Dialectical Intelligence Analysis

William G. Foote

March 28, 2018 (updated November 1, 2018)

Dialectic exists whenever there are opposing sets of data, understandings of the meaning of the information from data, judgments, priorities, decisions, priorities, and methodologies. Within the subjects of analysis, social, economic, military, religious, technological, and political systems form opposing forces of progress and decline. Embedded in the intelligence process analysts may oppose other analysts and policy makers while policy makers may oppose other policy makers, foreign and domestic. Ultimately the root of opposition is often the conflicting horizons of analysts, subjects of analysis, and consumers of the analysis. Limited horizons may also be rooted in inaccessible data, inappropriately applied techniques, irresponsible priorities, and short-fused times to decision.

Intelligence analysts, whoever their employers may be, are "knowledge workers." They engage in a decision process at one end of which is the production of knowledge. Understanding how analysts (or anyone for that matter) understand thus requires an understanding of the cognitional theory, epistemology, and metaphysics they implement. This paper extends Bruce (2014) to consider the dialectical analysis inherent in cognitional inputs to epistemology and the overarching objectivity of a metaphysics as horizon. Dialectic can help analysts progress to more efficient and reliable intelligence products especially by highlighting the sources of error, deviation, and distortion.

This paper proceeds in five parts:

1. Motivations for the use of dialectic analysis stem from existing challenges for the provision of high quality intelligence product in government and private sector settings.

2. Understanding analysts in the analytic process and within the analytic product locates limiting horizons as a source of error, deviation, and distortion.

3. A framework begins to be developed from first principles of the operation and objects of knowledge as a judgment of fact and value to decision makers.

4. The framework develops analyst dialectical operations using the highly stylized setting of several regional actors very interested in crude oil production and shipping.

5. The paper concludes with further thoughts and some next steps.

1. Some further motivation

The "bottom line" of dialectical analysis:

Without a rigorous method of resolving and transcending opposing beliefs, frameworks, data collection, analysis, and communication of relatively biased intelligence product, intelligence analysis will generate unreliable, confusing, and ultimately non-actionable product dangerous to the physical, cyber, operational, and financial security of any entity and its beneficiaries.

The admonitions of Richards J. Heuer continue to ring true for all analysts, whether government or private sector.

Intelligence analysts should be self-conscious about their reasoning processes. They should think about how they make judgments and reach conclusions, not just about the judgments and conclusions themselves.

(Heuer 1999)

The "complete analyst" is described by Lt. General Michal Flynn, et al.

"Analysts must absorb information with the thoroughness of historians, organize it with the skill of librarians, and disseminate it with the zeal of journalists.

(Flynn, Pottinger, and Batchelor 2010)

Epistemology (as a "theory of knowledge") enters the fray:

Epistemology should be a core idea in any discussion of intelligence. It suggests the importance of identifying how different ways of knowing can profoundly impact the judgments that analysis produces.

(Bruce 2014)

Heuer and Flynn et al., along with Moore (2009) and a manifest interest and use of critical thinking, and with Bruce (2014) and the development of an epistemology of intelligence analysis, continue to caution analysts to perform a much deeper dive into the reasons and approaches used to develop intelligence product for consumers. These analysts conclude that even with advanced critical thinking skills, including those embedded in devils advocacy, even with the elimination of currently understood bias, analysts and their consumers might simply pay the wrong attention to the wrong issue with the wrong thinking, with potentially catastrophic results.

A different but cognate movement is discernible among private sector business analysts. "Analytics" is the perview of enterprise systems where the emphasis is on data-driven decision support. Traditional structured systems analysis of transactional data have been complemented and sometimes substituted by a variety of new approaches such as machine learning, artificial intelligence, and botnets. Organizational focus has also shifted over the years from transaction systems to decision support and competitive intelligence. However, market and company failure persist even with advanced systems, more data, and increased emphasis on the decision maker.

For both government and private sector analyst the problem is the same. Target and decision centric intelligence demand efficient and comprehensive products that can themselves develop further insights and actionable product. The solution is to continue to build comprehensive analytical methodologies that are grounded in a practical use of a theory of knowledge. The challenge is overcoming not only the biases of requirements,

analysts, and policy makers, but incorporating the biases of the subjects of analysis and policy making.

2. The analyst, the intelligence process, and the product

For any analyst, process and content become blurred. Faulty process will yield good outcomes only by chance and rarely at that. Solid content, informative for decision makers, is nearly always the result of a holistic process that builds relationships among the objects of the analysis and estimates of intelligence that forewarn, and thus forearm, the decision maker.

2.1 Alignments, or not

Analysts, and their consumers, appeal to shared, somewhat aligned, principles to reconcile opposing conclusions. Yet often enough the principles themselves are radically opposed. One solution is to be rid of the opposition. While that may work in a very short run time frame, in a longer run, this means being rid of potentially contrasting, clarifying, and residual points of view.

In statistics, for example, the most important part of the analysis is not simply identifying the trend, e.g., percent of the population under insurgent control. When we analyze the trend we can get to the drivers of trends, e.g., population distributions, political party duration, life expectancy indices, and so on. We use data that is accessible and may be corrupted and not credible or plentiful enough to make any reliable judgment about the trend. But the point of statistical analysis to work through the deviations from the trend. The "residual" is not explained by the drivers of the trend and forms the insights needed to understand how trends can change. The dialectic here is an analysis of the movement from current trends to deviations from trend and onto to the persistence of the current trend or the creation of a new trend, a "kink" in the curve, where behavior is now significantly different.

As in statistics, so it is in systems. Systems are always on the move from decline to progress and on to progress or more decline. For example, a steady increase in the standard and quality of living might all of a sudden succumb to a steep decline with thousands of refugees crossing borders and wreaking environmental changes that decimate populations with generations of new pathogens. A clear conflict exists between the progressive trend and the current outlying decline. Does this bode a new trend? The trouble with declines is that they might breed new and progressive leaders as there is now an unmet demand for quality of life. On the other hand, the decline may continue with the iron totalitarian hand of a dictator's oligarchy in power. Here dialectical analysis would involve the differing horizons of opposing groups and their supposed and actual influence on the many layers of society, clearly a very long-run analysis across teams of analysts.

Clarifying the horizons, their genesis, and alignment, can help reconcile opposing principles. But principles are cultural inheritances and their application is intensely personal. Further considerations will come to an abrupt stop, even when we remain convinced that the other person is wrong. What is needed is a method that can uncover the

sources of error. After all, even culturally inherited principles first occurred to someone, and that someone may or may not have been biased. So there is considerable merit to investigating the innate methods by which we construe – and sometimes misconstrue basic principles.

2.2 Knowns and unknowns

Static approaches to intelligence analysis seek to understand unchanging essentials, logic, and a relatively unbending policy with force of law the rule. With the ongoing emergence of conflicting cultures, interests, and resource requirements, across state and non-state actors, intelligence analysts, fomented by the likes of Heuer, turn to understanding the innate methods of mind by which analysts discover what they do not yet know and create what does not yet exist. These approaches propose to overturn the errors of missing trend drivers and thus obfuscating the trend itself. They implicitly also begin to uncover the oversights of deviations from trend, and the distortions of decline. Here oversight is the lack of insight into deviations, while distortions are the current and projected inability of a system to return to progress.

Heuer, Bruce, and Flynn et al. presuppose a philosophy of intelligence analysis, and by implication a way to correct error, oversights, and distortions. Here philosophy is the attempt to illuminate the effort of intelligent, reasonable, free, fully responsible self-constitution of the analyst in the concrete context of fulfilling intelligence requirements. It is not at all about the specifics of those requirements, or even the specifics of methods to fulfill those requirements. It is about understanding how the analyst understands and then uses that understanding to organize a presentation of the facts as results of the analysis structured by insights to a consumer of intelligence. If this is true, then the organizing issue is horizon.

Horizon analysis is a tool implicit in structured analytic techniques, the trade craft of the analyst. Horizon defines the limits of knowledge, and worse, the ability to know further. There are the questions that the analyst can answer, and that totality constitutes what the analyst knows. Then there are the questions that one can raise, find important and worth knowing about, understand what they mean, and know how to solve at least in some remote fashion, even if one does not know the answers to them. That forms the second, surrounding circle of the "known unknown." Finally, there are the questions that cannot be raised at all, or that if raised are not understood, or that if understood in some fashion are not considered worthwhile, or that if considered worthwhile have no apparent method of solution. This constitutes the still further region of what may be named the "unknown unknown." The limit between the last two, between "known unknown" and "unknown unknown," is the horizon.1

1 This taxonomy of the "known" can be traced back at least to the 1440 treatise by Nicolas of Cusa De Docta Ignorantia ((Cusa and Hopkins 1985)), which might be translated as a judgment of fact with Known Ignorance and as an implied judgment of value with Learned Ignorance. The latter is a distortion where an analyst might habitually wander into a field of

Bruce (2014) begins such a journey across the knowns and unknowns, and thus implies various horizons. He details an epistemological analysis of the intelligence analytical methodology along four competing "epistemologies." "Dialectic" is excluded by Bruce as an irrelevant form of reasoning for intelligence analysis. However in an argument by "retortion," the very withdrawing concern from dialectic is actually yet another form of dialectic. The analyst uses dialectic whether the dialectical analysis is identified as such or not.2 This is because the very process of knowing itself is dialectical in nature, especially the self-correcting dynamic of learning what is, what is not, and what is in priority to other outcomes. Error and correctness are in opposition as are deviation and trend, and progress and decline. Intelligence product subsumes analysis and it thus in a push and pull of opposition. Analysis subsumes data collection and also exhibits opposing roles, functions, inputs and outputs.3

2.3 Empirics and humans

The empirical method behind the success of natural sciences progresses from a mind that collects data, formulates and verifies hypotheses about relationships in the data, and verifies if the relationship exists or not. But human actors are not molecules, electrons, or seemingly simple organisms. Humans, whether terrorists or bankers or private company executives or government officials, make meaning and assign values to judgments of what is is is not. The making of meaning, that is, interpretation, is intentional.

such limitation of data and vision about the data as to yield an embedded ignorance about how trends, deviations from trends, and distortions of progress might exist and evolve.

2 Bruce (2014) rightly discusses potential groundings of knowledge: authority, habit, rationalism, empiricism, and science. He dismisses "[a] fourth system, dialectical reasoning, ... devised by Friedrich Hegel and made famous by Karl Marx but is omitted from discussion here given its limited applicability to intelligence." The Greek roots of dialectic, especially as opposed to eristic, are discussed by Walton (1998). Hegel derived his dialectical approach from Fichte. Marx deployed a mechanistic materialist dialectic that clearly is not appropriate for intelligence analysis.

3 For Bruce, knowledge is "justified true belief" and "science" wins the race to provide probable assurance in the judgments of analysts. However, in the context of the current paper, knowledge is a "virtually unconditioned judgment." Both definitions rely on the data of sense (quantitative indicators) and of consciousness (beliefs and qualitative indicators). Even quantitative indicators are created from a belief in the particular indicator and its usefulness in the particular analysis. When a judgment is "unconditioned" then the judgment of what is includes the judgment of what is not, and the judgment of what is valuable necessarily carries with it the judgment of what is not valuable. Necessarily then, judgment is the result of a dialectical process. In fact, an epistemology is fundamentally incomplete and defective without dialectic. Also in fact, Bruce skillfully couches his arguments and builds a very useful checklist using implicitly a dialectical approach at least by contrasting opposing positions.

In fact all empirical method is intentional in that an analyst intends, as subject, to study, the intended, that is the object of the analysis, for another set of humans, policy makers who in turn intend to act on the analytical findings. It is in this context of the moving horizons of analysts, subjects of the analysis, and decision makers, that intelligence analysts must squarely face a view of data wider than that provided by metrics of frequency and severity of outcomes. They must include the data of the consciousness of individual decision makers and their developing relationships as makers of meanings and values. This is what makes the very human science of intelligence analysis so different from simply experimenting on protons and electrons (and even that is intended by researchers).

When analysts compound the data of sense, e.g., oil rig counts, or the geograhical disposition of an insurgent force, with the data of consciousness, e.g., the influence of profit incentives on an oil executive's judgment and subsequent decision, one may ascend through hypothesis to verification of the operations by which a decision maker deals with what is meaningful and what is valuable. After all knowledge of what is valuable to a decision maker is the motive force for a decision.

Alternatively, when analysts only rely on the sensory data available they evade the deeper meanings and operations on those meanings that decision makers use to make value. This is where intelligence estimates can risk missing even the known unknown, fail to prepare decision makers with appropriate realistic scenarios, and lead to further declines in analytical capability for the next round of estimation. The intersecting boundaries of known unknown and the scarier unknown unknown across all actors is the horizon or field of experience that the analyst must contend with.

Similarly over reliance on the data of consciousness of the decision maker, and instead on unfounded "intuition," will result in the failure to collect relevant data. With insufficient or even the wrong or corrupted data collection and analysis ultimately misestimate a growing situation as well as deplete the stock of data itself for inclusion in the next round of estimation.

3. Developing a framework

How can an analyst include the compound of the data of sense with the data of consciousness of those making the decisions that result in more sensory data? At the risk of introducing yet another philosophical term, we can say that all intelligence analysis is part phenomenology. It is thus one account, one description, one presentation of data structured by insight.

Data can be nominal, ordinal, interval or ratio in nature insofar as it represents a categorization, a rating scale, a sequence of measurements, and measurements against a benchmark. Insight is the unifying and organizing act of understanding that is the value added by the analyst to the decision maker. Just as there is insight into the relationships among the various objects of an inquiry, there is the inverse insight that the analyst does not have the right data, right framework, or even the remit, to discover the unknown unknown (indocta ignorantia of Cusa and Hopkins (1985)).

Insight is not automatic and depends on the dialetical tension between what is known already and what possibly can be known as well as on what known to be a deviation or distortion, but not yet understood well enough even to pose a question. With a definition of data, and two versions of data that matter to an analyst, all coralled together with a notion of insight, we can now ask some really pertinent questions to help us with horizons and frameworks.

3.1 Four questions

An encompassing framework presaged by Collier, Herbert, and others (see Collier (2005) and Herbert (2001)) and set forth by Lonergan (1957) would have analysts answer the following questions to provide a start at eliminating personal, group, and systematic bias in building the intelligence product. The questions track across cognition, epistemology, yes, a metaphysics, all aiming at a systematic framework also known as a methodology.

1. A cognitional theory asks, "What do I do when I know?" It encompasses the operations that give rise to judgments of fact and value. These operations include all of the techniques in Heuer and Pherson (2011). But we must also include the integrating notion that "operators" are intending to know in the first place. When that intention is unbiased, then horizons become less limiting. Less limited horizons then play to strengths of an analyst's use of a particular analytical technique applied to a growing base of data.4

2. An epistemology asks, "Why is doing that knowing?" It demonstrates how these occurrences may appropriately be called "objective." This is where dialectic certainly resides. We have subjects and objects (analysts and the objects of analysis; insurgents and other subjects who are the objects of their insurgency). What is objective is found in the widest possible horizon where there are no further relevant questions to be asked, and answered.5

3. A metaphysics asks “What do I know when I do it?” It lines up decision maker's priorities because it identifies corresponding structures of the realities we know and value, what is possible, what is not. It provides objective criteria for determining what is, as opposed to what is not, and what is valuable, as opposed to what is worthless.6

4 For cognition to work properly, analysts would need to collect the right data, apply the right technique, and make the best and most probably judgment. Inherent to this standard work flow is the overriding priorities placed on the analyst of time, place, relevance, sufficiency, and allowance for ambiguity.

5 Objectivity does not mean absolutely correct and right. It does mean a conclusion, a judgment, that is verifiable in the data of sense (e.g., measures of insurgency force levels) and of consciousness (e.g., rated and vetted importance of insurgency to the welfare of a region), for which there are now, and given all variety of circumstances, virtually no conditions and no further relevant questions.

6 The analytic community is getting used to "meta" objects. For example the "meta" data of an organization's performance ontology might include a metrics taxonomy (hierarchical

4. A methodology asks, "What therefore should we do?" It lays out a framework for collaboration among collectors, analysts, policy makers, assets, and others, based on the answers to the first three questions. It is this framework that analysts use as reference, and guide. Importantly it gives analysts employed at various levels in various organizations a common vocabulary, road map, and expectations from the analytic function.7

When questions 1 through 3 are answered and implemented through question 4, the analyst and consumer of the analyst’s product begin to partake of the dialectical method. What limits analysis and the intelligence product ends up being the limitations of the horizons of the analyst, the consumer of the analysis. The subjects of the analysis, the personages, the policies, the history, the technical, social, economic, and political, and cultural matrix against which the analysis is run, also have limiting horizons. With a limited horizon, the analyst can only ask questions within the horizon, and rely on answers only within the horizon. This is the source of error of any analysis as well as the consumption of an analysis by a policy maker. The same source of error becomes, dialectically, a spring for coordination, collaboration, and critical review of analytic outcomes.

3.2 Many horizons

Following the analysis of Lonergan (2001) the multiplicity of horizons may be considered along three paths.

1. As an assumption of the analysis and a matter of fact, horizon provides material for a set of intelligence requirements, a record of past events and results, including failures of intelligence, and an instant reason for the analysis.

levels with priorities and connections) with attributes (definitions, relationships in the hierarchical ranking, formulae, input data, output locations). "Metadata" does not include the names of actual metrics, or the IP addresses of report locations. It is an armature off of which hangs analytic content. So "metaphysics" categorizes and prioritizes the inputs, process, outputs, and stakeholders of the analytic enterprise. It does not include the realizations of actual data inputs, the mechanics and operations of analytics (cognition), outputs or specific priorities (the midnight deadline).

7 Everyone has a methodology, even if not explicit. For example, the use of COSO (Committee of Sponsoring Organizations of the Treadway Commission) has made very explicit the role, function, and operation of enterprise risk management (ERM) in government (see OMB Circular A-123) and non-government organizations (see the Sarbanes-Oxley Act) alike. Having an ERM analytic framework has become a sine qua non of management for at least the past 10 years. Prior to that, many organizations practiced a far less unified, and hard to verify and thus make objective, process of risk management that could only be called "heroic recovery."

2. The multiplicity of horizons may be considered as a problem to be explained, and then one gets as solutions to the problem such as in Moore (2009) and Bruce (2014), and their many citations of efforts to improve intelligence product.

3. This multiplicity may be considered as an issue calling for deliberation, judgment and decision. So that along with Bruce (2014) there are the many analytic techniques of Heuer and Pherson (2011), especially a species of dialectical analysis they call "challenge analysis."

It is on the third level that the multiplicity of horizons is a question assigned to cognitional theory, epistemology, a metaphysics, and ultimately of methodology. The multiplicity is a cognitional and epistemological question when we ask whether some horizon is the field against which analysis proceeds, whether some horizon is coincident with the limits of all that there can be. If we answer that question affirmatively, if we say that some horizon is the field, then how can that horizon be determined?

But if we affirm that some horizon is the field, we are involved in the second question, How do you determine which horizon coincides with the field of inquiry and of decision? That question is simultaneously ontological and epistemological. A given horizon also defines the field, so that what is outside that horizon is nothing and thus meaningless and insignificant in the absolute context of the inquiry made by the analyst. Thus to select the true horizon is to lay to lay down the criteria of what is and what is not, what is significant or not (think here of statistical hypothesis testing), and what is meaningful or not (think here of decision priorities).

3.3 Dealing with naysayers

As Moore (2009) and Bruce (2014) develop the positions of pure empiricists and others, we also need to meet the challenges posed by the pragmatists, the skeptics, and the relativists would deny that any horizon is the field of inquiry.

• The empiricists would say, "Let’s do science (and statistics)." Of course we will collect data. But some of that data is about humans making decisions across multiple fields of experience. In effect is is "doing science (and statistics)" about science. The empiricists, as Bruce (2014) so well points out, need an interpretation of their results that cannot, by definition, lie within the narrowly data-focused purview of empiricism.

• The pragmatist would say, "Let’s perform some experiments and see what happens." After all, the pragmatist, along with the rest of us, lives in the concrete world where oil wells are drilled, bankers finance pipelines, insurgents disrupt pipelines, and national oil companies seek to advance the economic and financial welfare of their nations. A guiding hand is needed to make pragmatic useful.

• The skeptic would want to inquire some more. But it is worse than that! Our knowledge is based on the knowledge of a contingent world, and our knowing is a contingent event. To demand the absolute and to be content with absolutely nothing else results in a skepticism that will forever spin its wheels of indecision, except to decide not to decide.

• The relativist would say that all answers are very interesting but none can be definitive. Just like the skeptic, the relativist on the one hand says anything goes, and on the other is paralyzed by the lack of necessity and absolute certitude.

Grant that time constrains every analysis. The analyst has to get to a judgment and avoid the common malady of "analysis paralysis." True? and here is where the accumulation of insight evolves in to a habit of solid leads and even "hunches." Following these paths will make analyses efficient, and allow for the possibility of further challenge at the same time.

The relations of all oil rigs, oil executives, bank accounts, policies and policy makers, insofar as they concern the practical problem of living in the concrete of the here and now of the intelligence requirement, is the world of common sense. The relations of beings to one another according to their agreed upon and even natural laws is the world of science. Again, the two truths are simply the result of applying the appropriate criteria to two cases of knowledge that is sought

4. A tale of four decision makers

4.1 Some recent discoveries

Up to this point the exposition hase been a dry, abstract, seeminging logical exposition of the fundamental process and product of intelligence analysis. we have discovered that

1. What really matters to the objectivity of the analysis is the multiplicity of horizons of analyst, decision maker, and objects of the analysis. The analysis will only be as good as the narrowest horizon.

2. Within the multiplicity of horizons, the analyst, decision maker, and objects of the analysis are not just rivers, rigs, or regions, they are people who intend meaning, and based on their intended meanings act, often very differently from one another. This means that all intelligence analysis must compound the data of sense with the data of consciounsness: rig counts and executive priorities and incentives both matter.

3. "Reality" ends up not being just the bitumen in the road, but the representation of a tarmac surface that links locations, on which certain lorries can carry loads, with bridges that wash out in the rainy season, that is critically important to at least one decision maker, and so on. Thus pure data analytics is devoid of meaning and significance unless analysts imbue the results with interpretable context. But with multiplicity of horizons there will be multiplicity of interpretations and significances. That is the "reality" the analyst and the consumer of analytics faces.

Given the many possible combinations of oppositional views, data, judgments, and criteria, only a dialectical analysis can hope to at least illuminate the size, and extent, of any known unknown.

Let's begin to apply these discoveries.

4.2 Four actors and an analyst

Imagine these four decision makers who face a commmon possible opportunity or threat in the development, production, transportation, financing and trading of oil from a nation-state's offshore oil reserves.

1. Privately held oil company executive. The company continues to log wells in the west African continental shelf. New proven reserves are discovered. The lead manager must decide timing, extent of drilling, production, arrangement of local and non-local resources, including labor, equipment, and funding, and perhaps partnering with local entities through a bilateral investment agreement or a production contract.

2. National oil company (NOC) executive. The NOC attempts to shadow and take over exising and potential drilling sites in line with national economic and security directives. The executive can do so through economic and financial means including buying out leases, managing the flow of local resources, including labor, equipment and funding, negotiating production contracts, and perhaps partnering with non-conventional actors.

3. State-sponsored insurgent. The terrorist organization's aims are dispersed through local factions. They view the private company's discoveries as opportunities to advance the organization's multiple aims, including disrupting the status quo locally, funding new operations, imposing a new order.

4. Banker. The banker stands as intermediary amid the other three actors and a latent actor called the world market for crude oil and financial capital.

The intelligence analyst must develop a multi-period estimate of the threats to regional security generated by these actors. So, where does dialectic rear its head? We start with the "horizons" each of the four actors has, and also consider the analyst's own horizon in the process.

4.3 Conflicting horizons

While the common thread of benefitting from offshore oil reserves unites these actors, each has different time, resource, stakeholder, financial, and experiential horizons.

• The corporate oil executive faces quarterly earnings reviews by shareholders who may believe the company is over-extending its reach into developing these reserves. The company lives from quarter to quarter. The executive must compete for resources world wide and understands the impact of local instability on basic rig operations.

• The NOC executive must answer to a state that faces insurgencies, poverty, social unrest, and the requirements of an oligarchic ruling caste. Timelines are of random size: a day, a week, a month, a year? given the volatility of the environment. The NOC executive used to work as a project manager for the privately held oil company.

• The insurgents develop and take advantage of opportunities to disrupt established institutions and extort funding from other actors through hostage-taking. Time is on their side.

• The banker can launder the terrorist's funding and oligarchs' payoffs as well as finance private and NOC projects, float sovereign state budgets, and trade on its own account. The banker also straddles calendars and lays in plans given various time-based contingencies.

Against this context, the analyst answers to the intelligence requirements laid down by a government or private sector consumer, who makes policy, with goals, tolerance for risk, and agenda that may or may not align with the analyst's experience, approach, data, and general horizons around the decisions, objectives, and constraints faced by the analyst.

Collision of horizons and differences in decision making are the subject of dialectal method. The actors and the analyst's horizons may intersect, but none of them coincide. They will all make various decisions using disparate objectives subject to different constraints. Far from simply not seeing "eye to eye," dialectic plumbs the depths of conflict found in opposing methods, data collected, timing of analysis and production of intelligence product, consumers of intelligence product.

4.4 Enter dialectic

The oppositions also inhabit differing intelligence agency views expressed in policies, analysis, production, consumption, asset generation and retention. Conflict will be found in the execution of collection by collectors, who may differ among themselves; analysis by analysts each with their bias; communication of product to consumers and their predilections. Untying the gordian knot of opposing intelligence execution is the job of dialectic.

Given there are many possible permutations of actor, and analyst, horizons, it is power that really matters. There is power in the four decision makers: they can make and implement a decision that will positively or negatively affect the other decision makers. What is legitimate power or not depends on authority as power legitimated by authenticity. Authority is that portion of a tradition produced by attention, intelligence, reason, and responsibility. As only a portion of a heritage, authority is a dialectical reality, to be worked out in mutual encounter among the decision makers.

Authenticity stems from three dimensions found in any community, including the one defined by the four actors. The intelligence analyst stands outside of this community, but has her own community to deal with. The actors as a group form a community that is itself a dialectical reality. It can only be so if there are opposing forces at work. We already thought about opposing horizons. These horizons can be further refined.

• Decisions may be based on simple actions and their reactions among the four decision makers. At this primal level, the actors are superannuated versions of insect colonies, prides of lions, and packs of dogs. They pursue one goal after another in action or

reaction to others's similar pursuits. This is the knee-jerk decision of a banker who has just been discovered embezzling funds from an insurgent customer.

• Opposed to simple action-reaction is the compendium of practical insights a whole tradition of decision makers have melded into conventional wisdom. This dimension corresponds to Bruce's "habit of thought" form of epistemology, perhaps with a sprinkling of "authority." But conventional wisdom cannot progress, as it is, by definition, the current convention to which everyone in the community adheres, sometimes, for dear life. This is the existing business model of the private oil producer, the political party system that instructs the NOC executive to take or not take action, the world financial market that even the banker cannot usually supersede.

• Finally opposed to action-reaction and conventional wisdom are the values that ground why a community, and specifically, a key decision maker in that community, should or should not act in a particular way. This constitutes a moral dimension of right and wrong that provides a prioritization of actions with consequences. The private oil company executive, if U.S. based, and listed as an SEC registrant, must abide by U.S. laws, including disclosure of material conditions that affect investments like oil rigs in potentially high security risk environments. A culture of risk aversion might attend such decisions so that the executive might avoid funding from parties possibly known for terrorist associations.

Here the analyst needs to use the notion of dialectic to understand how these four decision makers as a community is a system on the move cycling through progress and decline, the concrete resultant of the mutual conditioning of these three opposing forces:

• When action-reaction dominates a community, its decision makers often do not have the luxury of considering options, developing resources, or perform any action that takes time, and money. This dominance, if it becomes embedded in the normal course of analytic work flow, will ultimately allow anyone to justify anything through arguments such as exigent circumstance.

• When conventional wisdom chooses to ignore the action-reaction dynamics of a group, or the values that hold a community together, a community can devolve into a reliance on rules in spite of fresh sources of the data of sense and consciousness. These rules may indeed have emanated from breakthroughs and the progress over the decline of human communities. But they stagnate and result in oligarchies that protect their current power base.

• Where the calculus and rules of pleasure and pain dominate values, analysis is biased, and deeper human needs will be ignored. As deeper needs are ignored, common sense becomes overwhelmed, progress turns into decline, and the guiding like of shared values gives way to a knee-jerk reactionary culture.

Communities move, pushed and pulled by these principles, now converging toward, now diverting away from genuine progress. In the very concrete of specific factors, timing, force levels, trajectories, trends, and estimaate it is these push-pull dialectics that the analyst must understand and project into analytic judgments of fact and value. The analyst is then

in the role of prognosticating possible and then probable outcomes of various mutual encounters and resulting decline or progress.

4.5 What's an analyst to do?

In this case, an intelligence analyst must develop an estimate in a region. For example the banker might finance future oil production for the NOC-private oil company by leveraging capital laundered from insurgent "deposits." Building a case around these positions, the analyst can impose a classical approach using correlations, a statistical view grounded in identifying what is not correlated, a genetic evolution of controlling factions, and finally a dialectical view of what has yet to develop in the region.

Thus endeth the sketch. But here are six practical take-aways for the analyst in this particular situation:

1. Map actors's horizons and use an associative matrix to vizualize the interaction of horizons. Attributes such as timing, geography, technology, socio-economics, finances, environment, people, and politics can help delineate the segmentation of horizons.

2. Using a linked analysis develop a rating scale to describe the degree and direction of strength of similarity of horizons across actors. Summarize the scores across rows or columns. The rating divided by the total scores gives an indication of likelihood of similarity. Rank order the similarities and differences in a tornado graph to jump start the development of known knowns, known unknowns, and the possibility of error, deviation, and distortion in that fuzzy area of unknown unknowns.

3. Use Delphi (oracle) experts, devil's (multiple too) advocates, low probability / high impact and other challenge analyses along with the horizon linkages to formulate scenarios around potential outcomes.

4. Given a scenario, use horizon linkages to generate competing hypotheses. Analyze according to ACH.

5. Use other structured analytics as needed to excavate the known unknowns of the complex interaction of various actors in the case. Build an outcomes, likelihood, priority table linked to changes in horizon attributes to sensitize decision makers to various permutations of decision possibilities.

6. Add another item to the Bruce (2014) checklist. Suppose "X" is an analytic judgment of fact (e.g., so many oil rigs) or value (e.g., opposing beliefs that drive revolution).

Is X true because limited horizons have been reconciled with judgments of fact and value? Implication: Authentic analyst judgments require identification and reconciliation of opposed and limited horizons. If the judgment truly stands on its own (virtually unconditioned), then the analyst has exhausted relevant questions. When new information and changes in horizons arrive, then the existing judgment may require revision.

Horizon analysis is at the pinnacle of analysis because the analytical judgments start with and ultimately can only be accepted given analyst and policy maker horizons.

5. Further thoughts

Use of devil's advocacy, red teams, black hats, and other oppositional methods are often used to challenge a single prejudicially held view by building the best possible case for an alternative explanation of a sequence of events or an estimate of probable future outcomes.

These approaches are all a species of dialectic at work. To Heuer's point, the devil's advocate may also fail to excavate a reasonable alternative explanation due to personal bias. Thus, it is not enough to find a yet another competing hypothesis, gather alternative data, and producing an opposing conclusion. Analysts do have an obligation to policymakers to understand where their own judgments might be weak and open to future challenge. It is at least as important to understand the biases of decision makers, analysts, devil's advocates, as well as policymakers.

However, there still remains the need to determine whether the analyst's or the devil's advocate is the correct intelligence estimate. This determination may reside in the analyst or the policymaker or both. This determination will depend on the ability of both analyst and policymaker to transcend their roles and biases, encounter the reality represented by the estimate to generate very possibly a new judgments of fact (this might happen) and value (it is important).

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