Ideas about definition of mindset
• Author: Yuriy Polyulya •Building a definition of “Engineering Mindset” is my long-term project, and this is the first post intended to set the foundation for discussion.
Given the opportunity to compare my engineering education with scientific extension in the same specialization, I’ve reflected on the fundamental differences between being an engineer and a scientist within engineering disciplines. The most apparent distinctions lie in roles, goals, and objectives.
For scientists, the goals are relatively well-established: “to describe reality.” For engineers, however, the definition is less straightforward, as it typically revolves around specific problem definitions where generalization presents challenges. The most compelling definition of an engineering goal I’ve encountered is: “to change reality.”
Scientific and engineering mindsets are often intertwined, but they have distinct traits. Looking at the roots of the difference between the two, it becomes clear that the main differences lie in goal, focus, and approach.
| Property | Scientist | Engineer |
|---|---|---|
| Goal | To describe reality | To change reality |
| Focus | Generalization discovery, research, experimentation | Specialization problem-solving, invention, optimization |
| Approach | Inductive hypothesis testing, data collection, analysis | Deductive design, build, test, iterate |
| Result | Knowledge theory, model, simulation | Product device, system, process |
| Purpose | Understanding advancing human knowledge | Application solving practical problems |
| Success Metric | Explanatory power accuracy, peer validation | Functionality efficiency, reliability, scalability |
| Time Orientation | Future knowledge long-term insights | Present solutions immediate implementation |
When examining this from a goal-oriented perspective—“describing reality” (scientists) versus “changing reality” (engineers)—we can observe a complete spectrum of roles with numerous gradations between pure engineers and pure scientists. This spectrum includes engineers solving invention problems and scientists developing applied theories, as illustrated in Figure 1.
Any point in Figure 1 represents a possible specialization profile, such as R&D Engineers or Applied Scientists, each addressing defined problems through their unique blend of scientific and engineering approaches. To complete this picture, we must enrich our understanding of these goals with their underlying objectives:
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To describe reality - to create the most compact, elegant, and predictive description possible, capturing essential phenomena with mathematical precision.
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To change reality - to transform the existing state into one that more closely approximates an ideal final result, balancing constraints of time, resources, and feasibility.
This intersection is where skills and mindset become critical. But what exactly constitutes this mindset?
Mindset is a set of cognitive frameworks that enables us to identify optimal processes for reaching goals and evaluate the quality of both process and results. The key properties of an effective scientific-engineering mindset include:
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Simulation - the ability to model complex systems mentally, manipulating variables and focusing on critical parameters while recognizing that these models are abstractions rather than perfect reflections of reality. This cognitive scaffolding allows prediction of behavior under various conditions.
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Abstraction - perhaps the most fundamental property of an advanced mindset, abstraction enables identification of underlying patterns by filtering out noise and non-essential details. Rather than mere simplification, abstraction represents a sophisticated generalization that requires rational validation and typically depends on simulation results.
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Rationality - the discipline of decision-making based on evidence and logical frameworks. Rationality serves as the verification mechanism for both simulation and abstraction, checking for inconsistencies and rule violations. It demands intellectual honesty and willingness to discard appealing but incorrect solutions.
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Awareness - the meta-cognitive ability to recognize the limitations of one’s simulations and abstractions. Awareness encompasses understanding the boundaries of current knowledge and acknowledging the potential side effects of mental frameworks. Without this self-reflective capacity, identifying and correcting errors in simulation and abstraction becomes impossible.
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Optimization - The systematic pursuit of solutions that maximize desired outcomes while minimizing costs. Since humans are natural satisficers[1] (accepting “good enough” rather than optimal solutions), true optimization requires deliberate practices that challenge our tendency toward premature solution acceptance.
Note: In my assessment, other properties of mindset derive from these core attributes, with the exception of domain knowledge. Domain knowledge, while essential, represents a collection of facts and principles rather than a cognitive property—it serves as the raw material upon which these mental frameworks operate.
The most innovative breakthroughs often occur at the intersection of scientific understanding and engineering application, where descriptive power meets transformative capability. Those professionals who can navigate this spectrum with fluidity, applying both mindsets as circumstances demand, become the most versatile problem-solvers in their fields.
[1] Satisficing is a decision-making strategy that aims for a satisfactory or adequate result, rather than the optimal solution.