SYSTEMS
MOVEMENT:
Autobiographical
Retrospectives
Bernard P.
Zeigler
Arizona
Center for Integrative Modeling and Simulation
Department
of Electrical and Computer Engineering
University of Arizona
Tucson,
Arizona 85721, USA
E-mail:
zeigler@ece.arizona.edu
Web:
http://www.acims.arizona.edu/MEMBERS/participants.shtml
Photograph of the author
Systems
Movement: Autobiographical Retrospectives is
a special section of this journal the purpose of which is to produce, via
invited autobiographical articles, historical information and insights
regarding the thought processes and individual motivations of leading figures
in the systems movement. This valuable information is normally not included in
regular publications that tend to focus on results rather than the creative
process leading to those results. The autobiographical articles are likely to
help us to improve our understanding of how, and why, the systems movement has
progressed since its emergence in the mid 20th century. Each article
in this section is published strictly by invitation extended to individual
authors by the Editor, and are based on the recognition that these individuals
have made major contributions to systems movement.
Autobiographical Retrospectives:
The Evolution of
Theory of Modeling and Simulation
by
Bernard P.
Zeigler
Introduction
This is [a] short treatise in which I pretend to interview myself in order to elucidate some of the key turning points and most important issues that have been at work in my career, particularly in how it relates to systems thinking and research. Coincidently, it is fifty or so years since the computer age really took hold and a number of anniversaries and other such retrospectives have come out. Within this context, I critically examine the role of systems thinking, and particularly mathematical systems theory, in my development of theory and frameworks for the practice of modeling and simulation. The two (simulation and systems) became more intimately entwined than I might have anticipated -- success in the first (adoption of my ideas by the modeling and simulation community) became bound up with another kind of success in the second (wider acceptance of the benefits of integrative and cross-cutting science). I close with some frank views about why system thinking has not had the major impact that it should have had and what needs to be done to turn that situation around – especially with the new century’s changing face of science toward increased holism and multi-disciplinary collaboration.
John Mcleod, the
founder of Simulation Councils, the precursor of the Society of Modeling and
Simulation, International (www.scs.org), is still an active participant in
simulation affairs. Indeed, many of his
contemporaries, and those who joined with him in the early years, have
contributed their historical recollections and insights to a recent issue
commemorating the Fiftieth Anniversary of the Society which [that]
occurred in 2002. Currently, I am
President of the Society and my own intellectual development must represent
many parallel developments of other baby boomers and fellow members of the
Society. We all grew up in probably the most scientifically expansive age of
all time. And The exponential growth in knowledge we are experiencing is
in no small measure due to the growth in computer technology and the modeling
and simulation paradigm it enabled. To
set the backdrop for this profile of my development as a systems researcher, let
me I recall some of my decade milestones in relation to the development of
modeling and simulation.
- Fifty years ago, I was just entering high school struggling with homework and other teenage concerns, oblivious to computers, let alone the analog computers, digital computers or simulations that were just coming into practice. But of course, others were pioneering these new technologies and even forming interest groups and newsletters to share the excitement of their development.
- Forty years ago, I became aware of analog computers through an article in the engineering student magazine of McGill University where I was studying engineering physics.
- Thirty years ago, I became fascinated with the simulations of real neural networks that my dissertation advisor, John Holland[1] had done, and that many of his other students were undertaking.
- Twenty years ago, I had written a book proposing that computer-based modeling and simulation was the modern equivalent of basic arithmetic, so fundamental that all disciplines should be expressing and testing their theories with these tools.
- Ten years ago, I had synthesized ideas from systems theory and object-oriented programming to develop a discrete event modeling and simulation framework for complex systems studies.
Today, I am writing a retrospective as requested by George Klir, editor
of the International Journal of General Systems. It is to be a profile that, as defined in his email
invitation, is to “highlight critical career turning points … dealing with
those most important issues that have been at work with this particular
individual in relation to systems thinking and research.” This is clearly an honor, but also entails
a responsibility to discover just those events that were critical turning
points and unravel those issues that in retrospect, proved to be most important
in how I see things and what I do today. It seems a task that would be better
suited for a historian were it not for the fact that the data needed by such a
professional can only be surfaced by my own introspection. After a none-too-transitory period of a profound
bewilderment I decided to approach the task in the following, perhaps
unorthodox, manner. I would be my own
interviewer, asking myself questions that a critical interviewer might, and
then crossing over the table to answer these questions as best I might. So here forthwith is an interview with
myself[2].
Questions and
Answers
Question: In his autobiographical profile in Systems Research, George Klir (1988) indicated that he first realized that many of the same problems were of concern to the scientists who were seeking his help with computerization in the early days of computing and this later led to his vision of a general systems science that cut across the individual disciplines. Was there any comparable epiphany that you recall in your career as a systems theorist?
Answer. No, I can’t say that I suddenly had an “aha” experience that set me on the path leading to the present. How I got into systems theory was different from George’s entry in that his works, and some early systems theory founders, were already on the scene when I finished my doctoral thesis and was looking for a direction to drive my career. My thesis was called “On the Feedback Complexity of Automata” (Zeigler 1968) wherein I proved the truth of a conjecture by one of my committee members, Art Burks.[3] The conjecture stated that there is no upper bound to the feedback complexity (number of feedback connections, suitably measured) of digital networks. The conjecture had been believed but had remained resistant to proof to that date (Zeigler, 1972). Come to think of it, how I proved that conjecture might provide a common theme to my development. I noticed that a recently developed algebraic theory of finite automata (Arbib, Krohn and Rhodes, 1968) could be interpreted in the right way to solve a problem about digital networks that apparently had little to do with the algebraic theory itself. In other words, the answer was implicit in a kind of recognition that 2 +2 = 4 --- there was no invention of a new problem or a new approach, just a fortunate combination of two weakly related streams of knowledge[4]. Likewise, my contribution to systems theory was not in presenting a new theory but in the application of the existing theory to an area that I believed would be a fruitful ground for its application -- modeling and simulation (M&S).
Q. So how did you come to be interested in systems theory?
A. I’m not sure how I came across the works of early mathematical system theorists; it may even have been that George Klir was in part responsible for it when he gave a seminar to the University of Michigan Department of Communication Sciences[5], where I was working on a doctorate in the late sixties. I remember his promotion of the start up of this journal and my excitement at some of the problems he was discussing. In any event, here are some of the early mathematical systems theorists and their influence on my thinking:
|
Math-oriented System
theorists |
Influence on my
thinking |
|
Lotfi Zadeh |
Linear System theory (Zadeh and Desoer, 1972) |
|
George Klir |
Epistemological Hierarchy of Systems, Systems Science and Problem Solving Methodology (Klir, 1985) |
|
Mihajlo Mesarovic, Wayne Wymore, Michael Arbib |
General System Formalism (Mesarovic 1975;Wymore 1976; Padulo and Arbib, 1974) |
(I’ll use these little tables every so often to provide some historical context in a nutshell.)
Q. And how did you pick M&S as the field of its application?
A. I noticed that there was a lot of activity in simulation of nervous tissue using cellular automata (Burks, 1970) by fellow members of the Logic of Computers Group, the research group headed by Burks and Holland, at the University of Michigan. Most of it employed cellular automata as an empirical representation and simulation medium without reference to any guiding theory. So it seemed to me that there was a place for theory in addressing some of the issues involved such as what phenomena could be represented by cellular automata and whether there were more efficient simulation methods than those in practice. Cellular automata were essentially a discrete formalism introduced by von Neumann (1958) for modeling self-replication but were here being used for studying real world phenomena with undeniably continuous aspects. So it seemed that mathematical systems theory, with its generalization of systems to include both continuous and discrete, could throw some light on these simulations. I’m not sure whether I thought that applying the theory to the simulations would take no more than a few years of effort, but I probably didn’t foresee that it would fully occupy my professional attention to this date.
Q. So you say that you have always been a synthesizer of existing ideas rather than an inventor of truly new ones. How then would you characterize your role in the emergence of DEVS (discrete event systems specification), for which you are known?
A. That’s an interesting
question. Is DEVS a new innovation
requiring the kind of intellectual quantum leap we associate with, say Newton’s
laws of motion, or was it a predictable result of the fusion of systems theory
with M&S? Certainly, philosophers
of science can argue that usually there is much more continuity to the
emergence of new ideas than might seem apparent on first glance, and that
sooner or later, another Newton would have emerged, independently of the one
that did. So let’s examine the situation leading up to my first definition of
DEVS. Actually, DEVS stands for
Discrete Event Systems Specification and this name belies a close connection to
the fusion of systems theory and simulation.
As I suggested in the table above, early mathematical systems theorists
had provided a generalization, or rapprochement as Arbib called it (Kalman,
Falb and Arbib 1969), of continuous and discrete concepts that seemed to be
essentially the same though clothed in different garb. The generalization
emerged from two primary sources. One was the continuous (mainly linear)
systems of the control and circuits theories of (mainly electrical)
engineering. The other was the automata theory (mainly finite state) developed
for early digital systems design and nascent computer science. Remarkably, and indeed this was the reason
for giving it some credibility, the generalization turned out to be much the
same even though it looked a lot different on paper as written by the different
mathematical system theorists[6]. What I did, was to take one of these
formalisms which [that] seemed most easy to work with[7]
and explicitly define two subclasses, discrete time systems and differential
equation systems, within it. This was clearly a step that would have been
considered to be latent within the knowledge base of the time and a minor
incremental observation. However, it led to DEVS in a natural manner. To see
this, I should back up and say that there was a little more to the definition
of two classes than I have let on. What I actually did was notice that the
mathematical system formalism was phrased at a different level than the
notations that were commonly in use, for example, finite state diagrams, or the
usual systems of differential equations.
I explicitly made the link between these notations and the theory by
casting them as short hand specifications
for their respective classes. Thus, the Discrete Time System Specification
(DTSS) and Differential Equation System Specification (DESS) formalisms emerged[8]. Now, it became obvious that there was a
missing specification for the models underlying the event based simulations
that were being conducted in the specialized programming languages that were already
in use at the time:
|
Simulation Languages |
Influence on my thinking |
|
CSMP |
Continuous models ( Speckhart and Green 1976) |
|
Simula |
Object orientation (Birtwistle, 1975) |
|
Simscript |
Event processes (Markowitz, 1963) |
|
GPSS |
Internal implementation of simulation functionality (Schriber, 1974) |
|
GASP (later SLAM) |
Combined continuous/discrete (Pritsker, 1974) |
|
LISP |
Symbol manipulation, reflection (McCarthy, 1962) |
In this way, DEVS, Discrete Event Systems Specification, came about as filling the missing gap in mathematical systems theory, much as new elements are anticipated by Mendeleev’s periodic table of elements. For this to happen, someone would have to have been familiar with state of event-oriented simulation languages and understood the essence of the mathematical systems theory of the time. Looking back on the subsequent, and largely divergent, development of these fields, it seems unlikely that a person with these inclinations would come along for some time.
I should note that it wasn’t the formalization of discrete events that DEVS accomplished. Tocher (1964) had suggested a similar formalization for simulation models while Petri had introduced one that had events, but did not include time[9]. It was the formalization of discrete events within mathematical systems theory that was the essence of the contribution to science if that is what it to be considered.
Q. So how does embedding discrete events into a systems theory formalism make a difference?
A.
I could wax eloquent about how mathematical systems theory provides a
rigorous foundation for simulation in general, and discrete event simulation in
particular, and that would be true. But
to get to the heart of the matter, the one thing that the theory makes
operational is the concept of state in dynamics and time. The essence is
captured in the composition property of the transition function:
where is the transition
function, and
is a concatenation
of two functions of inputs over time,
and
. This composition
property will only work over all input functions, if the state space in which
resides is properly
defined. In essence, the property says that the state space must retain enough
information to allow the system to continue from where it was left by the first
input so that it can arrive at the same place it would have, had there been no
interruption in the middle. Imagine running a simulation with many variables
and saving only a few to start up from the same point later. The ones that you
can’t do without form a true, or minimal, state space for the system. A minimal
state space makes the formalism a lot more elegant and it is much easier to
work with models and simulations in the formalism – otherwise, there always
seems to be something missing and something ad hoc needs to be added to fill
the gap. Now, strange as it may seem, only DEVS, among all the discrete event
formalisms, has such a proper state space, and the reason that it does, is that
by being embedded into systems theory it has to satisfy the composition
property. None of the other formalisms
start or end with this requirement and so they never get the concept of state
quite right (for a compilation of discrete event formalisms see (Ho,
1992)). In more detail, in DEVS the
true state space is composed of a constant state and an elapsed time in that
state. The constant state part, by itself, does not satisfy the composition
property even though that is what most other formalisms consider as a state.
Indeed, I was led to find the missing part of the state space, the elapsed
time, by trying to satisfy the composition property unsuccessfully without it.
Q. So systems theory was essential in getting the definition of DEVS right, but once it was given a proper birth, couldn’t DEVS live a life of its own, independent of its systems origins?
A. No, because its nature as a shorthand notation for a subclass of systems always required going back to the parent systems formalism for the actual semantics. Moreover, the elapsed time component meant that you couldn’t simply revert to the timeless abstractions characterizing computations in computer science. So systems theory was at once a support, and at the same time, a crutch that couldn’t be dispensed with. Like it or not, I was stuck with this 900 pound gorilla and would have to make the best of it. And since most modeling and simulation researchers were not trained in mathematical systems theory, this would most likely be a solitary effort for quite some time. One thing that had to be done became obvious. This was to educate these colleagues in the basics of mathematical systems theory so they could understand and work with it.
Q. Is that how you came to write your first book, Theory of Modeling and Simulation (Zeigler, 1976)?
A. Yes, that and a rejection of a paper I submitted to a journal introducing the DEVS formalism. The reviewer said that the paper was poorly written, there were too many spelling mistakes, and the theory was long and hard to follow. The last sentence seemed something of a “try-to-be-nice” afterthought – perhaps the theory was too difficult to present in the confines of a journal article, and maybe I should try to write a book instead. Strangely enough, there were apparently extraneous materials along with the review in the envelope that I received. These gave unmistakable clues to the identity of the referee and indeed they pointed to a deservedly famous, but notoriously acerbic, figure in the control theory world. To this day, I’m not sure if the editor intentionally included these obvious violations of anonymity to suggest that there was weighty authority to this review and I should take it seriously. Or perhaps it was just an oversight on his part. In any event, I realized that even reviewers of mathematical journals could not be relied upon to know the mathematical systems theory background. It became clear that a book was essential because the systems theory background to DEVS would have to be collocated with it in order to have it make a successful debut to a skeptical world[10].
Q. Can you place Theory of Modeling and Simulation into the general context of systems research at the time it was published?
A. I think one can divide the relevant history into five segments: pre-computer age systems and cybernetics pioneers, early circuits and controls theorists, early computer science pioneers, early artificial intelligence researchers and early M&S influences. No doubt this partition is somewhat oversimplified and tends to stereotype highly multidimensional people into one dimension. However, it may be a good first approximation if, as in all modeling, it helps to get at the insights we are after. Concerning pre-computer age pioneers, I would list the following:
|
Pre-computer age
systems and cybernetics pioneers |
Influence on my
thinking |
|
Ludwig von Bertalanffy |
Originator of the general systems idea (von Bertalanffy, 1969) |
|
Norbert Wiener |
Cybernetics as crosscutting science, feedback abstraction (Wiener, 1948), |
|
W. Ross Ashby |
State-determined systems and modeling insights, e.g., every regulator must contain a model, the critical need for theory of simplification (Ashby, 1961) |
|
Kenneth Boulding |
System self image (Boulding, 1956) |
Probably no one would argue with these giants as pioneers of cybernetics and systems but there might be some dissension about their pre-computer age status. The latter is not based on their chronological age but on whether the modern computer played a fundamental role in their thinking, especially in contrast to the following pioneers of the modern computer:
|
Early computer
science pioneers |
Influence on my
thinking |
|
Allan Turing |
Turing machine model of computation (Turing, 1954) |
|
John Von Neumann |
Cellular automata, stored program computer (Von Neumann, 1958) |
|
Steven Kleene |
Finite state/regular expressions (Kleene, 1956) |
|
Noam Chomsky |
Language hierarchy – generation, recognition (Chomsky, 1969) |
These computer science pioneers developed what was called automata theory, the foundations for today’s computer science theory. Coming mainly out of mathematics and logic, their theories are abstract frameworks for what is computable in principle. This contrasts with the hypotheses about how real world computation occurs as made by artificial intelligence theorists such as:
|
Early artificial
intelligence researchers |
Influence on my
thinking |
|
John H. Holland |
Parallel processing, embeddings in cellular spaces, adaptive systems (Holland. 1975) |
|
Herbert Simon |
Heuristic problem solvers, hierarchical systems and complexity (Simon, 1981) |
|
Marvin Minsky |
Limitations of {hill climbing, ultrastability, neural nets}, need for symbolic processing (Minsky, 1961) |
As I suggested above, there were, and continue to be, two separate streams that contributed to mathematical systems theory, computer science being one of them. The other is controls and circuits, with some of my influences being:
|
Early circuits and
control theorists |
Influence on my
thinking |
|
Rudolf Kalman |
Controllability, observabililty (Kalman, Falb and Arbib, 1969) |
|
William Siebert |
Linear systems methods (Siebert, 1986) |
|
Michael Athens |
Optimal control (Athans and. Falb 1966) |
Theory of Modeling and Simulation
At the time of the writing of the Theory of Modeling and Simulation book, others were thinking more fundamentally about modeling and simulation, such as:
|
Modeling and
Simulation Theory |
Influence on my
thinking |
|
Robert Rosen |
Dynamic systems and models moving away from differential equations (Rosen, 1978) |
|
Leo Apostel, |
Approximate modeling relations (Apostel, 1961) |
|
Stan Ulam |
The stability of homomorphisms, error propagation (Soon-Mo Jung, 2001) |
|
Tuncer Ören |
Numerous aspects of advanced simulation methodology (Ören and Zeigler, 1979) |
|
Maurice Elzas |
System entity structure (Elzas, Ören and Zeigler, 1989) |
I distinguish them, and myself, from general systems theorists in that for us the focus is more on how we can come to know the real world, e.g., how do we construct and simulate models, then on building theories about what the world is really like.
As I said before, the Theory of Modeling and Simulation directly derived from mathematical systems theory and from the early computer simulation languages. However, the indirect influences suggested by the above enumeration were equally important in shaping the content of the theory. For example, the Theory of Modeling and Simulation makes a sharp distinction between models and simulators. Its concepts about models, such as behavioral equivalence, can be traced to those deriving from control theory as well as automata theory, hence from systems theory. In contrast, its concepts of simulators, such as simulation computational complexity measures, are derived from computer science.
In this historical context, perhaps the main contribution of the book was to place the separate elements into a common framework and to provide a first integration of their insights and techniques into a methodology that could address concerns of modeling and simulation.
Q. Why not “the” or “a” to start the title (as in “The Theory of Modeling and Simulation”)?
A. “The” would have suggested that the framework and integration were the only ones possible, while “a” would have suggested that the one being advanced was rather arbitrary. As suggested above, I believe that the framework and integration were natural extensions and combinations of existing advances in a number of areas and so were not arbitrary. Yet the existing constraints need not guarantee a unique solution and there could still be better ways of doing this integration that might arise in the future. So leaving out both “the” and “a” was the compromise.
Q. Why then did you write several more books and then a second edition of Theory of Modeling and Simulation?
A. I think I fit some of the attributes of scientists as characterized by the philosopher of science, Lakatos, in “The methodology of scientific research programmes” (Hollinger and Brendan, 2000). He emphasized that progress occurs through the unpacking of an initial idea into its implications. This unpacking occurs under the imperative that the scientist as champion of the idea needs to convince the critics and unbelievers by concrete outcomes understandable to them. Certainly my belief that adoption of DEVS, and its parent systems theory, could lead to much improved practice in modeling and simulation was not widely shared initially in the M&S community although that has changed considerably (Nance and Sargent, 2002). So there was, and still is, the motivation to demonstrate specific advantages of the theory to get it adopted in practice. This goal is not necessarily an altruistic one, since success in this manner feeds back into the reputation needed to obtain grants and rise up the academic ranks. I’ll illustrate these points in the following table:
Book Title
|
Content
Theme
|
Personal
Science Programmatic
Context |
|
Multifaceted Modeling and Discrete Event Simulation (Zeigler, 1984) |
Worked out more of
the DEVS theory and laid the basis for the implementation of DEVS-based
modeling and simulation environments. Introduced the concept of system entity
structure as a means of organizing models for multifaceted systems.
|
Recognized as the
publication of the year by the College of Simulation of Management Sciences
Institute.
|
|
Object‑Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems, (Zeigler, 1990) |
The advent of object-oriented
programming languages provided a highly compatible computational basis for
implementations of DEVS theory.
DEVS-Scheme was the first theory-based discrete event simulation
system. Hierarchical, modular
construction, supported by DEVS-Scheme (and subsequent DEVS-based
environments), was demonstrated as a powerful means to develop complex
intelligent systems.
|
I undertook what turned out to be a major programming effort punctuated by many frustrating limitations of the new object-oriented languages. But once done, it could then be replicated by many researchers and software developers. Received IEEE Fellow recognition for DEVS theory. |
|
Objects and Systems: Principled Design with Implementation in C++/Java (Zeigler, 1997) |
Developing the
software for DEVS-Scheme required state-based approaches to object behavior
specification. This book proposed
that these approaches could be taught to undergraduates, contributing to the
development of true professionalism in software engineering.
|
Initially buried in the avalanche of C++, Java and UML books, it is now finding its niche sandwiched between programming and formal methods courses. |
|
Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (Zeigler, Kim and Praehofer, 2000) |
Updated the first edition in areas where a lot of developments had occurred especially related to DEVS, which came into clearer perspective as a unifying computational medium for both continuous and discrete systems. I invited two major contributors to the development of DEVS theory to participate as co-authors. |
Received the McLeod Founders Award (highest recognition for professional contribution) of the Society for Computer Simulation (now Society for Modeling and Simulation, International.) |
Q. Where does the theory of modeling and simulation stand today? Where will theory and systems thinking, in general, be tomorrow?
A. There really haven’t been any competitors aimed at the exactly same issues and certainly no one else has published an alternative theory of modeling and simulation. So in that sense, using “the” in the title is becoming more appropriate. However, there are large research communities that could be considered to be doing modeling and simulation, who do not necessarily feel the need for a theory of Modeling and Simulation, let alone accept mine. For example, people working in computational science are concerned with making complex scientific codes run faster typically using high performance computers. They tend to blur the distinction between model and simulator, seeing only code that can be partitioned and instrumented. As a consequence, the underlying model is inviolate and a major degree of freedom is sacrificed. Another large community surrounds “complex systems” a phrase that has come to include a variety of ideas including chaos theory, fractals, adaptive systems, artificial life, and the like. While the origins of these ideas lie largely in cybernetics and systems, the current generation of researchers has lost its memory of, and relationship to, those roots. As a consequence, I believe, the field has fragmented without much unity and with much reinvention of the wheel.[11] On a more positive note, there is a significant and growing community of both researchers and simulation modeling practitioners centered on the DEVS formalism, along with its direct link to systems research and thinking[12].
In general, I think that the need for systems thinking is on the rise with the decline of reductionist sciences (such as physics) and the rise of holistic ones (such as bioinformatics). Unfortunately, given the memory span of about five years that we live with today, the original insights of the pioneers have been all but forgotten. The problem, I think, is that many scientists are wary of general concepts and abstractions that require interpretation to apply to one’s primary field. Progress, at least in the short run, moves faster using discipline-specific methods. Along with tangible advances, funding and personal careers follow in a self-propagating cycle. Disciplines thus spin away from each other and from cross-disciplines that try to keep knowledge integrated and reusable. Not part of influential leadership bodies, such as funding agency program committees, those who would have liked to continue the advance of general systems research were largely isolated from the action and left without access to the problems needing solution and the resources to solve them.
Heading, as we are now, into an age of holistic and multi-disciplinary science, the need is ever greater for the integration offered by general systems approaches and the tools provided by integrative modeling and simulation. Somehow we must overcome the amnesia that took hold and revive the teachings of the old ones. Some are trying to draw attention back to the original works. But these masterpieces are likely to be perceived as time-warped relics with dated examples and pre-computer age methods. Rather, the essential insights must be extracted and repackaged in modern garb to take hold and play the role they are will be sorely needed to fulfill. And more critically, those of us who still remember these messages must keep advocating them to the unconverted while inspiring the next generations to adopt them as their own.
A Final Note
This autobiographical perspective is necessarily somewhat self-centered, for which I apologize profusely, mainly because it leaves out the many people that have contributed to my career along the way. These are, in general terms, teachers, mentors, doctoral and masters advisees, graduate and undergraduate students, academic and professional colleagues, funding agency program managers, editors, wives and children. I have indeed been fortunate that there are so many professional colleagues that it would increase this story many fold to do their individual contributions justice. It must suffice to say that I learned something essential, with and from, each one – the brief account here is indeed, the tip of the iceberg. For the interested reader, the web site http://www.acims.arizona.edu offers much more of the current research, including educational and software products, that are touched on here.
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Klir, G. J. (1988) "Perspectives on the evolution of systems science: An autobiographical profile", Systems Research 5(2), 145-156.
Markowitz, H. M. (1963) Simscript : A Simulation Programming Language (Prentice-Hall, Englewood Cliffs, NJ).
McCarthy, J. (1962) LISP 1.5 Programmer's Manual ( MIT Computation Center and Research Laboratory of Electronics, Cambridge, MA).
Mesarovic, M. D. (1975) General Systems Theory: Mathematical Foundations (Academic Press, New York).
Minsky, M. (1961) "Steps toward artificial intelligence", Proc. IRE 49(1), 8-30; reprinted in Computers and Thought (McGraw-Hill, New York, 1963).
Nance, R. E. and Sargent, R. G. (2002). "Perspectives on the evolution of simulation", Operations Research 50(1), 161-172
Ören, T. I., and Zeigler, B. P. (1979) "Concepts for advanced simulation methodologies", Simulation 32(3), 69-82.
Padulo, L. and Arbib, M. A (1974) System Theory: A Unified State-Space Approach to Continuous and Discrete Systems (Saunders, Philadelphia, PA).
Pritsker, A. A. B. (1974) The GASP IV Simulation Language (John Wiley, New York).
Rosen, R. (1978) Fundamentals of Measurement and Representation of Natural Systems (North-Holland, New York).
Sarjoughian, H. S. and Cellier, F. E. (2001) Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies: A Tribute to the 60th Birthday of B. P. Zeigler (Springer, New York).
Schriber, T. J. (1974) Simulation Using GPSS (John Wiley, New York).
Siebert, W. (1986) Circuits, Signals, and Systems (McGraw-Hill, New York).
Simon, H. A (1981) The Sciences of the Artificial (MIT Press, Cambridge, MA).
Soon-Mo, J. (2001) Hyers-Ulam-Rassias Stability of Functional Equations in Mathematical Analysis (Hadronic Press, Palm Harbor, FL).
Speckhart, F. H and. Green, W. L. (1976) A Guide to Using CSMP - the Continuous System Modeling Program : A Program for Simulating Physical Systems (Prentice-Hall, Englewood Cliffs, N.J.).
Tocher, K. D. (1964) The Art of Simulation (Van Nostrand, Princeton, N.J.).
Turing, A. M. (1954) Mechanical Intelligence (North-Holland, New York).
Von Neumann, J. (1958) The Computer and the Brain (Yale University Press, New Haven, CN).
Von Bertalanffy, L. (1969) General System Theory: Foundations, Development, Application (G. Braziller New York).
Wiener, N. (1948) Cybernetics or Control and Communication in the Animal and the Machine (John Wiley, New York).
Wymore, A. W. (1976) Systems Engineering Methodology for Interdisciplinary Teams (John Wiley, New York).
Zadeh, L. A. and Desoer, C. (1963) A Linear System Theory (McGraw-Hill, New York).
Zeigler, B. P. (1968) On The Feedback Complexity of Automata, Doctoral Dissertation, University of Michigan, Ann Arbor, MI; later published as a book (Management Information Services, Detroit, 1970).
Zeigler, B. P. (1972) "Proof of a conjecture by A.W. Burks and H. Wang: Some relations between net cycles and state cycles", Information and Control 21(2), 185‑195.
Zeigler, B. P. (1976) Theory of Modeling and Simulation (Wiley Interscience, New York);
reprinted by R.E. Krieger, Malabar,
FL, 1984.
Zeigler, B. P. (1984) Multifaceted Modeling and Discrete Event Simulation (Academic Press, London).
Zeigler, B. P. (1990) Object‑Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems (Academic Press, Orlando, FL).
Zeigler, B. P. (1997) Objects and Systems: Principled Design with Implementations in C++/Java (Springer-Verlag, New York).
Zeigler, B. P., Kim, T. G., and Praehofer, H. (2000) Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, Second Edition (Academic Press, Boston, MA).
Bibliography of Bernard P.
Zeigler
Books
1968
On The Feedback Complexity of Automata, Doctoral Dissertation, University of Michigan, Ann Arbor, MI; later published as a book (Management Information Services, Detroit, 1970).
1976
Theory of Modeling and Simulation (Wiley Interscience, New York); reprinted by R.E. Krieger, Malabar, FL, 1984.
1979
Methodology in Systems Modelling and Simulation (North Holland, Amsterdam); with Klir, G.J., Elzas, M.S., and Ören, T.I.
1984
Simulation and Model Based Methodologies: An Integrative View (Springer-Verlag, New York); with Ören, T.I., Klir, G.J., and Elzas, M.S.
Multifaceted Modeling and Discrete Event Simulation (Academic Press, London).
1986
Modelling and Simulation Methodology in the Artificial Intelligence Era (North Holland, Amsterdam); with Elzas M.S. and Ören, T.I
1989
Modelling and Simulation Methodology:
Knowledge Systems Paradigms (North
Holland, Amsterdam); with Elzas M.S. and Ören, T.I.
1990
Object‑Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems (Academic Press, Orlando, FL).
1997
Objects and Systems: Principled Design with Implementations in C++/Java (Springer-Verlag, New York).
2000
Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (Academic Press, Boston, MA); with Kim, T,G. and Praehofer, H.
2002
Committee on Modeling and Simulation Enhancements for 21st Century Manufacturing and Defense Acquisition Modeling and Simulation in Manufacturing and Defense Acquisition: Pathways to Success (National Academy Press, Washington, D.C.).
Papers
1970
"System theoretic analysis of models: Computer simulation of a living cell", J. of Theoretical Biology 29(1), 36‑56; with Weinberg, R.
1971
"Feedback in homomorphic realizations", IEEE Transactions on Computers C‑20, 685‑688.
"Canonical realization of general time systems", Information Science 12(1), 179‑186.
1972
"Proof of a conjecture by A.W. Burks and H. Wang: Some relations between net cycles and state cycles", Information and Control 21(3), 185‑195.
"Towards a formal theory of modelling and simulation: Structure preserving morphisms", J. of the ACM 19(4), 742‑746.
1974
"A conceptual basis for modelling and simulation", Intern. J. of General Systems 1(4), 213‑228.
1975
“Statistical simplification of neural nets”, Intern. J. of Man-Machine Studies 7(3), 371-393
1976
"The hierarchy of system specifications and the problem of structural inference." In: F. Suppes and P.D. Asquith (eds.), PSA 1976: Philosophy of Science Assoc., East Lansing, MI, 227-239.
1977
"Persistence and patchiness of predator‑prey systems induced by discrete event population exchange mechanisms", J. of Theoretical Biology 67(4), 687‑714.
1978
"Structuring the organization of partial models", Intern. J. of General Systems 4(2), 81‑88
1979
"Concepts for advanced simulation methodologies", Simulation 32(3), 69‑82; with Ören, T.I.
1982
"Simulation of cell space models", Intern. J. of Theoretical Physics 21( 6‑7), 572‑588.
1984
"Abstraction in methodology: A framework for computer support", Information Processing and Management 20(1‑2), 63‑79; with Rada, R.
"Multifaceted modelling methodology: Grappling with the irreducible complexity of systems", Behavioral Science 29(3), 169‑178.
"Systems theoretic representation of simulation models", Trans. of American Institute of Industrial Engineers 16(1), 19-34.
1985
"Complexity and emergence", Intern. J .of General Systems 10(2-3), 163‑168; with Foo, N.
"Theory of discrete event specified models: Modularity, hierarchy, experimental frames", Intern. J. of General Systems 10(1), 57-84.
"A Hierarchical Information Processing Model for Adaptation to Technological Change", Systems Research 2(4), 304‑317; with Reynolds, R.
1986
"Discrete event simulation", IEEE Spectrum 23(12), 32‑36, with Garcia, R. and Garcia, M. P
"Artificial Intelligence in Modelling and Simulation: Directions to Explore", Simulation J. 48(4), 131‑144; with Ören, T.I.
"A hierarchical information processing model for adaptation to technological change", Systems Research 2(4), 304‑317; with Reynolds, R.
1987
"Hierarchical, modular discrete event models in an object oriented environment", Simulation J. 49(5), 219‑230.
"Knowledge representation from Newton to Minsky and beyond", Applied Artificial Intelligence 1(1), 87‑107.
1988
"Entity structure based design methodology: Application to LAN protocol design", IEEE Trans. on System Engineering 14(3), 375‑383; with Sevinc, S.
"DEVS formalism: A framework for hierarchical model development", IEEE Trans. on Software Engineering 14(2), 228‑241; with Concepcion, A.I.
"Some properties of modified Dempster‑Shafer uncertainty management operators in rule based inference systems", Intern. J. of General Systems 14(4), 345‑357.
"Design of a simulation environment for laboratory management by robot organizations", J. of Robotics and Intelligent Systems 1(3) 299‑309; with Cellier, F.E. and Rozenblit, J.W.
1989
"A knowledge‑based environment for investigating multicomputer architectures", Information and Software Technology 31(10), 512‑520; with Kim, T.
"The DEVS formalism: Event‑based control for intelligent systems", Proc. of the IEEE 77(1), 27‑80.
"Knowledge‑based design of artificial worlds", Biosystems 23(2-3), 95‑112.
1990
"AIDECS: An AI‑based distributed environmental control systems", AI in Engineering 5(1), 33‑41; with Kim, T.
"System entity structuring and model base management", IEEE Trans. on Systems, Man & Cybernetics, 20(5), 1013‑1024; with Kim, T., Lee, C., and Christensen, E.R.
"Object-oriented business process modeling and simulation: A discrete-event system specification (DEVS) framework", Simulation Practice 6(1), 533-571; with Nidumolu, R., with Menon, N.
"Knowledge‑based design and simulation environment (KBDSE): Foundational concepts and implementation", J. of Operations Research Society 41(6), 475‑489; with Rozenblit, J.W., Hu, J., and Kim, T.G.
"Design, analysis and implementation of a telemedicine remote consultation and diagnosis session playback using discrete event system specification", IEEE Transaction on Information. Technology in Biomedicine. 1(3), 179-188; with Shah, P.J. and Martinez, R.)
"Variable structure modelling methodology: An adaptive computer architecture example", Trans.of Society for Computer Simulation 7(4), 291-320; with Kim, T. and Lee, S.
"Knowledge-Based Simulation Design Methodology: A Flexible Test Architecture Application", Trans.of Society for Computer Simulation 7(3), 195-228; with Rozenblit, J.W.
"Mapping hierarchical discrete event models to multiprocessor systems: Algorithm, analysis, and simulation", J. of Parallel and Distributed Computing 9(3), 271‑281; with Zhang, G.
1991
"Model base management for multifaceted systems", ACM Trans. on Modeling and Computer Simulation 1(3), 195‑218; with Luh, C.
"Guest editor's introduction to special issue on high autonomy systems: Modelling and Simulation", Intern. J. of General Systems 19(3), 193‑199.
"Qualitative physics: Towards automated systems problem solving", J. of Experimental &Theoretical Artificial Intelligence 3(3), 219‑246; with Fishwick, P.
1992
"Symbolic discrete event simulation", IEEE Trans. on Systems, Man, & Cybernetics 22(6), 1428‑1443; with Chi, S.
"A multimodel methodology for qualitative model engineering", ACM Trans. on Modelling and Computer Simulation 2(1), 52-81; with Fishwick, P.
"Endomomorphic modelling concepts for high autonomy architecture", Applied Artificial Intelligence J. 6(1), 19‑44.
"Systems formulation of a theory of diagnosis from first principles", IEEE Trans. on Reliability 41(1), 38‑48 .
1993
"Hierarchical model‑based diagnosis for high autonomy systems", Intern. J. of Intelligent and Robotic Systems. 9(1), 1‑15; with Chi, S.
"Extension of the DEVS‑scheme knowledge‑based simulation environment to real‑time control", IEEE Trans.on Automation and Robotics 9(3), 351‑354; with Kim, J.
"A simulation environment for intelligent machine architectures", J. of Parallel and Distributed Computing 18(1), 77-88; with Louri, A.
"Abstracting event‑based control models for high autonomy system", IEEE Trans. on Systems, Man & Cybernetics 23(1), 42‑54; with Luh, C.
"High-autonomy control of space resource processing plants", IEEE Control Sytems. Magazine 13(3) 29-39; with Schooley, L., Cellier, F., and Wang, F.Y.
"Discrete event simulation of forest landscape response to fire disturbances", Ecological Modelling 65(1), 177‑198; with Vasconcelos, M.
"Extending the DEVS formalism for massively parallel simulation", J. of Discrete Event Dynamical Systems 3(2-3), 193-218; with Wang, Y.
"Frameworks for the evaluation of discrete event dynamic systems", J. of Discrete Event Dynamical Systems 3(2-3), 113‑118; with Sanders, W.H.
"Integrating system formalisms: How object orientation supports cast for intelligent systems design", J. of System. Engineering 3(4), 209‑219; with Praehofer, H. and Rozenblit, J.
1994
"DEVS-based intelligent control: Space-adapted mixing system example", Cybernetics and Systems 25(3), 471-510; with Chi, S.
1995
"DEVS approximation of infiltration using genetic algorithm optimization of a fuzzy system", J. of Computer. Modeling and Analysis 33(11), 215-238; with Moon, Y., Lopes, V., and Kim, J.
"Simulation of fire growth in GIS using discrete event hierarchical modular models", Advances in Remote Sensing 4(3), 54-62; with Vasconcelos, M. and Pereira, J.
"Implementing the DEVS formalism on a parallel SIMD architecture", Intern. J. in Computer Simulation 5(3), 229-245; with Wang, Y.
1996
"A knowledge based simulation environment for hierarchical flexible manufacturing", IEEE Trans. on Systems, Man, & Cybernatics- Part A: Systems and Humans 26(1), 81-91; with Cho, T.H. and Rozenblit, J.W.
"A high performance and simulation environment for intelligent systems design", J. of Intelligent Systems & Control 1(1), 83-100; with Kim, J.
"A Framework for multi‑resolution optimization in a parallel distribution environment: Simulation of hierarchical GAs", J. of Parallel and Distributed Computing 32(1), 77-88; with Kim, J.
"Hierarchical distributed genetic algorithms: A fuzzy logic controller design application", IEEE Expert 11(3), 76-84; with Kim, J.
"Designing fuzzy logic controllers using a multiresolutional search paradigm", IEEE Trans. on Fuzzy Systems 4(3), 213-226; with Kim, J.
"Designing fuzzy net controllers using GA optimization", IEEE Control Systems 13(15), 66-72; with Kim, J. and Moon, Y.
"Variable structure models in object-oriented simulation", Intern. J. of General Systems 24(4), 359-375; with Uhrmacher, A.M.
1997
"Adaptive queueing: A case study using dynamic structure devs", Intern. Trans. in Operational Research 4(2), 87-98; with Barros, F.
"Simulation of intelligent hierarchical flexible manufacturing", IEEE Trans. System, Man and Cybernetics 27(1), 116-125; with Cho, T.H.
"The DEVS environment for high-performance modeling and simulation.", IEEE Computational Science and Engineering 4(3), 61-71; with Moon, Y. and Ball, G.J.
"DEVS representation and aggregation of spatially distributed systems: Speed versus error tradeoffs", Trans. of the Society for Computer Simulation 13(5), 179-190; with Moon, Y.
"Successive approximation in multifacetted modelling methodology: Human performance models." Transactions. of the Society for Computer Simulation 14(1), 25-36; with Young, M. and Vahie, S.
1999
"Automatic generation of system entity structure alternatives: Application to initial manufacturing facility design." Transactions. of the Society for Computer Simulation 16(4), 173-185; with Couretas, J. and Patel, U.
"Exploiting HLA and DEVS to promote interoperability and reuse in Lockheed’s corporate environment", Simulation J. 73( 4), 288-295; with Hall, S.B. and Sarjoughian, H.
2000
"Engineering distributed systems: Simulation-based co-design", IEEE Computer 33(3), 110-113; with Sarjoughian, H. and Hild, D.
"Theory of quantized systems: DEVS simulation of perceiving agents", Intern. J. of Cybernetics and Systems 31(6), 611-648; with Sarjoughian, H. and Praehofer, H.
"DEVS and HLA: Complementary paradigms for M&S?", Trans.of Society for Computer Simulation 17(4), 187-197; with Sarjoughian, H.
2001
“Collaborative distributed network system: A lightweight middleware supporting collaborative DEVS modeling”, Future Generations Computer Systems. 17(2), 89-105; with Sarjoughian, H.S. and Park, S.
"Design and development of a data distribution management environment", Simulation 77(1-2), 39-52; with Lee, J.S. and Venkatesan, S.M.
"A Layered modeling and simulation architecture for agent-based system development", IEEE Proceedings 89(2), 201-213; with Sarjoughian, H. and Hall, S. B.
“Introduction to agent modeling and simulation", IEEE Proceedings 89(2), 127-30; with Uhrmacher, A.M. and Fishwick, P.
2002
"Space-based communication data management in scalable distributed simulation", J. of Parallel and Distributed Computing 62(3), 336-365; with Lee, J.S.
"DEVS-DOC: A modeling and simulation environment enabling distributed codesign", IEEE of Systems, Man. & Cybernetics 32 (1), 78 -92; with Sarjoughian, H. and Hild, D.
“The brain-machine disanalogy revisited", Biosystems 64(1-3), 127-140.
"Quantization-based filtering in distributed discrete event simulation", J. of Parallel and Distributed Computing 62(11), 1629-1647; with Cho, H. J., Kim, J. G., Sarjoughian, H. and Lee, J. S.