Chapter 9 Engineering as Willing

  Part I Reflections on Practice

Abstract

The abstract argues that while science is seen as a systematic approach to acquiring knowledge, engineering should be viewed as a systematic approach to applying will. Using Bernard Lonergan's transcendental precepts as a backdrop, it compares scientific and engineering methods. In science, the intellect is primary, aiming for objective knowledge. In contrast, engineering prioritizes the will, focusing on pragmatic, subjective outcomes often determined by managers or clients, not the engineers themselves.

Engineering problems are usually not well-defined and require heuristic approaches, leading to diverse solutions and models from different engineers, none of which is definitively correct. Engineering decision-making involves necessary trade-offs, making it more intentional than rational. The abstract suggests that recognizing the intentional nature of engineering can help society value willing as much as knowing and involve engineers more explicitly in solving various technological and other challenges.

9.1 Introduction

The introduction of this section discusses the nature of engineering, emphasizing that philosophy is about asking questions rather than finding definitive answers. It starts by examining the term "engineer," suggesting that it implies a person who exercises ingenuity, a key aspect often overlooked in common perceptions of engineering.

The classic definition of engineering, dating back to 1828 by Thomas Tredgold, describes it as the art of harnessing natural power for human use. However, this definition is seen as not fully capturing the everyday work of engineers. A more fitting, albeit humorous, definition found in a structural analysis textbook by Brown (1967) suggests that engineering involves working with uncertain materials, shapes, and forces in a way that keeps the public unaware of these uncertainties.

Interestingly, both definitions describe engineering as an art rather than a science. The introduction contrasts definitions of 'science' and 'art' from Merriam-Webster's dictionary, noting that science is associated with knowledge, while art is linked to skill. This leads to questions about whether engineering is more about skill than knowledge, and whether engineers should consider themselves artists rather than scientists.

"The dictionary ( Merriam-Webster’s 1993 ) provides three relevant definitions of science:

• “a department of systematized knowledge as an object of study”;

• “a system of knowledge covering general truths or the operation of general laws”; and

• “principles and procedures for the systematic pursuit of knowledge.”

Notice that one word is common to all three: knowledge. Apparently, science is all about knowledge. Is engineering all about knowledge? In light of these definitions, is engineering a science? Should engineers be calling themselves scientists?

What about art? Once again, the dictionary ( Merriam-Webster’s 1993 ) provides three definitions that are potentially relevant:

• “skill acquired by experience, study, or observation”;

• “an occupation requiring knowledge or skill”; and (my personal favorite),

• “the conscious use of skill and creative imagination.”

Once again, notice that one word is common to all three: skill. Apparently, art is all about skill. Is engineering all about skill? In light of these definitions, is engineering an art? Should engineers be calling themselves artists?"

The chapter aims to explore the idea that while science is a systematic approach to knowing, engineering could be seen as a systematic approach to willing, i.e., making decisions, often without complete rational justification. This perspective challenges conventional views of engineering and its relationship with both science and art.

9.2 Knowing and Willing

Section 9.2 discusses the relationship between knowing and willing in the context of Bernard Lonergan's transcendental precepts. These precepts, not rules but levels of awareness and function inherent in every person, outline a process for legitimately gaining and applying knowledge. Lonergan's model is adapted as follows:

Experience: The first level involves being attentive to the data presented.

Understanding: The second level requires being intelligent in considering possible explanations.

Judgment: The third level involves being reasonable in evaluating the most likely explanation.

Deliberation: An added fourth level calls for being considerate in exploring potential actions.

Decision: The final level involves being responsible in making choices accordingly.

These levels guide individuals from adopting beliefs about the past and present world (through experience, understanding, and judgment) to making choices about the future world (through deliberation and decision).

"Attentive experience, intelligent understanding, and reasonable judgment lead people to adopt beliefs about how the world was in the past and is now; considerate deliberation and responsible decision lead people to make choices about how the world will be in the future."

The discussion highlights that while Lonergan focused on the process of knowing, his precepts also necessitate willing. Acts of will are required to be attentive, intelligent, reasonable, considerate, and responsible. This is particularly true for deliberation and decision-making, which demand not just recognizing an obligation but also actively fulfilling it. These stages involve setting priorities and choosing the best path from multiple options, guided by a well-informed conscience. This conscience, disciplined through the habitual exercise of these precepts, helps in consistently choosing the better option and avoiding the worse.

9.3 Science and Engineering

Section 9.3 discusses the differences between science and engineering using Lonergan's framework, which clarifies the interactions and distinctions between knowing and willing. The section contrasts various concepts:

Intellect vs. Volition: Intellect is associated with reasoning and understanding, while volition relates to the act of choosing or deciding.

Adopting Beliefs vs. Making Choices: Science often involves adopting beliefs based on evidence and reasoning, whereas engineering involves making choices, often under conditions of uncertainty.

Having Reasons vs. Having Motives: In science, having reasons refers to logical reasoning or evidence supporting a theory. In engineering, having motives relates to the goals or objectives driving a project.

Exercising Judgment vs. Reaching a Decision: Scientific work exercises judgment in interpreting data and theories, whereas engineering reaches decisions on designs or solutions.

Being Reasonable vs. Being Responsible: Science aims for reasonable conclusions based on evidence, while engineering requires responsibility in creating practical, effective solutions.

The scientific method involves observing phenomena, proposing hypotheses, and conducting experiments, primarily driven by intellect with the goal of gaining objective knowledge. In contrast, engineering uses state-of-the-art heuristics to create optimal changes in uncertain situations with limited resources. Machine Learning Techniques, Simulation and Modeling Heuristics, Agile Methodology in Software Development, A/B Testing in User Experience Design, Lean Manufacturing, Simplified Structural Engineering Calculations, Failure Modes and Effects Analysis (FMEA) would all be some examples of state-of-the-art heuristics.

In engineering, although intellect is involved, the will is primary because the goal is pragmatic and often subjective, with outcomes usually determined by external factors like client needs or resource availability. This highlights that while knowledge is essential in engineering, it is often insufficient on its own, underscoring the importance of creativity and decision-making in the engineering process.

9.4 Social Captivity

Section 9.4 discusses the concept of 'social captivity' in engineering, as observed by Goldman (1991). This refers to two interdependent types of captivity: intellectual and social. Intellectually, engineering is often wrongly perceived as merely applied science, which limits the scope of what engineering can encompass. Socially, the practice of engineering is constrained by external factors, such as the need for someone to hire an engineer for a specific project, and the inability of engineers to influence the decision-making process of managers or clients about the necessity or desirability of a product or facility. Additionally, engineering designs are often subject to non-technical constraints imposed by others, including aesthetic and functional considerations. These two types of captivity—one intellectual, the other social—are not separate, but interdependent; the first anchors the second, which in turn reinforces the legitimacy of the first. 

Goldman's thesis further suggests that technology and innovation are dominated by market-driven values rather than technical knowledge. Even when engineers become managers or clients, their decisions are influenced by the agendas and priorities of the organizations they represent, not necessarily by the expertise of engineers. Consequently, engineering often becomes instrumental in nature and it is used by non-engineers to achieve their objectives, which may be arbitrary. This results in the willfulness of engineering being both enabled and restricted by the willfulness of the institutions that utilize it.

9.5 Heuristics and Design Procedures

Section 9.5 discusses the role of heuristics and design procedures in engineering, as outlined by Koen (2003) and others. Heuristics are defined as tools that provide plausible assistance in problem-solving but are ultimately unjustified, unjustifiable, and potentially fallible. This concept challenges the Western tradition rooted in the search for certainty and universality, contrasting sharply with the nature of engineering.

Goldman's work highlights the differences between science (and philosophy) and engineering. While science seeks objective, theoretical knowledge characterized by necessity, certainty, and universality, engineering is marked by contingency, probability, particularity, concreteness, and practice. Engineering relies on subjective know-how and experience-based opinions aimed at action and utility, using heuristics that, although not provable in an absolute sense, are justified based on past successes.

Individual engineers possess unique sets of heuristics and meta-heuristics, which they use to transform technical and non-technical requirements into viable solutions. These heuristics form design procedures, similar to hypotheses in science, but do not guarantee specific outcomes. Addis notes that different design procedures can lead to similar designs and vice versa, showing no logical connection between the two.

Design procedures often involve developing mathematical models to capture important aspects of reality. Engineers face the challenge of determining which features to focus on and what assumptions to make for manageable yet meaningful assessments. Two strategies used in modeling are abstraction (neglecting certain aspects of reality) and idealization (simplifying complex aspects).

Models serve as epistemic tools, aiding engineers in understanding design problems and evaluating solutions. While model analysis aligns with scientific principles, model construction, and adjustment require skill and creativity, aligning with the dictionary definition of art. This highlights the artistic aspect of engineering, involving the creative use of skill and imagination.

9.6 Engineering Intentionality

Section 9.6 discusses the concept of engineering intentionality, emphasizing that engineering is not deterministic and often involves making decisions among multiple options without a single "right" answer. This challenges the application of a theory of rationality to engineering, suggesting instead that intentionality is a more fitting concept.

Key points include:

Tradeoffs in Engineering: Engineers often face tradeoffs due to technical, budgetary, legal, and political constraints, which are central to the design process and represent the essence of engineering intentionality.

Inductive vs. Deductive Reasoning: Engineering predominantly involves inductive reasoning, requiring the addition of new information and creative input, as opposed to deductive reasoning, which leads to certain conclusions based on premises.

Engineering as Information Creation: Engineers are information creators, as there is no natural law dictating specific designs. Different engineers might approach the same problem differently, influenced by their unique perspectives and tacit knowledge gained through experience.

Modeling in Engineering: Engineering involves setting up models for deductive analysis, with the real work lying in the creation and adjustment of these models. This aspect emphasizes that engineering is more art than science, especially in unconventional projects.

Problem Recognition and Definition: Engineering includes identifying and defining problems, not just solving them. Engineers convert design criteria into engineering terms, akin to language translation, making the process indeterminate and subject to various influences.

Human Behavior Governed by Motives: The chapter concludes that design and human behavior are governed more by motives than by reasons, highlighting a traditional bias favoring knowing (contemplation) over willing (action). This suggests a need to reassess how engineering and its decision-making processes are understood and valued.

9.7 Conclusion

The conclusion of this section argues that engineers, as exemplars of willing, should challenge the common perception of a gap between theory and practice in their field. Addis (1990) suggests viewing the field as divided into 'engineering science' vs. 'engineering design', focusing on the distinct purposes of these activities: understanding the natural world versus efficiently producing useful artifacts in uncertain contexts.

Engineering outcomes are not simply right or wrong due to the multitude of parameters and criteria involved. Engineers are relied upon for their judgment, informed by education and experience. Contrary to the view of engineers as mere number crunchers, the author emphasizes that data and numbers require interpretation by knowledgeable individuals.

Samuel Florman (1996) is cited, as urging engineers and citizens in a technological society to reflect on their roles. The author posits that all human situations involve uncertainty and resource constraints, requiring the use of successful heuristics and a balance of willing and knowing. Wisdom, a key aspect of philosophy, becomes crucial when multiple paths are available.

The conclusion suggests that engineers, with their training and temperament, are well-positioned to assist society in addressing challenges beyond just the technological realm, due to their ability to navigate uncertainty and apply practical wisdom across various aspects of human existence.