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Part II
STATE OF THE ART IN
ORGANIZATIONAL MODELING
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Part II
State of the Art in
Organizational Modeling
P
art II reviews the multitude of individual, organizational, and societal
(IOS) modeling approaches, methods, and tools that are potentially
useful for addressing the military modeling needs described in Chap-
ter 2. Models take many forms, ranging from loose conceptual models to
precise mathematical models (Lave and March, 1975). They include agent-
based models, cognitive models, expert systems, dynamical systems, and
input-output models. Here we survey and explore many different types of
models relevant to our questions. We describe each, show their strengths
and limitations, and discuss research and development efforts that could
make the approaches more useful for addressing military modeling needs.
The diverse expertise of the committee members contributed greatly to
the completeness of this review but also made it challenging to agree on an
organizing framework for presenting the review results. Refined through
multiple iterations, the organizing framework that we developed represents
a significant product of the study.
CATEgORIES OF MODELS: INITIAL EMPIRICAL RESuLTS
As a first step in organizing our review, we took an empirical approach
to organizing the various terms and approaches used in IOS modeling.
Using the methods of cultural domain analysis (see Chapter 3 for a descrip-
tion), we developed a perceptual map of the field of modeling based on
committee members’ perceptions.
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BEHAVIORAL MODELING AND SIMULATION
Methodology
The first step in our investigation was to collect “free lists” of models
from each member of the committee. Effectively, we asked, “What are all
the kinds of models you can think of?” A large number of unique “kinds
of models” were elicited with little overlap, implying that the domain itself
lacks a high degree of cultural coherence. A total of 240 items were elicited,
with approximately 35 items per member. Much of this lack of overlap was
due to differences in the level of specificity for the kinds of models listed.
For example, some of the items were specified at the level of named models,
such as DyNet, EpiSims, NetWatch, etc., while others were at a very general
level, such as conceptual models or verbal models. Aggregating across all
lists, a master list of distinct terms was obtained after standardizing word
forms. Also, an attempt was made to keep all items at the same level of
specificity, in this case at a more general level.
The second step was to take the 38 most frequently mentioned items at
a more general level of specificity and construct a pile-sorting task, in which
each committee member was asked to sort the items into piles according to
how similar the kinds of models are. They could use as many or as few piles
as they wished. The task was conducted online using interview software
that simulated cards and allowed the virtual cards to be placed into piles.
When they were done, the program recorded the membership of each pile.
Then, an aggregate proximity matrix X, whose rows and columns corre-
sponded to “kinds of models,” was constructed such that each cell Xij of
the matrix recorded the number of respondents that placed the ith kind of
model in the same pile as the jth kind of model.
The final step was to visualize this proximity matrix using a standard
network visualization package called Netdraw (Borgatti, 2002). In this
approach to visualization, a line is drawn to connect two items if the simi-
larity of the two items exceeds a certain user-defined threshold (DeJordy,
Borgatti, Roussin, and Halgin, 2007; Johnson and Griffith, 1998).
Results
The resulting map is shown in Figure II-1. In the map, a line is drawn
between two modeling techniques if at least 28 percent of the respondents
placed the items together in the same pile. (A cutoff of 28 percent was
chosen because above that level the main section of the network becomes
disconnected.)
The results show three basic clusters of modeling techniques. The first
cluster, at the top left of the map, consists of multiagent models in which
the agents are connected to each other by social ties or interactions. In these
models, the combination of agents and ties forms a single interconnected
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Multiagent
Representative_agent
Simulation
Cellular_automata Multiagent_network
Agent-based Social_network
Computational
Dynamic_network
Process
System_Dynamics
Dynamical_systems
Mathematical
Differential_equation
Markov
Difference_equation
Linear Time_series Multiattribute
Nonlinear
Statistical Cultural
Group_decision-making
Equilibrium Organizational_culture
Econometric
Organizational_learning
Input-Output Cognitive
Optimization
Game_theory Machine_Learning
Genetic
Expert_systems
Influence Risk
Behavioral
Conceptual
Decision_theory
FIguRE II-1 Perceived similarities among types of models.
Part II-1.eps
broadside
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4 BEHAVIORAL MODELING AND SIMULATION
and interdependent system. For convenience, we refer to the models in this
cluster as the computational network models cluster (although it contains
some models for which this would not be the ideal name).
The computational network models cluster is connected to the next
cluster via the system dynamics node. This new cluster consists of low-level
statistical and mathematical techniques that have broad application across
many different settings. Although these techniques are often thought of as
tools rather than models, statisticians would recognize that they do indeed
constitute models. For convenience, we refer to this cluster as the math-
ematical systems models cluster.
At the bottom right of Figure II-1, there is another cluster of models
focused on the cognition or culture of the agents. We call these the cogni-
tive models. The difference between these and the computational network
models at the top of the map is one of emphasis rather than substance.
The cognitive models are defined by their focus on the details of cognition.
The objective of the cognitive models is to understand the patterns of who
believes or chooses what. In contrast, the computational network models
are defined by the processes that the modeler builds into the system and
may not represent cognition at a detailed level. The outcomes of the com-
putational network models may well be the same as those of the cognitive
models, and the processes of the cognitive models often involve the same
multiagent interactions of the computational network models: it is only the
focus of the investigation that is different. Finally, as noted at the bottom
left, three model types—influence, behavioral, and conceptual—did not
cluster with other types.
FOuR-PART ORgANIzINg FRAMEWORK FOR MODELS
On the basis of the empirical clustering results described above and
further discussion, the committee developed a four-part categorization
for reviewing modeling approaches: (1) macro models, (2) micro models,
(3) meso models, and (4) integrated, linked micro-meso-macro models.
No single one of these approaches is the correct one, and the best model-
ing approach depends on the nature of the problem to be solved. It is a
common theme throughout this book that models constitute “use-driven
research” (Stokes, 1997) and cannot be developed or evaluated without an
in-depth understanding of the uses to which they are to be put.
A macro model considers interactions between macro-level variables,
such as unemployment, crime, education, poverty, and resources. Macro
modeling approaches like system dynamics enable one to identify feedbacks
and to see system-level effects without getting bogged down in details.
At the other extreme, one can model the cognitive or affective processes
of individual actors or at least their outcomes—individual decisions and
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5
STATE OF THE ART IN ORGANIZATIONAL MODELING
actions. These more micro modeling approaches include cognitive models
from psychology, expert systems models, and rational choice models, which
include game theory and decision theory.
Fifty years ago, this distinction between micro and macro would have
been thought sufficient. One can look at the trees, or one can look at the
forest. Over time, social scientists have come to appreciate the importance
of the level in between—the meso level (Miller and Page, 2007). To com-
plete the metaphor, one can think of stands of aspen trees with a shared
root system. The stand is a part of the forest, yet it does not function merely
as the sum of its individual trees, given the sharing of resources. The social
analog of a shared root system is social capital. People join movements,
participate in riots, and support government in part based on the actions
of their friends and peers. Predictions based on individual attributes can
almost always be improved by adding in social factors.
We highlight two types of meso models: network models and agent-
based models. Both modeling approaches have produced flurries of atten-
tion over the past decade. Network models allow one to formalize, measure,
and test loose conceptions of social capital, centrality, and connectedness.
Agent-based models allow one to include diverse, purposive agents who
interact in space and time. As the name suggests, agent-based models origi-
nate with the agents, but these agents can self-organize, creating emergent
meso-level structures that take on meaning and have predictive value.
The fourth category links micro, meso, and macro models. Agent-based
models, and to some extent game theory and network models, achieve this
double linkage. Yet only recently have researchers begun to create hybrid
models that include agents who employ sophisticated psychological models
and whose macro effects link to a system dynamics model. These hybrid
models have great potential for addressing the needs identified in Chap-
ter 2. Thus, we make this linkage between levels explicit. Although we do
not have a separate chapter on integrated models, agent-based models and
network models are discussed in Chapter 6, and the challenges of achieving
such multilevel model integration are discussed in Chapter 8.
PART II guIDE
Chapter 3 discusses conceptual models and cultural models. Adequate
conceptual models provide the foundation for development of computa-
tional and mathematical models. Cultural models occupy a special position
in our review because our interest is in understanding people at multiple
levels of aggregation. The questions that concern us require the ability to
model individuals, teams, communities, and entire societies. At each of
these levels, cultural factors are at work, so we first explain what we mean
by culture and cultural effects.
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BEHAVIORAL MODELING AND SIMULATION
The discussion next turns to a review of formal modeling approaches
(Chapters 4, 5, and 6) organized using the four-part framework described
above. For each modeling approach we describe the current state of the
art, the most common applications of the approach, and its strengths and
limitations for the problems described in Chapter 2, and we provide sug-
gestions for further research and development.
In Chapter 7, we turn to online games as a methodology. Gaming,
the creation of an environment in which real people can play against one
another or against artificial players, can be thought of as a methodology,
but as these gaming models apply many of the other types of models, and
as they involve people interacting with the games, we set them apart. Online
gaming environments are both consumers of models—to create artificial
players and the social effects of player actions—and potential testbeds for
generating data to develop and test models of the communications and
actions of large numbers of individuals interacting in a simulated world.
Chapter 8 discusses important methodological issues that are common
across many modeling approaches, including modeling frameworks, tools,
and data, and it includes a discussion of model verification and validation.
Model validation is a key issue for complex social models, and we argue
for a “validation for action” approach that considers how the model is to
be used rather than attempting to evaluate model accuracy or model fidelity
without considering context of use. Chapter 9 summarizes the state of the
art in IOS modeling and its applicability to the requirements and uses dis-
cussed in other chapters.
REFERENCES
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Field Methods, 4(1), 88–107.
DeJordy, R., Borgatti, S.P., Roussin, C., and Halgin, D.S. (2007). Visualizing proximity data.
Field Methods, (3), 239–263.
Johnson, J.C., and Griffith, D.C. (1998). Visual data: Collection, analysis, and representa-
tion. In V. de Mjmck and E. Sabo (Eds.), Using methods in the field. Walnut Creek, CA:
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