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• #### Appendix D: Biographical Sketches of Committee Members and Staff 397-404

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4 Macro-Level Formal Models T his chapter presents modeling approaches for representing the behavior of humans in groups and organizations. It discusses system dynamics models first, followed by a discussion of several approaches to organizational modeling. SySTEM DyNAMICS MODELS What Is System Dynamics Modeling? System dynamics modeling is a method of modeling the dynamic behavior of complex systems by breaking down these systems into sim- pler interconnected components (“blocks”) connected together via links or “wires” that connect one block’s outputs to another block’s inputs. This breaking down or recursive modeling continues until simple blocks can be defined in terms of well-understood interactions between the block’s inputs, outputs, and its “internal state.” Within any given block, this state is defined by the associated state variables, which are usually related by a set of differential equations that underlie the dynamics of that block.1 To provide a quick illustration of the basic concepts involved, if one were to model the dynamics of two cars traveling down a straight road, one behind the other, one might specify four blocks: one for each car and one for each driver. Each car would have (a) two states: a speed and a 1 The use of differential equations reflects the history of system dynamics modeling and its roots in electrical and mechanical engineering and control systems theory. 

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 MACRO-LEVEL FORMAL MODELS position/location down the road; (b) a single input (or control) of accelera- tion, determined by the driver’s application of the gas or brake pedal; and (c) a single output, the position/location down the road.2 Simple differential equations, based on the laws of physics (and the vehicle acceleration/braking dynamics) would then be used to define the relation of the input (control) of the driver’s use of the gas pedal or brake to the car’s output, the position down the road. The second car would be modeled similarly. The trailing car driver would be likewise modeled as a block, with perhaps two inputs, distance and closing speed to the front car, and a single output, gas/brake pedal usage. The differential equations or “control law” relating driver inputs to driver outputs would be specified by well-understood manual control dynamics (see, for example, McRuer and Krendel, 1974). The lead driver could be modeled in “open-loop” fashion, as a block with no input but with a randomly varying output of gas pedal pressure, leading to ran- dom speed behavior. By specifying each individual block’s behavior (via the inputs, the outputs, and the differential equations underlying the internal dynamics) and by linking up the appropriate inputs to the appropriate outputs of the four-block system, one then has a general system dynamics representation of the dynamics of the two-car, two-driver “system.” The fundamental power of this approach lies in four areas: 1. System dynamics concepts are tightly bound to the twin notions of (1) the dynamic behavior of systems over time and (2) feedback and cross-connectivity between different elements of the system. Dynamic behavior can evolve simply because of a system’s internal dynamics and its initial conditions (e.g., a frictionless swing set to infinite harmonic oscillation by an initial offset from the vertical). But the dynamic behavior is considerably more interesting when it is driven by the dynamics of yet some other system (e.g., some- one pumping the swing ever higher and eliciting nonlinear swing behaviors), through a cross-coupling or feedback loop involving real physics or abstract information. And when these loops are contaminated by noise (an erratic “pumper”), time delays (a slow- to-respond pumper), and/or distortion in the form of frequency- or amplitude-selective feedback channels, then the opportunity exists for often unanticipated and sometime surprising behaviors across the system as a whole. These are often the characteristics of com- 2 Two states suffice for a simple kinematic representation of the longitudinal (fore-aft) control of vehicle location; additional states would be added for finer grained representation of the situation if one were interested in modeling the effect of the detailed dynamics of the brake calipers, for example. The approach would be the same, however, via the introduction of yet another block placed between the driver’s brake pedal and the block representing the vehicle kinematics.

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5 MACRO-LEVEL FORMAL MODELS to make the theoretical analysis of such systems intractable, so that system dynamics analysts must then rely on simulation execution and analysis in order to understand or predict system behavior. A specialized version of system dynamics modeling, and the main focus of this section, focuses on a fairly explicit representation of the system states, called “stocks” (entities that accumulate or deplete over time) and their associated “flows” (the rates of change of stocks) (Forrester, 1968). In essence, Forrester6 transformed the generic nth order differential equations characterizing general system dynamics theory into n first-order differential equations that are intuitively simple to understand and, via the associated programming language Dynamo, into a transparent graphic representa- tion of the key interrelationships among variables (Richardson and Pugh, 1981). Using Dynamo to implement these first-order relations, it becomes a relatively simple exercise in computational model development by the nonspecialist who may not have been schooled in differential equations and their specification or solution. Feedback and interconnections are intro- duced by defining how the level of one stock controls the flow of another. Nonlinearity is introduced via simple limits on stock levels and flow rates. A simple example is given in Box 4-1, which illustrates how two states (birth rate and death rate) define the flow of a third state (net growth rate). This is a simple open-loop example with no feedback, but it is not a diffi- cult exercise to close the loop, for example, by postulating how population growth rate might influence economic growth rate, which could induce consumer confidence and, through that, cause birth rates to increase. An example showing this level of loop closure is given in Figure 4-1, which illustrates one component of a larger system dynamics model of the spread of an epidemic (Sage and Armstrong, 2000). The three state vari- ables (stocks) are X1, the population susceptible to infection (susceptible population), X2, the population that is actually infected (infected popula- tion), and X3, the population that has developed an immunity to the infec- tion (immune population). Note that boxes are used to represent these states graphically. The associated flows are LR (loss of immunity rate), IR (infection rate), and RR (recovery rate). Note that the valve symbols are used to indicate how the flows control the stock levels, via the following intuitive graphic analogy: flow into a block increases the stock level, while 6 Although Jay Forrester’s name is the one most closely associated with the system dynamics concept, his work owes much to the electrical engineering pioneers at Bell Laboratories work- ing with feedback circuits and notions of system stability in the 1920s and 1930s (see, e.g., Black, 1977); the discipline of cybernetics developed at the Massachusetts Institute of Tech- nology by Norbert Weiner and colleagues during the 1940s and 1950s (Weiner, 1948); and, more recently, practitioners who have done much to popularize its application to important problems in the social sciences, most notably Richardson and colleagues (see, e.g., Richardson and Pugh, 1981; Richardson, 1991).

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 BEHAVIORAL MODELING AND SIMULATION BOX 4-1 The Equation, Variables, and Mathematical Representations for Birth and Death Used in Population Modeling Description of variables: b(t) : Average birth rate per unit person in the population at time t D(t) : Average death rate per unit person in the population at time t mn(t) : Expected value Mathematical representation of birth rate, death rate, and average rate of popula- tion growth: b(t)mn(t) : Total average birth rate D(t)mn(t) : Total average death rate dµn (t ) = [ β (t ) − ∆ (t )] µn (t ) : Average rate of population growth (the difference dt between the total average birth rate and death rate) X1 Susceptible Population IR(t) ) Infection Rate X22 x LR(t) ) Infected Infected Loss of Immunity Population Rate population RR(t) ) Recovery Rate X3 Immune Population FIguRE 4-1 Example of a system dynamics model that shows the partial system dynamics description for propagation of 4-1.eps epidemic. a potential SOURCE: Adapted from Sage and Armstrong (2000, p. 235).

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 MACRO-LEVEL FORMAL MODELS flow out decreases it.7 The diagram captures the following qualitative and, for the mathematically inclined, quantitative notions:8 • For the states: — The susceptible population X1 will increase as the recovered lose immunity (LR) and decrease as the susceptibles become infected (IR). Or9 d(X1)/dt = LR – IR n — The infected population X2 will increase as the susceptibles become infected (IR) and decrease as the infected recover (RR). Or d(X2)/dt = IR – RR n — The immune population X3 will increase as the infected recover (RR) and decrease as the immune lose immunity (LR). Or d(X3)/dt = RR – LR n • For the flows (not illustrated for simplicity): — The infection rate (IR) increases both as the susceptibles (X1) increase and as the infected (X2) increases, due to the net- worked nature of spreading infections. Or10 IR = a*X1*X2 n — The recovery rate (RR) is directly proportional to the infected (X2). Or RR = b*X2 n — Likewise, the loss of immunity rate (LR) is directly propor- tional to the infected (X3). Or LR = b*X3 n Note the complete loop closure relating the three states, and the potential for continuing growth and decay of an infected population over time. Note also the potential for nonlinear behavior over time, because of the fundamental nonlinearity introduced via the infection rate equation (IR = a*X1*X2). The structure of system dynamics models can be characterized by four hierarchical levels, as shown in Figure 4-2.11 All interactions and impacts 7 Not explicitly shown is how the flows are influenced by the stock levels. 8 Note that in this set of equations and in subsequent sets, the asterisk (*) is not meant to represent a convolution operation or function composition, but rather a simple multiplication, in line with DYNAMO code conventions, as well as FORTRAN syntax, which was a popular computational language at the time of DYNAMO’s introduction. 9 d( )/dt is used to denote the first-order derivative of the associated variable. 10 The constants (a,b,c) are chosen on the basis of underlying knowledge of dynamics of infection, recovery, etc. 11 This description borrows heavily from Sage and Armstrong (2000, p. 237).

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 BEHAVIORAL MODELING AND SIMULATION Closed Boundary Around a System Rate and Level Variables as Basic Structural Elements Rate Variables Representing Level Variables Representing Activity Within Feedback Loops Accumulations Within Feedback Loops Detection of Control or Discrepancy Goals or Observed Policy Action Between Goals Objectives Conditions Based on the and Observed Discrepancy Conditions FIguRE 4-2 The four hierarchical levels of system dynamics modeling. 4-2.eps SOURCE: Sage and Armstrong (2000, p. 237). in the system dynamics model take place inside a boundary. Within this boundary, variables are chosen to represent the key states that define overall system behavior. A derivative variable is chosen to control a flow into the state or level variable, which integrates or accumulates this level. Informa- tion concerning the level is used to control the rate variable (state feedback to the same associated state). In other words, we define a rate variable as the time derivative of a level or state variable and determine rate variables as functions of level variables. Some useful readings on system dynamics modeling methodology are Roberts, Anderson, Deal, Garet, and Shaffer (1983); Sterman (2000); Ogata (2003); and Karnopp, Margolis, and Rosenberg (2006). A more detailed description of system dynamics modeling and the equations it uses is available in Sage (1977) and Sage and Armstrong (2000). Comprehen- sive approaches to modeling complex projects—including industrial and military—are described by Williams (2002).

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0 BEHAVIORAL MODELING AND SIMULATION More Recent Applications of System Dynamics Modeling More recently, there has been a resurgence of interest in system dynamics modeling, most particularly in public policy and business areas. Sterman’s text on Business Dynamics (2000) presents a number of case studies that demonstrate successful applications across a number of areas, including global warming, the war on drugs, reengineering the supply chain of a major computer firm, developing a marketing strategy in the automobile industry, and planning process improvements in the petrochemicals indus- try. The Department of Defense (DoD) has also taken a keen interest in this approach, particularly for modeling diplomatic, information, military, and economic (DIME) actions, and political, military, economic, social, infor- mation, and infrastructure (PMESII) interactions. It is not our intent here to survey all of these efforts, but merely to provide a few illustrative examples to indicate the potential of system dynamics modeling in this area. For example, Robbins’ Stabilization and Reconstruction Operations Model (SROM) (Robbins, Deckro, and Wiley, 2005) analyzes the orga- nizational hierarchy, dependencies, interdependencies, exogenous drivers, strengths, and weaknesses of a country’s PMESII systems using a complex set of interdependent system dynamics representations. SROM models a country system in a holistic manner as a national model, which, as shown in Figure 4-3, is then defined in terms of its n regional submodels that interact with each other and the national model. Each regional submodule contains six functional submodels: the demographics submodel, the insurgent and coalition military submodel, critical infrastructure, law enforcement, indig- enous security institutions, and public opinion. Each submodel is comprised of approximately 600 model parameters, 90 random variables, 80 states (stocks), and 190 rates of change (flows). National Sub-Model Region 2 Region 1 Region N Sub-Module Sub-Module Sub-Module FIguRE 4-3 Top-level nation SROM. SOURCE: Robbins et al. (2005, p. 19). 4-3.eps

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 MACRO-LEVEL FORMAL MODELS Figure 4-4 shows a portion of the critical infrastructure model of SROM. The model captures a sequence of influences among variables, start- ing from the power supply at an electrical substation. The generated power is fed into an industrial water plant, which produces water consumed by oil field work. An oil field produces crude oil to be refined by a refinery. Finally, refined fuel is used to generate power, which in turn is supplied to various power substations, thus forming a closed loop. SROM has been demonstrated in modeling and analysis of Iraqi recon- struction and recruiting efforts (Robbins et al., 2005). Parameters were set to reflect prevailing conditions in Iraq on May 1, 2003, including • Regional makeup (governorates) • Regional population • Population subgroup distribution • Population support for coalition • Oil and gas infrastructure • Power infrastructure • Transportation infrastructure • Economic—regional gross domestic product Robbins (2005) claims that the SROM allows analysts to more precisely investigate the multifaceted process that is nation building: “[Because] the complexities of nation-building involve many different but interrelated systems and institutions, understanding the significance of the dynamic relationships between these systems and institutions is paramount to operational success. The system dynamics model proposed in this study allows decision-makers and analysts to investigate different sets of decision approaches at a sub-national, regional level” (p. 135). The Pre-Conflict Anticipation and Shaping (PCAS) program (Popp et al., 2006) was an attempt to evaluate alternative DIME/PMESII model- ing efforts to predict nation-state collapse and to anticipate instabilities that might lead to conditions necessitating military intervention. One of the approaches, led by Nazli Choucri, developed a “state stability model” using a system dynamics approach; a high-level view of the model is given in Figure 4-5. Power Industrial Oil Oil Power Substation Water Plant Field Refinery Generators Industrial Refined Power Crude Water Fuel FIguRE 4-4 SROM infrastructure model. SOURCE: Robbins, Deckro, and Wiley (2005). 4-4.eps

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 BEHAVIORAL MODELING AND SIMULATION + Military Capability + Dissident Institutional – + + + Social Capacity Cohesion Anti-Regime Activity – + – – – + Population + + – External – Regime Force Resources + and Violence – State Institutional + + GNP Capacity + – + Civic Capacity + and Social Liberties + + Regime + + Legitimacy 4-5.eps FIguRE 4-5 High-level view of system dynamics implementation of state stability model. redrawn SOURCE: Popp (2005). According to Popp (2005, p. 18), it “shows loads, demands and stresses on state and the causal dependencies; shows feedback loops, tipping points and unintended consequences; [and] shows the internal and lateral pressures that can lead to conflict.” By looking at the loads (demands) placed on the system (nation-state) and evaluating those demands in terms of the system’s capabilities, an assessment of stability can be made based on how much demands exceed capacity. Finally, O’Brien’s Integrated Crisis Early Warning System (ICEWS) is a new program at DARPA/IPTO aimed at following on from the PCAS exploration just described. According to the announcement of the research program, its goal “is to develop a comprehensive, integrated, automated, generalizable, and validated system to monitor, assess, and forecast national, sub-national, and international crises in a way that supports decisions on how to allocate resources to mitigate them. ICEWS will provide Combat- ant Commanders (COCOMs) with a powerful, systematic capability to anticipate and respond to stability challenges in the Area of Responsibility (AOR); allocate resources efficiently in accordance to the risks they are designed to mitigate; and track and measure the effectiveness of resource allocations toward end-state stability objectives, in near-real time” (see

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 BEHAVIORAL MODELING AND SIMULATION State of the Art in Organizational Modeling Here, we focus on simulation or computational organizational models. A number of books contain overviews and examples of many models in this area (Carley and Prietula, 1994; Carley and Gasser, 1999; Lomi and Larsen, 2001). Some models consider organization theory questions; others are more oriented to organizational design questions; and some can be used for both purposes. We begin with the theory models and then consider the design models, with comments when the models can be used both ways. Organization Theory Models There are numerous organization simulations or computational orga- nizational models; here we review a few of them. Most, but not all, are agent-based models in which the organization is represented as agents that are linked together by communication or authority structures or both. The earliest computational organizational model was a behavioral theory of the firm in which the organization was modeled in terms of goals, expectations, and choice (Cyert and March, 1963). Simple systems were used to demonstrate how nonrational behavior could generate behavior similar to that observed in real organizations. This was then extended in the now canonical model, the garbage can model of organizational choice (Cohen, March, and Olsen, 1972). This was a simple Fortran program in which basic matching and accumulation functions were combined to show how variations in the problem access, salience of problems, and energy of the participants altered the level of work and the quality of outcomes. The Lin and Carley models look at organizations as networks of com- munication linkages among agents, such that agents learn only from the information that they get from the outside world or that is provided to them by another agent in the organization (Lin and Carley, 2003; Lin, Zhao, Ismail, and Carley, 2006). Using these models, they investigated questions of crisis response. They conducted a “matched-set” validation experiment, in which they compared the behavior of 69 real-world organizations faced with industrial crises with the behavior of the simulated versions of those same 69 companies. Using what-if analysis, they were then able to show that the type of decision making employed by the organization—for exam- ple, following standard operating procedures or following the dictates of historically based experience—often led organizations to false conclusions about their performance. This work was generalized and extended to produce the OrgAhead model. OrgAhead is a multiagent model of organizational design and the examination of the impact of learning and strategic adaptation on that design (Carley and Svoboda, 1996). In this model, learning occurs at the

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 MACRO-LEVEL FORMAL MODELS operational and structural levels, using experiential and expectation-based learning models. From a technical standpoint, the model uses simulated annealing15 to alter the communication and authority lines and number of agents. The agents are information-processing units with a simple learning component. OrgAhead can be thought of as an operationalized grounded theory. The basis for OrgAhead is the body of research, both empirical and theoretical, on organizational learning and organizational design. The model has built into it several theories of different aspects of organiza- tional behavior. From the information-processing tradition comes a view of organizations as information processors composed of collections of intel- ligent individuals, each of whom is boundedly rational and constrained in actions, access to information by the current organizational design (rules, procedures, authority structure, communication infrastructure, etc.), and his or her own cognitive capabilities. Organizations are seen as capable of changing their design (DiMaggio and Powell, 1983; Romanelli, 1991; Stinchcombe, 1965) and as needing to change if they are to adapt to changes in the environment or the available technology (Finne, 1991). Dif- ferent organizational designs are seen as better suited to some environments or tasks than others (Hannan and Freeman, 1977; Lawrence and Lorsch, 1967). Aspects of the model have been tuned to reflect the findings of various empirical studies related to these theories. The set of theories that are unified into a single computational theory of organizational behavior interact in complex fashions to determine the overall level of organizational performance. Harrison and Carroll (1991) investigated the effect of turnover on organizational culture for different prototypical organizations and poli- cies. Their model is stated as a set of mathematical functions, which are then simulated and yield data that are analyzed as if they were field data. The model is essentially a cultural diffusion model operating at the group level. On the basis of “virtual experiments” conducted with the model and a follow-on analysis of the resulting simulation-based data, they found that some employee turnover can help stabilize the culture of the organiza- tion, suggesting that some previously held truths about turnover are not general. An alternative information diffusion model is Construct, developed by Carley to examine the coevolution of structure and culture that results from individual information exchange and the formation and dissolution 15 Simulated annealing is a technique to find a good solution to an optimization problem by try- ing random variations of the current solution. A worse variation is accepted as the new solution with a probability that decreases as the computation proceeds. The slower the cooling schedule, or rate of decrease, the more likely the algorithm is to find an optimal or near-optimal solution (see http://www.nist.gov/dads/HTML/simulatedAnnealing.html [accessed August 2007]).

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40 BEHAVIORAL MODELING AND SIMULATION of social networks (Carley, 1991). Construct has been used to examine the impact of new technologies on the workplace (Carley and Schreiber, 2002), performance under diverse leadership styles (Schreiber and Carley, 2004), and the emergence of organizational vulnerabilities (Carley, 2004). NK models, originally suggested by Kauffman, are simple optimiza- tion models, often operationalized using genetic algorithms, in which N is the number of actors and K is degree of connectedness among the actors (Kauffman and Weinberger, 1989). NK models have been applied to organization theory questions of adaptation (Levinthal, 1997), search and stability (Rivkin and Siggelkow, 2003), modularity and innovation (Ethiraj and Levinthal, 2004), imitation and benchmarking (Rivkin, 2000), and other basic questions about organizations. The explicit modeling of rugged landscapes permits one to understand the limitations of organization expla- nations that implicitly assume smooth performance surfaces. It also yields greater insights into the persistence of variety among organizations. The SimVision model (earlier called VDT) is a project organization model (Levitt, Thomsen, Kunz, Jin, and Nass, 1999) which explicitly models the project tasks (similar to a critical path method network) and the hierarchical organization structure. In essence, this model is the merger of Gantt chart technology with a limited information-processing model for the agents. The project tasks are linked by the project network, and each task is assigned directly to an agent in the hierarchy. SimVision has been used as a laboratory for organization experiments.16 For example, Carroll, Burton, Levitt, and Kiviniemi (2006) found that “fast tracking” or concurrent engi- neering of projects quickly leads to increased coordination demands that do not reduce total project time; additional personnel can also increase project time as they require time to manage; and decentralization increases coordi- nation demands. Earlier, Kim and Burton (2002) found that decentraliza- tion reduces project time but may also decrease quality. Long, Burton, and Cardinal (2002) demonstrated that three simultaneous control approaches are better than any single control approach. These studies began with orga- nizational questions and observations of real organizations as base models. The simulation experimental manipulations (“virtual experiments”) went beyond real-world observations to investigate plausible conditions of what could happen for a better understanding of potential outcomes. Field obser- 16 In the studies cited here it must be remembered that the conclusions drawn from analysis of the simulation-based data (in turn generated by virtual experiments in the simulation domain) are not to be confounded with conclusions drawn from an analysis of homologous real-world data. This is in keeping with our earlier footnote regarding how simulation-based data can be analyzed as if it were real-world data. It often can, but the fundamental issue still remains regarding the validity of applying the simulation-based conclusions to real-world organizational behavior. Naturally, the more validated the model, the more likely one is to be correct in cross-applying one’s conclusions.

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4 MACRO-LEVEL FORMAL MODELS vations and generalizations are limited in their applicability and should be used with caution in the design of future organizations. Simulation studies provide deeper insight into what is possible and what is desirable for organizational redesign and change. SimVision can also be applied as an organizational design model. Organizational Design Models The term “organizational design” is used both to mean the design of the organization and the process of design. The two meanings are different but closely related. In a special issue of Organization Science, Dunbar and Starbuck (2006) focus on the process of organizational design in its many facets. The articles give insight into how design can be accomplished and the challenges encountered. SimVision was applied to investigate organization theory questions. But it was originally created as an organizational design tool to help project managers optimize projects and project management implemen- tation (Levitt, 2004) This included avoiding unforeseen bottlenecks and finding options to compress project time. One of the insights is that project managers adapted quite well to minor variations from the normal base case but less well when there were large changes in requirements. The simula- tions were extremely useful in helping project managers reframe the project and redesign the project. Pattipati and colleagues (Pattipati et al., 2002; Levchuk, Levchuk, Luo, Pattipati, and Kleinman, 2002a, 2002b; Levchuk, Levchuk, Meirina, Pattipati, and Kleinman, 2004) have used multiobjective optimization algorithms to develop organizational designs optimized to meet mission requirements for military command and control organizations, focusing specifically on Joint Task Force command teams. These designs specify both structure and process by specifying roles in the organization defined in terms of control of resources, responsibility for tasks, and requirements for coordination. Designs are then tested in simulations of organizational performance and finally tested in field experiments in which military officers play the roles that were designed using the model. Studies have shown that optimized organizational designs based on the model result in performance that exceeds that observed under more traditional designs suggested by military subject matter experts (Entin, 1999). A key find- ing of this work is that sufficient training is essential for the officers to function effectively in the innovative organizational structures developed using the model. Carroll, Gormley, Bilardo, Burton, and Woodman (2006) describe an organizational design process at the National Aeronautics and Space Administration (NASA), where SimVision and other organizational design

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4 BEHAVIORAL MODELING AND SIMULATION tools were used as decision aids in creating a new organization. The chal- lenge was to create an organization that had multiple functional experts, was geographically disperse, and had severe resource constraints in which project time and quality were paramount. The design team began with the construction of the design structure matrix; it gave a good beginning but generated questions as well as answers. Next, they used OrgCon—an expert system organizational diagnosis and design tool—to model the proposed organization at a high level in terms of structural properties, such as formal- ization and decentralization. One purpose of this modeling was to identify “misfits” (Burton and Obel, 2004) that suggested a need for change; they found few of them. But many questions remained. Then they created a SimVision of the proposed design to obtain greater detail and better under- standing of how the organization would actually work. Using variations in the design, they confirmed that the design developed with the aid of the tools was reasonable. Perhaps most importantly, the usual organizational design approach would have resulted in an organization that would have failed to meet the goals and would have incurred delays and unanticipated costs. The results indicate that the tools can make a difference and lead to better designs; furthermore, the theory-based notion of organizational misfits aids in the process. It can be a bridge between theory and design and theory and practice, as managers find the identification of misfits and their correction both intuitive and practical. NASA had been accustomed to using simulations in engineering design but not in organizational design. Nonetheless, the culture was amenable to the application of such tools for organizational design. Similarly, OrgAhead was built to explore the relative effectiveness of different organizational designs. For example, it was used to determine the adaptability and performance characteristics of different designs under consideration by the Naval Strategic Studies Group. Construct, referred to earlier, has also been used to evaluate various organizational designs under different turnover regimes. Moreover, when data are collected on the who, what, where, and how of organizations, such data can first be assessed for points of vulnerability in ORA and then Construct can be applied to the same empirical description of the real organization to forecast its behavior in terms of information diffusion and performance with or without turn- over (Carley, Diesner, Reminga, and Tsvetovat, 2005). Levis and Wagenhals (2000) and the subsequent work with Shin, Kim, Bienvenu, and Shin led to the development of a Petri net model for design- ing and assessing organizational architectures (Bienvenu, Shin, and Levis, 2000; Wagenhals, Shin, Kim, and Levis, 2000). Modeling agents, their resources, and the decision process, this overall approach makes possible the fine tuning of detailed designs of core groups in organizations. This approach has been used consistently to evaluate command and control

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44 BEHAVIORAL MODELING AND SIMULATION rarely include more than six to eight team members. However, the devel- opment of agents that can represent the behaviors of members of the organization in a realistic way opens the door for “hybrid” experiments in which most roles in the organizations are played by agents, with only a few played by live subjects. Research is needed on the best ways to use this hybrid experimentation capability to advance organizational science: the types of questions that can best be addressed in such experiments, the best ways to “control” such experiments in the classical sense of experimental control, the level of fidelity needed in the agents, and the statistical tech- niques needed for analysis of the results. REFERENCES Abdel-Hemid, T., and Madnick, S.E. (1991). Software project dynamics: An integrated approach. Englewood Cliffs, NJ: Prentice-Hall. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, and P. Terna (Eds.), Simulating social phenomena (pp. 21–40). Berlin, Germany: Springer. Bienvenu, M.P., Shin, I., and Levis, A.H. (2000). C4ISR architectures III: An object-oriented approach for architecture design. Systems Engineering, (4), 288–312. Black, H.S. (1977). Inventing the negative feedback amplifier. IEEE Spectrum, 4, 54–60. Buck, J.T., Ha, S., Lee, E.A., and Messerschmitt, D.G. (1994). Ptolemy: A framework for simulating and prototyping heterogeneous systems. International Journal of Computer Simulation, Special Issue on Simulation Software Development Component Development Strategies, 4. Burton, R.M. (2003). Computational laboratories for organization science: Questions, validity and docking. Computational and Mathematical Organization Theory, (2), 91–108. Burton, R.M., and Obel, B. (2004). Strategic organizational diagnosis and design: The dynamics of fit, third edition. Boston: Kluwer Academic. Carley, K.M. (1991). Designing organizational structures to cope with communication break- downs: A simulation model. Industrial Crisis Quarterly, 5, 19–57. Carley, K.M. (2004). Estimating vulnerabilities in large covert networks using multi-level data. In Proceedings of the North American Association for Computational Social and Organi- zational Science (NAACSOS) 004 Conference, June 27–29, 2004, Pittsburgh, PA. Carley, K.M., and Gasser, L. (1999). Computational organization theory. In G. Weiss (Ed.), Multiagent systems: A modern approach to distributed artificial intelligence (pp. 299– 330). Cambridge, MA: MIT Press. Carley, K.M., and Prietula, M.J. (1994). Computational organization theory. Hillsdale, NJ: Lawrence Erlbaum Associates. Carley, K.M., and Schreiber, C. (2002). Information technology and knowledge distribution in C3I teams. In Proceedings of the 00 Command and Control Research and Technology Symposium Conference, Naval Postgraduate School, Monterey, CA. Carley, K.M., and Svoboda, D. (1996). Modeling organizational adaptation as a simulated annealing process. Sociological Methods and Research, 5(1), 138–168.

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