Jared Tarbell, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
What enables the wise sovereign and the good general to strike and conquer, and achieve things beyond the reach of ordinary men, is foreknowledge.
Sun Tzu, Art of War, VI bc.
Intelligence agencies deals everyday with a massive amount of information collected by different gathering methods and they have to process them in order to produce assessments, reports and briefs for politicians and decision-makers. This is a real challenge, given the limited time, the huge quantity of data they have to go through and the imprecise and incomplete nature of these data. This intellectual challenge is the task of intelligence analysis, that stage of the process that has to “make sense” out of all this information received at the agencies’ headquarters. Both the institutional and the academic world have been concerned with issues about intelligence analysis, as it seems to be one of the most critical parts of intelligence: it is here that raw information are transformed and politicians receive the materials on which decide from here. Thus everyone would like to improve intelligence analysis as much as possible and many articles and books have developed and offered techniques and tools to reduce analysts’ errors while increasing their analytical ability.
However, almost all of these attempts have disregarded the problem’s foundations, i.e. the conceptual basis of intelligence itself, and have focused their work on the ultimate practical aspects of the analytical tradecraft. Therefore this paper will deal with this often-ignored problem and it will give a possible account to improve and clarify intelligence analysis. To delineate this account, it will start with questioning the definition of “intelligence” and this will be based on knowledge. Then, analysis is the part of the intelligence cycle whose purpose is to reach and communicate knowledge, based on collected information. Since epistemology is the study of knowledge, the paper will proceed giving an epistemological definition of knowledge and that definition would be the aim and the process of all the analysis’ process. With this renewed awareness of the nature and end of analysis, some common methods of the analytical tradecraft will be outlined and evaluated on our epistemological account. Eventually it will be shown how this founding account would give the intelligence world a more precise, targeted and reliable way of conducting analysis, capable of producing better assessments to be disseminated among decision-makers.
A true definition of intelligence
Michael Warner has given us a complete summary of various definitions of intelligence in the debate. It is worth to cite two of the most valid ones:
Reduced to its simplest terms, intelligence is knowledge and foreknowledge of the world around us—the prelude to decision and action by US policymakers.[1]
Intelligence, as I am writing of it, is the knowledge which our highly placed civilians and military men must have to safeguard the national welfare.[2]
These two definitions given by intelligence practitioners focus on the fundamental epistemic aspect of the intelligence activity. After he has taken into account this aspect, Warner argues that these definitions are not enough and he offers a different one: “Intelligence is secret, state activity to understand or influence foreign entities”.[3] This definition considers more the secret feature, the state’s monopoly and the practice of covert actions abroad. However, these aspects do not seem as fundamental as the epistemic one for the intelligence activity. Indeed, secrecy is something that belongs to the entire military strategy and defense policy, since strategic and tactical plans formulated by high-grades officials and politicians would not be likely to be widespread in the international realm. Then there is not much to say about the state’s prerogative of the intelligence, as we can see how privates companies or organizations are more and more involved in intelligence and espionage activities nowadays. Again, we can say that covert actions are a quite different craft from the proper intelligence one, focused on information gathering and analysis. These actions are true operations conducted abroad to influence or modify political assets in foreign countries, so they are much more a military or paramilitary activity than a proper intelligence’s competence.
Thus we can claim that the sought definition of intelligence must be centred on knowledge, as this is the feature that most characterised its work in governments’ policies, military operations, industrial and marketing strategies and even in a NBA team’s game plans.[4] Intelligence can be simply and clearly defined as the knowledge necessary to formulate and implement decisions in any kind of complex environment where there is multiple actors’ interaction.
But what is knowledge?
As intelligence has been defined as knowledge, knowledge should be defined as well, in order to really understand what we are talking about when we consider intelligence.
Now, a precise discipline that studies knowledge itself existed for centuries and it has formulated many different theories about its nature and origins: that is epistemology (from ancient Greek episteme: knowledge and logos: discourse). Plato can be recognized as its founder and in his Teaetetus he states that knowledge is “justified true belief”[5]. This means that a cognitive subject S knows the proposition P if and only if:
This definition underlines the importance for knowledge of the truth of the proposition, because it would be illogical to say that we know something false, of the subject’s cognitive disposition to believe in that, so that the proposition would be a real belief or opinion of him, and of his justification for believing it. The last feature is the one that has risen all the epistemological debate until today, focused on the equation of “true beliefs + something = knowledge”. In fact no ones argues that a proposition should be true and believed to be known, but as it seems that we need something more than just believe something true for the status of knowledge[7], justification is the aspect that must be explained and that epistemologists are always looking for in their theories.
Alvin I. Goldman offers the best account to define that ‘missing ingredient’ added to “true belief” to give real knowledge. He proposes a causal theory that states that the belief in P is causally connected to the fact P. So the theory requires a causal connection between the fact believed and the belief itself, that is, the fact must have some role in the generation of the subject’s belief. [8] This is particularly interesting in the case of testimony, when one cognitive subject transfers his belief to another one, namely the exact case of intelligence activities in general. This causal connection requirement can be easily understood through a simplified version of Goldman’s scheme.
This illustration clearly shows how there is a continuous causal chain from the fact p up to the belief of S in it and how we would not say that S would know p in the case one arrow was missing (so that it would be the case that S believes p, but T did not have a direct causal contact with p or maybe S did not have such a connection with T).
So we have demonstrated the necessity of having a continuous causal chain between the fact believed and the belief itself. But there should be an initial point from which the epistemic chain starts and that should be able to guarantee the true belief’s justification. Thus Goldman elaborates the theory of reliabilism to characterize epistemic justified beliefs. This states that a belief is justified when a reliable process or a reliable source produces it.[10] Taking the previous scheme, we can state that T is justified in believing p if a reliable process produces his belief (for example, a physical perception of an object[11]) and S is justified in believing what T tells him if T is a reliable source (for instance, he is sincere and he believes p thanks to reliable processes). In this way, all the causal chain would be a reliable process, based on reliable sources and reliable ways of transmitting beliefs, and eventually this will ensure the subject knowledge.
Thus, we have delineated a clear account to define knowledge. Now we can also outline a general way to get knowledge itself from such an account, that is, to base the search for a true belief’s justification on reliable causal chains made up by reliable sources and reliable methods of getting beliefs. Consequently, intelligence will have to look for these features of justification in its beliefs, since it has the aim to produce knowledge for decision-makers, as given in the established definition of intelligence.
Intelligence analysis produces knowledge
The intelligence process has always been illustrated as a cycle that begins and ends with the consumer of intelligence products. It is classically constituted by direction, collection, analysis and dissemination. Many critiques and modifications have been made to this cycle[12], but they usually regard the relations and orders between these stages.[13] In fact, every intelligence operation would see the set of some requirements by decision-makers, the gathering of the information required, the analysis of this information into assessments and finally the dissemination of these ones into the policy-makers’ community. Therefore the analysis’ role in the intelligence cycle remains important for our purposes. Indeed, the analysis phase takes raw information collected by different methods (such as HUMINT, SIGINT, IMINT, OSINT etc.) and has to make sense of them since “data do not speak for themselves”.[14] As it appears, this is actually the most complicated stage of the process, since it involves intellectual and complex reasoning about the information gathered in order to elaborate the products which will serve the consumer’s needs for critical decisions.
Indeed many academic and technical works on the nature of analysis and the way to improve it have been published after great intelligence failures in history. This has happened precisely because analysis can be considered the most turning part of the entire cycle and so if a failure occurs many people will look into analysis’ features and faults to find whom to blame for that.[15] It is worth to consider some works written just after the Yom-Kippur War[16] and after the Iraq’s Weapons of Mass Destruction case[17], both seen as failures of assessment. These attempts argue that it is necessary a more intellectual and human-centred account to improve intelligence analysis in dealing with contemporary uncertain and unconventional threats and issues. Even CIA Directorate of Intelligence’s Analytic Tradecraft Notes stress the need for a reform of the analysts’ community in terms of more intellectual, critical thinking and logical methods for their office duties.[18] Unfortunately these considerations are limited to the techniques and tools to be used in an agency’s analytical task and they do not deepen the subject neither try to find a base of these techniques.
But, as we have asserted previously, intelligence is knowledge and analysis is the process’ stage assigned to knowledge production. At this point it is clear how epistemology, namely theory of knowledge, can contribute to intelligence analysis. Indeed analysis has the charge to ascribe the status of knowledge to the beliefs that it receives from collectors, at least when this ascription is possible: this is what “make sense of the data that do not speak for themselves” [19] actually means. So we can say that the intelligence analyst is an epistemologist[20], as he handles complex epistemic situations where he has to consider the gathered beliefs that can give knowledge and the beliefs that cannot give it. Matthew Herbert has given us this significant intuition but, as many others, he does not give a precise account of what he means about knowledge. So it is not clear from his statements how the analyst should investigate the nature of the beliefs received and how he should act in discriminating beliefs and knowledge.
Therefore the epistemological account we have delineated would give analysis a precise way to understand its function and its activities and to implement them in order to accomplish the final intelligence’s end (knowledge production). When analysts receive all collected information, they should look for the causal chain of beliefs’ transmission, made up by reliable sources of beliefs. If they could find this, then they would have found the justification for this beliefs and thus succeeded in guaranteeing them the status of knowledge, making them sensible and ready to be exploited by consumers. If they could not find this, it would have meant that they couldn’t guarantee them the higher epistemic status and those will remain simple beliefs. Then this epistemic limit should be pointed out to the consumers. This fact reminds us that there is always something not knowable, since we cannot reconstruct all the causal chain without missing any “arrows” or maybe we deals with unreliable sources. Failures of intelligence assessments confirm this claim, like the case of Iraq’s WMD in which there were sources like ‘Curveball’[21] that were not reliable and then the analysts could not retrace all the beliefs’ passages through the chain.
Foundation of analytic methods
Thus we have understood the decisive help epistemology can give to intelligence analysis since it entails its very foundations and this can lead appropriately analysts’ job. This thesis is primarily logical and theoretical and it does not involve specific cognitive or social issues that affect analysis. On the other hand, intelligence community has been facing these last problems for decades and has proposed various methods to limit or resolve them[22]. For they have focused their research in those terms, they lacked a founding account of those methods. Thence, they have left analysts without a clear aim and framework for their activity.
However, we can now reconsider those methods through the lens of the epistemological account of analysis we are claiming and see how these methods’ aim is to question analysts about gathered beliefs’ reliability and causal chain. We can list the most significant ones:
Devil’s Advocacy: it consists in building a strong alternative explanation for the beliefs taken into account and this helps the investigation of their ground and source.
Competing Hypotheses: the evaluation of alternative hypotheses about beliefs to confirm or disconfirm them, aims to test the epistemic status of these hypotheses.
Key Assumptions Check: testing deeply assumptions and preconceptions is the implicit practice of an epistemologist looking for beliefs’ justification.
Red Cell: putting analysts’ mind in enemy’s shoes strengthens the process of inquiring beliefs’ epistemic ground.
Therefore all these analytical techniques assess the reliability of sources and processes that caused the beliefs analysts receive from collectors. Their proper aim is to understand if knowledge can be obtain from them or not. This procedure will allow formulating correct and precise judgements on facts and information that intelligence agencies have to analyse for policy-makers. This epistemological account makes clear that all these tools are suitable for the analysis’ requirements and that their worth feature is the appropriate way of assessing beliefs through inquiry into their reliable sources and causal chains of transmission. Then different methods among these ones are valid for different problems in different contexts and the single analyst should select the right one for his particular case. Again, he will always have the epistemological framework we have provided to conduct his tasks properly.
Conclusions
We have taken into account the problem of intelligence analysis and how it is possible to improve it. To do this, it was necessary to reconsider first the definition of intelligence, in order to understand what we are really looking for when we do intelligence activities. Given a clear definition based on knowledge, we had to define it too and here epistemology offered the possibility to clarify this tricky concept with Goldman’s theories of causal chain and reliability. Then, since analysis is the knowledge-producer stage of the intelligence cycle, we have characterized more precisely what intelligence analysis has to do with collected information and how it should behave towards it. In this way we have provided analysis with a new grounding epistemological account, which is essential to reconsider classic analytical methods of the intelligence community and characterize them in a precise way and with a precise purpose.
The aim of this argument is to found intelligence and mainly analysis, as it is the turning point of the cycle for our definition, on a clear and philosophically strong basis. Indeed this lacks in all the contributions scholars and practitioners have given the discipline of intelligence studies, even the ones who tried to improve analysis in a more intellectual and philosophical way. This groundwork for intelligence analysis is not just a useless abstract discourse on the ideas behind intelligence practices. On the contrary it does constitute a precious aid for the intelligence community, as it provides a clear understanding of what practitioners do and look for, and why. So it can improve the way they work and produce assessments for decision-makers and at the same avoid conceptual confusion about analytical methods and allow the use of precise procedures with a clearly stated end.
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[6] E. Gettier, “Is justified true belief knowledge?”, in Analysis, 1966, 23, p. 121.
[7] E. Gettier, “Is justified true belief knowledge?”, in Analysis, 1966, 23, p. 123.
[8] A. I. Goldman, “A causal theory of knowing”, in The Journal of Philosophy, 1967, 64:12, pp. 357-372.
[9] (p) is the fact that causes a person T to believe(B) p. T then asserts(A) p. This assertion causes another person S to believe that T is actually asserting p. Thus S can infer that T believes p and from this he infers that p is a fact and finally he believes p as well.
[10] A. I. Goldman, “What is justified belief?”, in Pappas G. S. (ed.), Justification and Knowledge, D. Reidel, Dordrecht, 1976, pp. 1-23.
[11] Here Goldman delegates the task to define a “reliable process” to the natural sciences.
[12] P. Gill and M. Phythian, Intelligence in an Insecure World, Polity Press, Cambridge, 2012, p. 13.
[13] The classic version puts these stages in a hierarchical order, while many scholars argue that today’s needs push for a more networked structure.
[14] P. Gill and M. Phythian, Intelligence in an Insecure World, Polity Press, Cambridge, 2012, p. 105.
[15] The first case in this sense is traceable after the Cuban Missile Crisis, when famous CIA analyst S. Kent proposed to reform mind-sets and approaches, guilty of engaging in mirror imaging and restrictive thinking. S. Kent, “A crucial estimate relieved”, in Studies in Intelligence, 1964, 36:5, pp. 111-119.
[16] I. Ben-Israel, “Philosophy and methodology of intelligence: the logic of estimate process”, in Intelligence and National Security, 1989, 4:4, pp. 660-718.
[17] J. B. Bruce, “Making analysis more reliable: why epistemology matters to intelligence”, in George R. Z. and Bruce J. B. (ed.), Analyzing Intelligence: origins, obstacles and innovations, Georgetown University Press, Washington DC, 2008, pp. 171-190. George R. Z., “Fixing the problem of analytical mind-sets: alternative analysis”, in International Journal of Intelligence and Counterintelligence, 2004, 17:3, pp. 385-404.
[18] Directorate of Intelligence, A Compendium of Analytic Tradecraft Notes, CIA, Washington DC, 1997.
[19] P. Gill and M. Phythian, Intelligence in an Insecure World, Polity Press, Cambridge, 2012, p. 105.
[20] M. Herbert, “The intelligence analyst as epistemologist”, in International Journal of Intelligence and Counterintelligence, 2006, 19:4, pp. 666-684.
[21] M. Herbert, “The intelligence analyst as epistemologist”, in International Journal of Intelligence and Counterintelligence, 2006, 19:4, p. 675.
[22] R. J. Heuer,“Limits of intelligence analysis”, in Orbis, 2005, 49:1, p. 90.
Matteo Bucalossi
Matteo Bucalossi è nato nel 1994 e vive a Brescia. Ha conseguito la laurea in filosofia all’Università San Raffaele. Attualmente è iscritto alla Georgetown University di Washington (US) in data analysis e sempre alla stessa università a conseguito un MA in Security Studies. E' autore di un pezzo nel volume La guerra fredda - Una guida al più grande confronto del XX secolo.
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