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Understanding the role of Intelligence in the Onset of War:

A Quantitative Approach1

Jacquelyn Schneider

Arizona State University

jgschne1@asu.edu

Abstract: Explanations for war onset span the theoretical sphere: the distribution of power, misperceptions, the dynamics of capabilities, or domestic motivations. Scholars may disagree about the causal mechanisms that affect the likelihood of war, but all agree that information and uncertainty about information plays a role in the onset of war. Despite the fact that information is often alluded to within the causes of war, very little literature discusses the effects of institutional information, or organized intelligence, on conflict initiation. Further, the literature that does examine the relationship between intelligence and war is dominated by intensive single-event case studies, which fail to provide generalizable understandings about the role of intelligence in the onset of war. I attempt to solve these problems by examining the presence and effect of intelligence failures in crises from 1935 to 2007. Based on the data collected from source reporting and scholarly analysis, I use logit analysis to test the effect of including an intelligence variable within existing understandings of why states go to war. I find that intelligence adds a statistically significant explanatory value to understanding why states go to war. Further, my analysis confirms the assumption that intelligence failures have a positive correlation with the likelihood of war onset. The research provides a valuable opening for the inclusion of intelligence within the war onset research agenda and suggests that quantitative explorations of intelligence failure can elucidate important relationships between intelligence and crisis escalation.

Why do states go to war? War is costly, unpredictable, and risky (Fearon 1995)—

and yet it occurs. Explanations for this puzzle span the theoretical sphere: the

distribution of power (Waltz 1979, Mearsheimer 2001), misperceptions (Lebow 1984),

the dynamics of capabilities (Van Evera 1999), and domestic motivations (Schultz 1999,

Bueno de Mesquita et al 2003). The causal mechanisms may vary, but what all of these                                                         1 Prepared for presentation at the 2013 Annual Meeting of the International Studies Association, San Francisco, CA, April 3-7, 2013. Do not cite or quote without author’s permission

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explorations seem to agree on is that variances in information have a significant effect on

war (Fearon 1995, Jervis 1976, Bueno De Mesquita 1984, Morrow 1989). War is, on

some level, a product of uncertainty (Clausewitz 1976).

In the past, this uncertainty, or variance in information about capabilities,

intentions, or outcomes has been included more as an immeasurable error term than an

understandable part of war onset (Gartzke 1999). My exploration here aims to tackle that

error term by defining and measuring a significant part of the uncertainty that affects war

onset. In particular, I posit that we can understand variances in uncertainty (and therefore

the error term) by proxying uncertainty with intelligence (or the institutional collection,

processing, analysis, distribution, and reception of private information) in crisis decision-

making. In other words, explanations for why states go to war can better capture true war

dynamics by including an intelligence variable that operationalizes the inherent variance

in information by which states and their leaders make decisions to go to war. By

understanding the error term as a variable, I am able to explore not only how intelligence

affects the onset of war, but also how intelligence can further explain existing theories

about the causes of war.

In this paper, I first provide a definition of intelligence and next introduce

structural, behavioral, and domestic explanations for war onset. I then examine the role

intelligence could play in expanding the explanatory capability of each of these theories.

Using these theoretical categories, I then develop a model of war onset that introduces

intelligences to the existing war explanation. This model is tested with a data set of

crises drawn from the International Crisis Behavior Database (ICB) (Brecher and

Wilkenfeld 2000), each coded for intelligence failure through archival and historical

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research of intelligence’s role within crisis decision-making. This intelligence variable is

used alongside ICB variables for power, perception, and domestic regime to test the

additive value of intelligence to understanding war onset.

By attempting to understand war onset’s error term as both identifiable and

measurable, I introduce an important explanatory variable to the currently unsettled

debated about the causes of war. To be clear, I do not suffer from the belief that

inclusion of intelligence will explain all conflicts. There are going to be times in the

international system when two countries, even faced with all the correct information

about the repercussions of their actions, will choose to go to war with each other. My

aim is not to replace the current explanations, but to add to their explanatory ability. In

addition, in introducing intelligence as a variable in the causes of war, I hope to provide a

theoretical opening for systematic analysis of intelligence within the causes of war

literature.

Furthermore, I hope that the use of a large-N quantitative method will fill a large

hole within the current intelligence literature, which is dominated by single event case

studies (Bar-Joseph and Kruglanski 2003, Wohlstetter 1965, Jervis 2010, Lebow 1983).

While this literature is rich with detail and lends important knowledge about

intelligence’s role in the decision-making process, it has two major weaknesses. First,

the literature focuses on a few key (and obvious) intelligence failures—particularly

failures involving British, American, and Israeli conflicts. Secondly, the literature’s

focus on singular events fails to provide more generalizable conclusions about

intelligence failures and their relationship with war.

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What is intelligence?

In order to make these more generalizable conclusions, it is essential to define

intelligence. Are diplomatic discussions intelligence? Newspaper articles? Is

intelligence a product, an action, an actor, an institution, or all? A true definition of

intelligence must not only incorporate all these understandings of intelligence, but also

demarcate intelligence from a broader understanding of information or informational

processes (Warner 2002). As Sherman Kent, the father of U.S. intelligence analysis,

noted “the main difficulty seems to lie in the word 'intelligence' itself, which has come to

mean both what people in the trade do and what they come up with. To get this matter

straight is crucial: intelligence is both a process and a product” (Kent 1946).

To begin first with intelligence as an actor, we must identify what separates

intelligence from policy makers. Intelligence exists, not as an individual actor, but as its

own institution. It is not a spy or a ring of spies, but an institution created by the state as

an official representation of the state’s informational needs on foreign activities.2

Accordingly, intelligence is an official organization or set of organizations responsible to

a country’s leadership. But intelligence is also a process. It is the initial collection of

private information, the analysis of that information, the dissemination to the appropriate

parties, and the reception of that information by decision-makers. The information

received by decision-makers then informs their perception of requirements and begins the

intelligence process again. Therefore, the process of intelligence is a feedback cycle of

information processing. In this way, one can discuss intelligence analysis or collection as

                                                        2 A clearer example of this may be to look historically to determine when intelligence began to exist as an institution. Spies and reconnaissance have existed since it seems the beginning of warfare. However, the first real evidence of the institutionalizing these capabilities and personnel occurs after World War I, when the British, French, and Germans created organizations specifically devoted to the collection, analysis, and dissemination of information about foreign capabilities and intentions (Kahn 2001).

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a part of intelligence, but the overall concept of intelligence implies that the totality of the

process. Therefore, intelligence is also the collection, analysis, dissemination, and

reception of private information.

This leads us to understanding that intelligence is also a distinctive type of

information. If all information were intelligence, then there would be no difference

between journalism and intelligence, between business research and intelligence

estimates (Agrell 2002). What makes intelligence unique is that it is considered private

information, information that the consumer (the state) does not want to share with others.

While the way that information is obtained may classify it as more or less private, the

overall understanding of the information is that it is for the use of the state only. This

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also assumes that the information is targeted, i.e. the state has identified as a need to

know piece of information for making policy decisions. In sum, intelligence is private

information on foreign entities deemed relevant to policymaker and defense consumer’s

needs. And, finally, intelligence must be a product, disseminated to the consumer as a

representation of the totality of the process. This could be a long-range analytical

product like the National Intelligence Estimate or a tactical report, like a predator feed of

insurgent activity or a communications intercept of an adversary conversation. In this

sense, intelligence is disseminated to consumers as a finished product representing the

totality of the processes.

Therefore, intelligence is:

An official organization or set of organizations responsible to a country’s leadership,

tasked with the collection, analysis, and dissemination of private information on foreign

entities deemed relevant to policymaker and defense consumer’s needs, which is

disseminated to consumers as a finished product representing the totality of the

intelligence process.

What, then, is an intelligence failure? Jervis offers a starting point, declaring that,

“the most obvious sense of intelligence failure is a mismatch between the estimates and

what later information reveals” (Jervis 2010 pg. 2). But Jervis admits that this definition,

while valid, is also unsatisfyingly constrictive. He broadens his conception of

intelligence failure to include “a falling short of what we expect from good intelligence”

(Jervis 2010 pg. 2). Based on these criteria, an intelligence failure can be defined as

intelligence’s failure to correctly estimate enemy capabilities or intentions or

intelligence’s failure to meet policymakers expectations of good intelligence as evidenced

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by a failure in acquisition, analysis, dissemination, and/or appreciation of pertinent data.

Intelligence success, conversely, is the correct estimation of enemy capabilities or

intentions or meeting policymaker’s expectations of good intelligence. This

understanding of success allows for mini-failures within the intelligence cycle as long as

final product estimations are correct and/or satisfy policymaker’s desires.

Causes of War and Intelligence

Why do wars begin? Fearon provides a categorization of three types of

explanations: rational, irrational (or emotion-based), or regime-dependent (Fearon 1995).

Placed in more theoretical terms, the explanations for the cause of war can be divided

into structural (rationalist), behavioral (irrational), or domestic explanations (regime-

dependent). The first categorization, structural explanations, points to redistributions of

power and the anarchical international system as key determinants in states’ decisions to

go to war (Waltz 1959, 1979, Mearsheimer 2001, Organski and Kugler 1981). As Waltz

asserts, “war occurs because there is nothing to prevent it . . . In the absence of a supreme

authority there is then the constant possibility that conflicts will be settled by force”

(Waltz 1959, 188). For these theorists, war is best understood as a state-level

phenomenon in which the re-balancing of power is the primary war driver and is most

often material—a country’s economic or military ability to compel or deter others from

actions which would negatively affect their position within the international system.

Behavioral explanations, on the other hand, focus much more closely on agent-level

decisions. For behavioralists, war is a problem of misperception and decision-making

biases. It is the distinction between a balance of power and a balance of threat (Walt

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1985) and takes into account the inherently human nature of war. Finally, the third type

of explanations, domestic, looks at the role of regime and regime survival in decisions to

go to war. For domestic advocates, the decision for war is initiated and motivated by

inherently domestic survival incentives.

Structural

Since structural explanations for war focus on power distributions, intelligence’s

main role in these explanations is the ability to provide accurate estimates of adversary

capabilities. We can think of this as one of four types of estimates. The first and second,

overestimation of power and underestimation of power, involve the inaccurate

assessment of an adversary’s capability to wage war. For example, an underestimation of

the amount of tanks an enemy has in its order of battle, an overestimation of a state’s

industrial capacity, or an underestimation of a state’s ability to implement a new tactic.

Both of these estimates assume that the assessment is delivered in a coherent and timely

manner to the decision-maker. The third category of intelligence, no estimation of

power, assumes that intelligence fails to provide an analysis of the adversary’s power

capabilities. This could be because of lack of ability to collect information, lack of

forward thinking to collect information, lack of dissemination within the intelligence

community, or lack of access to the decision-maker.3 Finally, the last category of

intelligence’s relationship with structural explanations for war is a correct estimation of

power capabilities.

                                                        3 The last two reasons for no estimation may also be explained within domestic cause of war explanations, so we should expect a relationship between this type of structural intelligence interaction term and the domestic intelligence interaction term. Therefore, we can expect that a structural interaction variable with no estimation of power may display a statistical relationship with the domestic-intelligence interaction term.

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Overestimation of power has often been discussed within the intelligence

literature as a bureaucratic tendency for military intelligence to overestimate its

adversaries. For example, the U.S. tended to overestimate Soviet missile capability,

despite intelligence that would suggest their capabilities were not as great as expected

(Aronsen 2001). This can be advantageous to deterrence—overestimating an enemy’s

capability (especially if the enemy is a pure security-seeker and not a greedy state) can

keep the opposing state from pursuing aggressive policies because it fears the adversary

has the capability to respond. The argument has been made that the overestimation of

Soviet military capabilities served as a check on U.S. potentially aggressive policies

during the Cold War (Botti 1996).

However, overestimation of an adversary’s capabilities can also lead to a decision

for war. George W. Bush’s recent decision to invade Iraq was predicated on a severe

overestimation of Iraq’s weapons of mass destruction program (Jervis 2006). This could

suggest that overestimation of capabilities increases the likelihood of war when the

estimation presents an image of the enemy as being on the precipice of an either

revolutionary technology or major capability advantage (as suggested by power transition

theory). Therefore, you would expect that overestimation of threat would be most likely

to lead to war when a state felt like a preemptive attack (or first-mover advantage) would

be necessary to win a conflict. In terms of the structural causes of war vocabulary,

overestimation of threat may provide an impetus towards war when it changes a state’s

understanding of who has the first move advantage. As Kenneth Waltz argues (Sagan

and Waltz 1995), preemptive attack might be necessary to stop an aspiring nuclear state

from developing nuclear capability. Once a state has crossed the nuclear threshold, an

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adversary loses the power of conventional advantage. Policy options are held captive by

nuclear deterrence. This is why the estimation of nuclear weapons capabilities becomes

so important to decisions to go to war.

Intelligence may also underestimate the adversary’s power capabilities. This

could be because the opposing state exercises good denial and deception tactics, or

because the intelligence community fails to collect on game-changing tactics,

technologies, or industries that qualitatively change the nature of a state’s power. For

example, the United States underestimated Hussein’s weapons of mass destruction

program before the Gulf War because Saddam executed excellent denial and deception of

facilities (Dolley and Leventhal 2001). Sometimes an underestimate of adversary power

can have dire consequences. For instance, in the Falklands War, the Argentine junta

underestimated Britain’s capability to respond to the Argentine invasion of the Falkland

Islands. In particular, their mistaken belief that the British submarine Endurance was out

of area caused them to greatly underestimate the potential success of the invasion (Welch

1997, Freedman 2008).

Behavioral

While structural explanations focus on the role of power in war, behavioral

explanations center more on the role of intentions and perceptions. Therefore, perception

of threat is more important than absolute power capabilities. Conflict, then, is not

predetermined by structural constraints. Instead, it is the “world in their minds” which

drives misperceptions and conflict (Vertzberger 1990). Accordingly, intelligence’s role

in these explanations is in either overestimation of threat, underestimation of threat, or

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the correct estimation of threat. Overestimation of threat can cause a state to act with

restraint against an adversary (especially if the state believes that the adversary has the

capability to win in a conflict). However, overestimation of threat can also lead a state to

believe that the adversary poses an immediate or existential threat to their existence and

therefore initiate war to garner either a first-move advantage or a hail-mary to save their

state. A good example of the overestimation of threat is Lyndon Johnson’s decision to

send forces to Vietnam. The initial event that triggered his decision, the Maddox attack,

was later debunked as an intelligence failure (Hanyok 2010). The “torpedo attack” that

was reported to Johnson was actually a false radar return. Meanwhile, domino theory

analysis from some elements of the intelligence community incorrectly assessed a North

Vietnamese victory as potentially devastating for the U.S. and its interests (Rovner 2011).

While overestimation of threat may be thought of as a failure of commission

(Walker and Malici 2011), the underestimation of threat may be thought of as a failure of

omission—a failure to deter. By underestimating threat, a state may fail to take the

appropriate defensive actions in order to deter an adversary from going to war. For

example, the United States in the Korean War underestimated the threat of North Korean

invasion and the North Koreans underestimated the possibility that the U.S. would

intervene for South Korea. Not only did the United States fail to signal its resolve in

order to deescalate tensions, but it also failed to posture forces on the peninsula. Because

the U.S. didn’t believe the North would invade (underestimated the threat), the North

Koreans in turn also underestimated the threat and initiated war (Cumings 2004, deWeerd

1962).

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Domestic

Domestic explanations for war focus on the second image of Waltz’s Man, the

State, and War. These explanations posit a relationship between domestic regimes and

decisions to go to war. They can be thought of broadly as one of two types of

explanations: war explained by the type of regime (Maoz and Russett 1994, Rousseau et

al 1996, Gartzke 1998), and as an extension of that argument, war explained by regime

motivations for survival (Bueno de Mesquita et al 1999, Bueno de Mesquita et al 2003,

Reiter and Stam 2002).

Domestic explanations for the causes of war point to regime characteristics and

their calculation for survival. A state that chooses war for domestic reasons believes that

war will enable their regime to hold onto power. Therefore, intelligence’s relationship

with domestic causes for war centers on a regime’s inability to properly organize or

utilize intelligence because of domestic reasons. These reasons could be related to

distrust of intelligence as an organization, a need for intelligence to reach a certain

conclusion for domestic reasons, an ineffective organization of the intelligence

community, an excessive harmony between intelligence and decision-making, or a

decision-making apparatus that fails to allow for effective use of intelligence. This

breaks down broadly into four categories: politicization, neglect, polythink, and failure to

share (Mintz and Derouen 2010, Rovner 2011). When no evidence of these is found,

then more than likely the intelligence community and the decision-making apparatus

utilized proper dissemination and reception of intelligence. Therefore, we should expect

that in these situations of proper dissemination and reception of intelligence, then the

only role intelligence would play in war onset would be structural or behavioral.

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Politicization is one of the most well-known and well-documented examples of

domestic intelligence factors that may lead to war. As Rovner argues in his exploration

of politicization and intelligence, “domestic political pressures create incentives for

policymakers to oversell the amount and quality of information on security threats,

regardless of the nature of personal relationships or organizational design” (Rovner 2011,

11). In this construct, while politicization may affect individual or bureaucratic decision-

making, the domestic variables serve as the causal mechanism for intelligence to be

politicized. Here, I understand politicization as a deliberate (if subconscious) altering of

the intelligence process—whether that be in the collection requirements, the analytical

products, or who the information is disseminated to—in order to fulfill domestic

objectives. In this sense, a regime that directs intelligence to devote all resources to one

adversary and not look at the other would not necessarily be guilty of politicization (this

is after all, how requirements for the intelligence community are generated). However, if

that regime directed the intelligence community to ignore the other adversary because it

believed that the other adversary’s actions would make them lose ground with their

selectorate, then that would be politicization. As Rovner argues, politicization can be the

direct or indirect manipulation of intelligence by the regime, the influence of regime

norms on intelligence analysis, the use of intelligence by political parties to influence

their political control, and actions taken by intelligence to support regimes perceived as

friendly to intelligence’s interests (Rovner 2011, 205).

President Johnson’s need to secure domestic support for his advertised attrition

strategy in Vietnam serves as a compelling example of politicization due to regime

manipulation. Because Johnson had publicly committed to this strategy, the failure of

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attrition in Vietnam would inflict serious audience costs for the leader and threaten his

hold on power. Because Johnson had publicly invested in this strategy, Johnson needed

North Vietnamese casualty estimates that showed North Vietnamese were losing more

members than they were able to recruit. This “crossover point” would then be used to

demonstrate to the American public that the U.S. was winning the war (and not caught in

a stalemate). Military estimates supported Johnson’s position and, leading up to the 1968

election, he pushed for a consensus from the intelligence community that the crossover

point had been reached. Unfortunately for Johnson, CIA casualty estimates (based on a

different methodology) did not support the military’s estimates. Choosing to politicize

the intelligence, Johnson ignored the CIA analysis and instead insisted on the military’s

estimates as the official order of battle in calculating Vietnam strategy success. This

allowed Johnson to continue massive bombing campaigns despite contradictory evidence

about their effectiveness. And, most importantly, it allowed Johnson to combat mounting

anti-war dissension with what he advertised to the American public as intelligence facts

(Rovner 2011).

Denial, as opposed to politicization, is a more passive form of failure in the

relationship between intelligence and the decision-maker. It can manifest itself in one of

two ways: the denial of information presented to the decision-maker and the denial of a

relationship between intelligence and the decision-maker. Rovner provides an example

of the first form of denial in Johnson’s initial decision to escalate conflict in Vietnam.

First, in 1964, Johnson commissioned an intelligence estimate to evaluate the domino

theory. He believed that the fall of South Vietnam to the communists would lead to a

domino effect within Southeast Asia, leaving the Soviets with a dangerous foothold in the

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region. Though the theory was foundational to Johnson’s national security, it was not an

explicit point presented to the American people. Therefore, Johnson had significant

domestic flexibility in decisions about the domino theory. Despite this flexibility, when

the estimates came in refuting the domino theory, Johnson’s administration ignored the

findings. No significant domestic group had come out for or against the Vietnam War or

domino theory and therefore Johnson had no need to evaluate the intelligence (Rovner

2011).

Many autocratic rulers exemplify the second form of denial: the denial of a

relationship between intelligence and the decision-maker. For instance, after-action

reporting from Hussein’s security apparatus indicate that Hussein refused to hear accurate

reporting of an impending and potentially dangerous attack by the United States in both

the first Gulf War and the subsequent invasion in 2003. Taped recordings of a 1990

discussion about the U.S.’ possible intervention against the Iraqi invasion of Kuwait

(found in the aftermath of the Operational Iraqi Freedom invasion) reveal a decision-

maker who refused to even meet with intelligence:

America is a complicated country. Understanding it requires a politician’s alertness that is beyond the intelligence community . . . I said I don’t want either intelligence organization to give me analysis—that is my specialty . . . we agree to continue on that basis . . . which is what I used with the Iranians (as cited in Woods 2006, 12)

Intelligence may also fail to reach a decision-maker because of internal

intelligence failure to disseminate. In this scenario, the failure occurs because the

community itself did not think the information was important or because it failed to

create an effective mode of dissemination. While the most recent example may be the

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failure for the intelligence community to warn of an impending attack on the World

Trade Center (9/11 Commission), a similar phenomenon occurred in the 1999 Kargil

War. As Indian after action reports documented, Indian intelligence failed to disseminate

warnings of a potential Pakistani action because it “was conditioned by the two decade

old mindset that Kargil was unsuitable for cross-LOC military action” (Kargil Review

Commission 1999). Therefore, the warning reports stayed at the ground level among

some small Army units, instead of disseminating warning to political leadership (Lavoy

2009).

The failure to share intelligence encompasses not only that final failure to provide

intelligence to the decision-maker, but also the failure to provide intelligence within the

intelligence community. It is often argued that Pearl Harbor is an example of this type of

failure. Though the correct information was collected, the community did not have an

effective means of sharing warnings and therefore was unable to put together an accurate

depiction of the Japanese threat (Wohlstetter 1962).

Table 1. Theoretical explanations of how intelligence failure affects onset of war

The Model: Causes of War and Intelligence

Empirical and case study evidence suggests that each cause of war explanation

offers significant explanatory value to understanding conflict onset. However, they

remain unable to satisfactorily explain anomalies—the black swans of states’

interactions. As it currently exists, these explanations can be captured within the a simple

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additive explanatory model, where each variable represents the totality of

operationalizations within each intellectual category and the error term represents the

uncertainty in which conflict often occurs (also posited by Gartzke to be where the real

explanation for war lies, Gartzke 1999):

Model 1: War is in the error term

War= structural variable + behavioral variable + domestic variable + error term

This model allows us to conceptualize explanations for the causes of war and to even test

each term for their relative significance, but it fails to explain any relationship between

the error term and the variables. Further, in accepting the error term as an immeasurable

element of the equation, we are left intellectually unsatisfied about what causes conflict

to occur. In order to solve this problem, I create a model in which intelligence is

included as a measurable way of understanding the error term.

Model 2: Intelligence as an additive explanatory variable

Likelihood of War= structural variable + behavioral variable + domestic variable +

intelligence

Based on my theoretical argument, this model, which substitutes intelligence for

the error term, should have significantly more explanatory capability than Model 1. In

this simple additive model, the intelligence variable should capture the variance

previously unexplained by the three core explanations for war onset. It will therefore

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provide a more robust overall explanation for war onset and explain anomalies within the

existing theoretical arguments.

Methods: Measuring the Effects of Secrets on both War and Non-Events

There are two major challenges in understanding the role of intelligence on war at

an aggregate level: first, tracing the impact of intelligence on not only war but the non-

event of peace; and, second, the difficulty in determining intelligence failure. The first

problem can be solved through effective case selection and the second through in-depth

case analysis and a clearly defined operationalization of the intelligence variable.

Most case study work exploring the role of intelligence and the onset of war

examines cases in which war begins (Bar-Joseph and Kruglanski 2003, Freedman and

Gamba-Stonehouse 1991, Shlaim 1976). This selection on the dependent variable leads

to rich analysis of specific cases, but biases our overall understanding of intelligence and

war. Are there examples of intelligence failure in which war did not occur? Intelligence

success and war onset? Is there enough variation within the overall sample of both war

and peace to challenge the common perception that intelligence failures have a

correlation with war initiation? In order to account for this bias, I chose a sample of 265

inter-state crises beginning after 19354 from the ICB. By utilizing crises instead of cases

of war, I am able to examine both war and peace. Further, by using crises as opposed to a

random sample of dyads and years, I limit my analysis to politically relevant dyads and

instances for which intelligence should have been used to solve the policy puzzle. Even

with this case refinement, information about crisis intelligence can be difficult to find for                                                         4 Cases used from ICB satisfied the following criteria: 1) crisis trigger date after 1 Jan 1935, 2) triggering entity not a non-state actor, 3) at least two actors, and 4) crisis either an entry of a major actor in a war, an initiation of a war, or not an intra-state war.

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less salient crises, crises between smaller powers, and crises involving closed regimes. In

total 53 cases were successfully coded for intelligence success or failure.5

Table 2. Sample characteristics

To solve the second problem of determining intelligence failure, I chose a

dichotomous operationalization of intelligence based on the definition of failure outlined

above. Therefore, intelligence was either a failure (1) or not a failure (0). Cases where

evidence could not be found about intelligence’s involvement in the policy decision were

not coded and not included in the final sample. Based on after-action reporting, post-

incident interviews, and historical or journalistic accounts, an incident was categorized as

a failure if there was a clear example of an incorrect intelligence estimate of threat or

capabilities that significantly altered the ability for policymakers to utilize the most

appropriate policy and/or evidence that intelligence did not meet policymakers’

expectations.

For example, if intelligence failed to detect an impending invasion or significantly

underestimated an opposing country’s development of offensive weaponry, the case was

coded as a failure (whether this information was used by policymakers or not). But there                                                         5 Because evidence of intelligence involvement in policy was more likely to be found for democracies, English-speaking nations, and highly salient events, there is a potential bias in the data. Further analysis of cases will be required to determine how influential this bias may be.

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were other examples in which intelligence had a good estimate of enemy capabilities or

intentions and failed to communicate it effectively with policy makers. This would also

be considered a failure because intelligence had the correct information but failed to

communicate to the policymaker, despite the policymaker’s articulated need for that

information.

Operationalization of the existing causes of war explanations was based on the primary

causal mechanisms posited by each theoretical camp. For structural explanations, which

focus on material capabilities, I utilized ICB’s power discrepancy variable which captures

GDP and military capabilities of each actor in the crises and assesses an overall power

disparity between opposing sides. According to structural explanations, war is most

likely in two possible situations: crises with low power discrepancies and crises in which

the initiator has a greater power capability (positive power discrepancy). In other words,

war is more likely to occur when the power discrepancy is greater than zero.

Behavioral explanations focus on the perception of threat. Perception of an

adversary is based on two significant factors: the history of conflict between the states

(Diehl and Goertz 2000, Thompson 2001, Rasler and Thompson 2006) and the perception

of the gravity of the threat (Jervis 1976). Therefore, in order to capture the power of

these two perception issues on intelligence failures, I utilized two measures. The first,

taken from the ICB, codes for gravity of threat where one=economic threat, two=limited

military threat, three=political threat, four=threat to territory, five=threat to influence,

six=threat of grave damage, and seven=threat to existence. The second measure, taken

from Thompson’s dataset of enduring rivalries (Thompson 2001), captures the power of a

rivalry relationship to the onset of war. This measure is dichotomous: the gravity of

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threat measure was multiplied by one if the dyad was not an enduring rivalry and by two

if the dyad was an enduring rivalry. They hypothesis here is that states are more likely to

go to war as the gravity of the threat increases and if the states exist within an enduring

rivalry.

Finally, I measured the domestic explanation using the ICB’s coding of regime

type. According to the domestic causes of war genre, democratic regimes are less likely

to go to war with one another, but not necessarily less likely to go to war with a non-

democracy (Maoz and Russett 1993, Reiter and Stam 2002, Schultz 2001). One possible

explanation for this phenomenon is that democracies do a better job of assessing the

probability of victory or defeat. Because a democratic regime is more concerned about

the effect of failure on their survival, democracies are less likely to enter a war that they

cannot win (Bueno de Mesquita et al 2003, Chiozza and Goemans 2011). Democracies,

therefore, have a better assessment of the capabilities and intentions of adversaries. This

may be because of a better relationship between intelligence and the policymaker (for

example, authoritarian regimes like Saddam Hussein and Stalin have been documented as

ignoring intelligence estimates) or because democracies have better intelligence

apparatuses because they can expend more resources on outside threats versus domestic

threats. Crises were assigned a zero if none of the actors were democracies, a one if at

least one actor was a democracy, and a two if all actors were democracies. Therefore, we

should expect less likelihood of war between democracies and a greater chance of

conflict when one or more of the actors is not a democracy.

  22 

Table 3. Explanations of war operationalizations

Results

Table 4. Logit analysis of Model 1, explanations without intelligence

To examine the robustness of the current explanations of war without intelligence,

I ran a logit analysis of the effect of power, threat, and regime type on the onset of war. I

found no statistical significance for any of the variables. In fact, estimates for all three

variables were close to zero, indicating that the odds of any of the variables changing the

likelihood of war onset was one.

Table 5. Logit analysis of Model 2, explanations with intelligence

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Model 2, which included both the causes of war explanations and intelligence,

offered more insight into war onset. Though power, threat, and regime remained

statistically significant, with little overall impact on the likelihood of war onset,

intelligence had a statistically significant effect. In fact, war was 5.8 times more likely to

occur with intelligence failure. Overall, Model 2 provides a better explanation of the

likelihood of war onset. Table 6 displays the result of a likelihood-ratio test comparing

Model 1 to Model 2. The results indicate that Model 1 can be rejected in favor of the

intelligence-included Model 2.

Table 6. Likelihood ratio test: Model 1 vs. Model 2

Conclusion

The findings presented here represent a first step in understanding the role of

intelligence in explaining the onset of war. The statistical analysis suggests that

intelligence can help elucidate why some crises escalate to conflict and others diffuse

without violence. However, the addition of intelligence to the discussion of why conflict

occurs does not represent new theory about why states go to war. Inherent in all the

current genres of war explanations is the role of information in conflict decisions. What

this analysis provides is a quantifiable and observable way to capture both information

and the uncertainty that bounds that information. In providing intelligence as a variable

to explain the error term, we can attempt to both confirm assumptions about information

and war, as well as test how intelligence helps fill in gaps where existing theoretical

explanations leave off. Also, by placing a highly-qualitative body of work within a large-

  24 

N quantitative framework, this paper attempts to solve the “rigor and parsimony”

problems identified by Jervis (Jervis et al 1985 pg. 5) by exploring intelligence in a

generalizable, systemic way.

This paper represents merely a launching-off point to explore the role of

intelligence in conflict. While mathematically, Model 2’s additive inclusion of the

intelligence variable is the most logical transmutation of previous explanations, actual

case studies of intelligence and the decisions to go to war (De Weerd 1962, Best 1996,

Bar-Joseph and Kruglanski 2003, Bar-Joseph and Adamsky 2006) suggest that this un-

interactive model is not representative of the way intelligence affects decisions to go to

war. For instance, bad intelligence about an enemy’s power capabilities is not enough

alone for a state to feel the need to go to war. In addition to that bad intelligence, the

state must also have a theory that explains why an adversary’s increase in capability

would affect their sovereignty or interests. So, for example, the U.S. consistently

overestimated and underestimated Soviet capabilities during the Cold War, and yet war

did not occur (Aronsen 2001). Bad information cannot in itself cause conflict; if that

were the case, then every time a nation incorrectly counted tanks, conflict would ensue.

Intelligence requires the existence of other theoretical explanations of war in order to be a

causative element of war.

But how does intelligence interact with the causes of war to explain war onset?

Model 2 introduces intelligence as a univariate interaction between intelligence and all

three causes of war. In this construct, intelligence’s effect is constant across all three

variables. Therefore, if a state’s intelligence overestimates, then it overestimates

invariably across all three causes of war explanations. This seems intuitively acceptable

  25 

in at least some instances; it appears logical that a state that overestimates an opposing

state’s power capabilities could also overestimate revisionist intentions or the threat to

domestic regime survival. Indeed, this could be explained by a state’s tendency to “fit”

intelligence within a pre-conceived image of the other (Heuer 1999).

Model 3: Intelligence as a Multivariate Intelligence Effect

War= ((intelligence) *(structural variable)) +((intelligence) *(behavioral variable)) +

((intelligence)*(domestic variable))

In this final model, intelligence has not only an interactive effect on the causes of

war, but is also variable within each interaction. Therefore, intelligence can be both an

overestimation of power projection capabilities and an underestimation of threat. This

would allow us to capture the inherent variability of intelligence within each cause of war

and the dynamics of competing intelligence assessments. It is clear, therefore, from this

model that intelligence can both increase and decrease the chances of war. It does not

have a stand-alone effect on war, but instead is influenced by the cause of war to which it

contributes.

Future research should explore these multiplicative effects. Hopefully in

understanding these relationships between intelligence and the explanations for war, we

can not only provide a more robust model for war onset, but also generate pragmatic

insight into how intelligence reform may affect conflict initiation.

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