The world is currently, and always has been, awash in complex problems of education, sustainable energy, health-care delivery, urban resilience, and national security [1]. The complexity of these public-private enterprises is daunting, at least in part because these enterprises are laced with behavioral and social

phenomena. The systems community needs to help society address these problems, partly because no one else is willing to stand up to help under-stand and manage the phenomena associated with these issues [2].

Enterprise ArchitectureThese complex public-private enterprises operate at multiple levels as shown in Figure 1. Human phenomena in terms of decisions, behaviors, and performance happen in the context of physical infrastructure, capabilities,

When the System Is an Enterprise

At the Cutting Edge

Digital Object Identifier 10.1109/MSMC.2016.2520064 Date of publication: 28 April 2016

by William B. Rouse

2333-942X/16©2016IEEE16 IEEE SyStEmS, man, & CybErnEtICS magazInE October 2015

©iStockphoto/peShkova

October 2015 IEEE SyStEmS, man, & CybErnEtICS magazInE 17

and information. Physical phenomena (expressed by physics), processes, and flows provide the infrastructure, capabilities, and information while also being influenced by human decisions, behaviors, and performance. Eco-nomic phenomena, both macroeconomic and microeco-nomic, underlie economic investments in competitive physical capacities. This level also pays attention to phys-ical, economic, and social consequences. The economic level is responsible for investing in and managing every-thing, whereas the physical level operates and maintains the processes that actually deliver capabilities. Social phenomena include the values, norms, politics, and eco-nomic incentives devised and enforced by cities, firms, and a wide range of organizations. These entities are con-cerned with economic and social returns as well as com-petitive advantages. Their intent is to foster the context within which economic entities will invest to create the physical capabilities that result in desirable human deci-sions, behaviors, and performance.

Policy Flight SimulatorsWhether the context is education, sustainable energy, health-care delivery, urban resilience, or national securi-ty, there are always many stakeholders in how issues are addressed, framed, and resolved. Most of these stake-holders will not be technically educated; therefore, data, statistics, and models will seem very esoteric to them. Yet the resolution of problems requires their enthusiastic support. This issue led us to develop large, interactive visualizations of our models. As we have worked with groups of senior decision makers and thought leaders using these interactive visualizations, they have often asked, “What do you call this thing?” I suggested “multi-level simulations,” but I could tell from the polite respons-es that this did not really work. At some point, I responded “policy flight simulator” and immediately knew this was the right answer. Numerous people said, “Ok, now I get it.” This led to a tagline that was also well

received. The purpose of a policy flight simulator is to enable decision makers to “fly the future before they write the check.”

Policy flight simulators are designed for the purpose of exploring alternative management policies at levels ranging from individual organizations to national strate-gy [3]. To develop policy flight simulators, we need to computationally model the functioning of the complex system of interest to enable decision makers, as well as other significant stakeholders, to explore the possibili-ties and implications of transforming these enterprise systems in fundamental ways. This pursuit always starts with reference to the multilevel architecture pic-tured in Figure 1. The overall goal is to create organiza-tional simulations that will serve as policy f light simulators for interactive exploration by teams of, often disparate, stakeholders who have inherent conflicts but need and desire to agree upon a way forward [4].

People’s Use of SimulatorsThere are eight tasks associated with creating and using policy flight simulators:

◆ agreeing on objectives—the questions—for which the simulator will be constructed

◆ formulating the multilevel model—the engine for the simulator—including alternative representations and approaches to parameterization

◆ designing a human–computer interface that includes rich visualizations and associated controls for specify-ing scenarios

◆ iteratively developing, testing, and debugging, includ-ing identifying faulty thinking in formulating the model

◆ interactively exploring the impacts of ranges of param-eters and consequences of various scenarios

◆ agreeing on rules for eliminating solutions that do not make sense for one or more stakeholders

◆ defining the parameter surfaces of interest and “pro-duction” runs to map these surfaces

Policy flight simula-tors are designed for the purpose of exploring alternative management policies at levels ranging from individual organizations to national strategy.

18 IEEE SyStEmS, man, & CybErnEtICS magazInE October 2015

◆ agreeing on feasible solutions and the relative merits and benefits of each one.

The discussions associated with performing the above tasks tend to be quite rich. Ini-tial interactions focus on agreeing on objectives, which includes output measures of interest, including units of measure. This often unearths differing perspectives among stakeholders. Attention then moves to discussions of the phenomena affecting the measures of interest, including relationships among phenomena. Component models are needed for these phenomena, and agreeing on suitable vetted, and hopefully off-the-shelf, models occurs at this time. Also of great importance are uncer-tainties associated with these phenomena, including both structural and parametric uncertainties.

As computational versions of models are developed and demonstrated, discussions center on the extent to which model responses align with expectations. The overall goal is to computationally redesign the enter-prise. However, the initial goal is to replicate the existing organization to see if the model predicts the results actu-ally being currently achieved. Once attention shifts to

redesign, discussion inevitably moves to the question of how to validate the model’s predictions. As these predictions inherently

concern organizational systems that do not yet exist, val-idation is limited to discussing believability of the insights emerging from debates about the nature and causes of model outputs. In some cases, deficiencies of the models will be uncovered, but occasionally unex-pected higher-order and unintended consequences make complete sense and become issues of serious discussion.

Model-based policy flight simulators are often used to explore a wide range of ideas. It is quite common for one or more stakeholders to have bright ideas that have sub-stantially negative consequences. People typically arrange many alternative organizational designs, inter-actively explore their consequences, and develop criteria for the merit of an idea. A common criterion is the assur-ance that no major stakeholder can lose in a substantial way. For our health-care delivery simulators, this rule was able to pare the feasible set from hundreds of thou-sands of configurations to a few hundred [5].

Policy flight simulators serve as boundary-spanning mechanisms across domains, disciplines, and beyond initial problem formulations, which are all too often more tightly bounded than warranted. Such boundary spanning can externalize arguments among stakeholders. The alternative per-spectives are represented by underlying assumptions and the elements that com-pose the graphically depicted model pro-jected on the large screen. The debate then focuses on the screen rather than being an argument between two or more people across a table.

Research IssuesPolicy flight simulators meet a need for interdisciplinary teams to be supported when they explore “what if” questions, look for insights, and attempt to reach con-sensus on how to move forward. We would like policy flight simulators to provide valid representations of the phenomena of interest. This issue has been explored in depth in [6] and [7]. One concern is how

Social Phenomena(Cities, Firms, Organizations)

Economic Phenomena(Macro- and Microeconomics)

Human Phenomena(Individuals, Teams, Groups)

Physical Phenomena(Physics, Processes, Flows)

Values, Norms, Politics,and Economic Incentives

Economic Investments inCompetitive Physical

Capacities

Physical Infrastructure,Capabilities, and Information

Economic and Social Returnsand Competitive Advantages

Physical, Economic, andSocial Consequences

Decisions, Behaviors,and Performance

Figure 1. multilevel architecture of public–private enterprises.

The purpose of a policy flight simulator is to enable decision makers to “fly the future before they write the check.”

October 2015 IEEE SyStEmS, man, & CybErnEtICS magazInE 19

best to decompose phenomena of inter-est, represent these phenomena, and recompose these representations into overall models of complex systems and enterprises. At the most basic level is the concern about consistency of assumptions. Are inputs, outputs, process, and parameters based on shared defini-tions of variables, units of measure, and coordinate sys-tems? These must be handled correctly for anything to make sense.

Much more difficult is the problem of entangled states. When simultaneously solving multiple sets of equations, is the solution an equilibrium solution or is it premised on indefensible assumptions, which could lead to unstable responses? For example, traffic flow is typically modeled using partial differential equa-tions. In contrast, driver decision making is usually modeled with agent-based models. Thus, we quickly encounter situations where traffic flow affects driver decisions that, in turn, affect traffic flow. Our assump-tions about this causality has an enormous impact on, for example, congestion pricing rates that are intend-ed to discourage drivers from venturing onto the roads at peak demand times. Drivers may interpret high prices as signals that demand is high and conse-quently seek the higher-priced rather than the lower-priced solution.

We conducted surveys in four industries regarding how they address these model composition issues. The indus-tries included automotive, building equipment, commercial aerospace, and semiconductors. Everyone agreed about the problems of model composition. They were particular-ly concerned with avoiding model-induced design errors. Their concern was that the models used to answer the last set of questions would provide misleading or outright wrong answers to the next set of questions. We need to understand these modeling issues at a much deeper level. The question is not simply one of how to get disparate models to compute together. Instead, we need a more dis-ciplined level of understanding of how different represen-tations do and do not fit together, as well as the consequences of forcing the fit so that computation is fea-sible but meaning is lost.

ProspectsIn 2014, I was a panelist at the IEEE International Conference on Systems, Man, and Cybernetics Norbert

Wiener Symposium in San Diego, California. In my talk, I  expanded on Norbert Wie-ner’s classic book Cybernetics: On Control and Com m­

unication in the Animal and the Machine. I broadened the perspective to “Control and Communication in Ani-mals, Machines, Organizations, and Societies,” and I empathized that this broader perspective leverages many ideas that were articulated long ago by Wiener, Shannon, Weaver, and many others. The difference is that we now have enormous computational capabilities, large-scale interactive visualizations, and massive data sets including numeric data, text, and images. We now can pursue research questions that past luminaries—the shoulders on which we stand—could not have imag-ined as tractable. We need to leverage this legacy to make a difference.

About the AuthorWilliam B. Rouse (wrouse@stevens.edu) is the Alexan-der Crombie Humphreys Chair in the School of Systems and Enterprises at Stevens Institute of Technology and the director of the Center for Complex Systems and Enterpris-es. He is also a Professor Emeritus and a former chair of the School of Industrial and Systems Engineering at the Georgia Institute of Technology.

References[1] E. Pate-Cornell, W. B. Rouse, and C. M. Vest, Perspectives on Complex Global

Challenges: Education, Energy, Healthcare, Security, and Resilience.

Hoboken, NJ: Wiley, 2016.

[2] W. B. Rouse, Modeling and Visualization of Complex Systems and

Enterprises: Explorations of Physical, Human, Economic, and Social

Phenomena. Hoboken, NJ: Wiley, 2015.

[3] W. B. Rouse, “Human interaction with policy flight simulators,” Appl. Ergonom.,

vol. 45, no. 1, pp. 72–77, Jan. 2014.

[4] W. B. Rouse and K. R. Boff, Organizational Simulation: From Modeling

and Simulation to Games and Entertainment. Hoboken, NJ: Wiley, 2005.

[5] W. B. Rouse and N. Serban, Understanding and Managing the Complexity

of Healthcare. Cambridge, MA: MIT Press, 2014.

[6] M. J. Pennock and W. B. Rouse, “The challenges of modeling enterprise systems,”

Proc. Int. Symp. Eng. Syst. (CESUN), Hoboken, NJ, 2014.

[7] M. J. Pennock, W. B. Rouse, “Why connecting theories together may not work: How

to address complex paradigm-spanning questions,” in Proc. IEEE Int. Conf. Syst.,

Man, and Cybern., San Diego, CA, 2014.

We now can pursue research questions that past luminaries— the shoulders on which we stand—could not have imagined as tractable.