow the uncertainty, but there is a natural error of lack of clarity or precision that is present. Aleatoric uncertainty is also known as statistical uncertainty or irreducible uncertainty.

Modeling Example

Both kinds of uncertainties are present in Large Worlds. And they are usually overlapping.

For example, I began my career developing quantitative groundwater (hydrogeologic) models. The leading-edge quantitative models were developed with early generations of the control-volume finite-difference (CVFD) flow software MODFLOW and the solute transport and reactive solute transport software MT3D. Both software applications are still in use 20 to 30 years later, albeit with several generations of improvement. They are now officially endorsed by the United States Geological Survey (USGS).

Some of the issues with quantitatively assessing the uncertainty associated with some nasty chemicals and chemical compounds included: the assumed boundary conditions at the edges of the model; grid spacing (both model and field sampling points); relationships and inter-actions between known, and possibly unknown, compounds; geophysical conditions, such as aerobic or anaerobic environments, and the effect on chemical fate and transport; the type and precision of groundwater sampling that had been performed; the accuracy and reliability of analytical laboratories and field testing; and the accuracy and reliability of the models themselves.

All of this to say that there were many sources of uncertainty – some based on our then-current knowledge of the world and others related to the assessment approaches we had chosen, or were limited to using.

Uncertainty is Everywhere

Similar Large World examples can be found related to air quality assessments, atmospheric modeling, weather forecasting, climate change models, predicting wildfires, disease and epidemic modeling, biological assessments, nuclear engineering, and others.

The good news is that we are certainly much improved at quantitative prediction where variables behave independently, such as in many physical sciences.

The bad news is that we still have a long way to go when it comes to accurately predicting outcomes in which variables depend on and interact non-linearly, such as in biological processes and human behavior.

Uncertainty in Practice

The distinctions between epistemic and aleatoric uncertainty continue because they clarify what can be reduced versus what must be managed.

Epistemic uncertainty - reducible with more data, monitoring, research, or model refinement.
Aleatoric uncertainty - inherent variability that must be accommodated through design margins, resilience, or probabilistic methods.

Environmental agencies still rely on distinctions in epistemic and aleatoric uncertainty to justify monitoring programs, adaptive management, probabilistic risk assessments, and funding for data collection.

Uncertainty in Environmental Communication

Federal and state environmental agencies implicitly or explicitly use epistemic and aleatoric uncertainty logic in risk communication and hazard modeling.

Leaders need to know whether uncertainty is a knowledge gap (fixable) or a natural variability (not fixable). There are competing frameworks, but framing environmental issues using epistemic and aleatoric uncertainty remains powerful and intuitive.

The Limits of Eliminating Uncertainty

Reducing uncertainty and being more objective are certainly the right, noble things that should be done. However, the reality is that our knowledge of the future is not perfect and even the most quantitative models require subjectivity. Only statistical Frequentists, working in Small Worlds, believe or advocate otherwise.

Reduce uncertainty and subjectivity – yes.

Eliminate uncertainty and subjectivity – never.

Embrace uncertainty and subjectivity – always.

North Carolina State University’s Ralph Smith is an excellent source in the field of uncertainty quantification. For me, he is also an example that, although you may travel far for expert advice and guidance, sometimes you discover one of the best sources is in your own backyard. See G.L.S. Shackle for more on the nature of our knowledge. The US National Weather Service is an excellent reference for more details on quantitative modeling for weather forecasting, the USGS for quantitative hydrogeologic and geologic modeling, and the Centers for Disease Control (CDC) on quantitative and qualitative modeling related to diseases and epidemics.

This article was first published by JD Solomon on LinkedIn.

Solomon, J. D. (2018, October 29). Risk and uncertainty: Eliminating uncertainty. LinkedIn. https://www.linkedin.com/pulse/risk-uncertainty-eliminating-jd-solomon

Environmental Risk and Uncertainty: The Role of Subjective Probability

JD Solomon
Feb 26

Probability is based on our current knowledge of future uncertain events, which always makes probability subjective. JD Solomon Inc. provides practical solutions for environmental risk and uncertainty. — Probability is based on our current knowledge of future uncertain events, which always makes probability subjective.

Seldom do we run to failure the things that matter most. Take, for example, any one of the three high-service pumps that supply drinking water and firefighting capability to a city with a population of 1 million people. An accurate assessment of the probability of failure is not possible – we simply do not have the data, and never will. In Large Worlds, we don’t run the things that matter most to failure (at least statistically speaking). And, on the limited failures that occur, we seldom do the detailed root cause analysis that is required to determine the causation of the failure. Enter subjective probability.

Subjective Probability

Every event that refers to future occurrences is uncertain. What we refer to as probability is a reflection of our current knowledge. Probability is simply one valid method to express our degree of certainty (or uncertainty) in quantitative terms. Only clairvoyants and fortune tellers can predict the future with complete certainty.

When it comes to risk and uncertainty, all probability is subjective.

Objective, Numeric Analysis

Rational thought, as defined by objective, numerical analysis, is a modern one. Many technical professionals were trained in statistical hypothesis testing and, consequently (and usually unconsciously), have a “baked-in” statistical Frequentist tendency. It is simply flawed thinking that a conclusion drawn from the past will objectively predict the future.

It is not simply by chance that probability and risk originated about the same time, nearly 300 years ago, to help interpret an uncertain world.

So what does this mean?

The Laboratory versus The Real World

In the logic of Small Worlds, where variables can be isolated and assumed to be independent, there is potential meaning in the concept of objective probability.

In Large Worlds, in reality (and not in laboratories), all probability is subjective probability.

Probability is based on our current knowledge of future uncertain events, which always makes probability subjective.

All Models Are Subjective

Beware of those who say we should eliminate all subjectivity by using mathematical/quantitative models. At best, the insights from models help reduce some of the subjectivity but will never eliminate it altogether. All models include the subjectivity of the modeler, and even our cherished Monte Carlo simulations require subjective evaluations of the input probability distributions.

Embrace Subjectivity (and Uncertainty)

Subjective probability is not a flaw in our thinking but a reflection of how the real world actually works. The systems that matter most—our critical assets, our organizations, our communities—operate in Large Worlds where perfect data will never arrive and tidy statistical assumptions will never hold. The responsible path forward is not to cling to the illusion of objectivity, but to acknowledge the role of judgment, experience, and evolving knowledge in every assessment we make. When we accept subjectivity as inherent rather than inconvenient, we make better decisions, communicate more honestly, and manage environmental uncertainty with the maturity it deserves.

See John Moubray and reliability-centered maintenance (RCM) for more on the limitations of historical data for the things that matter most. In addition to early references related to probability and risk, more modern references to personal probability can be found in Frank Knight (1921), and to subjective probability in L.J. Savage (1954) and Kahneman and Tversky (1972). Karl Pearson, Fisher, and Neyman & Pearson are key references for statistical hypothesis testing. See G.L.S Shackle for more on the dynamic nature of knowledge and the limitations of objective probability.

This article was first published by JD Solomon on LinkedIn.

Solomon, J. D. (2018, October 18). Risk and uncertainty: The role of subjective probability. LinkedIn. https://www.linkedin.com/pulse/risk-uncertainty-role-subjective-probability-jd-solomon