Baccalaureate Degrees in Systems Science
A Joint Development of the Interdisciplinary Arts & Sciences Program and the Institute of Technology
University of Washington — Tacoma
Co-Developers:
Michael Kalton, Professor, Interdisciplinary Arts & Sciences
George Mobus, Associate Professor, Institute of Technology
Contact: gmobus@u.washington.edu
Is Education Fulfilling Its Mandate and Mission?
If education as delivered in the modern American university were fulfilling its mission, would the world look the way it does? We are struck by a simple observation of the state of the world: there is more hunger, poverty, war, crime and disease extant than at any time in history. Even accounting for a growing population, one might reasonably expect that if we are a society that learns from prior mistakes and gains in wisdom as a result then shouldn't the world look quite different by now? Shouldn't we have managed to quell the negative tendencies of society so that life is substantially improved for the majority of humanity? One would think that with all the knowledge afforded by science and the clever technologies we have developed that education would have produced a generation of able citizens capable of making rational decisions in community, politics and economics. The evidence provided in the daily news suggests this is not the case.
More recently, researchers in higher education effectiveness have shown strong evidence that higher education is failing in its promise to produce citizens with critical thinking, complex reasoning and written communications skills [1]. The causal factors illuminated by this study are many, but they are all related.
If education is not fulfilling its mandate — to produce capable citizens — then we must ask why not? Clearly this is not an easy question to answer. It isn't even an easy question to ask. But we need to ask it nonetheless. The world is getting more complex and integrated. Globalization has accelerated the growth in complexity. If we are having problems coping with the world as it is right now, how much more so will this be the case in the future if globalization were itself to accelerate? The only salvation is for education to fulfill its mandate.
But we assert that on the present course, laid down during the 20th century, this cannot happen. Over the course of the 20th century society has increasingly seen the benefits of higher education as economic, but more importantly, tied to specific job-related skills and knowledge. The rise and rapid growth of professional schools attests to the increased emphasis on job-market preparation as the goal of higher education. The correlation between the amount of higher education and salary potential has been interpreted as a causal relation and as a result parents and high school counselors have directed most high school graduates toward college as the only viable option for future success in earnings. Businesses have bolstered this view by insisting on a preference for more educated workers at all levels in the organization. The education system has attempted to respond to this social demand for more productive workers by offering more discipline-specific majors under the assumption that curriculum content should be more directed toward this end. This may have been a serious mistake, both for society and for education.
As a society we have become overly enamored with the twin ideas of specialization and professionalization. Our education system is now primarily geared to produce professionals who are highly specialized on the theory that such specialists will do better work within their disciplines. Insofar as the details of work are concerned that might be true. But enterprises, governments, and non-governmental organizations where specialized expertise is employed are not sets of isolated cells of specialized activity. They need to be and often are integrated systems where what each individual does has an effect on all other work. Naturally one wants to do the best job they can and this often means the application of specialized knowledge. But does this translate into needing an education process that mostly prepares one to specialize without doing justice to showing how one's work relates to the rest of the world?
A metaphor that is often used is the 'can't see the forest for the trees' theme. In this case it goes much further. We have gone to the point that we teach people how to examine the bark on one particular tree, paying lip service to the fact that the bark is only one component of a complex living system. The worker becomes so engrossed in the intricate details of the bark on that one particular tree that she fails to note that the tree itself may be dying. And she will never realize what the forest offers. She won't even notice if the forest were to catch fire until her tree burns.
For those few who still think it's important to take in the whole forest and have caught glimpse of smoke, the continuing attention to specialization and lack of awareness it engenders is downright dangerous. If we want the world to progress for all of humanity, not just a lucky few, then we need to reconsider the meaning of specialization, professionalism and what our individual relationships with the world are about.
Being a citizen of anywhere, in this globalized world, means more than just doing your job. It means understanding the world and how your actions and decisions affect it. This all starts with education. What one learns in school will be the basis of life-long learning and that is the basis for being a capable citizen. The problem to solve here is to resolve the seeming dichotomy between learning to do a single job well and learning about the world in general. There are only twelve to twenty years for a person to learn what they need to know. And there is so much knowledge out there to know. How can a person possibly absorb it all?
Fortunately the dichotomy is illusory. First off, it isn't necessary to learn everything or even a tiny fraction of everything — if you think of knowledge as a collection of facts and figures then obviously a single brain couldn't do it. Knowledge isn't a collection of facts. There is an inherent structure both to the way the world is organized and to how we perceive and conceive of that world. If one learns the fundamental nature of this structure — the organizing principles underlying it — then one has a powerful intellectual tool for life-long learning, comprehending diversity, and becoming an effective problem solver in any domain of knowledge that one finds interesting. These principles point to the means by which the brain can manage complexity without suffering information overload. Systems thinking engages the power of abstraction in the hierarchy of knowledge organization.
Learning these principles empower the individual to think in a way that is at once both natural and, if practiced with discipline, powerful. That power comes from systems thinking and using the systems approach to learning and problem solving. Education should first teach systems thinking and the tools of the systems approach if it wants to truly fulfill its mandate and mission.
Caveat and Disclaimer
Lest it sound like we are claiming that systems thinking is the solution to all the world’s problems we hasten to assure that we are not. Perhaps if everyone in the world thought systemically with the level of discipline we are calling for then such a claim might be warranted. Obviously that will never be the case. What we are claiming, however, is that if enough people in the world were to learn and use systems thinking/approach that we could reverse the downward spiral in education and society that we seem to be in right now. At least we think it is a key toward achieving that goal.
Systems Thinking and the Systems Approach
Systems thinking is a style of thought, a perspective on how the world is organized and how it works. Everyone thinks systemically to some degree. We don't see the world as a bunch of unrelated objects floating around. We naturally see and remember interactions between objects and we naturally see patterns of behavior and form. The brain is wired to do that. But, at the same time, for most people this style of thinking is not rigorous or reliable.
There is an analogous situation with mathematics. Every human being has some natural capacity for what is called numeracy or using counting numbers to quantify aspects of the world. Every human also has the natural ability to think about sets (aggregates of similar objects) and their manipulations. These basic abilities may be augmented by learning arithmetic and possibly algebra. But without training in formal mathematics most people use mathematical reasoning in a piecemeal, ad hoc, and casual manner. Education in formal mathematics helps to systematize and strengthen what was a more heuristic approach.
Systems thinking, like mathematical reasoning, is extraordinarily useful for solving many difficult problems in real life. What is called the systems approach is the application of systems thinking to solve complex problems. In order to use the systems approach reliably it is necessary to follow a rigorous disciplined methodology. And that is where systems science comes in.
Why Systems Science?
All sciences are disciplinary approaches to discovering, organizing, and using knowledge. Systems science is certainly no different in this regard. But systems science is different in one sense and that is that the concepts of systems can be applied to all other sciences. In other words, systems science is more general and organizes a set of concepts which are common to all other areas of knowledge.
Systems science is a field that can be pursued for its own sake, just as chemistry or sociology might be. Systems scientists are involved in discovering the deep principles of systems and demonstrating how these are applicable in solving problems or explaining nature. But systems science has another, broader usefulness in terms of providing a set of principles and thinking discipline that allow the possessor to operate effectively in different specific disciplines. This means that someone schooled in systems science is actually in a very good position to work in any field with a minimum of further education in the particulars of that field.
This is a strong claim. What is it about systems science that gives us the audacity to make such a bold statement?
We must hasten to admit that no general studies of this claim have been done to provide evidence. Rather we need to appeal to the nature of the contents of systems science, to a theory from psychology about learning efficiency and transfer of skills, and to a few anecdotal examples of people who have successfully changed careers by recognizing the systemic patterns inherent in the subjects.
Systems Science Content
There are several concepts and principles embodied in the knowledge content of systems science that are found to be generally applicable in every other field of study. We will cover a few of these here to provide examples.
At a most fundamental level systems are composed of components that interact with one another to one degree or another. Some components and their interactions act to form a boundary giving definition to the objectness of the system. Boundaries may be indistinct or fuzzy, but we don't recognize something as a system unless a discernable boundary exists. Systems exist within an embedding environment, and generally systems can exchange some of their components with other systems (entities) in that environment. They definitely exchange energy with the environment. Finally, the components of systems are themselves, systems. That is, components can be decomposed to expose inner sub-components. The inverse is true as well. All systems, by virtue of being embedded in environments, are components in larger systems — super systems.
There are many more principles involved in describing systemness and the life of systems (e.g., how systems change over time, dynamics of how the components interact, etc.). These are just the basic definitional principles. Now let’s examine some examples of these in real life.
Systems of Thought
One might be tempted to point out that thoughts and concepts, held in the mind, are abstract and therefore do not meet some of the above criteria, yet we call these systems when they are related by some internal logic. Take for example one’s concepts of fairy tales or fantasies — dragons, elves, and the like. These are things that do not exist in the real world (as far as any scientist knows) yet they can have their own reality in our minds. Until recently this metaphysical argument might have been taken as self-evident and justified. But recent developments in neuroscience, specifically in how brains encode and work with concepts, shows us that concepts are real enough in the sense that they are organizations of neural firing patterns which persist over time and between occurrences. That is a conceptual representation of a dragon (or dragonness) involves the firing pattern of real neurons. Furthermore, the firing patterns can be shown to be circumscribed within specific brain tissues giving an object-like quality to thoughts. Finally, these mental objects can exchange energy in the sense that the excitation of one set of neurons, say representing a dragon, can excite another set, say representing a dragon slayer. One’s subjective experience would be of the thought of a dragon bringing to mind the thought of the dragon slayer.
Social Systems
Organizations, such as enterprises, clubs, and governments meet all of the above criteria. Organizations have been analyzed in terms of their components (people, resources, knowledge bases, etc.) and their interactions (processes) for as long as there have been organizations. Management science is largely about the study of how things get done and decisions get made in organizations. And the most powerful tools in the management scientist’s armamentarium are systems science concepts.
Demographers study characteristics of populations. A population is defined by a systemic consideration of some shared characteristics and interactions among the members. Political scientists are interested in the political theories that motivate and drive people to make decisions about policy and who they want as their leaders. A political party meets the criteria of a system and, overlapping with organizational studies as mentioned above, can be best understood from the systems science standpoint.
Living Systems
The most advanced work in systems science comes from systems biology. As the name implies biologists study living organisms and ecology from a systems perspective. It is not hard to see how an individual organism is a system, with its skin boundary, its intake of food, water, and air, its various organ sub-systems, and so on. The same can be said for populations of a species and communities of mixed species living in the same region. In systems biology the principles of systemness have been extraordinarily successful in helping biologist to recognize what they should be looking for in examining living processes. That is, systems science acts as a guide in doing the biological science.
Of course, to be historically honest, it is probably more the case that systems science owes a great deal to biologists who were doing systems work before there was a systems science as such! Much of our modern understanding of systems principles were due to biologists approaching their subject from a more theoretical perspective.
As these few examples show there are shared principles operating in many fields. It is quite possible to talk about the body of an organism, the body of a group, or the body politic and have these principles operative in all cases. If one is familiar with the principle of exchange of particular components with an environment, say the intake of O2 by an animal and the exhaust of CO2 in biology, you are clued into looking for some kind of exchange of components between an enterprise and its environment, say products sold and money received.
Systems science is at the base of problem solving. Within a single discipline this is true. One becomes a much more holistic thinker with the principles of systems science at their disposal. They can see the patterns and expose the problems more readily. Moreover, they are equipped to understand the requirements and constraints from a systems perspective, which gives them greater power in seeing feasible solutions and not waste time on local solutions that will prove infeasible in the larger context.
There has been a relatively recent development in many areas of science and the economy in recent decades. Projects are being undertaken that require multiple disciplines working together. One of the things that makes such large-scale interdisciplinary work possible is the fact that individuals involved are able to communicate across disciplinary boundaries. And a large part of that communication is predicated on parties recognizing the systemic principles operating in the other disciplines. A good example has been the decoding of the human genome which brought together computer scientists, geneticists, chemists and a variety of engineering disciplines. The computer scientists needed to design algorithms for sorting through and organizing the huge number of DNA sequence snippets that were the result of chemical breaking of genes into pieces. Geneticists had long recognized the information coding function of DNA and as a result were able to explain the genetic alphabet and how long strings of these nucleotide letters created coding sequence patterns that the computer algorithms could work on. In essence the computer scientists and the geneticists were able to recognize that DNA coding was similar to computer coding (strings of characters). Information theory is an integral part of systems science.
More and more, today, the advances of the natural sciences and social sciences depend on multi-disciplinary work involving teams of people from different disciplines. The need to facilitate their ability to work across disciplines is great and should not be left to chance or personal aptitudes. Systems science as a core or base education can provide a cognitive framework for all disciplinarians that will allow people with this education to participate fully in such cross-disciplinary projects.
Psychology of Learning
There are two specific areas in learning theory that are relevant with regard to the general applicability of systems science. The first involves the concept of transferability of knowledge from one disciplinary domain to another. This refers to the ability of a person to learn specifics in a new field of endeavor (to them) based upon what they have already learned in a different field. The question is, how much basic knowledge can one transfer from one field to another and how does this transfer translate into efficient learning in the new field. Studies of this phenomenon have shown several things to be true[2]. First there must be some kind of seed knowledge in place or initial learning accomplished before one can tackle a new field. Second, if what one has learned has been represented in a too contextualized form, i.e. depends too heavily on specifics of the original field, then one has difficulty using that knowledge to learn in the new field. Third learning involves active processes, especially comparing and contrasting what one already knows with what one is faced with in the new domain. Fourth, one learns new knowledge based on what one has learned before. This last one actually spills over into the second major area of learning theory.
Evidence is rapidly building in neuroscience to support what many teachers and psychology learning theorists have known and argued for a long time; namely, that the previous learning of a schema or generic pattern learned in context can vastly improve the rate of learning new knowledge[3]. A schema is a conceptual framework that provides a general pattern that defines the organization of knowledge in a domain. For example, a script for how to order food in a restaurant is often given as an example of a schema. Once you know the general pattern of behavior you can go into any restaurant and order a meal. The schema had to be learned originally and generally, at least in this case, by inductive learning, that is by generalizing across what was observed over several experiences of going to a restaurant with someone who already knew how. Once in place the schema provides a guide to performing the behavior in many situations. But the real power of a schema is when you encounter a new situation that doesn't immediately fall out from what you know. For example the method of ordering food in some foreign countries is quite different from what you experience in the US. If you blindly follow the script the food server may look at you dumbly, not knowing what you are doing. But given the existence of the schema and recognizing that something is different gives you the opportunity to quickly learn what those differences are and apply them. The schema tells you that there is some method for ordering food, but it doesn't include the exact script that works in these new surroundings. So you kick into learning mode, observe how others are doing it, and you're back in business (and will get fed).
The fact that a schema already encoded in the neural tissues of the brain can improve the efficiency of learning new knowledge is a key to why systems science is so valuable. The principles of systems science are, effectively, the most general schema the brain can possess. Knowing what to look for when encountering a new ‘system’ makes it possible to quickly learn what is specific to this instance and incorporate it into your knowledge base. A related notion is one of scaffolds in education. Here the idea is to prepare students for learning highly specific knowledge in a domain by first providing a support structure of knowledge that will permit a student to rapidly see where the specific bit fits in the overall scheme. Other educators have extolled the use of conceptual maps that provide an outline of the ‘territory’ of the subject. The concepts in systems science form not just a concept map but a map of how to construct more specific maps. All of these ideas point to the role that establishing some organizing framework for learning greatly improves the learning rate as well as retention. Moreover if the framework is rooted in meaning for the learner then so much the better.
Meaning comes from what is relevant to the needs of an individual. If an individual recognizes him or herself as a system and as a component within a system and that interactions with environment affect his or her life, then they will be more motivated to grasp the power of systems science in order to have a greater sense of control of their lives. It has always been a point of curiosity as to why, often times, the psychology department of a typical university will have the largest number of declared majors or pre-majors among freshmen. Not everyone finishes in the major, but many start. The common belief is that people are motivated to learn something about themselves, how they think, etc. which is why there is such a strong interest in the degree. Clearly any subject that purports to explain the human condition will provide strong incentives for learning. Systems science provides a more general framework even for psychology. The brain is a system, as is our social organizations.
Skill and Knowledge Transfer and Life-long Learning
Thomas Friedman, the New York Times columnist and author (“The World is Flat”) has identified an important trait in the successful worker in the modern global economy. By successful, he means one earning a better than average salary and continuing to do so for many years. That trait is what he calls being a ‘versatilist’. Being versatile means being able to adapt to changing conditions, to changing knowledge, and to changing goals. Adaptability, in turn, comes from being prepared with the right knowledge and skills at the right time. Someone who has successfully pursued a given career and then successfully changed to another career is an example of a versatilist. And, the need for more people to be able to do so will be even greater in the future. One path to changing careers is to go back to school and learn a new expertise. For many people caught in the obsolescence of their knowledge and skills (e.g. mainframe COBOL programmers in the late 1900’s) this often implies re-training in a new field, which means attending classes and not earning a paycheck during that period. In the case of COBOL programmers, for instance, it wasn't just a matter of learning a new computer language. The colleges had been pumping out lots of programmers knowing the newer languages so the competition for jobs was fierce. Often one needs to shift to a whole different discipline in order to become employable again.
A popular drumbeat in today’s educational arena is the idea of preparing students to be life-long learners. The general conception is that people who have learned how to learn are better prepared to change directions or careers by being able to learn new skills and knowledge. This need is beginning to become more apparent, according to Friedman, as the world moves toward a global economy.
The reality in the world of continuing education for job retraining is that many people, finding themselves in need of new skills and knowledge are faced with a challenge. Most are older adults. Many have not participated in formal education for many years. And many have difficulty learning the needed material in a timely manner. It is tough and the education system doesn't have a clear solution for these students. Instead the emphasis now is on teaching learning skills to younger students so that they will be prepared in the future for the inevitable need. But it is unclear exactly what these students should be taught.
We posit that given the above descriptions of the benefits of systems science on thinking and learning capabilities, students of systems science will find that their deeper understanding of systems will be a basis for learning new details in new disciplines. Life-long learning, in this view, begins with an understanding of how the world is a system of systems and that is the scaffolding for learning new knowledge later in life.
Conclusion
We assert that the time to develop a new curriculum in systems science is now. Many of the components of systems science, such as control theory, information theory, and others, have been a major part of many fields since the middle of the 20th century. Aspects of systems science have now found purchase in nearly every branch of disciplinary knowledge. And the systems approach is deeply embedded in all of the sciences to some degree or another.
In the 1950s and through the ‘60s several people advanced the idea of a discipline of ‘General Systems Theory’. Ludwig von Bertalanffy[4] published his work, by that same name, in 1969. There was a strong effort to consolidate many of the components of systems theory under a common disciplinary umbrella. Unfortunately there were still too many aspects of systems science that were more hand waving than solid science, for example the issues of non-linear dynamics were just beginning to be explicated and the field of chaos theory had not yet been developed. Other examples include emergence and universal evolution theory. These areas were seen as huge holes in the comprehensiveness of systems theory and an inability to demonstrate a sense of completeness in the theory (without it seeming to be too general and therefore not explanatory) made it difficult to further develop the theories into a full-fledged science.
Currently, especially with the development of incredibly cheap high-power computation, we are seeing a comprehensive integration of all of the components of systems theory and a much stronger basis for those that were nascent in the mid-century. Systems biology and climate studies are fields that are wonderful examples of systems science at work. We believe this is the time to introduce students to the science of systems as a basis for their understanding of the world in the 21st century. We are working on a baccalaureate degree program to fulfill this end. We feel that a Bachelor of Arts (qualitative systems science) will provide students with a modern version of what the liberal arts degree provided in the 19th and early 20th centuries. A Bachelor of Science (quantitative systems science) would prepare students for many technical fields. We can envision post-baccalaureate programs that would much more efficiently provide disciplinary details (e.g. engineering, computer science, or biology). Students going through these kinds of programs will be far more prepared for the kinds of jobs that will be developed in the 21st century.
References
[1] Arum, R. and Roksa, J. (2011). Academically Adrift: Limited Learning on College Campuses, The University of Chicago Press, Chicago.
[2] Bransford, H.D., Brown, A.L., and Cocking, R.R. (eds). How People Learn: Brain, Mind, Experience, and School, National Academy Press, Washington, D.C., page 53.
[3] C.f. Dorothy Tse, Rosamund F. Langston, Masaki Kakeyama, Ingrid Bethus, Patrick A. Spooner, Emma R. Wood, Menno P. Witter, and Richard G. M. Morris (6 April 2007), Schemas and Memory Consolidation, Science 316 (5821), 76. Rats learn to associate a place with a taste much more rapidly if they have already been given a chance to learn the spatial context of the new location.
[4] von Bertalanffy, L. (1969). General Systems Theory, George Braziller, New York.