structure and effectiveness of intelligence...
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Structure and Effectiveness of Intelligence Organizations
Robert BehrmanEngineering and Public Policy
Carnegie Mellon UniversityPittsburgh, PA 15213
Abstract: This paper lays out an abstract model for analyzing the structure and function of intelligence organizations and the activities of units within them. Metrics of intelligence organization effectiveness derived from the intelligence and decision making literatures are presented; then social network and computational methods of analyzing the developed model in terms of the discussed metrics are presented. Methods of validating the model are discussed. Implications of this model for the analysis of intelligence and intelligence-using organizations are discussed, and areas in need of further research are identified. This study, although preliminary, provides an initial attempt to model and analyze intelligence organizations in terms of their effectiveness.
Structure and Effectiveness of Intelligence Organizations
1. Introduction
Popular concern over well-known intelligence failures, a widespread
disagreement over whether the current intelligence and law enforcement
infrastructure is capable of handling the additional demands of the counter-
terrorism mission, and recognition of lack of interagency cooperation have
prompted concern over the structure and function of the United States
intelligence community. New missions and a different global/political climate
from the one of the cold war have placed additional and different demands on
intelligence agencies: they must be able to collect against new targets, many of
which require different collection methods; meet new or different international
and interagency sharing requirements, often with nations in which we do not
have a long-standing cooperative intelligence relationship; they must cooperate
with civilian service agencies and law enforcement agencies; and they must do
all of this while continuing to meet military and tactical intelligence requirements,
maintaining efficiency, and operating under closer public scrutiny. In order to
meet these demands, many solutions are being discussed - increases in the
scope, power, authority, and size of the national intelligence structure; a
restructuring and recentralization of the intelligence community (examples
include the creation of a new agency to handle domestic intelligence) (Berkowitz
and Goodman, 2000); establishing intelligence coordinating positions
(“intelligence ombudsmen” or liaisons); or major changes in structure, such as a
“networked intelligence structure” (Berkowitz and Goodman, 2000; Alberts,
Garstka, and Stein, 1999; Comfort, 2002). Nor is this discussion of intelligence
confined to strategic and military intelligence – the business sector,
notwithstanding its own cloak and dagger stories, has long invested in
intelligence collection, research, and analysis designed to increase the accuracy
of business decisions; in short, in intelligence organizations. All of these
solutions involve structural changes in command and communication networks of
intelligence organizations, but there has been little analysis of these networks in
either the network analysis or the organization theory literature.
This article will discuss a method for a formal, abstract analysis of the
structure and function of intelligence organizations, the activities of the units
within them, and the correlation between these and the effectiveness of the
organization. The first part of this paper will develop an abstract model of
intelligence organizations and define terms used in the analysis. The second
part of this paper will discuss how to measure the effectiveness of intelligence
organizations, and will discuss the application of social network and
computational analysis methods to the model in order to generate these
measurements. In the third part of the article, methods of applying and validating
this model will be discussed, weaknesses in the model and its theoretical backing
will be identified, and possible areas for future research and experimentation will
be mentioned.
2. Modeling Intelligence Organization Structure
The action of intelligence organizations is typically modeled in terms of
the ‘intelligence cycle:” plan, collect, process, produce, disseminate, repeat.
Though there is skepticism within the literature about the usefulness of the formal
process, the planning, collection, and processing phases all need to be modeled
as capabilities of the organizations. During the planning phase, intelligence
consumers generate information requirements and send them to intelligence
organizations. These information requirements are used to generate tasks for
units within the intelligence organization, which are then prioritized and sent to
the units that can handle them. During the collection phase of the intelligence
cycle information is gathered by collection assets and reports are generated and
sent to units that use them. For simplicity, the ‘process’, ‘produce’, and
‘disseminate’ phases of the intelligence cycle are modeled as one phase, which
this paper refers to as the processing phase. During this phase, reports are
‘read’ by processors, and either sent to intelligence consumers or databases or
discarded. This simplification of the process, produce, and disseminate phases
of the intelligence cycle is supported by the intelligence literature – Berkowitz and
Goodman group the process and production phases together in a phase called
analysis, and separate the disseminate phase (Berkowitz and Goodman, 1989);
however it will become clear during the forthcoming discussion of communication
ties within the model why this paper chooses to model the dissemination phase
within the processing phase. The three phases identified in this discussion of the
intelligence cycle and used within the model correspond to the functions of three
different types of units within the model: decision makers, collectors, and
processors. By modeling these phases as the actions of specific units, multiple
intelligence cycles within specific sectors of the intelligence organization can be
identified and failures or inefficiencies in the operation of the organization can be
identified.
The model that will be developed will be a ‘sociogram’ of the type
discussed in Scott, 2000; it will consist of various nodes that will represent units
within the intelligence organization that are linked by ties, which represent both
communication networks and hierarchical position. Additionally, regions of the
sociogram will be identified as agencies, the intelligence organization, or the
environment. All elements of the graph – ties, nodes, regions – will have
‘attributes,’ which are parameters governing the handling of phenomena by the
element.
Two types of ties will be modeled in an intelligence organization – tasking
and reporting ties. These are directed ties, in that A being able to task or report
to B does not imply that B can task or report to A. These ties do not merely
indicate communication; instead they indicate a combination of communication
and, along with certain attributes of the phenomena that travel along them and
the units at the ends, hierarchical position and command relationships. The
presence of a tasking tie indicates that a unit can issue tasks to another unit,
which the receiving unit is compelled to either obey or forward as is appropriate.
‘Obey’ is determined by the function of the unit that receives the task – for
example, collectors obey tasks by collecting the information that is required by
the task or by queuing the task for later execution, while processors obey tasks
by producing certain reports from received and stored information. ‘Forward,’ in
the case of tasks, means that the unit can send the task to another unit that it has
tasking ties to. The presence of a reporting tie indicates that a unit can send an
intelligence report to another unit, which the receiving unit can use, forward, or
ignore as appropriate. ‘Use’ is determined by the function of the unit –
intelligence consumers receive reports and use them to make decisions,
processors use reports in order to produce synthesized reports that are then sent
to intelligence consumers or stored in databases. Ties in the model have certain
attributes: time, type, and security. Time is the amount of time it takes a
phenomenon to move along the tie. Type is a descriptor of the tie, e.g. “radio,”
“email,” or “shout across the room;” that may be useful to certain non-quantitative
analyses of the network. Security is a measure indicating how ‘secure’ – from
environmental organizations compromising or overhearing the communication –
the tie is. Certain criteria tasks may only travel along ties with a certain security,
and the tie should be more secure than the sensitivity of reports traveling along it.
Certain phenomena in the intelligence organization model have been
mentioned repeatedly but not discussed at length: tasks and reports. Tasks
indicate requests for information or action, generated by decision makers, and
sent to other units within the intelligence organization for execution. Tasks can
take the character of formal commands – subordinate units, such as processors
and collectors, are enjoined to obey them; or they can take the character of
requests – decision makers who receive tasks can choose to forward them to
units subordinate to them or not, or alter their priority. Tasks travel ‘down’
tasking ties from an originating or forwarding unit to a receiving unit that forwards
the task, queues it, or completes it. Tasks have certain attributes: criterion,
problem, time, deadline, priority. Criterion can indicate the type of unit that must
accomplish the task (for example a collector with type 1, or an actor with type 3).
Problem indicates which problem the task is intended to generate reports
answering. Time is a parameter affecting the amount of time it takes a unit to
finish the task. Deadline indicates when the task must be completed. Priority
indicates whether the unit will attempt to finish the task before or after other tasks
in its queue. Reports indicate any unit of intelligence information that is to be
communicated – from formal reports, to oral conversations, to analytic products
such as planning maps. Reports travel ‘up’ reporting ties from an originating or
forwarding unit to a receiving unit, that either uses, queues for reading or
forwarding later, forwards, or discards it. Reports have certain attributes:
Criterion, problem, accuracy, perishability, sensitivity, length, and report number.
Criterion indicates which sort of collection asset originally generated the report.
Problem indicates which decision maker needs the information from the report.
Accuracy indicates how useful it is to the decision maker. Perishability indicates
how long it takes for the report to become less accurate or worthless. Sensitivity
indicates what type of reporting tie is suitable for transmitting the report. Length
indicates how long it takes a unit to consider information from the report. Report
number is an arbitrary parameter that differentiates the information in the report;
decision makers can only use each report number one time for each problem.
Note that report number is not necessarily unique – the decision maker may
receive the same report from two different units, or multiple collectors may notice
the exact same information. Note that phenomena can be copied
indiscriminately – tasks can be assigned to more than one unit, and reports can
be disseminated to multiple units.
Nodes within this model do not correspond to people, per se; instead they
correspond more to duty positions and functions. Nodes can indicate a single
person – e.g. the president or a CEO in the case of a decision maker – or a
group of people, such as an analysis team in the case of a processor. For this
reason, usually when nodes are discussed in this paper they are referred to as
units. In certain cases, for example modeling very small organizations, the same
person or group may be represented by more than one node – for instance a
market researcher (processor) who also conducts surveys for data (collector).
Nodes have functions, which describe its operations; and attributes, which
describe its phenomenon handling.
The most important type of units in the intelligence organization are
decision makers - proper representation of decision makers is critical to the
modeling of the intelligence process, since they are its natural end. Although not
specifically modeled per se (except in the simulation), decision makers have
some method by which they go about making decisions. Decision makers can
use intelligence provided by the intelligence organization to affect this process,
and metrics of intelligence organization effectiveness (to be discussed later) will
almost certainly deal with modeling and evaluating this process. In its simplest
form, this decision making process is modeled by the ability to generate tasks.
Decision makers are the only unit in the intelligence organization that can come
up with tasks on their own. For the purpose of modeling, decision makers do not
carry out tasks, instead they forward tasks to units that carry them out
(processors, collectors, actors). Because they are the origin of tasks, decision
makers always have tasking ties to at least one other unit (which can be of any
type). Decision makers can also receive tasking ties from other units but since
they cannot execute tasks on their own they must forward these tasks to other
units within their control. Because they use reports, decision makers also tend to
receive reporting ties. It is possible to conceive of a situation in which a decision
maker does not receive reporting ties, but such a situation is useless to an
understanding of the intelligence organization to model this. Decision makers
have certain attributes: problems, priority, and comprehension. Problems
indicate issues or topics that the decision maker is responsible for making
decisions on. For each problem, a decision maker may need certain amounts of
accurate information (that is, sum of the comprehended accuracy of used
reports) from certain criteria of collectors to make a ‘good’ decision. Not all
decisions makers have problems – some decision makers are included in the
organization solely to plan the forwarding of tasks, which is modeled as a
separate function. Power is a relative parameter that indicates how the decision
maker can handle tasks forwarded from other decision makers – if the receiving
decision maker has a greater power value than the sending decision maker, it
can handle the task as it desires; if the receiving decision maker has a lower
power value it may be forced to increase the priority of the received tasks or to
decrease the priority of other tasks that it will forward. Finally, comprehension is
a parameter that affects a random distribution of how much of the accuracy of a
received report the decision maker can apply to its problems – a decision maker
with a low comprehension is more likely to receive less information from a report
than a decision maker with a high comprehension. Decision makers have two
primary functions in this model: they generate tasks and forward tasks. The two
functions of decision makers correspond to the ‘plan’ phase of the intelligence
cycle: they make requests for information that they then turn into specific tasks
for units within the intelligence organization. A decision maker can make a
decision (that is, using the decision making process) to task units to collect
information to fulfill the information requirements of his problems. A decision
maker who receives a task from another unit can choose to forward it to a unit
that he has tasking ties to, to ignore it, or to change certain attributes of it (like its
priority). This allows decision makers to choose the relative importance of
subordinate decision makers’ requirements, or to choose how cooperative they
want to be with other decision makers.
The next type of units within the intelligence organization to be considered
is collectors. Collectors act as an interface between the intelligence organization
and the environment. Collectors notice changes in the environment and respond
to specific tasks to gather information on the environment. Collectors receive
tasks from decision makers and send reports to processors or, in certain
circumstances, directly to decision makers. Collectors have criteria. The
criterion of a specific collector indicates which kind of tasks it is capable of
responding to. Collectors sole function is to generate reports – they can receive
tasks from other units, ‘work on them’ for an amount of time dependent on the
time attribute of the task, and then generate a report on the task. Collectors that
receive tasks while working on other tasks either queue what they’re doing and
work on the new task or queue the new task (dependent on the relative priority of
what they’re doing and the received task(s)). Note that it is possible to model
collectors that do not receive tasks, and randomly generate reports that they
send to processors. This could model ‘listening’ to the environment or access to
open source information (the decision makers may receive so many reports that
they have to task collectors to read the newspaper).
Processors perform the process, produce, and disseminate elements of
the intelligence cycle. That is to say, they receive information from collectors,
process it for worth, usefulness, etc.; produce intelligence summaries or reports
for intelligence consumers; and send reports to databases for storage and/or
send referential information to referential databases. Processors receive tasks
from decision makers and automatically forward them to databases and
referential databases that they have access to. Processors receive reports from
collectors or databases, and send reports to processors, databases, or decision
makers. Processors have two functions: synthesize received reports into
analyzed intelligence products, and store information in databases and referential
databases. Processors can receive tasks from decision makers calling for
analyzed intelligence products of a certain type (that is, the problem attribute of
the task). They then query all available databases for reports on that problem,
read these reports (that is, ‘work on’ the reports for an amount of time equal to
the sum of the length of the reports), and combine the information from these
reports (that is, sum the accuracy of the reports) into one report (with a shorter
length value), which they send to the decision maker and/or to a database of
synthesized reports. Processors can also forward received reports to databases,
which they are assumed to do automatically. Processors do not necessarily
have any attributes, though they could be coded specifically for problem or
criterion. Additionally, a ‘skill’ attribute might be appropriate for certain
processors, which represents their ability to combine the most information into
synthesized reports.
Databases represent the memory stores of the intelligence organization.
Databases contain intelligence information stored for later retrieval. Databases
are contributed to by one or more processors. For example, each processor may
have a database that it maintains privately, while there may be a shared
database contributed to by every processor in the organization. Similarly, some
or all processors or decision makers may be able to query the database for
information. Databases use received reports by storing them, and forwarding a
copy of the report (that is, a new report with the same attributes). Databases
receive tasking ties from every unit that has access to the information in them,
and have a corresponding reporting tie back to the tasking unit. Databases
receive reporting ties from processors responsible for maintaining them.
Databases have a criterion attribute and/or a problem attribute that represents
the type of information that can be stored in them; a size attribute, which
indicates the number of reports that can be stored in them; and a time attribute,
which indicates how long it takes to retrieve and forward the information stored
within.
Referential databases contain information about units within the
organization itself. They may contain contact information for units within the
organization, to facilitate communication between units, or they may contain
information on what reports are stored in which database. In order to model this,
referential databases are represented as receiving tasking ties from units with
access to them and having tasking ties to units that they contain information on
(for example, databases or processors). Referential databases forward received
tasks to the appropriate unit, and forward generated reports back to the unit that
queried them. Referential databases are primarily included as a means of
modeling large organizations – they can represent phone directories or
information directories. Referential databases have the same attributes as
databases, though a size attribute models the number of units that it can forward
to and the time attribute indicates how long it takes to forward the task to the
receiving unit (the time attribute does not take into account the forwarding of the
report, since this is (in reality) a communication between the tasking unit and the
final tasked unit. This time is the sum of the reporting times between the
referential database and the two units.
There is one other type of node that can be modeled – actors. These
units do not generate reports or tasks, so they are largely unimportant to a
structural understanding of the intelligence organization, but they are important to
model in order to complete the intelligence organization’s environmental interface
and in order for use in certain analysis and metric methods. Actors represent the
other part of the intelligence organization’s environmental interface – the ability of
the intelligence organization to affect the environment. Actors receive tasks from
decision makers, and carry them out. They have no specific attributes, except
possibly for a problem coding (indicating the type of problems they can act on),
and they do not generate any other effect within the intelligence organization.
The last thing that needs to be discussed in the modeling of intelligence
organizations are the sub-organizations and agencies within the intelligence
organization and the environment that the intelligence organization operates
within.
The intelligence organization is the entire organization that is modeled – it
corresponds to the cooperative organization of intelligence agencies and unit that
attempts to provide intelligence to intelligence consumers. Though the
intelligence consumers that the intelligence organization serves may not be
physically or formally “within” the organization, they are modeled as part of it in
order to understand the way that the intelligence agency serves them and the
way that their requests are handled. For example, the U.S. national intelligence
structure is an intelligence organization designed to serve national level
intelligence consumers (the president and legislature, etc.).
Agencies are individual, specific, formal entities within an intelligence
organization. Agencies are composed of at least one decision maker, and a
number of other assets (collectors, processors, actors, or other decision makers).
Agencies are useful for designating command relationships and other ‘political’
relationships within an intelligence organization; for example, one decision maker
may wish to task an asset within another agency, but since he does not have
direct control of the asset he must request a decision maker within the agency to
forward the task. Both agencies and the intelligence organization as a whole
have ‘rules,’ which are overarching guidelines for the handling of phenomena
within the organization. For example, a rule could be “all reports with a sensitivity
of greater than or equal to 3 and an accuracy of greater than or equal to 75
should be sent directly from the collector to the tasking decision maker.”
Finally, there is the environment, which is the ‘world’ in which the
intelligence organization exists and operates. It is the source of information
collected, the target of intelligence consumers’ decisions, and is affected by
actors and random events. Events can take place in the environment that would
affect decision makers’ problems, and collection assets have a chance to notice
these events based on how ‘busy’ they are working on tasks. Similarly, actors
can cause events, and collectors may be tasked to find out information about the
effect of the action. A proper modeling of collector response to environmental
change is essential to the analysis of the damage assessment and indicators and
warnings missions of intelligence organizations.
3. Metrics of the effectiveness of intelligence organizations
The task of measuring the effectiveness of intelligence organizations is not
easy: there is little consensus within the organization theory literature on how to
come up with a universal or generalizable metric of organization performance.
The easiest relative metric is a metric of goal completion, but even this has
problems, since the goals of intelligence organizations are not always clearly
stated.
The fundamental mission of an intelligence organization is to provide
intelligence to support intelligence consumers’ decisions. Unfortunately, this
mission, as stated, does not lend much insight in how to measure exactly how
well an intelligence organization is doing this. Other tasks identified for
intelligence organizations include: providing ‘indicators and warnings’ of changes
in the environment; collecting information on other organizations within the
environment; assessing the impact of organization actions upon the environment;
and being adaptable and flexible enough to provide intelligence to intelligence
consumers with differentiated goals and circumstances; that is, being able to
adapt to changes in the environment and the structure of the intelligence
organization.
Other constraints are placed on the intelligence organization from outside
sources. Intelligence organizations are expected to be resource efficient, i.e.
they make the best use of available resources and technology at all times; they
maintain accountability for intelligence failures (misinformation) or unethical or
sloppy conduct (really bad decisions); and they prevent the disclosure of
sensitive information. Combining the internal and the environmental approaches
to the goals of intelligence organizations can create a meaningful multiple-
constituency framework for the analysis of intelligence organization (Connolly,
Conlon, and Deutsch, 1980).
Possible quantitative measurements for intelligence organization
structures can be derived from the above goals and constraints upon intelligence
organizations. Perhaps the most meaningful metric of intelligence organization
success is its ability to support intelligence consumers’ problem solving: consider
the intelligence organization over a period of time and see if the intelligence
generated was able to solve the decision makers’ problems. If so, the
percentage of problems solved over the problems not solved would be an
appropriate measure of the success of the intelligence organization.
If meaningful quantification of intelligence requirements for problems
proves to be inappropriate or intractable, the speed of the intelligence
consumers’ decision process can be use. This would use one of the cyclical
decision making, such as Boyd’s OODA loop, as a representation of the decision
makers’ decision process. The amount of time it takes to go through the loop
one cycle would be an appropriate metric of the ability of the intelligence
organization’s effectiveness. Similarly, the speed of the intelligence
organization’s function could be represented for intelligence consumers – that is,
the intelligence cycle could be represented in terms of the number of iterations it
takes for each intelligence consumer to return to the ‘plan’ phase of the
intelligence cycle. Both of these loop based metrics would provide useful details
on the comparative effectiveness of the intelligence organization with respect to
different clients.
Finally, one of the most popular metrics of intelligence organization
effectiveness is the lack of intelligence failures; that is, ensuring that the
intelligence organization does not report false information or fail to report
information of value to intelligence consumers. Though there is no provision in
the model as developed for false information, this would not be difficult to include;
the failure to report information constraint can be modeled in other metrics of
effectiveness such as proportion of problems solved.
When assembling these metrics, certain concerns should be kept in mind.
Intelligence consumers and processors are boundedly rational – they can only
review a certain amount of incoming information, and the intelligence
organization should keep away from information overload. Metrics such as
intelligence cycle or decision cycle speed can model these phenomena. Also,
units are boundedly capable: they can only perform a certain number of tasks,
and if they keep receiving new orders or distracting orders they will not get
anything done – ‘chasing the tail’ behavior, as described by Alberts, Garstka, and
Stein. A problem solving metric can identify these phenomena.
4. Analysis and experimentation methodsThe end purpose of this model is not solely to measure the effectiveness
of the intelligence organization, but to determine structural effects on the
effectiveness of intelligence organizations. For this purpose, social network and
computational analysis methods can be applied to the models of intelligence
organization structure to determine the correlation between structural
measurements and effectiveness measurements.
Social network analysis provides many useful measurements of
organization structure that can be applied to this model. These measurements
include centrality and cognitive load measurements, path length measurements,
density and degree measurements, and meta-matrix representations. For the
purpose of many of these measurements, it is convenient to separate the tasking
and reporting communications structures, and to analyze them separately.
Centrality measurements are generally based on the number of paths that
a node lies on. Centrality measurements are generally taken into account for
sociograms with non-directed ties; however these measurements can be applied
to directed graphs, in this case with meaningful results. High centrality in the
tasking communications structure indicates that the unit is responsible for
forwarding tasks. In a large intelligence organization, the units with the highest
task centrality should be referential databases; since the function of these units is
automatic, however, it may be useful to ignore them. In smaller intelligence
organizations and organizations in which referential databases are ignored, task
centrality should be highest among decision makers responsible for planning. If
planners are not highest in task centrality, it means that decision makers without
formally designated planning authority are nonetheless responsible for most
planning decisions (as may be the case with decision makers who are solely in
command of collection assets of a certain criteria that is highly demanded). High
centrality in the reporting structure means that the unit is responsible for
forwarding multiple reports, which should again correspond to either databases
or referential databases. Under these circumstances, databases should not be
ignored – a database with high report centrality is contributed to by many
processors, or at least many centrally placed processors, and therefore contains
a large amount of information; these databases should correspondingly have a
high tasking indegree, to ensure that significant portions of the intelligence
organization have access to this information. A high centrality on both the
tasking and reporting structures is indicative of large cognitive load, and should
correspond to the largest databases and the processors serving the most
important decision makers (since processors have tasking ties with databases
and bidirectional reporting ties with databases).
Average path length is also an important measurement of intelligence
organization structure. Long average tasking path lengths between intelligenge
consumers and collectors mean that consumers are largely sheltered from the
intelligence planning and task allocation process; long average reporting path
lengths between intelligence consumers and collectors mean that the intelligence
consumers are receiving highly analyzed and probably somewhat late
information. On the other hand, short average path lengths in both networks can
contribute to high cognitive load measurements for large numbers of units in the
network, contributing to possible widespread cognitive overload (the information
overload and chasing the tail phenomena described above). Other
measurements of the communication network, such as density, are less
meaningful to the study of intelligence organizations, though the comparison of
separate density measurements for different types of reporting or tasking tie can
be measured to analyze use of information technology in the organization.
The meta-matrix – slightly edited for the attributes of units in the
organization – provides an extremely powerful tool for analysis of network
structure in the case of intelligence organizations. The different types of meta-
matrix values can be altered by unit types (decision maker-collector, decision
maker-decision maker, etc.), or certain attributes (decision maker-criterion,
criterion-criterion), to determine various types of relationship in large intelligence
organizations. Unfortunately, the intelligence organization model under
development is still at too early a stage to present mathematical formulae for
deriving these network structure measurements.
This model of intelligence organization structure and the metrics
previously discussed lend themselves to computer analysis and experimentation.
Computerized methods can be used to compare different intelligence
organization structures, different agency or organization rules, and/or different
communication attributes with ease. In the simplest model, phenomena, units,
ties, and agencies are modeled as discrete variable bundles with sets of
attributes. The movement of phenomena throughout the network can be tracked
and recorded in databases to provide a log of phenomena action and
transmission. This sort of ‘traffic analysis’ can precisely identify snagging points
in the organization – points of information overload or assets forced into chasing
their tail; or points of inefficiency within the organization – needlessly long
reporting or tasking paths, persistently idle units, or decision makers who are not
receiving sufficient reports or tasks. By varying organization structure in
successive iterations of the experiment and observing changes in traffic patterns,
design guidance for intelligence organizations can be derived. Finally, by varying
the attribute parameters of ties, nodes, phenomena, and rules within the
organization singly and jointly, a sensitivity analysis can be done on the findings
of the computerized experiment and correlation between attributes can be
measured. The simple traffic analysis, though powerful, is only one aspect of
virtual experimentation on this model. A codification of decision loops or
intelligence cycles for each decision maker in the organization can allow for
speed-optimizing tests to be run on various organization structures. In doing so,
conclusions about structural effects on command and control speed can be
reached.
All of this presumes, however, that the model works. This paper would be
remiss if it did not discuss methods by which this model of intelligence
organization structure could be validated.
First, and most practical, is the comparison to historical intelligence
organization charts and performance records. Though we are unlikely to gain
access to historical databases of U.S. intelligence organization activities,
historical records of intelligence organization activities in other nations are
available. A model could be developed based on these organizational charts,
and predictions of intelligence organization effectiveness could be compared with
historical judgments. If a computer model is developed, phenomenon movement
logs can be saved for time iterations of the simulation and compared to
corresponding message logs within the organization. Though the will necessarily
differ somewhat, general trends can be compared and the ability of the model to
make accurate predictions can be derived.
5. Conclusion, Strengths and Weaknesses
Though this model presents a potentially powerful tool for the analysis of
intelligence organizations, it is by no means complete. Its theoretical backing
with respect to many of the trends and developments in organization theory is
incomplete or non-existent, it is as yet not validated, and at presents it lacks the
ability to exploit many powerful tools in organization analysis. On the other hand,
this model shows great potential – it is scalable from the largest intelligence
networks to the smallest; it takes into account multiple agencies, assets, and
decision makers; it is simple and easy to understand; and makes a formal and
quantitative analysis of intelligence organizations possible.
The theoretical backing for this model is as yet incomplete. Though it is
based on a relatively thorough understanding of the command and control,
military and strategic intelligence, and tactical decision making literature, it does
not take into account many elements in the academic literature that could be
relevant. It has not been considered in terms of the rational decision making
literature, which could provide additional relevant insights on how to measure
intelligence accuracy and its effect on decision makers’ choices. Its network
structural measurements are well grounded, but it ignores entirely the literature
on dynamic networks, adaptive networks, and organizational learning. Additional
research in these fields could provide a method by which this model could be
applied to dynamic networks, which would increase its applicability and power
substantially. Finally, this network model ignores the literature on economic
incentives and decision making within organizations (a market decision making
process). Nothing about the model precludes an application of market based
resource and unit allocation schemes save for the lack of research no how to
apply these. A further study of this literature could provide insights on how to use
this model to consider personal power in intelligence networks and information
economics measurements to the decision makers’ decision processes; which
would be quite useful in expanding the applicability of the model to civilian or
business sectors.
References
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