Structure and Effectiveness of Intelligence Organizations



Structure and Effectiveness of Intelligence Organizations

Robert Behrman

Engineering and Public Policy

Carnegie Mellon University

Pittsburgh, PA 15213

412-268-1876

rbehrman@andrew.cmu.edu

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 methods

The 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.

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