Transdisciplinary Engineering Design Process

About the Author

Dr. Atila Ertas, Professor of Mechanical Engineering at Texas Tech University, received his masters and PhD from Texas A&M University. He had 12 years of industrial experience prior to pursuing graduate studies. He has been the driving force behind the conception and the development of the transdisciplinary model for education and research. His pioneering efforts in transdisciplinary research and education have been recognized internationally by several awards from the Society for Design and Process Science (SDPS). He is the director of the transdisciplinary PhD program at Texas Tech University. He is the creater and the founding president of the non-profit organization The Academy for Transdisciplinary Learning and Advanced Studies (TheATLAS). He is a Senior Research Fellow of the ICC Institute at the University of Texas Austin, a Fellow of ASME, a Fellow of SDPS, and a Founding Fellow of the Luminary Research Institute in Taiwan. He is also an honorary member of the International Center for Transdisciplinary Research (CIRET), France. He has earned a national and international reputation in engineering design. He is the author of a number of books and the editor/coeditor of more than 35 conference proceedings. His contributions to teaching and research have been recognized by numerous honors and awards. These include: President's Excellence in Teaching; Pi Tau Sigma Best Professor Award; Pi Tau Sigma Outstanding Teaching Award; Halliburton Award in recognition of outstanding achievement and professionalism in education and research; College of Engineering Outstanding Researcher Award; George T. and Gladys Hanger Abell Faculty Award for overall excellence in teaching and research; and President's Academic Achievement Award. He has published over 150 scientific papers that cover many engineering technical fields. He has been principal investigator or co-princial investigator on over 40 funded research projects. Under his supervision more than 190 MS and PhD graduate students have received degrees.

Preface

During the last decade, the number of complex problems facing engineers has exploded, and the technical knowledge and understanding in science and engineering required to attack these problems is rapidly evolving. The world is becoming increasingly interconnected as new opportunities and highly complex problems connect the world in ways we are only beginning to understand. When we don't solve these problems correctly and in a timely manner, they rapidly become crises. Problems, such as energy shortages, pollution, transportation, the environment, natural disasters, health, hunger and the global water crisis, threaten the very existence of the world as we know it today. Recently, fluctuating fuel prices and environmental concerns have sent car manufacturers in search of new, zero polluting, fuel efficient engines. None of these complex problems can be understood from the sole perspective of a traditional discipline. The last two decades of designing large–scale engineering systems have taught us that neither mono–disciplinary nor inter– or multi–disciplinary approaches provide an environment that promotes the collaboration and synthesis necessary to extend beyond existing disciplinary boundaries and produce truly creative and innovative solutions to large–scale, complex problems.

Large–scale, complex problems include not only the design of engineering systems with numerous components and subsystems which interact in multiple and intricate ways; they also involve the design, redesign and interaction of social, political, managerial, commercial, biological, medical, etc., systems. Further, these systems are likely to be dynamic and adaptive in nature. Solutions to such large–scale, complex problems require many activities which cross discipline boundaries.

One of the widely agreed to characteristics of transdisciplinary research is that it is performed with the intent of solving problems that are complex and multidimensional, particularly those related to sustainability in a human environment. Transdisciplinary research tends to focus on collaborations that transcend specific disciplines to define new knowledge.

The anticipated results of transdisciplinary research and education are: emphasis on teamwork, the bringing together of investigators from differing disciplines, and sharing of methodologies to generate fresh, invigorating ideas that expand the boundaries of problem solutions. The Transdisciplinary approach develops people with the desire to seek collaboration outside the bounds of their professional experience in order to explore different ideas. A truly transdisciplinary research and educational system is needed to address large–scale, complex problems and to educate the researchers and designers of the future.

Transdisciplinary education involves students from many areas of knowledge crossing disciplinary boundaries such as economics, modeling and simulation, optimization, reliability, statistical decisions, ethics, and project management, all of which are included in this book. Hence, students can understand issues from a broad point-of-view in order to synthesize potential solutions.

Over the past decade, awareness and understanding of the complexity of the environmental impact on human activity is growing. Issues of environmental change are of increasing concern for both developed and developing nations of the world. Design and processes are central to the concept of transdisciplinary education. Social, political and cultural aspects of problems and issues must be recognized if workable and economically feasible solutions are to be developed. Students will emerge from this transdisciplinary education program with a broad perspective of the world and its problems, including a wide range of tools that will equip them to address such problems and apply them to socially relevant issues. It is a program that will teach students to integrate and manage knowledge in technical, social and scientific areas that require the collaboration of engineers, planners, physicists, biologists, psychologists, sociologists, economists, and other specialists. Transdisciplinary methodologies and tools covered in this book can be applied in a wide variety of disciplines including economics, business, management, operations research, engineering, chemistry, genetics, and the social and behavioral sciences.

It is a pleasure to make grateful acknowledgment of the many valuable suggestions which have been contributed by Professor Jesse C. Jones. Two chapters, (Chapters 10 and 11), and a majority of the problems which are included were used without change from The Engineering Design Process, co-authored by Ertas and Jones. The first five chapters of this book are about the Transdisciplinary education, the remaining chapters are devoted to fundamental engineering knowledge adapted from the earlier book, with a significant amount of new material, example problems and case studies.

In conclusion, the author takes this opportunity to express his thanks to Ms. Lauren Newmyer, Mr. Utku Gulbulak, Dr. Adam Stroud, Dr. Turgut B. Baturalp and Dr. Bugra H. Ertas for their help in the preparation of this book.

Atila Ertas

1

Systemic Thinking and Complex Problem Solving

We live in a highly complex, technological world – and it's not entirely obvious what's right and what is wrong in any given situation, unless you can parse the situation, deconstruct it. People just don't have the insight to be able to do that very effectively.

Christopher Langan

1.1 Introduction

The world's population continues to increase rapidly, which causes technology to develop at a geometric pace. Modern communication systems offer each of us overwhelming mountains of information, much of which is disorganized, not relevant, redundant, or inaccurate, and thus may well provide more confusion than clarity. We are faced with the necessity to wrestle with and solve many large-scale problems if we are to maintain sources of clean water, clean air, food, energy, adequate medical services, political stability, and a civilized social structure. Improving the condition of our world will prove even more difficult.

The area of study known as complexity is a very popular area of research. Complexity arises from the nature of large interconnected systems and is escalated by the background, personal characteristics, and perspectives of the individuals working on the design teams. It is important for designers to understand complexity and how it affects the understanding and projection of system behavior. It is also important to manage complexity so that it does not overwhelm the design effort and prevent the development of effective solutions. This chapter presents an overview of complexity, discusses how complexity can increase almost without bound(s), and suggests ways to control the impact of complexity on design process.

1.2 What Is Complexity?

During the last two decades of designing large-scale engineering systems it has been demonstrated that mono-, inter-, and multi-disciplinary approaches do not provide an environment that promotes the collaboration and synthesis necessary for extending disciplinary boundaries and producing innovative solutions to large-scale, complex problems. Such problems include the design of engineering systems with numerous components and subsystems which interact in multiple and intricate ways with social, political, managerial, commercial, biological, and medical systems. Furthermore, these systems are likely to be dynamic and adaptive in nature. Solutions to such complex problems require activities that cut across traditional disciplinary boundaries; this is what we call transdisciplinary research and education.¹

Complexity is difficult to understand due to the variety of competing proposed solutions and explanations for what constitutes complexity. Many researchers have proposed that complexity can described by size, entropy, information content, thermodynamic and information required to construct, computational capacity, statistical complexity, as well as others.²

Size was proposed as a level of complexity based on the presumption that larger things are inherently more complex. Information content refers to the length of computer program required to define a message or pattern.² Many of the proposed definitions of complexity are based on identifying a quantifiable parameter for a system or problem; however, proposals to date have not provided an agreed-upon definition.²

A distinction must be drawn between complex and complicated systems because this association is a source of confusion. Complicated systems and complex systems may both have multiple individual interactive components; however, in complicated systems the behavior is well understood, while complex systems lack this clear understanding.³

An additional problem in defining complexity comes about due to the association of randomness as an indicator of complexity. This is likely due to the synonymous use of complexity with unpredictability, which is a characteristic of random systems.⁴

Although it is possible for complex systems to produce random outputs, this perception of randomness is often relative to individual observers and their knowledge base. The presumption of order or randomness cannot be definitely or certainly demonstrated; therefore, this is not an effective measure of complexity.⁴

To add further difficulty in providing a physical definition of complexity, Pierce argued, in one of the earlier texts addressing complexity, against it being a quantifiable parameter. He states: Complexity is that sensation experienced in the human mind when, in observing or considering a system, frustration arises from lack of comprehension of what is being explored.⁵ In this paradigm, complexity is dependent on the individual examiner of a system, not the system itself. Warfield presents seven necessary conditions for a situation to be complex:⁶

a human presence;

a generic purpose associated with the human presence;

an inquiry into the system by the human presence;

a human purpose related infrastructure to allow inquiry;

a system related environment;

a sensing mechanism to measure inquiry; and

cognition of the human presence.

In Warfield's analysis, the human observer is involved with every requirement of complexity. Complexity has been described as a degree of ignorance. Objects are more or less complex depending on our ignorance or lack of information about it, our ability to make distinctions and perceptions about it, and our ability to infer information from it. Encrypted messages highlight the concept of observer dependent complexity. Encrypted messages are commonly broadcast during wartime and are received by all parties within the broadcast range. Observers with the correct cipher, or knowledge related to the information, can interpret the message. Those without knowledge about the data may see the same information as without meaning and, in fact, random. This is underlined by the ideas presented by Gell-Mann, proposing that systems are hard to predict not because they are random, but because their regularities cannot easily be described, or are unknown.⁷

The perception driven definition of complexity is considered incomplete by many. Axelrod and Cohen argue that the source of complexity is fundamental to the system and cannot be eliminated.⁸ They go on to describe the structure of complex systems as being composed of artifacts and agents. The agents are the interacting entities with some level of functioning behavior. Agents have memory and capability, and can formulate strategies and interact with agents. The artifacts are unanimated objects, manipulated by agents and properties of the agents, such as location and capabilities. In this description, the agent's use of selective intervention is a source of complexity as it is very difficult to develop predictions for the system.⁸ This concept of complexity originating from a system was expanded with the identification of three sources of system complexity: environmental influence, initial conditions of the system, and the system structure itself.⁹ Stability was also studied by Simon by examining the chaotic nature of complex systems from minor changes in environment or initial condition.¹⁰

The two distinct aspects of complexity were characterized by Warfield as cognitive complexity and situational complexity. Situational complexity is complexity inherent to the system being examined. Cognitive complexity refers to the complexity associated with interpretation by the observer.¹¹ This duality will be examined in greater detail later in this chapter.

Time has also been introduced as having an impact on complexity. The identification of unstable responses of the system related to influencing parameters indicates that the system is not stable in time.¹⁰ Works by Suh identify time, and specifically time periods, as a key parameter for prediction.¹² It is argued that the lack of ability to predict system behavior is due to an excessively long period of prediction.¹² This indicates that a less daunting task is to predict the state of a system incrementally, in near time, rather than at the end state, such as many years from now. It is argued that the introduction of incremental or periodic analysis will reduce complexity and improve the ability to predict system behavior.¹²

Suh provides one of the most quantifiable definitions of complexity by focusing on functional requirements (FRs) of a system rather than the physical components.¹² In this way, the relationship of system output relative to a desired output range defines the system's complexity.¹² Figure 1.1 describes the concept of complexity represented by a system output in the form of a distribution as it falls compared to a desired range (design range). The overlap range between the design range and system range shown in Figure 1.1 is the conformance or predictability of success. If there is no overlap area between two ranges, then there is a finite probability that the system will fail in time. The use of a small range relative to the distribution will constitute higher complexity in the image.

Figure 1.1 Uniform probability density function of an FR.

The predictability in achieving a desired outcome with a specified system becomes the measure of complexity. This effectively defines complexity, but does so with a relative quantity, instead of a physical measure.¹²

This is illustrated with the example of cutting a rod to a length. The length accuracy (design range) may be 10 micrometers. If the tool can always cut the rod with an accuracy of 10 micrometers, then the FR is satisfied and we assume that the complexity is zero. However, to perform this with a chop saw would be complicated due to its inaccuracy. Thus, cutting the rod within the design range ( 10 micrometers) may not be possible. In this case, we assume that the task is complex. The same task with a laser capable of extremely accurate cutting is much less complex. The relative overlap of system capability relative to desired outcome gives a quantifiable measure of complexity provided that the interrelations between entities are known and fixed.¹² This is ineffective in circumstances where the response of agents varies significantly and rapidly, as is common in human systems.

It is clear that the definition of complexity itself is not simply defined. To carry the discussion forward we will focus on the common denominators of complex systems instead of focusing on formal definitions. Examining complexity in the context of known complex systems proves very insightful, and provides a basis for a useful working knowledge. Although there is much ambiguity in the formal definition of complexity as a concept, the attributes of a complex system are relatively well known. A complex system is loosely defined as a system in which large networks of components with no central control nor any simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.² This behavior is unexpected considering the collective ability of the individual pieces. These systems should exhibit complex collective behavior, information processing, and apparent adaptation.² Simon specifies the four characteristics of complex systems as follows:¹⁰

Complex systems are frequently hierarchical.

The structure of complex systems emerges through evolutionary processes, and hieratic systems will evolve much more rapidly than non-hierarchic systems.

Hierarchically organized complex systems may be decomposed into sub-systems for analysis of their behavior.

Because of their hierarchical nature, complex systems can frequently be described, or represented, in terms of a relatively simple set of symbols.

We can conclude from these four statements that complex systems, at least ones that are likely to be of interest to us, are not random, but rather, have structure. This statement agrees with Axelrod and Cohen's comments mentioned earlier. Further, this structure is based on the interactions between the various parts (components and sub-systems) of the system. Simon also indicates that complex systems, because of their hierarchical nature, evolve and change rapidly with time. Axelrod and Cohen explain the adaptation of systems due to the intervention of agents (agents are individuals, organizations, political entities, computer software routines, etc.). Simon's third aspect of complex systems indicates a likelihood that they can be decomposed for study and analysis. This characteristic, which provides hope for dealing with complex systems, may not be easily realized. The interconnections and interactions of the parts of a complex system will not typically be obvious without significant study. Finally, Simon notes that complex systems can often be represented by seemingly simple arrangements of symbols which follow from the hierarchical structure of the system.

Warfield expresses his view of complex systems a bit differently:¹¹

For better or worse, our society has accepted the idea of large and complex systems. If we are going to have them, it behooves us to learn how to manage them….

One of the primary motivations comes from recognizing that society today involves large sociotechnical systems whose performance is far from ideal. It is clear that many of these large systems have taken their present forms primarily through evolutionary change that did not involve any systematic overview design, but may have involved some systematic design of parts. Other systems are said to have been designed, but still fail in ways that produce disasters.

Warfield defines three classes of systems:

(class A) systems found in the physical world;

(class B) systems based on intellectual technology or artificial intelligence;

(class C) systems synergistically composed of class A and class B systems.

Class B and C systems often do not have readily available metrics to measure performance. Relative metrics must be generated to evaluate and conceptualize these systems.¹¹

Maxwell, Ertas, and Tanik draw the parallels between class C systems and Warfield's definition of a socio-technical system.¹³ These types of systems are increasingly commonplace and must be understood to be effective in modern society. This will require a transdisciplinary process to design and manage the complexity associated with these systems.¹³ Complex systems are hierarchical, with many agents and subsystems that interact, often in non-trivial and varying ways that make them difficult to understand and predict. They can be completely decomposed within their hierarchic structure; high levels of redundancy make them relatively simple to represent symbolically. They adapt and evolve over time, and can contain physical and artificial entities within them. Finally, the understanding and management of complex systems is a transdisciplinary methodology. Herb Simon stated:¹⁴

Today, complexity is a word that is much in fashion. We have learned very well that many of the systems that we are trying to deal with in our contemporary science and engineering are very complex indeed. They are so complex that it is not obvious that the powerful tricks and procedures that served us for four centuries or more in the development of modern science and engineering will enable us to understand and deal with them. We are learning that we need a science of complex systems, and we are beginning to construct it.

Herb Simon

In The Sciences of the Artificial, Simon further states:¹⁰

The proper study of mankind is the science of design, not only as the professional component of a technical education but as a core discipline for every liberally educated person

Herb Simon

Our success in solving complex problems through design will depend largely on our ability to manage the complexity associated with these problems.

1.3 Source of Complexity

Considerable work has been done to identify and classify the sources of complexity; the majority of work has been done in human systems. Human systems are affected by several sources of complexity which fall under three categories: logical, gnosiological, and computational.

Systems impacted by logical complexity are not predictable at all. Logical complex systems are observable and comprehendible by an observer, but behavioral relationships cannot be generated to represent them. This classification of complex systems has two subclasses: logical and relational. Logical complexity applies to systems that are irreducible, problems within the system isolated. Order cannot be recognized in system data, and each theory is unavoidably temporary. The relational complex human system occurs when the modification of a human in a system is effective due to the presence of other actors in the system. This interactive modification of behavior, also known as the Hawthorn Effect, represents a shifting between individual and group behavior patterns. Individuals within groups become less objective in their observed behavior.⁴ The cases of logical complexity indicate that the observer is unable to develop any relationships between components in a system and, to them, it truly appears random.

Gnosiological systems are only predictable through an infinite computational capacity. Four subcategories exist for this type of complex human system: pure gnosiological, evolutionary, semiotic, and semantic. The pure gnosiological complex system exists when there is an irreducible subjectivity in perceiving the environment or system. The viewer is unable to draw distinctions from objects in the system, and without distinction no information can be obtained. In an evolutionary complex system, the characteristic of human systems to face continuous change is the source of complexity. The ability of humans to learn drives continuous change in the reaction of individuals to certain conditions. This affects the ability to predict behavior greatly. The second type of gnosiological complex system is the semiotic complex system. This source of complexity arises due to an inability to derive meaning from system behavior due to ambiguity. The lack of self-evident facts in individual behavior prevents the building of computationally predictive models. The semantic complex human system arises when ambiguity arises due to differences in language and culture of individuals in a system. Ambiguity in expression, interpretation, and translation of information provides a source of unpredictability in a system.⁴ This correlates with Warfield's description of design group dynamics, including knowledge, point of view, values, and objectives influencing the complexity of a system.¹¹

Systems of the computational category are only predictable through transcomputational ability.⁴ The third class of sources of complexity can be reduced into three subcategories: pure computational complexity, chaotic complexity, and self-organizational complexity. Pure computational complexity is a source arising from the inability to perform computer predictive models at a rate equal to event occurrence. Suh would argue that this is actually an instance of complicatedness instead of complexity.³ Chaotic complexity is a source arising from an inability to perfectly model behavior of objects in a system, particularly with large quantities of individual members. Self-organizational complexity is the source of complexity resulting from the unexpected and unexplainable emergence of behavior from an otherwise chaotic system. This category aims to classify the inability to model system behavior.

Of these three categories of complexity, the first two categories essentially describe human limitations: the inability to recognize a pattern or relation, and the ambiguity of interpretation. The third category describes the difficulty in describing a physical system, or situational complexity as described by Warfield.¹¹

1.4 Two Aspects of Complexity

It can be seen from the proposed definitions of complexity that a divergence is occurring. While some define complexity with physical metrics, others suggest it is dependent on the observer.²,⁴,⁵ It has been proposed that there exist two distinct classifications of complexity: situational and cognitive complexity, also referred to as real and imaginary complexity.¹¹,¹²

Warfield defines situational complexity as the complexity associated with a system being analyzed, and does not account for the observer's ability to perceive the systems' behavior.¹¹ Suh, who takes a relative perspective on complexity, reinforces this by defining real complexity as a measure of uncertainty associated with achieving a task.¹² Many of the other proposed physical descriptions of complexity, including information content, free energy, statistical complexity, computational requirements, level or hierarchies, and size, would align as instances of situational complexity as they are measures independent of the observer.²

Cognitive complexity, as Warfield describes it, arises from the aspects in the system that makes interpretation difficult.¹¹ Imaginary complexity, as Suh calls it, is not a real complexity but is cognitive and arises from an observer's lack of familiarity or ability to understand the system. Imaginary complexity can also exist even in the absence of real complexity.¹² This complexity aligns with early descriptions of complexity by Pierce, in which complexity is not quantifiable but, instead, persists as a sensation of frustration from the inability to interpret a system.⁵ Biggiero illustrated cognitive complexity with his observations that objects are more or less complex depending on the observer's ignorance of them.⁴

Example 1.1

Discuss the complex system of the stock market.

Solution

The stock market consists of traders behaving in their best interest, which produces an emergent systemic behavior.² The collection of interacting components represents the situational complexity in the system. Now imagine an observer standing on the floor of the New York Stock Exchange evaluating the performance of the market. The observer's ability to assess the market performance comprises the cognitive complexity. Now let us take away the observer's (and only the observer's) access to the various indexes and displays of stock prices. The observer has lost much of his ability to perceive the stock market. He must rely on observing the behavior of the other brokers and listening to interactions occurring in their immediate vicinity. To the observer, the system has become more complex because of a diminished ability to perceive and evaluate the system. This is an example of an increase in cognitive complexity without any change in the situational complexity of the system.

Both situational and cognitive complexity must be accounted for when defining complexity. Every system with any number of components will have some level of situational complexity, however small it may be. There also must exist some level of cognitive complexity, due to the finite cognitive abilities of humans.¹⁰ Human cognitive abilities must always be considered as the human is involved with all aspects of complexity, according to Warfield.⁶

1.5 Complexity and Societal Problems

Societal problems are real-life problems that are highly complex because of their dynamic character and impact on society.¹⁵ They are highly transdisciplinary, with social, cultural, economical, political, and emotional issues intertwined with technology. They cannot be easily solved by experiment, and the implementation of a correction to a problem changes the social system in a complex way.¹⁶ Analyzing a complex societal problem requires knowledge from diverse domains: bringing together non-academic experts and academic researchers from different disciplines to share concepts, methodologies, processes, and tools. Cronin stated:¹⁷

There is a need for transdisciplinary research (TR) when knowledge about a societally relevant problem field is uncertain, when the concrete nature of problems is disputed, and when there is a great deal at stake for those concerned by problems and involved in dealing with them. TR deals with problem fields in such a way that it can: a) grasp the complexity of problems, b) take into account the diversity of life world and scientific perceptions of problems, c) link abstract and case specific knowledge and d) constitute knowledge and practices that promote what is conceived to be the common good.

1.5.1 Causes of Societal Problems

Many actors are connected with societal problems that have legal, environmental, political, technical, safety, transportation, and economic aspects. Even though the real reason for complex societal problems is not always clear, many are caused by humans. In particular, some of the fundamental problems are political and economic, and rooted in human nature. Human involvement is apparent in complex societal problems such as antisocial behavior, sexually transmitted diseases, drug and alcohol abuse, wars, crime, and others. The main cause of social problems is unemployment, poverty, economic deprivation, urban problems, inflation, hunger, water crises, and diseases which are also social problems themselves. To solve such problems, important interactions between problem elements need to be considered. Failure to do so leads to unexpected and often undesirable solutions.

1.5.2 Process for Societal Problem Solving

As shown in Figure 1.2, societal problem-solving processes include problem orientation and problem-solving style.¹⁸ Positive problem orientation is a constructive problem-solving process by which a team attempts to identify or discover effective solutions using a transdisciplinary approach to produce positive solution outcomes. Negative problem orientation is a dysfunctional problem-solving process which contributes to an impulsivity/carelessness or avoidance style; both are likely to produce negative problem solution outcomes.¹⁹

Figure 1.2 Societal problem-solving process.

(Adapted from D'Zurilla, T.J., Nezu, A.M., and Maydeu-Olivares, A., Social Problem-Solving Inventory-Revised (SPSI-R): Technical Manual, Multi-Health Systems, North Tonawanda, NY, 2002. Used with permission.)

When the problem solution results are negative or unacceptable, two cases are possible:

give up and stop working on the problem solution, or

recycle or return to the problem-solving process by changing the problem-solving goals for more realistic outcomes.

For example, consider a magnitude 8 earthquake – it can be tremendously destructive with collapsing buildings and loss of life, but the destruction is often compounded by mudslides, fires, floods, sicknesses or tsunamis. Assume that such a devastating earthquake happened in a poor country with terrible diplomatic relations with other countries in the same region. Perhaps the first attempted goal would be to solve compounding problems individually. Pride is the main reason they may not want to ask for help. The reality is that these problems cannot be solved in a short time and the initial outcome would likely be negative. The following two cases are possible:

What can a poor country do to solve the many issues that this tragic event causes? Government mismanagement and lack of resources may delay the cleanup process and the result will not be a solution for the societal problems. In other words, give up and end with disaster.

Finding that the problems are not solved in a timely manner could be disastrous; their goals would have to be changed and plans should be developed to ask for help from other countries to minimize further damage and undesirable consequences. The solution is to return to the problem-solving process, to find a better way forward with collaborative effort with the help of other countries.

1.5.2.1 Transdisciplinary Societal Problem Solving

As mentioned earlier, for the positive problem orientation process, a transdisciplinary (TD) approach can be used. A schematic representation of the TD process is shown in Figure 1.3. In this figure, the process starts by understanding the problem (issue or situation), identifying the TD team members from diverse disciplines, and selecting or developing an appropriate methodology to solve the problem in hand. Chain links shown in Figure 1.3 represent the required interactions among the three elements of triangle. The three connections (interrelationships) are usually more difficult to deal with than the three elements accomplished separately (problem, team, and methodology).

Figure 1.3 Transdisciplinary societal problem-solving process.

Problem. As shown in Figure 1.3, the societal problem in hand can be divided conceptually into three groups: social problems, policy problems, and organizational problems. A social problem is an issue that influences a significant number of individuals within a society – social problems are closely related to the well-being of people such as obesity, poverty, domestic violence, homelessness, hunger, and healthcare problems.

Some of the technical policy problems can be very complex and highly resistant to solution; these are wicked problems. Examples of such problems are: housing and urban renewal policies, sustainable communities, and large infrastructure projects.

In the case of complex organizational problems, there are no simple and clear solutions. Issues facing corporate, government, and non-profit organizations are extremely complex. Some of the examples of complex organizational problems are: global marketing, starting new joint ventures, achieving goals such as strategic planning, working more efficiently, addressing conflicts, and promoting diversity.

Transdisciplinary team. As shown in Figure 1.3, the transdisciplinary team is divided into three groups:

Stakeholders – the people who have a stake in the problem being considered (have skin in the game).

Issue experts – the people who have specialized knowledge that is related to a problem under consideration.

Structural modelers – the people are concerned with describing things in a system and how these things are related to each other.

These three groups from diverse disciplines can be brought together in different combinations of people to collaborate together in solving complex unstructured problems. In some cases, issue experts may not be knowledgeable of the stakeholder's concern and interest and it may be necessary to involve stakeholders even in structuring the problem. The structural modeler establishes the reliability of the structural approach to the problem, hence arranging the basis for further development of content along the lines of the structure.

1.5.2.2 Methodology

Figure 1.3 indicates that methodology includes transdisciplinary generic tools, collective intelligence management (CIM) and development of collective intelligence. Use of TD tools and creating collective intelligence are a necessary part of a methodology for solving societal problems. The process is also involved in dealing with academic and non-academic experts for conducting CIM in developing possible solutions for societal problems.

1.6 Understanding and Managing Complexity

The understanding and management of a complex intervention can be a lengthy process. First, it is essential to understand the complexity of a situation in order to manage it. Complexity will either be managed or it will overwhelm the people or society. The understanding of complexity and the principal paths to the management of complexity shown in Figure 1.4 will be discussed in this section.

Figure 1.4 Understanding and managing complexity.

1.6.1 Understanding Complexity

Although there are many definitions and measures of complexity in the literature, the concept has proven to be very difficult to understand. The following factors can be considered for definitions and measures associated with complexity:²⁰

Numeric size of basic elements in a system. Although a larger size corresponds to a higher degree of complexity, numeric size in itself is not adequate to define complexity in its whole.

Variety of elements. Even though disorder alone cannot adequately define complexity, many researchers have used the degree of disorder or entropy in information theories as the measure for variety or complexity. Drożdż et al. stated that complexity lies somewhere between order and disorder.²¹

Relation between elements. Relations (interactions, etc.) among the components of a system contribute to complexity. Individual components of a system are held together through the relations of its internal structure. As an example, a chess pattern can be of great complexity to a player because the player counts on the relations between the elements, not just the number and the variety of the elements.²⁰

Observer dependency. As shown in Figure 1.5 (similar figures were used by Grassberger in experiments),²² the variety of the images changes as the disorder of image pixels and the number of pixels increases from left to right. However, the middle image was selected by the observers as the most complex one. This experiment shows that complexity depends on how observers process information.

Figure 1.5 Human perspective of complexity.

(Adapted from Grassberger, P., Information and complexity measures in dynamical systems, in H. Atmanspacher and H. Scheingraber (eds), Information Dynamics, Plenum Press, New York, 1991.)

Task dependency. The complexity also depends on the task requirements a person is involved with. For example, if the task is to count ants in a colony, then the complexity of the ants does not vary with the number of the ants and the variation in the shape or color of the ants. If the task is the investigation of the social life of an ant colony, then the complexity assessment will change with the behavior of the ants.

Thus, we may conclude from Simon's words and from our own deliberate consideration of ongoing efforts to wrestle with complex problems that complexity is a major obstacle, and that design and process are the keys to solutions of large-scale problems. Our success in solving complex problems through design will depend largely on our ability to manage the complexity associated with these problems. In summary, the degree of complexity is a combination of three factors: numeric size, variety, and relation. All three factors must be considered within the constraints of task requirements.²⁰

1.6.1.1 Twenty Laws of Complexity

Miller developed 20 laws of complexity in order to quantify and evaluate complexity. These laws aim to identify commonalities that can be useful in understanding the aspects of complex systems.²³

Law of triadic compatibility. This law states that the human mind's ability to recall and conceptually explore seven conceptual components simultaneously limits the size of system that the human brain can process to three components and the four possible combinations (interactions) of them. Systems of a larger size than this are difficult for the human brain to develop an understanding of component relationships.

Law of requisite parsimony. This law also represents a limitation of human capacity with respect to the flow of information. A human being can only collect and process information at a certain rate. A flow of information exceeding a person's ability to internalize it will result in overburdened thinking and the reasoning produced will be flawed and untrustworthy. In this way, systems generating huge quantities of data may appear complex simply due to the inability to process information into behavioral predictions.

Law of structural underconceptualization. An example of the law of structural underconceptualization is where system behavior is being developed and the relationships and components of a system are incomplete. The lack of a complete mapping of the system presents challenges to defining behavior and increases complexity.

Law of organizational linguistics. According to this law, individuals within organizations and disciplines operate in a linguistic domain to allow communication of ideas. These varying layers of these organizations and disciplines develop working linguistics to promote better communication within the level but hinder communication to different layers. Consider how scientists of different disciplines (e.g. physics, chemistry, biology, medicine) would describe an event like an X-ray of a human body and their ability to convey their ideas to members from different disciplines.

Law of vertical incoherence of organizations. In organizations there exist repeating patterns of behavior that can be categorized and subcategorized to describe behavior and certain problem resolutions within the structure. This represents a means of simplifying behavioral descriptions by building generalizations.

Law of validation. The validity of knowledge within a discipline or organization requires a high degree of consensus within the specific community. Divergence from this consensus requires an extensive burden of proof and may represent a barrier to developing new concepts and understanding.

Law of diverse beliefs. When dealing with complex issues, often teams of experts are assembled of differing background in order to encompass the conceptual scope of the problem. The use of diverse teams utilizing different linguistic domains produces an inability to cooperatively approach a problem from a unified point of view. This provides further inability to understand behavior and increases complexity.

Law of gradation. This law acknowledges that all conceptual bodies of knowledge can be graded into varying layers and subcategories. The applicability of each level of the science is situation dependent. It is uncommon for every grade of a discipline to be utilized to evaluate a problem. The division of knowledge into subcategories allows specialization into specific tasks by individuals. It is no surprise that academic disciplines are hierarchical in nature, as this is a common structure for humans and allows the discipline to evolve quickly.¹⁰

Law of universal priors. The prerequisites for science and human understanding are humans possessing common language, reasoning of relationships, and an archival representation of information. This seems very basic, but can explain difficulties experienced when attempting to learn entirely new systems or concepts. This law is particularly important in a global economy involving participants from a broad spectrum of nations and cultures.

Law of inherent conflict. Similar to the law of diverse beliefs, groups assembled of individuals of different backgrounds and linguistic domains will disagree on the relative importance of different factors affecting a complex issue. This law also has significant impact in collaborative global design efforts. This was experienced in the development of the Boeing 787 Dreamliner when designs were modified by different groups, resulting in a structural failure.²⁴

Law of limits. For all activities and complex problems, there exist limits that define the relationships and performance. The understanding of these limits, their relationship to other components, and how they may change is key to designing for complexity. The development of the 787 Dreamliner provides an example of limits contributing to complexity. The limits of the titanium supply chain were not known, and severe shortages emerged in the system as production began.²⁰

Law of requisite saliency. When a designer is developing design targets for a system, it is seldom the case that each factor affecting the target is of equal weight. With each system there is an inherent need to develop a fundamental understanding of the relative importance of factors in a system to define performance.

Law of success and failure. When designing solutions to complex problems, there exist prerequisites that the design group must possess in order to achieve success, including leadership, financial support, component availability, design environment, designer participation, documentation support, and design process. Without each component, the successful design of solutions could be jeopardized.

Law of uncorrelated extremes. During the learning process of a design group consisting of members from various backgrounds, it was found that the relative importance of the factors affecting the problem evolved away from the individuals' disciplines toward a more comprehensive understanding. In this way the initial perceptions of individuals could be described as a collection of uncorrelated extremes prior to the learning process.

Law of induced groupthink. Groups attempting to address complex issues under a time constraint will have a tendency to exhibit a behavior known as groupthink. Groupthink is a phenomenon that occurs with collaborative group decisions. The results of the group can represent courses of action that have not been well thought out by any individual group members. The collective decision can also be in stark contrast to the views of individual members.¹¹

Law of requisite variety. A designer attempting to address the behavior of a complex issue has numerous specifications or design variables to produce the desired outcomes.

Law of forced substitutions. In design groups experiencing difficulties of conceptualization and internal conflict, it is common to see personnel substitutions in an attempt to induce results. This may compromise the ability of the group to fully define complex issues and develop a predictive control of the complex issue. The change in the group could also be the source of additional complexity.

Law of precluded resolution. The absence of interpretive modeling to describe the structural patterns representing the complex issue will result in the failure to properly address the issue itself. Without a clear methodology the inherent nature of any diverse group or organization is to focus on persuading the group of their perceived requirements of the individual members. This results in a failure to address the actual issue set out by the group or organization.

Law of triadic necessity and sufficiency. Complex relationships exhibit a sort of modularity consisting of three relational components. All complex problems can be reduced to a combination of triadic modules. In order to effectively approach a complex problem of any magnitude, the triadic modules to represent the system.

Law of small displays. The natural tendency of individuals is to accommodate problems to the size of media most familiar, including paper and computer monitors. This is in contrast to sizing the display to the size of the problem at hand. Too limited a display will create difficulty in conceptualizing large and complex problems.

1.6.1.2 Relationships between Components: Block Diagram

A block diagram can be used to visualize relationships between components in a system. The system diagram provides a visual mapping of the relationships between components. Lines should connect each element with all the other elements it can affect or be affected by.

Consider a basic, simplified fuel oil system powering a two-stroke diesel engine, as shown in Figure 1.6. The system has three sub-systems: a fuel heating sub-system, a fuel supply sub-system, and a fuel injection sub-system. Fuel oil must be heated to a certain temperature to provide the correct viscosity for combustion. Booster oil pumps are used to pump the fuel oil through heaters to the engine-driven fuel pumps. The fuel pumps will discharge high-pressure fuel to their respective injectors. The energy source for heating fuel oil can be waste heat, electricity, or steam.

Figure 1.6 Fuel oil system to power two-stroke engine.

For this example, assume that the heating source is steam and produced by a boiler shown in Figure 1.7(a). The block diagram shown in Figure 1.7(b) can be used to visualize the relationships between components in the fuel heating sub-system. However, if a block diagram is used to visualize the relationships between the components in the entire fuel oil system as shown in Figure 1.6, the diagram becomes cluttered and difficult to interpret.

Figure 1.7 (a) Physical boiler sub-system; (b) boiler sub-system block diagram.

1.7 Managing Complexity

1.7.1 Processes to Manage Complexity

We have discussed the meaning of complexity, complexity escalation, and the limitations of human beings in dealing with complexity. Now we would like to suggest a process for harnessing or managing the complexity associated with a large-scale design project. A management methodology will be proposed for a real-world example of a design team addressing a complex system. The example involves the design of a fuel cell powered vehicle by a design team at Texas Tech University (TTU). The methodology will be compared with the applicable literature to produce a method to manage complexity.²⁵

The development of the TTU fuel cell vehicle, the TTU entry in the 2002 FutureTruck competition sponsored by the US Department of Energy and the Ford Motor Company, was initiated with a new 2002 Ford Explorer, an 80 kW fuel cell stack, and high-pressure tanks to store hydrogen. The development of this vehicle is truly a transdisciplinary design exercise including mechanical, electrical, chemical, control components, and sub-systems to be developed and integrated. Vehicle emissions, energy efficiency, and consumer acceptability are the important parameters to be considered.

As the quotes from Simon mentioned previously indicate, complex systems are frequently hierarchical in nature, and thus can be divided into sub-systems. Both Simon and Wolfram indicate that complex hierarchical systems can be represented in terms of simple symbols or rules. Thus, what needs to be done is to develop a simple representation of the system, and to divide the system into its individual sub-systems. If necessary, the sub-systems can also be divided along similar lines until a manageable hierarchy of systems and sub-systems is achieved. This process is not that easy to accomplish.

First, any large-scale design project will require a team effort – probably a large team. Teams, especially large teams, do not just happen. A manager cannot merely identify a number of persons and form an effective team, even if the appointed team members represent all of the knowledge areas related to the project. It takes time for team members to develop confidence and trust in each other. Confidence and trust are built on interactions and relationships. The team members must learn to communicate effectively among themselves. Face-to-face interactions are best, but there is no reason why internet or other electronic communication and interfacing cannot provide a healthy team environment. Confidence, trust, and a good working relationship can be attained with effort, frequent communication, and time.

Large teams should be organized to complement the hierarchical nature of the system to be designed. That does not imply a hard vertical structure; rather, a combination of a vertical and horizontal structures is needed. The horizontal aspect of the structure provides for easy and frequent communication among sub-teams working on the details of various sub-systems that must interact. In hierarchical systems not all sub-systems directly interact with each other, thus not every sub-team will need to communicate directly or frequently with every other sub-team. The vertical aspect of the team structure provides a unifying effect on the overall team by defining the relationships of the various sub-systems and related sub-groups, and it provides the capability to continually reorganize the sub-teams as required during the design process.

Frequent and regular meetings of the team members are very important. Members of sub-teams should communicate at least daily about the details of the sub-systems on which they are working. Sub-groups working on interacting sub-systems should meet at least weekly to ensure that the respective sub-systems are compatible. The entire team should meet frequently to ensure that everyone involved with the project has an understanding of how the overall project is progressing and is able to keep his or her part in perspective. Meetings, especially of the larger groups, do not require that everyone literally meet face to face at one location. Internet or televised meetings can be very effective, particularly if two-way communication is available.

The fuel cell vehicle development team was comprised of undergraduate students, graduate students, technicians, and faculty advisors – about 10 graduate students, about 50 undergraduate students, two technicians, and two faculty advisors. The students were divided into several sub-groups. Each sub-group was assigned to a specific portion or sub-system of the vehicle; however, everyone was expected to be familiar with the vehicle in general, and members of one sub-group frequently worked with other sub-groups to accomplish specific tasks. Regular weekly meetings of the entire team were held. During the weekly meetings, each sub-group would summarize accomplishments, problems, decisions made during the last week and anticipated accomplishments, etc., for the upcoming week. Thus, all team members were kept aware of what other sub-groups were doing and what decisions that might affect their activities were being made by other sub-groups.

It is very important for all team members to understand the basic objective of the project and how their part fits into the whole. An iterative process that must begin immediately and continue throughout the entire project involves defining the project objective(s), requirements, constraints, etc. This iterative process should include the team members, customers, and stakeholders. The vertical component of the team organization should allow for the upper sub-teams to address primarily the overall aspects of the project and the basic relationships of the major sub-systems, while the lower sub-teams address the more detailed aspects of various sub-systems and the relationships among sub-systems. This process continues because the full understanding of a large-scale, complex project can only be accomplished as an iterative process as more of the underlying aspects become visible to the team and because the project requirements, constraints, and resources will very likely change with time. To assist members of the student team with the objective of the vehicle design project and to provide as much background information as possible, a library was set up on the internet. This library contained all information collected on fuel cells, hybrid electric vehicles, and previous TTU vehicle projects. Team members could add any information to the library that they developed or identified as being relevant.

After the team has been organized, has significantly completed its background reviews, and has initiated the process to define the objective, requirements, and constraints of the project, it must then contemplate decomposing the project into sub-systems and components. To begin this process it is useful to utilize one or more creativity sessions with some or all of the team members to identify aspects of the system to be designed, sub-systems, components, potential concerns, etc. Various creativity models can be used, for instance brainstorming, or any of the methods described by De Bono.²⁶ Table 1.1 shows a list generated for the fuel cell powered SUV. One session will not likely produce an exhaustive list. Once an initial list is developed, each item should be critiqued, duplicates should be combined, and extraneous items should be deleted. For small and medium size projects, this list can be maintained on large sheets of paper and the entire team can simultaneously participate in the development process. For large projects, a computerized list may be necessary and creativity sessions may be limited to smaller sub-sets of the team; however, all team members should participate.

Table 1.1 Typical components, sub-systems, etc., required for fuel cell vehicle.

Decomposing the overall system begins with developing the list of components, sub-systems, concerns, etc., as described above and continues with the organizing of the list into a hierarchical structure. The topics on the list should be correlated by relationships. The more encompassing sub-systems should be identified and then the topics within each upper sub-system must be correlated and organized similarly.

Table 1.2 provides a possibility for the first tier of sub-systems related to the development of the fuel cell SUV. The six sub-systems listed in Table 1.2 indicate the primary systems to be required for vehicle operation. Of course other variations could have been tried. For example, the high- and low-voltage systems could be lumped together as the electrical system, or the control system could have been split and included partly in the fuel cell system and partly in the power train. The first arrangement may often not be best. In fact it may be necessary to organize the next level, or levels, of sub-systems under each of the blocks in the first tier. Similarly, each tier of the structure may change after possible scenarios for the previous tier are considered.

Table 1.2 Possible tier 1 sub-systems for fuel cell vehicle.

Interactions between sub-systems, components, etc., should be noted on the hierarchical structure. Indeed, there will likely be several interactions or linkages that horizontally connect sub-systems on different vertical legs of the structure. Such cross-linkages are extremely important to identify and include. The organizational process, which will be iterative at each step, must be repeated until all levels of sub-systems are identified. We emphasize that there are many potential hierarchical structures that could be developed for a system; a part of the iterative procedure is to consider several possible structures in the process of developing the best structure. A schematic diagram of a typical hierarchical structure for a system is shown Figure 1.8. Note the gray lines which indicate an interaction between sub-groups in different vertical parts of the structure. The mindmapping process as described by Wycoff²⁷ and others is a good model for a tool to help with the decomposition and organization of project. The graphical representation inherent in the mindmapping process clearly shows both the hierarchical structure and the interactions among sub-systems and components. Traditional mindmapping works well for small and medium size projects where the entire map can be displayed on a single large sheet of paper. At least one software package, Inspiration from Inspiration Software, Inc., which supports mindmapping is widely available.

Figure 1.8 Possible hierarchical structure for fuel cell SUV development.

The organizational process is iterative in nature. First, it is likely that additional items will be added to the initial list as the process progresses. Additional items will be recognized as part of the organizational process because the very efforts that are needed to organize the list will help clarify understanding of the system and provide additional insight. Secondly, as the project develops, additional information will be discovered and additional sub-systems, components, etc., will be identified. Further, additional and more involved interactions or linkages between various sub-systems will be realized. The organizational structure being developed must be expanded to include these new components and interactions. Thus, the task of organizing and fully understanding the system being designed is not completed until the design process is completed.

Alongside the decomposition process there should be a team member organizational process. The team for a large project is likely to be large. Thus, the structure and communication within the team and sub-teams are critical. The project organizational structure should clearly indicate the major areas of work and the important interactions among sub-systems, hence members of the design team should be grouped into sub-teams similarly and the project organization should be reviewed frequently to ensure that no aspects of the overall design are neglected.

In any endeavor that requires effective coordination of the effort of many people, leadership and management are critical issues. The leader must provide vision, inspiration, and resources for the team and the leader must remove obstacles which limit the efforts of the team. The vision for the project, major resources, etc., are provided by the official team leader, and team members must provide leadership with respect to specific efforts of the team or sub-teams. Clearly, team management is also important, but management is not leadership.

The working environment for the design team is important. Appropriate space and communication capabilities must be available. In earlier times, space meant a situation room with ample table space, means to display information on a scale providing easy access, and secretaries or computer terminal operators to record activities. Today, team members may be located remotely all over the world. Work space probably refers to computer input software, display, and storage, while communication is related to interactive computer sessions, e-mail, online conferences, other machine interfaces, and perhaps even telephone discussions. What is important is convenient communication among team members with easy transmission of data to each other and the capability to display data and ideas as structured images.

In summary, management of complexity requires an