"Systemic Analysis Approaches for Air Transportation Sheila Conway PhD"
Systemic Analysis Approaches for Air Transportation Sheila Conway PhD Candidate, Old Dominion University Researcher, NASA Langley Research Center Hampton, Virginia USA Sheila.R.Conway@NASA.gov Abstract “attempt to represent the environment in such a way as to maximally simplify problem- Air transportation system designers have solving.” He continues that a model is not to had only limited success using traditional be judged on its correctness in terms of its operations research and parametric modeling ability to yield absolute or complete truth, but approaches in their analyses of innovations. rather on its ability to provide insight. They need a systemic methodology for Simply stated, models are our windows to modeling of safety-critical infrastructure that understanding the world around us. Wilson is comprehensive, objective, and sufficiently (1998) concurs with this sentiment, saying, “A concrete, yet simple enough to be used with Model is the explicit interpretation of one’s reasonable investment. The methodology must understanding of a situation, or merely of also be amenable to quantitative analysis so one’s ideas about that situation. It can be issues of system safety and stability can be expressed in mathematics, symbols, or words, rigorously addressed. However, air but it is essentially a description of entities transportation has proven itself an extensive, and the relationship between them. It may be complex system whose behavior is difficult to prescriptive or illustrative, but above all, it describe, no less predict. must be useful.” Selecting an appropriate There is a wide range of system analysis model is then highly dependent on the subject techniques available, but some are more and the addressed concern. appropriate for certain applications than Wilson’s statement raises the issue that others. Specifically in the area of complex modeling itself does not impose a particular system analysis, the literature suggests that implementation, method or tool, but rather is both agent-based models and network analysis the process of interpretation. All models techniques may be useful. share the same interpretive goal, but many This paper discusses the theoretical basis different types of models, and their associated for each approach in these applications, and tools and techniques, may be able to achieve explores their historic and potential further use it. Selection should be based on the intended for air transportation analysis. purpose of the model. Modeling Systems. The real world is a Need for Complex System Modeling big place that can be difficult to understand. To avoid being overwhelmed by its sheer size Why Model? Models are necessarily or complexity, we are inclined to parse it into simplified abstractions of reality for manageable, and hopefully functional, parts understanding specific properties of a subject. through abstraction. This parsing can be at any Heylighten (1993) likens models to granularity (pixilation, or scale), but tradeoffs knowledge itself. As abstractions of reality, are made between the size of the parts and the he asserts they are vital simplifications and an utility of the abstraction: too big, and it is still 1 unmanageable, too small and it may no longer counterintuitive. In fact, there are numerous represent the phenomena of interest. A accounts of system changes that had system is such a parsing. The context in unanticipated and sometimes dramatic results, which it is formed must be kept with it. like the deregulation of the power industry in Kast and Rosenweig (1972) define a California (which caused rolling power system as an organized assemblage of smaller outages). Often these ‘surprises’ are results of units that form a unitary whole. Others oversimplification, ignoring system dynamics identify the interdependence and complexity or even misinterpreting the ‘system’ itself by that their relationships inherently contribute. not recognizing important elements, or Without models, we can’t do much with including superfluous, confounding ones. systems. In fact, Forrester (1987) held the One obvious issue with complex system belief that all “systems” are mental constructs modeling is compliance with the principle of of portions of the real world, and that we can’t Occam’s Razor. How to select a model that is even imagine a system without modeling. simple enough to field, yet captures the Such a bold statement brings to mind a riddle nuances of a complex system? Heylighten’s about a tree falling in the woods, but there is statements imply that comprehensiveness is no doubt that to understand or influence a not necessary. On the contrary, he implores system requires a model. Whether a mental us to look for the simplest model that explains model, a mathematical model, or a simulation, observed behavior. As a guiding principle, Forrester had a point: system models are a controlling scope, or the inclusion of detail, basis for action. We have expectations for will require substantial domain knowledge and our systems, and we would like to be able to explicit recognition of the context. A complex influence their behavior. However, a system’s system cannot be trivialized with a simple reaction to intervention is not always clear, treatment. nor easy to predict. As systems become more Rather than assume that everything within complex, with a greater number of interrelated a system can be equivalently described by a components, their response to intervention can single type of abstraction, matching a model become even more ambiguous. In fact, as to reality begins with an honest assessment of even linear systems become complex in the the fidelity of what is 1) known and 2) what structure of their interrelationships, their knowledge is desired. This issue of scope for behaviour can begin to appear chaotic. each model element is paramount to Modeling Complex systems. Modeling successful complex system modeling. One complex systems adds some additional might assume modelers are granted authority limitations and assumptions to the already to scope as they see fit, but unfortunately, that challenging task of systems modeling. The is not always the case. scientific method and good engineering Another broad category of issues is practice require us to observe, hypothesize, expectations: 1) Qualities inherent to complex predict, test, derive a conclusion, and repeat systems may make understanding analytical until a satisfactory solution is obtained. results open to interpretation. 2) Complex How is the safety of a new air traffic control system analyses may disappoint those who are technique to be tested without putting aircraft concerned with output rather than outcomes. and people at risk? Even if safe, what effect 3) Complex systems are often ill structured will the new operation have on traffic flow and changeable. Thus, modeling them can be management, or an airline business? difficult, costly, and never quite complete In a complex world, answers to such compared to more simple systems. questions are not always obvious, and can be 2 Finally, though important in all systems mechanical factors of the system and their modeling, being able to gauge a system’s interactions, or gross errors will occur. They robustness becomes imperative with go on to recognize that this is a tall order increasing system complexity. Complex indeed, and that a comprehensive ATS model system models have limited optimizing is a “grand challenge,” albeit necessary and ability. The nature of complexity requires attainable. models to address fuzziness, non-determinism, Actually, NASA has recognized the need and multiple objectives by affording for a more systemic study for some time. exploration of sensitivities to assumptions and They commissioned Krozel (2000) to review environmental uncertainties. all the research related to distributed air traffic management, a widely accepted development Air Transportation System Analyses concept. He identified not only existing research, but also research needs that were not “The changes that are coming are too big, being met. In summary, he found that at the too fundamental for incremental adaptation… time, there were no tools capable of assessing We need to modernize and transform our both new and traditional ATS operations global transportation system, starting right simultaneously, or their interactions. now.” (U.S. Transportation Secretary Norm Carley (1997) claims “social, Minetta, 2004). Ultimately in the case of air organizational and policy analysts have long transportation, national governments are recognized that groups, organizations, largely responsible for both setting policy, and institutions, and the societies in which they are implementing infrastructure implied therein. imbedded are complex systems.” When it To do so necessitates consideration of both the comes down to it, policy analysis is about effectiveness and repercussions of actions complex system design in light of uncertainty. within the air transportation system (ATS). Certainly, complexity and uncertainly will As Wieland et al (2002) point out, abound in ATS transformation. Influencing modeling the ATS, “with all its interrelated ATS performance within itself is complicated components – mechanics, human decision enough, but ATS policy reaches outside this making, and information flow, is a large effort arbitrary system boundary. Sheate (1995) involving multidisciplinary and ‘out-of-the- complains that standard ATS policy decisions box’ thinking. …The challenge is not only to have lead to a business market that decides represent physical NAS [National Airspace “where capacity is needed and therefore fails System] dynamics, but also to incorporate the both to maximize the use of existing airport behavioral and relational components of NAS resources and to recognize the importance of decision making that are an important part of environmental capacity constraints.” He the system. …A comprehensive model is argues for policy analyses that consider the incomplete and subject to first order errors interplay of system capacity, demand, and unless all such interactions are incorporated to aircraft capability. some degree.” Unfortunately, policy analysts in the ATS Wieland et al stress the necessity for ATS arena have continued to use methods more modeling at three different time horizons for suited to regularly-behaved systems to various purposes: tactical (predictive), develop strategy. Apparently, this is a strategic planning (investment and policy), pervasive problem throughout the policy and a posteriori analysis (also investment and community. Bankes laments that there are policy). Their claim is that a useful simulation “few good examples of the classical policy of the ATS intended for setting policy must analysis tools being successfully used for a model the economic, informational and 3 complete policy analysis of a problem where that describe them. Regardless of the complexity and adaptation are central.” He approach used, Andrews (2000) implores continues to say that policy analysis in the modelers to not loose touch with purpose of face of “deep uncertainty” must focus on their effort, and to build models that robustness rather than single-point “appropriately and credibly” simplify reality optimization. This reinforces the notion of within specific context of the system at hand. developing many different plausible Within the literature, there is consensus environmental scenarios, and recommending that: 1) Systems modeling is a useful a way of policy that is viable across their range. solving real world problems, particularly Addressing this same concern, Iyer (2000) when prototyping or experimenting with the offered that the “basic contribution of real system is expensive or impossible. complexity theory [to planning] is its focus on 2) Different types of applications call for systemic interactions at various scales…” that different modeling techniques (figure 1). can address uncertainty. Moss expresses the view that “Policy analysis has to start with observation and the specification of a problem to be solved.” From there, appropriate analysis tools can be defined. Moss, Iyer, and others suggest that deterministic and even stochastic approaches to complex policy development are incompatible, though agent-based modeling (ABM) may be workable. Though the ATS research community has Figure 1. Methods and application within attempted to model particular attributes of the problem context (adapted from Daniel) system, there hasn’t yet been a method Many different modeling approaches have capable of answering questions regarding the been offered in the literature. Generally systemic response to substantive changes in speaking, these different approaches are operations. To date, agent-based, elemental intended to address specific classes of simulations have proven too expensive and systems. A model’s capabilities have to unwieldy to complete. Parametric simulations match the system’s overall attributes. Many have failed to provide the flexibility to be used authors summarize these attributes in system as design tools. classifications, which, of course, vary within The dearth of appropriate analytical tools the body of work. Authors find their own is not due to a lack of demand, or trying. It dimensions on which to split the space of has simply proven to be very difficult. Calls possible systems. In the realm of complex for systemic simulation for operational design systems, which nearly all classification of the ATS continue to accrue, from the schemes include, authors have deemed most responsible government officials, to the modeling approaches unsatisfactory, leaving researchers in the trenches. precious few potential choices for those interested in complex system modeling. A (Discretized) Continuum of Daniel (1990) described the possible space Potential Modeling Approaches of systems in two dimensions, along the attributes of complexity and the number of Just as systems themselves differ in objectives a system operates to control. He objectives and complexity, so do the models suggests that classic Operations Research 4 (OR) modeling techniques are best applied as to say that soft approaches as well as when the system can be described in great traditional, “harder” ones will never support detail and a single optimization function is the effective policy analysis. How then to address primary focus of study. He reports the static complex systems in both a rigorous but nature of OR models is an inherent limitation. sufficiently realistic and tractable way? Moss Not only does OR require detailed system provides a suggestion, saying; “adaptive agent knowledge, the system is implicitly expected modeling [e.g. ABM] is an effective to remain unchanged. substitute” for other analyses in the complex He offered cybernetics as an approach that system realm. acknowledges the importance of both system Borshev and Filippov (2004) interpret the structure and the interaction of components potential systems modeling space differently that can cause dynamic behaviors. However, than Daniel. Borshev and Filippov’s he implies its limitations lie in the singularity orthogonal dimension to complexity is of its optimization goals. Others have discreteness, that is, the level of abstraction or enumerated additional challenges with aggregation in model elements. Interestingly, cybernetic deployment. they also make a distinction between system Daniel continues that of the many systems types that necessitate simulation vs. those modeling techniques described in the better served by analytical models. They literature, soft systems methods (SSM) are prefer analytical solutions when a closed form particularly well suited to complex systems: solution is obtainable. Thus, they imply one Complex systems are represented in the upper ought at least to consider such a model first, right of his systems space. They are context- because simulation, they argue, is not trivial. rich, non-linear problems that cannot be However, they continue by saying that “for expressed by a single set of objectives or complex problems where time dynamics is goals. SSM involve the development of a rich important, simulation modeling is a better picture of the problem, putting great emphasis answer,” narrowing the field of potential on framing the problem correctly within modeling techniques. context. However, these methods have been Akin to Andrews, Borshev and Filippov criticized for being unverifiable, non- suggest matching modeling techniques to the quantifiable, and lacking in rigor (Lane 1998). “nature of the problem,” and that any one Additionally, if effecting systemic technique will almost surely not be most improvement is a modeler’s goal, the ability appropriate for all systems. Rather they call of the output/outcome to be influential has to for modeling techniques that “would allow for be considered. For a safety-critical system integration and efficient cooperation between with minimum performance criteria, mental different modeling paradigms.” They discount constructs (and the flexibility they provide as other complex system simulation options, but “controlling” qualities as in SSM) have not conclude that there is a place for both system proven influential in many circles. Many dynamics (SD) and ABM. They found ABM systems, air transport included, demand well suited to systems where most knowledge rigorous evaluation before change is even is at the local level (e.g. agent-level) and little considered. or nothing is known about global Sterman (2002) warns that SSM often interdependencies. They also concluded that leads to “wildly erroneous inferences about SD could be more efficient, particularly if system behavior”, dramatic underestimation of agents are uniform and/or have little true the dynamics of systems, and incorrect “active” or autonomous behavior, and discuss conclusions. In fact, Moss (2002) goes so far the use of both techniques in combination. 5 While full-scale agent models can be as and feedfoward loops causing attenuation and complex and costly to develop as a large-scale amplification of system attributes respectively. parametric model, there may be a means of Often the metaphor of stocks and flows is validating models and educing a number of used to illustrate the approach. SD models are higher-order effects without constructing and time-dependent linked mathematical models running full-scale agent-based simulations. exploiting differential calculus. From the description above, it is clear that Borshev and Filippov note that SD is interaction among agents could be described similar in nature to dynamic systems, or by network structure: there are well-defined simply “dynamics,” taught in technical nodes (agents) and links (interfaces, engineering disciplines, but uses language and interaction protocols). Network analysis notation more familiar to systems analysts. (NA), developed in the field of network As with dynamics, rigorous treatment, theory, could be applied to a network defined unavailable with cybernetic models by the agents’ communications demands. equivalents, is possible. They comment that These may provide a relatively simple and dynamics are taught to mechanical, aero and reliable means of evaluating the aggregate electrical engineers “as a standard part of the performance of a complex system, similar to design process.” These members of the SD, with less effort than an ABM (Figure 2). academic community acknowledge the necessity for systemic dynamic analysis for ( re )F ra m in g design, at least for physical systems. th e p ro ble m Unfortunately, SD requires extensive system knowledge a priori, including all system elements and potential Co n ce pt M o de ls: A n alysis o f R ecom m e n d communications between them. This makes d escribin g m o de l Ne two rk e le m e n ts o r “a ge n ts” a n d D e scrib e d by A g en t d efinitio n s S ystem tran sfo rm s b a se d on fe asib le building a comprehensive model of a complex m od ification s in te ractio ns system an enormous effort. Systems that change frequently or have a high degree of E xe cu tio n a n d uncertainty may not be amenable to SD at all. An a lysis of A gen t-B a se d Despite this major drawback, many Sim ula tio n authors have used SD to model complex systems. In its favor, analysis and control Figure 2. ABM and NA Relationship techniques for the resultant mathematical By using dynamic or adaptive modelling models are well established and have proven methods when dealing with complex systems, to be highly serviceable. the possible modeling space is reduced Agent-Based Modeling (ABM). Agent- dramatically. The three methods identified in based modeling (ABM) techniques have been the literature as applicable for capturing proposed as an alternative to traditional dynamic behavior are worthy of further parametric models because they can exhibit consideration: higher-order behaviors based on a relatively System Dynamics (SD). Forrester, the simple rule set. ABM uses agents to execute father of SD, describes system dynamics as model functions. They are the active the discipline of interpreting real life systems components of an agent-based simulation. as simulation models. These models highlight Agents are ‘autonomous’ in that they have the structure and decision-making processes interfaces to the general simulation, but carry within a system that give rise to its behavior. within them their own ability to perform their As the name implies, SD is the study of the assigned tasks without a centralized controller. interactions of system elements via feedback 6 Agents are interactive entities that capture While the latter two arguments are similar salient but generally localized behavior of to those of Jennings, Bankes claims system elements. Using simple rules to dissatisfaction with the restrictions imposed determine each agent’s actions, higher-order by alternative modeling formalisms is driving systemic behaviors can emerge. Jennings modelers to agent-based solutions. In his (2000) offers further detail, saying agents: opinion, the most widely used alternatives, 1) have defined boundaries & interfaces. systems of differential equations and statistical 2) are situated in a particular environment. modeling, are viewed as imposing restrictive 3) strive for specific objectives. or unrealistic assumptions that limit many 4) are both reactive and proactive, and applications. He says “The list of assumptions 5) are autonomous (distinct from objects). that have been objected to is lengthy, but it Jennings would most likely agree that includes linearity, homogeneity, normality, ABM is not well suited to all systems. and stationarity.” However, he outlines his argument in favor of What Bankes fails to mention is that these ABM of complex systems, saying complex shortcomings are not necessarily avoided just system development requirements and ABM by deploying ABM approaches, and certainly are highly compatible. He argues that ABM is not by agent implementations of standard particularly well suited to complex systems methods. A model still has to be appropriately because it: defined to describe significant features for the 1) partitions a complex problem space. system served. Additionally, addressing 2) naturally abstracts complex systems, and issues such as homogeneity requires not only 3) captures dependencies and interactions. more effort in model specificity, but also more However, he also admits that these same information related to distributions of properties can lead to issues of variables or behaviors. These data may not be unpredictability and apparent chaotic available. A homogeneous population model behavior. Unpredictability is a problem in the might be of sufficient fidelity for describing simulation world because it makes internal some systems, while an assumed (but validation very difficult when exact results erroneous) normal distribution, for example, cannot be repeated. The lack of deterministic might yield misleading results. A more behavior is also a problem for validation. complex or detailed model (e.g. at the agent Jennings and others claim that these rather than the aggregate level) is not difficulties can be circumvented by formally necessarily more accurate. analyzed interaction protocols, limiting the Arthur (1994) suggests agents are a natural nature of agent interaction, and adopting rigid way to deal with ill-defined or complicated organizational structure among the agents. “reasoning” within a system, oft induced by Much hope is laid at the feet of ABM, inclusion of humans. He argues, “beyond a particularly in the social science realm where certain level of complexity, human logical complexity and uncertainty are paramount. capacity ceases to cope – human rationality is From recent literature, Bankes (2002) bounded.” Agents can be designed to mimic summarizes three reasons why ABM is the inductive behavior of people when placed potentially important: 1) the unsuitability of in unfamiliar or complicated environments. competing modeling formalisms to address the However, the example he provides, a problem problems of social science, (2) the ability to of deciding whether or not to frequent a bar use agents as a natural ontology for many based on the expected crowd, exemplifies a social problems, and (3) the ability to capture prime concern with assuming agent emergent behavior. “intelligence” (which has to be present to 7 differentiate the agent from a mere object in All networks can be analyzed by some Jennings terms). In his example, the agents basic, quantifiable measures including their select from a pre-determined set of schemata degree distribution and their average based on some outcome metric (actual number clustering coefficient (Wuchty et al, 2003). of bar patrons). Can this be considered true Stemming from these basic metrics, networks inductive behavior? The “induction” was often exhibit higher-order dynamic functions, accomplished [by the modeler] in the thought to be associated with their unique generation of the options, not by the agent in structures. These include robustness, fragility, their selection later on. percolation and searchability. If appropriate strategies were not included The ability of NA to differentiate in the agent’s definition, Arthur’s agents operationally unique airline route strategies would have never succeeded. Recognizing and their resultant distinctive structures is yet this, he does acknowledge that people’s to be shown. Due to the relatively small inductive ability [emulated by agents using number of nodes in air traffic networks, nodal lists, genetic algorithms, etc.] is a “deep separation distance and searchability tend to question in psychology” and thus can only be be straightforward to determine and not too marginally imitated. Generally speaking, instructive. However, because of the agent “intelligence” at best will be limited by criticality of the application, resilience to the degrees of freedom their internal models cascading failure, percolation, and congestion are allowed to explore, and may be further robustness are of utmost interest in the ATS. limited by the methods of exploration. It is not clear if NA will be able to reveal these Bonabeau (2002) claims that ABM is “by qualities sufficiently. Braha and Bar-Yam its very nature the canonical approach to (2004) suggest that the approach is worthy of modeling emergent phenomena” of complex pursuit, as functional classes of networks systems, necessary for analysis of non-linear might be expected to have differences in their behaviors, localized phenomena, and topologies, such as directedness. These in heterogeneous populations. However, like turn could be expected to lead to particular Jennings, he acknowledges difficulties in dynamic potentials. building agent models of large systems Latora and Marchiori (2001) call for the because of the myriad low-level details and measurement of average path length, the “extremely computation intensive and clustering coefficient, average degree, and therefore time consuming” model that results. degree distribution as do Strogatz, Watts, and Network Models. Network theory is an others, but also suggest the use of efficiency extension of graph theory. By definition, and cost. They define efficiency at both the nodes that constitute a network are local and global level as “the measure of how interconnected in some way or another by efficiently it [the network] exchanges links. The resultant network can be information.” They suggest that efficiency is categorized by its structure. In turn, this really a more general measure for path length structure imparts peculiar characteristics to and clustering, useful because other measures both the system as a whole and to the can only be defined for certain network sub- individual nodes. Following specific classes. Efficiency can be applied to any connectivity rules, some networks have some network, but it can be difficult to calculate. nodes that are highly connected while others Latora and Marchiori argue that the have only a few connections. In other Watts/Strogatz measures are only effective in networks, links are randomly formed but still quantifying a network in the “topological obey statistically generalizable patterns. abstraction, where the only information 8 retained is about the existence or absence of a paradigm. For example, a good modeler link.” Following the above arguments that would be hard pressed to generate any “hard” quality/cost of the links are paramount to model (e.g. ABM or SD) without some effort describing operational functionality, it appears to capture significant system context or a clear unlikely then that topological metrics alone understanding of the problem at hand. Using will be useful abstractions for describing air SSM adds formalism to this step that in turn transport networks. Using the Boston Subway may improve the product. as an example, they suggest that substituting What Mingers and others offer is balance efficiency measurements resolves difficulties to the process. Their claim, based on a in general application of network topologic number of examples, is that modelers will analyses to weighted and directed systems. tend to focus on the data at hand, and not on Once measured, Latora and Marchiori modeling the primary driving functions of a show that efficiency metrics can be used as system. Models tend to be concentrated on indicators of potential cascading failure, and directly measurable quantities, and ignore or can be used as a “measure of performance” of de-emphasize less well-behaved system the network. They showed marked components (such as people). Freeing the differences in the non-linear behavior (onset modeler to use all available and suitable of cascading failure) of two different, well- techniques rather than a single model for the documented network topologies. entire system should produce a better, more Multimethod Approaches. The use of tractable product with less effort. more than one method in a single modeling Regardless of the declaration of multi- effort may be the most promising approach to methodology or not, the concept is well complex system analysis. Multimethodology established in practice. may enable modeling of inhomogeneous elements of a complex system, each element Summary matched to an appropriate modeling method. This strategy may be important where a lack The majority of ATS researchers have of complete system knowledge inhibits the use joined Wieland et al. in suggesting that of a single model type. It might also be useful specific classes of tools represented by ABM when the scope of the system represented in a and/or network analysis are perhaps the only single scale would cause the model to become modeling solutions currently available that too cumbersome. Multiple methods could be offer systemic utility. Holmes and Scott applied in successive phases of an effort, or in (2004) say, “Proposed ideas for changing the parallel, representing different levels of NAS should not be contemplated lightly, due fidelity for various subsystem models. to the sheer size and complexity of the system. Mingers (2000) proclaims, "Multimethod Instead it will require a fundamental is not the name of a single method, or a reconsideration of how such complex systems specific way of combining methods. Rather it are analyzed and designed if the system to refers in general to utilizing a plurality of evolve remains productive and viable. methods or techniques, both quantitative and Traditional methods for analyzing changes to qualitative, within a real-world intervention." complex systems fail when applied to highly Declaring multimethodology as a distinct dynamic and interconnected system such as analytical approach or even as “new” may be the Internet or the NAS.” They outline a case a disservice to best practices within systems for using agents operating on networks as a science. Perhaps multimethodology is more a viable analytical alternative. matter of emphasis than a totally new The literature suggests that both NA and ABM are well suited to study emergent, 9 complex behavior within the context of air Tpolgies”, 4th Intg CNS Conf, Fairfax, VA transportation. From the outset, differences Iyer (2000)“Can Complexity Theory Enter the in both scope of effort to establish these two World of Planning” Critl Planning, v7 p25 models and expectations for their results Jennings, (2000) “On Agent-based software should be acknowledged. NA is focused at engineering” Artfcl Intlgnce 117, 277-296 systemic-level solutions, much like system Kast & Rosenweig (1972) “General systems dynamics, while ABM revolves around the theory” Acad Mgmt Jrnl, v.4 p. 47 “unit” of the system. Is the additional Krozel, (2000) “Free Flight Research Issues information (at the agent level) necessary or and Lit. Search”, NASA, NAS2-980005 even useful for a transport system study? Is Lane and Oliva, “The greater whole: towards the system so sensitive to assumptions of a synthesis of SD and SSM” Euro Jrnl of individual behaviors that ABM predictions are Opra’l Rsrch, v. 107 n.1 p.214-235, 1998. no better, or in fact worse, than more Latora & Marchiori(2001) “Efficient Behavior generalized network analyses? On the other of Small-World Networks”, Physl Rvw hand, are NA so aggregated that system Lters, Amer. Physical Society, v.87 n.19 dynamics are poorly described? Mingers (2000), “Variety is the spice of life: Either approach, or perhaps both in Combining soft and hard OR/MS” Intern’l combination as Mingers might suggest, may Transactions in OR 7, 6, pp. 673-691 provide clues for uncovering problems, Moss (2002) “Policy analysis from first provide confidence about systemic principles”, PNAS v.99 sup.3, p.7267 performance, and contribute to developing Sheate, “Transport policy: a critical role for mitigation strategies for systemic ATS issues. strategic environmental assessment” World Trnsprt Polcy and Prctice, v1 n4, p.17, 1995 References Sterman, “All models are wrong” Systems Dynamics Review, v. 18 n. 4 2002 Andrews, “Restoring Legitimacy to the U.S. Transportation Secretary Minetta, speech Systems Approach” IEEE Tech & Society to Aero Club of Wash DC, Jan 27, 2004 Mag, v. 19 n.4, p.38-44, winter 2000/2001. Wieland, Wanke, Niedringhaus and Wojcik, Arthur (1994), “Complexity in Economic “Modeling the NAS: A grand challenge Theory” Amer Econ Revw, v.84, n.4, p.406 for the simulation community” Soc. for Bankes(2002) “Agent based modeling: Computer Sim, San Antonio, TX, 1/2002 Revolution?” PNAS, v99 n3 p7199-7200 Wilson, E. (1998) Consilience: The unity of Bonabeau (2002) “ABM: Mthds & technqs for knowledge. Knopf, New York, NY sim human sys”, PNAS 99/3 p 7280-7287 Wuchty, Ravasz and Barabasi (2003) "The Borshchev & Filippov (2004) “From Systems Architecture of Biological Networks", Dynamics and Discrete Event to Practical Complex Systems Science in Biomedicine, ABM”, Sys Dymcs Soc Conf, Oxford, UK Kluwer Academic, New York Braha and Bar-Yam (2004), “The Topology of Large-Scale Engineering Problem-Solving Biography Networks”, Physical ReviewE, v.69 Carley (1997) “Extracting team mental models Sheila Conway is a researcher at NASA thru textual analysis” Jrl Org Bhvr, 18, p533 Langley Research Center in Hampton, Forrester, J. “Fourteen ‘Obvious Truths’” Sys Virginia and a Doctoral Candidate at Old Dynamics Revw, v.3 n.2 p.156-159. 1987 Dominion University. Her experience Heylighen (1993): "Epistemology", Principia includes engineering design and systems Cybernetica, URL: //pespmc1.vub.ac.be development. She is a commercial pilot and Holmes and Scott (2004), “Trnsprtn Ntwk an instrument flight instructor. 10