154 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003

Engineering Complex Systems:Implications for Research in

Systems Engineering

BETWEEN MY role as Chair of the School of Industrial andSystems Engineering (ISyE) at Georgia Tech and being

co-editor of theHandbook of Systems Engineering and Man-agement(New York:Wiley, 1999), I get many questions aboutsystems engineering, namely, what this field includes and ex-cludes.

A quick answer is, “take a look at the handbook.” A bit moreof an elaborate answer, especially if people are interested inwhat we are doing at Georgia Tech, can be cast in terms of themanufacturing, logistics, transportation, and health systems pro-grams available at ISyE. This answer might also include men-tion of ISyE’s cross-cutting areas of optimization, stochastics,statistics, economics, and human systems.

Thesetypesofanswersoftenwork,butoccasionallypeopleaskaboutthefundamentalnatureof“systemsengineering,” includingwhy, or if, this construct does not cover everything and, hence,nothing. This is a great question and in this brief note, I attempt toprovide an answer. I admit on the outset, however, that this noteprovides plenty of opportunities for disagreement, especiallyamongadvocatesofoneormoreof thepointsofviewelaborated.

To begin, it seems reasonable to assert that systems engi-neering involves the engineering of complex systems. This em-phasizes engineering as a verb, i.e., something one does. Engi-neering as a verb implies the creation of entities that are morethan just sums of parts. Creation in this manner requires anal-ysis and synthesis of complex systems.

• By system, I mean a group or combination of interrelated,interdependent, or interacting elements that form a collec-tive entity. Elements may include physical, behavioral, orsymbolic entities. Elements may interact physically, math-ematically, and/or by exchange of information. Systemstend to have purposes, although in some cases the observerascribes such purposes.

• By complex systems, I mean systems whose perceivedcomplicated behaviors can be attributed to one or more ofthe following characteristics: large numbers of elements,large numbers of relationships among elements, nonlinearand discontinuous relationships, and uncertain character-istics of elements and relationships. I include the idea ofperceived complexity because apparent complexity candecrease with learning.

Based on my perusal of a wide range of literature, as wellas many discussions with colleagues at Georgia Tech and else-where—see acknowledgment—it is clear that there are severalviews on what “analysis and synthesis of complex systems” ac-tually implies. In particular, there are multiple perspectives on

Digital Object Identifier 10.1109/TSMCC.2003.813335

the source of the “complexity” of the systems we are seeking toengineer.

I hasten to note that a primary differentiator among theseviews concerns systems science vs. systems engineering. Withinsystems science, there are both formal system views and naturalsystem views. Formal system scientists focus on worlds that hu-mans create while natural system scientists pursue worlds thatnature creates. Of course, there is often the strong desire to per-ceive formal worlds as close analogs of natural worlds. Systemsengineers adopt both formal and natural views depending on thecircumstances.

SYSTEMS OFHIERARCHICAL MAPPINGS

Many people view systems engineering in terms of theprocesses for designing, developing, deploying, and sustainingcomplex systems, notably, defense and public systems, butalso airplanes, manufacturing facilities process plants, etc. Thisview tends to be driven by hierarchical decomposition of avery complicated design task into component tasks, as well asmanagement of the execution of these tasks and integration oftask outcomes.

The emphasis is on defining a large number of reasonablystraightforward tasks whose outcomes flow together to createa successful complex system, with appropriate resolutionof multi-attribute tradeoffs across multiple stakeholders.Performance of these more straightforward tasks may drawupon expertise from various engineering disciplines, computerscience, economics, and so on. The key, however, is thatcomplexity is managed by dividing and conquering.

Note that the complexity here is typically due to large num-bers of interacting elements. The elements are usually well de-fined, in fact, designed. Relationships among elements are often“linear” in the sense that overall system behavior is a reasonablystraightforward composite of the behavior of its many elements.

SYSTEMS OFUNCERTAIN STATE EQUATIONS

Another view of systems engineering focuses on the “state” ofthe system of interest. The mechanisms whereby the state of thesystemevolvesareofcentralinterestastheyaffectsystemresponseand stability. The nature of appropriate feedback mechanisms forcontrollingsystemstateisacentraldesignissue.Observabilityandcontrollability are key constructs. Optimization of control is anoverriding goal. However, inabilities to fully specify state-tran-sition mechanisms and a wealth of other uncertainties limit thispursuit to formulation of constrained optimality.

The emphasis in this view is on formal depiction andmanipulation of mechanisms underlying complex behaviors.This more formalistic approach seldom “scales up” to the types

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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003 155

of problems addressed by the hierarchical mapping view ofsystems engineering. However, fundamental knowledge gainedfrom formal approaches may provide insights into successfulapproaches in more complicated domains.

Complexity in this view is often due to a large numbers ofstate variables and significant levels of uncertainty, includinguncertain state-transition mechanisms. Pursuit of optimal con-trol solutions can both be mathematically and computationallycomplicated. Such pursuits are often made possible by assump-tions of linearity.

SYSTEMS OFDISCONTINUOUSNONLINEAR MECHANISMS

Yet another view focuses on apparently simple underlyingphenomena that yield complex behaviors for systems with veryfew elements, perhaps even just one element with particular in-teraction terms. The nonlinear and/or discontinuous nature ofthe elements of interest lead to behaviors labeled as catastro-phes, chaos, and so on. The key point here is that systems ap-pearing simple can produce very complex behaviors. The re-sulting hypothesis is that many apparently complex phenomenacan be attributed to surprisingly simple mechanisms.

The nonlinear mechanisms view portrays complexity as a de-parture from our expectations of continuous, linear phenomena.There need not be large numbers of interacting elements. Thisperspective argues for understanding complexity by exploringunderlying mechanisms. Identification of such mechanismsmay lead to design solutions, for example, by adapting naturalmechanisms to human-made systems.

It is quite imaginable, perhaps typical, that nonlinear mech-anisms underlie the state-transition mechanisms central to theearlier state equations view. Formal systems approaches canflounder when addressing fairly small numbers of interactingnonlinear mechanisms. Complexity in such situations escalatesmuch more quickly with nonlinear than linear elements.

SYSTEMS OFAUTONOMOUSAGENTS

A fourth view is concerned with understanding the emer-gent properties of complexity. Rather than focusing on decom-position, this view emphasizes composition of large numbersof simple behaviors into overall system behaviors that exhibithallmark characteristics of complex systems. The simple behav-iors are created by autonomous “agents” acting independentlyin pursuit of their individual goals. These goals need not differacross agents. Further, agents may communicate about goals,resources, actions, etc. Reactions of agents to each other’s be-haviors result in emergent phenomena that could not have beenpredicted by dissecting mechanisms within individual agents.

Insights gained from studies of such phenomena includeunderstanding the nature of incentives, motivations, and pro-hibitions that will influence individual agents to contribute tocreating desirable collective behaviors. In this view, under-standing and managing complexity is more an experimentalthan axiomatic undertaking. This approach presents difficultiesin that many things can be demonstrated but few can be proven.

Complexity in this view is in part due to a large number ofelements, although were all the elements simply linear, nonin-teracting agents, the state equation’s view might be successfullyemployed. However, typical agents are not linear, they interact

TABLE ICONTRASTING VIEWS OFCOMPLEX SYSTEMS

with each other, and they learn from their own and others’ be-haviors, which may modify their intentions over time. Thus,formal approaches are seldom tractable.

CONTRASTING VIEWS

Table I contrasts the four views just outlined. Clearly, the ap-proaches and foci of these four views are quite different. Thiscontrast might merely be interesting were it not for the fact that,in some cases, all four views are addressing related phenomena,e.g., effects of turbulent flow on aerodynamic behavior and ve-hicle performance in high-density traffic.

In this example, view no. 1 might be employed for designingthe vehicle, no. 2 to explore the vehicle dynamics, no. 3 to modelthe turbulence, and no. 4 to understand traffic effects. Of course,thechoiceofapproachwoulddependontheproblemathand,e.g.,poor vehicle handling qualities vs. traffic congestion problems.

Nevertheless, in thissimpleexampleweseecomplexityatmul-tiple, related levels. The literature within each discipline wherethese views are pursued tends to argue for the essence of com-plexityoperatingattheirlevel.However,fromabroader,cross-dis-ciplinary perspective, none of these arguments are tenable.

SPANNING THE VIEWS

If we think in terms of multi-view representations of systems,there are several issues that span the four views just elaboratedand contrasted. One of these issues concerns the nature and flowof information. Uncertainties associated with this informationare also of interest.

The hierarchical mappings view addresses this issue explic-itly, both in terms of the information flows within the systemand information flows about the system, i.e., for design, devel-opment, maintenance, etc. The state equations view addressesstate information—including uncertainty associated with thisinformation—quite rigorously, but does not capture informationabout the system, for instance, for maintenance. The nonlinearmechanisms view is also purely focused on phenomena relatedto state transitions.

The autonomous agents view addresses information flowbottom up from information access and exchange amongindividual agents, including access and posting of informationfrom and to formal sources. While individual agents mayact probabilistically, a larger source of uncertainty is oftenunpredictable emergent behaviors. This is not just an issue,for example, of observation variability in terms of variancesaround known means, but instead relates to the possibility ofwhole courses of collective action being rather different thanexpected.

156 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003

Another spanning issue concerns decision making and con-trol. This concerns representation of the framing and makingof decisions to pursue courses of action, as well as the controlassociated with executing these courses of action. The hierar-chical mappings view addresses decision making, decision sup-port, and execution quite explicitly. However, approaches to de-cision making and control are usually far less formalized thanwith the state equations and nonlinear mechanisms views. Whilethe former view focuses on supporting decision makers’ needsand preferences, the latter two views place much more of anemphasis on prescriptions for how decision making and controlshould be addressed, e.g., in terms of optimal decision and con-trol strategies.

The autonomous agents view addresses this issue much moresimply. Agents’ decision making and control strategies are usu-ally quite elementary. The complexity arises from large num-bers of agents employing these strategies. The resulting inter-actions, including effects of incentives, regulations, etc., oftenserve to demonstrate how seemingly rational “rules of the game”can yield clearly undesirable overall system behaviors. Decisionmaking and control with this view is much more distributed thanwith the other views.

A third spanning issue concerns representation of humanbehaviors, both individually, and organizationally. The hi-erarchical mappings view often represents such behaviorsexplicitly, at least in terms of information and control require-ments for supporting these behaviors. This view also tends toplace great emphasis on human involvement in the process ofengineering systems. Thus, humans in this view both decideand control, as well as design and develop.

The state equation’s view includes such representations tothe extent that humans can be depicted “in the loop” of statetransitions and control. The nonlinear mechanism’s view maydepict human-related phenomena, but human informationprocessing characteristics are rarely explicit. For both ofthese views, strong assumptions are often made regarding aperson’s ability to decide and control optimally, perhaps withinmathematically tractable constraints.

The autonomous agents view is often primarily focused onhuman behaviors, particularly large collections of humans. In-dividual behaviors are represented in terms of decision and con-trol rules, perhaps including possibilities for learning. Organi-zational behaviors are represented in terms of rules for infor-mation flows, decision processes, and performance incentives.Some uses of this view represent organizations as agents oper-ating, for example, within marketplaces.

Clearly, the ways in which one chooses to address the span-ning issues just outlined affect the attractiveness of the alter-native views of systems and complexity. Both the problems athand and the tools available also strongly affect such choices.To the extent that information flow and portrayal for support ofhuman decision making and control are central systems issues,the choice among alternative views is fundamental.

RESEARCH INSYSTEMS ENGINEERING

Systems operate at a multiple of levels and exhibit apparentlycomplex behaviors at all these levels. The problem at hand very

much affects the best level at which to address the problem.For instance, managing the complexity of aircraft developmentis very different from understanding the complexity of bees’foraging behaviors.

This suggests that the nature of systems engineering is deter-mined by the range of problems addressed. From this perspec-tive, my “easy” answers to the questions people have, mentionedearlier, are pretty good. Yet, not satisfying, at least not to me.

My sense is that systems engineering at its richest is able tocross levels of description and integrate the above views to engi-neer complex systems in a more integrated and effective manner.Good systems engineering is able to uncover which views arecritical to effectiveness and employ the appropriate concepts,principles, methods, and tools for assuring effectiveness.

This suggests that investments in systems engineeringresearch should focus on elaboration of the multiple views andcreation of means for translating among these views. To a greatextent, we have many pieces of the puzzle within the variousdisciplines of engineering and science. What we do not have isa true sense of the whole puzzle in terms of both how and whythe pieces fit together.

Systems engineering is not another discipline like industrial,electrical, mechanical, and chemical engineering. It should bean integrative discipline that crosses the boundaries of thesedisciplines and, of course, others. Thus, systems engineeringis “everything,” in the sense of the exploration, understanding,and design of how everything fits together. From this perspec-tive, systems engineers provide a brokerage and communicationfunction that enables them to see the system from the perspec-tive of all stakeholders, ranging from the different engineeringdisciplines involved to users, customers, and the public.

Systems engineering research should focus on how to do thisandhowtodoitwell.Thissuggests the ideaofan“architecture”ofknowledgeacrossengineeringandsciencethatenablesforquicklylinkingpuzzlesandpuzzlepieces.Suchanarchitecturecouldalsobeinvaluablefor identifyinghigh-leveragepiecesthatprovidetheessence of the picture as well as missing pieces.

A tremendous amount of productive research is performedwithin the many disciplines of engineering and science. Theoutcomes of this research can only be fully leveraged by under-standingandcreatingmechanisms for integratingknowledgeandtechnologies to create efficient and effective complex systems.This should be—and is—the charter of systems engineering.

ACKNOWLEDGEMENT

This paper has greatly benefited from the comments and sug-gestions of C. Anderson, K. Boff, J. Casti, L. McGinnis, C.Mitchell, A. Pritchett, A. Sage, and C. White on an earlier ver-sion. This paper also draws upon discussions and debates in anongoing graduate seminar on systems involving several facultymembers at Georgia Tech.

WILLIAM B. ROUSE, Chair, Fellow, IEEESchool of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlanta, GA 30332 USA