Heuristics and Biases in Military Decision Making

58 Mission Command  MILITARY REVIEW

Major Blair S. Williams, U.S. Army, is

a Joint planner at U.S. Strategic Com-

mand. He holds a B.S. from the U.S.

Military Academy (USMA), an M.S.

from the University of Missouri, and a

Ph.D. from Harvard University. He has

served in a variety of command and

staff positions, including deployments

to Iraq and Afghanistan, as well as an

assignment as an assistant professor

of economics in the Department of

Social Sciences at USMA.

_____________

PHOTO: U.S. Army SSG Clarence

Washington, Provincial Reconstruc-

tion Team Zabul security forces squad

leader, takes accountability after an

indirect ﬁre attack in Qalat City, Zabul

Province, Afghanistan, 27 July 2010.

(U.S. Air Force photo/SrA Nathanael

Callon)

If we now consider brieﬂy the subjective nature of war—the means by which war

has to be fought—it will look more than ever like a gamble . . . From the very

start there is an interplay of possibilities, probabilities, good luck, and bad that

weaves its way throughout the length and breadth of the tapestry. In the whole

range of human activities, war most closely resembles a game of cards.

—Clausewitz, On War.

ARL VON CLAUSEWITZ’S metaphoric description of the condition

of war is as accurate today as it was when he wrote it in the early

19th century. The Army faces an operating environment characterized by

volatility, uncertainty, complexity, and ambiguity.

Military professionals

struggle to make sense of this paradoxical and chaotic setting. Succeed-

ing in this environment requires an emergent style of decision making,

where practitioners are willing to embrace improvisation and reﬂection.

The theory of reﬂection-in-action requires practitioners to question the

structure of assumptions within their professional military knowledge.

For commanders and staff ofﬁcers to willingly try new approaches and

experiment on the spot in response to surprises, they must critically exam-

ine the heuristics (or “rules of thumb”) by which they make decisions and

understand how they may lead to potential bias. The institutional nature of

the military decision making process (MDMP), our organizational culture,

and our individual mental processes in how we make decisions shape these

heuristics and their accompanying biases.

The theory of reﬂection-in-action and its implications for decision

making may sit uneasily with many military professionals. Our established

doctrine for decision making is the MDMP. The process assumes objec-

tive rationality and is based on a linear, step-based model that generates

a speciﬁc course of action and is useful for the examination of problems

that exhibit stability and are underpinned by assumptions of “technical-

rationality.”

The Army values MDMP as the sanctioned approach for

solving problems and making decisions. This stolid template is comforting;

we are familiar with it. However, what do we do when our enemy does

not conform to our assumptions embedded in the process? We discovered

early in Iraq that our opponents fought differently than we expected. As

Heuristics and Biases in

Military Decision Making

Major Blair S. Williams, U.S. Army

The author is indebted to

COL(R) Christopher Paparone,

MAJ Rob Meine, MAJ Mike Shek-

leton, and COL(R) Doug Williams

for reviewing this article and

providing insightful suggestions

for its improvement.

Originally published in the

Sep-Oct 2010 issue of MR.

59MILITARY REVIEW  Mission Command

SPECIAL EDITION

a result, we suffered tremendous organizational

distress as we struggled for answers to the insur-

gency in Iraq. We were trapped in a mental cave

of our own making

and were

unable to escape our

preconceived notions of military operations and

decision making.

Fortunately, some have come to see the short-

comings of the classical MDMP process. It is ill-

suited for the analysis of problems exhibiting high

volatility, uncertainty, complexity, and ambiguity.

The Army’s nascent answer, called “Design,”

looks promising. As outlined in the new version

of FM 5-0, Operations Process, Chapter 3, Design

is deﬁned as “a methodology for applying critical

and creative thinking to understand, visualize, and

describe complex, ill-structured problems and

develop approaches to solve them.”

Instead of a

universal process to solve all types of problems

(MDMP), the Design approach acknowledges

that military commanders must ﬁrst appreciate

the situation and recognize that any solution will

be unique.

With Design, the most important task

is framing a problem and then reframing it when

conditions change.

Framing involves improvisation and on-the-

spot experimentation, especially when we face

time and space constraints in our operating envi-

ronment. FM 6-0, Mission Command, Chapter 6,

states, “Methods for making adjustment decisions

fall along a continuum from analytical to intui-

tive . . . As underlying factors push the method

further to the intuitive side of the continuum,

at some point the [planning] methodology no

longer applies.”

In the course of intuitive deci-

sion making, we use mental heuristics to quickly

reduce complexity. The use of these heuristics

exposes us to cognitive biases, so it is important

to ask a number of questions.

What heuristics

do we use to reduce the high volatility, uncer-

tainty, complexity, and ambiguity, and how do

these heuristics introduce inherent bias into our

decision making? How do these biases affect

our probabilistic assessments of future events?

Once apprised of the hazards rising from these

heuristic tools, how do we improve our deci-

sions? This article explores these questions

and their implications for the future of military

decision making.

Behavioral Economics

The examination of heuristics and biases began

with the groundbreaking work of Nobel Laureate

Daniel Kahneman and Professor Amos Tversky.

Dissatisﬁed with the discrepancies of classical

economics in explaining human decision making,

Kahneman and Tversky developed the initial

tenets of a discipline now widely known as behav-

ioral economics.

In contrast to preexisting classi-

cal models (such as expected utility theory) which

sought to describe human behavior as a rational

maximization of cost-beneﬁt decisions, Kahne-

man and Tversky provided a simple framework

of observed human behavior based upon choices

under uncertainty, risk, and ambiguity. They pro-

posed that when facing numerous sensory inputs,

human beings reduce complexity via the use of

heuristics. In the course of these mental processes

of simplifying an otherwise overwhelming amount

of information, we regularly inject cognitive bias.

Cognitive bias comes from the unconscious errors

generated by our mental simpliﬁcation methods.

It is important to note that the use of a heuristic

does not generate bias every time. We are simply

more prone to induce error. Additionally, this

bias is not cultural or ideological bias—both of

which are semi-conscious processes.

Kahne-

man and Tversky’s identiﬁed phenomena have

withstood numerous experimental and real-world

tests. They are considered robust, consistent, and

predictable.

In this article, we will survey three

important heuristics to military decision making:

availability, representativeness, and anchoring.

In the course of intuitive decision making, we use mental heu-

ristics to quickly reduce complexity. The use of these heuristics

exposes us to cognitive biases…

60 Mission Command  MILITARY REVIEW

Availability

When faced with new circumstances, people

naturally compare them to similar situations resid-

ing in their memory.

These situations often “come

to one’s mind” automatically. These past occur-

rences are available for use, and generally, they

are adequate for us to make sense of new situations

encountered in routine life. However, they rarely are

the product of thoughtful deliberation, especially in

a time-constrained environment. These available

recollections have been unconsciously predeter-

mined by the circumstances we experienced when

we made them. These past images of like circum-

stances affect our judgment when assessing risk

and/or the probability of future events. Ultimately,

four biases arise from the availability heuristic:

retrievability bias, search set bias, imaginability

bias, and illusory correlation.

Retrievability bias. The frequency of similar

events in our past reinforces preconceived notions

of comparable situations occurring in the future.

For example, a soldier will assess his risk of being

wounded or killed in combat based on its frequency

of occurrence among his buddies. Likewise, an ofﬁ-

cer may assess his probability of promotion based

on the past promotion rates of peers. Availability

of these frequent occurrences helps us to quickly

judge the subjective probability of future events;

however, availability is also affected by other fac-

tors such as salience and vividness of memory. For

example, the subjective probability assessment of

future improvised explosive device (IED) attacks

will most likely be higher from a lieutenant who

witnessed such attacks than one who read about

them in situation reports. Bias in their assessment

occurs because the actual probability of future

attacks is not related to the personal experience of

either ofﬁcer.

Similarly, consistent ﬁxation on a previous event

or series of events may also increase availability.

Naval ofﬁcers most likely experienced a temporary

rise in their subjective assessment of the risk of

ship collision after the highly publicized reports of

the collision between the USS Hartford and USS

New Orleans.

The true probability of a future

collision is no more likely than it was prior to the

U.S. Marine Corps SSgt Tommy Webb of Headquarters Battalion, Marine Forces Reserve, teaches a class on grid coor-

dinates and plotting points on a map, 22 February 2010. The course emphasizes combat conditioning, decision making,

critical thinking skills, military traditions, and military drill. These professional courses must focus on critical reﬂection

when examining new problems in order to avoid bias.

US Marine Corps photo by Lance CPL Abby Burtne.

61MILITARY REVIEW  Mission Command

SPECIAL EDITION

collision, yet organizational efforts to avoid colli-

sions increased due to the subjective impression

that collisions were now somehow more likely.

People exposed to the outcome of a probabilistic

event give a much higher post-event subjective

probability than those not exposed to the outcome.

This is called hindsight bias.

When combining hindsight bias and retrievabil-

ity biases, we potentially fail to guard against an

event popularized euphemistically as a black swan.

Nassim Taleb describes black swans as historical

events that surprised humanity because they were

thought of as non-existent or exceedingly rare. We

assume all swans are white; they are in our avail-

able memory.

For example, in hindsight the 11

September 2001 terrorist attacks look completely

conceivable; therefore, we hold the various intel-

ligence agencies of the U.S. government publicly

accountable for something that was not even con-

sidered plausible before the event. Furthermore,

mentally available disasters set an upper bound

on our perceived risk. Many of our precautionary

homeland security measures are based on stopping

another 9/11 type attack, when in fact the next

attempt may take on a completely different context

that we cannot imagine (because our searches for

past experiences are limited).

Availability played a role in the current global

ﬁnancial crisis. Our collective memories contained

two decades of stable market conditions. The

inability to conceive a major economic downturn

and the ﬂawed assumption that systemic risk to the

national real estate market was minuscule contrib-

uted to creating a black swan event.

Taleb wrote

the following passage before the collapse of the

asset-backed securities market (a major element of

the current economic recession):

Globalization creates interlocking fragil-

ity, while reducing volatility and giving the

appearance of stability. In other words, it

creates devastating Black Swans. We have

never lived before under the threat of a

global collapse. Financial institutions have

been merging into a smaller number of very

large banks. Almost all banks are interre-

lated. So the ﬁnancial ecology is swelling

into gigantic, incestuous banks—when one

fails, they all fail. The increased concentra-

tion among banks seems to have the effect

of making ﬁnancial crises less likely, but

when they happen they are more global in

scale and hit us very hard.

Given the possibility of black swans, we should

constantly question our available memories when

faced with new situations. Are these memories

leading us astray? Are they making our decisions

more or less risky? Are our enemies exploiting this

phenomenon? Military planners have done so in the

past, seeking the advantage of surprise.

For example, the British were masters at exploit-

ing retrievability biases during World War II. They

employed the COLLECT plan in North Africa

in 1941 to obfuscate the exact timing of General

Auchinleck’s offensive (Operation Crusader)

against Rommel’s forces in Libya.

Via ofﬁcial,

unofﬁcial, and false channels, the British repeatedly

signaled speciﬁc dates of the commencement of the

operation, only to rescind these orders for plausible

reasons. These artiﬁcial reasons included the inabil-

ity to quickly move forces from Syria to take part

in the operation to the failure of logistics ships to

arrive in Egypt. Planners wanted to lull Rommel

into expecting the repeated pattern of preparation

and cancellation so that when the actual operation

began, his memory would retrieve the repeated

pattern. The plan worked. The British achieved

operational deception. They surprised Rommel and

after 19 days of ﬁghting ultimately succeeded in

breaking the siege at Tobruk. The repetitive nature

of orders and their cancellation demonstrates the

power of availability on human decision making.

Search Set Bias. As we face uncertainty in piecing

together patterns of enemy activity, the effectiveness

of our patterns of information retrieval constrain our

ability to coherently create a holistic appreciation of

the situation. These patterns are called our search

set. A simple example of search set is the Mayzner-

Tresselt experiment, in which subjects were told to

randomly select words longer than three letters from

memory. Experimenters asked if the words more

likely had the letter R in the ﬁrst position or third posi-

tion. Furthermore, they asked subjects to estimate

the ratio of these two positions for the given letter.

They also asked about K, L, N, and V. The subjects

overwhelmingly selected the ﬁrst position for each

letter given over the third position, and the median

subjective ratio for the ﬁrst position was 2:1.

fact, the aforementioned letters appear with far more

62 Mission Command  MILITARY REVIEW

frequency in the third position. This experiment

highlighted the difﬁculty of modifying established

search sets. When we wish to ﬁnd a word in the

dictionary, we look it up by its ﬁrst letter, not its

third. Our available search sets are constructed in

unique patterns that are usually linear. We tend to

think in a series of steps versus in parallel streams.

The effectiveness of our search set has a big

impact on operations in Iraq and Afghanistan. When

observing IED strikes and ambushes along routes,

we typically search those routes repeatedly for high-

value targets, yet our operations rarely ﬁnd them.

Our search set is mentally constrained to the map

of strikes we observe on the charts in our operation

centers. We should look for our adversaries in areas

where there are no IEDs or ambushes. They may be

more likely to hide there. In another scenario, our

enemy takes note of our vehicle bumper numbers

and draws rough boundaries for our respective unit

areas of operation (AOs). They become used to

exploiting operations between unit boundaries and

their search set becomes ﬁxed; therefore, we should

take advantage of their bias for established bound-

aries by irregularly adjusting our unit AOs. From

this example, we can see that to better structure our

thinking to escape search set bias, we should think

along a spectrum instead of categorically.

(Using

both methods allows us to think in opposites which

may enhance our mental processing ability.)

Imaginability Bias. When confronted with a

situation without any available memory, we use

our imagination to make a subjective premonition.

If we play up the dangerous elements of a future

mission, then naturally we may perceive our likeli-

hood of success as low. If we emphasize the easy

elements of a mission, we may assess our probabil-

ity of success too high. The ease or lack thereof in

imagining elements of the mission most likely does

not affect the mission’s true probability of success.

Our psychological pre-conditioning to risk (either

low or high) biases our assessment of the future.

Following the deadly experience of the U.S. Army

Rangers in Mogadishu in 1993, force protection

issues dominated future military deployments.

Deployments to Haiti and Bosnia were different

from Somalia, yet force protection issues were

assumed tantamount to mission success. We could

easily imagine dead American soldiers dragged

through the streets of Port-au-Prince or Tuzla. This

bias of imaginability concerning force protection

U.S. Army , SPC Eric Cabral

1LT Matthew Hilderbrand, left, and SSG Kevin Sentieri, Delta Company, 1st Battalion, 4th Infantry Regiment, patrol in search

of a weapons cache outside Combat Outpost Sangar in Zabul Province, Afghanistan, 27 June 2010.

63MILITARY REVIEW  Mission Command

SPECIAL EDITION

actually hampered our ability to execute other

critical elements of the overall strategic mission.

Biases of imaginability may potentially become

worse as we gain more situational awareness on

the battleﬁeld. This seems counterintuitive, yet

we may ﬁnd units with near-perfect information

becoming paralyzed on the battleﬁeld. A unit

that knows an enemy position is just around the

corner may not engage it because the knowledge

of certain danger makes its members susceptible

to inﬂating risk beyond its true value. These

Soldiers may envision their own death or that of

their buddies if they attack this known position.

Units with imperfect information (but well-versed

in unit battle drills) may fare better because they

are not biased by their imagination. They will

react to contact as the situation develops.

As an

organization, we desire our ofﬁcers and NCOs to

show creativity in making decisions, yet we have

to exercise critical reﬂection lest our selective

imagination get the best of us.

Illusory Correlation. Correlation describes the

relationship between two events.

People often

incorrectly conclude that two events are correlated

due to their mentally available associative bond

between similar events in the past.

For example,

we may think that the trafﬁc is only heavy when

we are running late, or our baby sleeps in only

on mornings that we have to get up early. These

memorable anecdotes form false associative bonds

in our memories. Consider the following example

regarding military deception operations from CIA

analyst Richard Heuer:

The hypothesis has been advanced that

deception is most likely when the stakes

are exceptionally high. If this hypothesis

is correct, analysts should be especially

alert for deception in such instances. One

can cite prominent examples to support the

hypothesis, such as Pearl Harbor, the Nor-

mandy landings, and the German invasion

of the Soviet Union. It seems as though

the hypothesis has considerable support,

given that it is so easy to recall examples

of high stakes situations…How common

is deception when the stakes are not high

. . . What are low-stakes situations in this

context? High stakes situations are deﬁn-

able, but there is an almost inﬁnite number

and variety of low-stakes situations . . .

we cannot demonstrate empirically that

one should be more alert to deception in

high-stakes situations, because there is

no basis for comparing high-stakes to low

stakes cases.

Heuer highlights the potentially pernicious

effect illusory correlation can have on our decision

making. Exposure to salient experiences in the

past generates stereotypes that are difﬁcult to con-

sciously break. In fact, we may fall victim to con-

ﬁrmation bias, where we actively pursue only the

information that will validate the link between the

two events. We may ignore or discard important

data that would weaken our illusory correlation.

In social settings (such as staff work), the effects

of illusory correlation and conﬁrmation bias are

reinforcing factors to the concept of groupthink,

whereby members of a group minimize conﬂict

and reach consensus without critically examining

or testing ideas. Groupthink generates systematic

errors and poor decisions. Scholars have identiﬁed

a number of military disasters, such as the Bay of

Pigs ﬁasco and the Vietnam War, as examples of

the dangers of heuristics associated with group-

think.

To avoid illusory correlation, we should

ask ourselves whether our intuitive or gut feeling

on the relationship between two events is correct

and why. This does not come naturally. It takes

a deliberative mental effort to ask ourselves a

contrary proposition to our assumed correlation.

Individually, we may be unable to overcome illu-

sory correlation. The solution potentially lies in

Exposure to salient experiences in the past generates stereotypes

that are difﬁcult to consciously break. In fact, we may fall victim to

conﬁrmation bias, where we actively pursue only the information that

will validate the link between the two events.

64 Mission Command  MILITARY REVIEW

a collective staff process where we organize into

teams to evaluate competing hypotheses.

Representativeness

Representativeness is a heuristic that people use

to assess the probability that an event, person, or

object falls into a larger category of events, people,

or things. In order to quickly categorize a new occur-

rence, we mentally examine it for characteristics of

the larger grouping of preexisting occurrences. If we

ﬁnd it to “represent” the traits of the broader category,

we mentally place it into this class of occurrences.

This heuristic is a normal part of mental processing,

yet it is also prone to errors. Representativeness leads

to ﬁve potential biases: insensitivity to prior prob-

ability of outcomes, base-rate neglect, insensitivity

to sample size, misconceptions of chance, and failure

to identify regression to the mean.

Insensitivity to prior probability of outcomes.

Consider the following description of a company-

grade Army ofﬁcer:

He is a prudent, details-oriented person. He

meticulously follows rules and is very thrifty.

He dresses conservatively and drives a Ford

Focus.

Is this ofﬁcer more likely to be an aviator or ﬁnance

ofﬁcer? If you picked ﬁnance ofﬁcer, then your ste-

reotype of the traits of a typical ﬁnance ofﬁcer may

have fooled you into making the less likely answer.

You may even hold the stereotype that aviators are

hot-shot pilots, who ﬂy by the seat of their pants. It

is common to view pilots as individuals who believe

rules are made to be broken, and money is made to

be spent on fast cars and hard partying. Given these

stereotypes, you chose unwisely because there are

statistically more aviators than ﬁnance ofﬁcers

who ﬁt the given description. As a branch, aviation

assesses approximately 20 times more ofﬁcers than

ﬁnance each year. It is always important to under-

stand the size of the populations you are comparing

before making a decision. Stereotypes often arise

unconsciously; therefore, it is important to remain

on guard against their potential misleading effects.

Base-rate neglect. Consider the following prob-

lem given to cadets at West Point:

While on a platoon patrol, you observe a

man near a garbage pile on the side of a

major road. In recent IED attacks in the

area, the primary method of concealment

for the device is in the numerous piles

of garbage that lay festering in the street

(trash removal is effectively non-existent

due to insurgent attacks on any government

employee—including sanitation workers).

You immediately direct one of your squad

leaders to apprehend the man. Based on S2

reports, you know that 90 percent of the

population are innocent civilians, while

10 percent are insurgents. The battalion S3

recently provided information from detainee

operations training—your platoon correctly

identiﬁed one of two types of the population

75 percent of the time and incorrectly 25

percent of the time. You quickly interrogate

the man. He claims innocence, but acts sus-

piciously. There is no IED in the trash pile.

What is the probability that you detain the

man and that he turns out to be an insurgent

rather than a civilian?

Most cadets answered between 50 percent and 75

percent.

This estimate is far too high. The actual

probability is 25 percent.

The 75 percent detection

probability from the platoon’s training provides

available individuating information. Individuating

information allows the lieutenant to believe that he

President John F. Kennedy addresses the 2506 Cuban Inva-

sion Brigade, 29 December 1962, Miami, FL.

Cecil Stoughton, White House, in the John F. Kennedy Presidential Library and Museum

65MILITARY REVIEW  Mission Command

SPECIAL EDITION

is individually differentiated from his peers due to

his high training score. This available information

potentially causes the lieutenant to order informa-

tion based upon its perceived level of importance.

The high detection ability in training may facilitate

overconﬁdence in actual ability and neglect of the

base-rate of actual insurgents in the population of

only 10 percent. The result is that the lieutenant is

far more likely to mistake the innocent civilian for

an insurgent.

Outside of the lieutenant’s mind (and

ego), the base-rate actually has a far greater impact

on the probability that the apprehended man is an

innocent civilian rather than an insurgent.

Insensitivity to sample size. Consider a problem

from Afghanistan:

We suspect two primary drug trafﬁcking

routes along the Afghan-Pakistani border.

A small village is located along the ﬁrst

suspected route, while a larger village is

located along the other suspected route.

We also suspect that local residents of each

village guide the opium caravans along the

mountainous routes for money. Human

intelligence sources indicate that thirty men

from the small village and sixty-ﬁve men

from the large village engaged in guide

activities over the last month. Furthermore,

coalition check points and patrols recently

conﬁrmed the G2 long-term estimate that

on average, twenty-ﬁve percent of the

male population of each village is engaged

monthly in guide activity. The smuggling

activity ﬂuctuates monthly–sometimes

higher and other times lower. Which vil-

lage is likely to experience more months

of over forty percent participation rate in

smuggling?

If you selected the large village, then you are incor-

rect. If you guessed it would be 25 percent for both

villages, you are also incorrect. The small village

would have greater ﬂuctuations in activity due to the

“law of large numbers.” As population size grows,

the average number becomes more stable with less

variation; therefore, the larger village’s monthly

percentage of guide activity is closer to the long–

term average of 25 percent. The smaller village has

greater monthly deviations from the long-term aver-

age value. This example highlights that insensitivity

to sample size occurs because many people do not

consider the “law of large numbers” when making

probability assessments and decisions.

Misconceptions of chance. Many people mis-

understand the elements of chance. For example,

suppose you observe roulette in a casino. The

following three sequences of red and black could

occur: RBRBRB or RRRBBB or RBBBBB. Which

sequence is more likely? The answer is that all

of these sequences are equally likely; however,

if you were like most people in similar experi-

ments, then you most likely picked RBRBRB.

This sequence is the most popular because people

expect the fundamental traits of the equilibrium

sequence (50 percent Black and 50 percent Red) to

be represented—yet if you stopped to do the math,

each sequence has a probability of 1.56 percent.

If the sequence was RBBBBB, then you most

likely would hear people say “Red is coming up for

sure”—this is the gambler’s fallacy. Many people

expect the equilibrium pattern to return after a long

run of black; however, the laws of randomness

have not changed. The probability of red is equal

to black. The implication is that we unconsciously

judge future events based on representativeness of

sequence, not on probability.

Now, consider the following question:

Which is more likely: 1) “Iran tests a nuclear

weapon in 2013” or 2) “Iran has domestic unrest

after its next election and tests a nuclear weapon

sometime in 2013?”

If you selected the second scenario, then you

are incorrect. The reason is the more speciﬁc the

description, the less likely the event. The two events

occurring in the same year are less likely than only

one event occurring; however, many people tend to

judge an event more likely as more speciﬁc infor-

mation is uncovered. This human tendency has

potential implications for military decision making

as situational awareness improves with technol-

ogy. Adding new details to a situation may make

that scenario seem more plausible, yet the mere

discovery of further information does not affect

the probability of the situation actually occurring.

Failure to identify regression to the mean.

Suppose we examine the training records of tank

crews during gunnery qualiﬁcation.

Observer-

controllers (OCs) may report that praising to a

tank crew after an exceptional run on Table VII

is normally followed by a poor run on Table VIII.

66 Mission Command  MILITARY REVIEW

They might also maintain that harsh scorn after a

miserable run on Table VII is normally followed

by a great run on Table VIII. As a result, OCs

may assume that praise is ineffective (makes a

crew cocky) and that criticism is valuable (makes

a crew buckle down and perform). This assump-

tion is false due to the phenomenon known as

regression to the mean. If a tank crew repeatedly

executed Tables VII and VIII, then the crew’s

scores would eventually converge (or regress) to

an average score over the long term. However, at

the beginning of this process, the scores are likely

to be highly volatile with some scores alternating

far above and others far below the average. OCs

may falsely assume that their social interaction

with the crew has a causal effect on the crew’s

future scores. Kahneman and Tversky write that

the inability to recognize the regression to the

mean pattern “remains elusive because it is incom-

patible with the belief that the predicted outcome

should be maximally representative of the input,

and, hence, that the value of the outcome variable

should be as extreme as the value of the input

variable.”

In other words, many times we fail to

identify settings that follow the regression to the

mean phenomenon because we intuitively expect

future scores to be representative of a previous

score. Furthermore, we attribute causal explana-

tions to performance that are actually irrelevant

to the outcome.

Anchoring

When facing a new problem, most people estimate

an initial condition. As time unfolds, they adjust this

original appraisal. Unfortunately, this adjustment is

usually inadequate to match the true ﬁnal condition.

For example, the average number of U.S. troops in

Iraq from May 2003 to April 2007 was 138,000.

Mounting evidence during this time exposed this

initial estimate as insufﬁcient, yet decision makers

were anchored on this number over the course of

this four-year period. They did not upwardly adjust

the number until Iraq was on the verge of a civil war

between Sunnis and Shiites. The anchoring phenom-

enon kept the value closer to the initial value than it

should have been. Historically, anchoring bias has

had harmful effects on military operations.

As previously identiﬁed, the British in World

War II were masters of exploiting human mental

errors. They exploited German anchoring bias with

the deception scheme called the Cyprus Defense

Plan.

Following the German seizure of Crete, the

British were concerned that the 4,000 troops on

Cyprus were insufﬁcient to repel a German attack.

Via the creation of a false division headquarters,

barracks, and motor pools along with phony radio

transmissions and telegrams, the British set out to

convince the Germans that 20,000 troops garri-

soned the island. A fake defensive plan with maps,

graphics, and orders was passed via double agents

a lost briefcase. The Germans and Italians fell for

the ruse. This deception anchored the Germans on

the 20,000 troop number for the remaining three

years of the war. In spite of their own analysis

that the number might be too high, intelligence

intercepts and post-war documents revealed the

Germans believed the number almost without

question. This exposes another negative effect

of anchoring: excessively tight conﬁdence inter-

vals. The Germans were more conﬁdent in their

assessment than justiﬁed when considering the

contradictory information they had. In summary,

the Germans were anchored, made insufﬁcient

adjustments and had overly narrow conﬁdence

intervals.

Biases in the evaluation of conjunctive and

disjunctive events. Anchoring bias appears in our

assessments of conjunctive and disjunctive events.

A conjunctive event is comprised of a series of

stages where the previous stage must be successful

for the next stage to begin. In spite of each indi-

vidual stage having a high probability of success,

the probability of total event success may be low

due to a large number of stages. Unfortunately,

When facing a new problem,

most people estimate an

initial condition. As time un-

folds, they adjust this origi-

nal appraisal. Unfortunately,

this adjustment is usually

inadequate to match the true

ﬁnal condition.

67MILITARY REVIEW  Mission Command

SPECIAL EDITION

researchers have shown that many people do not

think in terms of total event (or system) probability.

Instead, they anchor on initial stage probabilities

and fail to adjust their probability assessment. This

results in overestimating the likelihood of success

for a conjunctive event.

A disjunctive event occurs in risk assessment.

When examining complex systems, we may ﬁnd

that the likelihood of failure of individual critical

components or stages is very small. However, as

complexity grows and the number of critical com-

ponents increases, we ﬁnd mathematically that the

probability of event (or system) failure increases.

However, we again ﬁnd that people anchor incor-

rectly. In this case, they anchor on the initial low

probabilities of initial stage failure. Consequently,

people frequently underestimate the probability of

event failure. This overestimation of success with

a conjunctive event and underestimation of failure

with a disjunctive event has implications for mili-

tary decision making.

For example, military planners in 2002 and 2003

may have fallen victim to conjunctive event bias

during strategic planning for the Iraq invasion. In

order to realize success in Iraq, a number of military

objectives had to occur. These included—

● Ending the regime of Saddam Hussein.

● Identifying, isolating, and eliminating Iraq’s

WMD programs.

● Searching for, capturing, and driving terrorists

out of Iraq.

● Ending sanctions and immediately delivering

humanitarian assistance to support the Iraqi people.

● Securing Iraqi oil ﬁelds and resources for the

Iraqi people.

● Helping the Iraqi people create conditions for

a transition to a representative self-government.

For illustrative purposes, suppose planners gave

each stage a 75 percent independent probability

of success.

This level of probability potentially

anchored decisionmakers on a 75 percent chance

of overall mission success in Iraq, while the actual

probability of success is approximately 18 percent.

The total probability of accomplishing all of these

objectives gets smaller with the addition of more

objectives. As a result, the conclusion by strategic

leaders that Operation Iraqi Freedom had a high

likelihood of success was potentially overoptimistic

and unwarranted.

A more recent example of conjunctive event

bias occurs in procurement decisions. One of the

main selling points of the Future Combat System

Manned Ground Vehicle family (MGV) was tank-

level survivability combined with low weight for

rapid deployability. While the M1 tank relies on

passive armor for its protective level, the MGV

would reach an equivalent level via increased

situational awareness (“why worry about armor

when you are never surprised by your enemy?”)

and an Active Protective System (APS) that verti-

cally deploys an interceptor to strike an incoming

threat munition. The Active Protective System is a

conjunctive system that requires a chain of stages

to occur for overall system success: 1) detect an

incoming threat munition, 2) track and identify

munition trajectory, 3) deploy appropriate counter-

measure, 4) hit incoming munition, and 5) destroy

or deﬂect the munition.

Again for illustrative

purposes, assume that the individual probability of

success for each of these ﬁve stages is 95 percent.

Suppose that the M1A2’s passive armor is only

80 percent effective against the threat munition.

Anchoring bias occurs in that people may conﬂate

the 95 percent individual stage rate with an overall

APS system success rate. This is a false conclu-

sion. In this example, the overall APS probability

of success is actually 77 percent.

When compared

to the M1 tank, the APS is actually less survivable

than passive armor with this notional data.

We could also view the APS as a disjunctive

system. Instead of success rate, suppose the failure

rate of each component is ﬁve percent. Naturally,

a ﬁve percent failure rate looks better than the M1

tank’s 20 percent failure rate. Framed this way,

many people may erroneously anchor on a total

system failure probability of ﬁve percent, when

the disjunctive probability that at least one criti-

cal APS component fails is actually 23 percent.

Again, we ﬁnd that the APS is worse than the M1

tank’s passive armor. This simple example shows

that disjunctive and conjunctive events are opposite

sides of the same coin. Kahneman and Tversky

write, “The chain-like structure of conjunctions

leads to overestimation; the funnel-like structure of

disjunction leads to underestimation.”

The direc-

tion of the ﬂawed probability estimate is a matter

of framing the problem, yet the bias exists in both

types of events.

68 Mission Command  MILITARY REVIEW

Overcoming this anchoring phenomenon is dif-

ﬁcult. Even when test subjects are apprised of the

bias, research has shown anchoring and inadequate

adjustment persist. In dealing with highly volatile,

uncertain, complex, and ambiguous environments,

military professionals need to improvise and experi-

ment with a variety of new methods. These activities

are part of the critical task of reframing the problem,

outlined in FM 5-0. In order to avoid anchoring,

it may be necessary to reframe a problem anew;

however, this may be a difﬁcult proposition in a

time-constrained environment.

Summary

The volatility, uncertainty, complexity, and

ambiguity of our operating environment demand

that military professionals make rapid decisions

in situations where established military decision

making processes are either too narrow or inef-

fective. The fast tempo of operational decisions

potentially may render any elaborate approach,

either MDMP or Design, infeasible. As a result,

commanders and staff may ﬁnd themselves

engaged in more intuitive decision making. FM

3-0, Operations, states that intuitive decision

making rests on “reaching a conclusion that

emphasizes pattern recognition based upon knowl-

edge, judgment, experience, education, intelli-

gence, boldness, perception, and character.”

This

article has identiﬁed several heuristics that people

use to make intuitive decisions to emphasize the

potential cognitive biases that subconsciously arise

and can produce poor outcomes. When subjective

assessments, ego, and emotion are intertwined

with cognitive processes, we realize that intuitive

decision making is fraught with potential traps. We

must constantly strive to avoid these mental snares

and plan to compensate for them when they arise.

The solution may lie in the organizational embrace

of the concept of reﬂective practice as advocated

by previous authors in this journal.

Instead of

the usual striving toward a “best practices” meth-

odology, which is also full of potential heuristic

biases, reﬂective practice calls for “valuing the

processes that challenge assimilative knowledge

(i.e. continuous truth seeking) and by embracing

the inevitable conﬂict associated with truth seek-

ing.”

Institutionalizing this approach may help

us to avoid some of the intrinsic human mental

frailties that inhibit good decision making. MR

DOD

The XM1203 Non-Line-of-Sight Cannon was a mobile 155-mm cannon intended to provide improved responsiveness and

lethality to the unit of action commander as part of the U.S. Army’s Future Combat Systems project, Yuma, AZ, 2009.

69MILITARY REVIEW  Mission Command

SPECIAL EDITION

1. Carl von Clausewitz, On War, trans. and ed. Michael Howard and Peter Paret

(Princeton University Press, 1976), 85-86.

2. The speciﬁc terms volatility, uncertainty, complexity, and ambiguity (VUCA)

gained favor in the curricula of the military senior service colleges. For a history of

its pedagogical evolution, see Judith Stiehm, The U.S Army War College: Military

Education in a Democracy (Temple University Press, 2002).

3. The origins for these concepts come from Nobel Laureate Herbert Simon

and Charles Lindblom. Simon’s concept of “satisﬁcing” and Lindblom’s notion of

“muddling through” challenged the dominant technical-rational view (still prevalent

in the operations research community) that optimally efﬁcient solutions can be

found to inherently social problems. See Charles E. Lindblom, “The Science of

“Muddling Through,” Public Administration Review 19 (1959): 79-88, and Herbert

A. Simon, Administrative Behavior, 4th Ed. (Simon and Schuster, 1997). Later

theorists applied it to business organizations (Karl E. Weick, “Improvisation as a

Mindset for Organizational Analysis,” Organization Science 9, no. 5 [1998]: 543-55)

and to codes of professional knowledge (Donald A Schön, Educating the Reﬂective

Practitioner [Jossey-Bass, 1987]). There are a number of recent works that apply

these concepts to the military: Don M. Snider and Gayle L. Watkins, The Future

of the Army Profession, 2d Ed. (McGraw-Hill, 2005) and Christopher R. Paparone

and George Reed, “The Reﬂective Military Practitioner: How Military Professionals

Think in Action,” Military Review 88, no. 2 (2008): 66-77.

4. Donald A Schön writes that if “we think critically about the thinking that got us

into this ﬁx or this opportunity . . . we may, in the process, restructure strategies of

action, understandings of phenomena, or ways of framing problems,” , Educating

the Reﬂective Practioner (Jossey-Bass, 1987), 28.

5. “Technical-rationality” is the positive epistemology that has largely structured

our current view of knowledge. It is the view that we can reduce the elements of a

complex system, analyze them individually, and then reconstruct them into a holistic

appreciation of the system. Simultaneous causality and endogeneity make this type

of analysis very difﬁcult when analyzing social situations.

6. Plato uses this metaphor to describe a group of people unable to perceive the

true nature of the world because they are chained in a cave of their own making.

See Gareth Morgan, “Exploring Plato’s Cave: Organizations as Psychic Prisons,”

in Images of Organization (Sage, 2006).

7. Field Manual (FM) 5-0 (Washington, DC: U.S. Government Printing Ofﬁce

[GPO]), 3-1.

8. At its core, Design calls for an open mind that examines problems from

multiple lenses. It is not a systems engineering process with a sequence of steps

similar to MDMP. It calls for a broader intellectual examination of a problem. Unfor-

tunately, educating many in our profession to examine problems in this manner will

most likely meet institutional resistance. We are a culture of doers, not thinkers.

We decisively execute rather than thoughtfully deliberate. Process checklists are

easy to use and require little thought in a time-constrained environment. Under-

standing and using Design may require more ofﬁcers with liberal arts educations

over engineering training. The full embrace of a Design-type methodology to face

volatile, uncertain, complex, and ambiguous environments may require the com-

plete re-tooling of the core curricula at West Point, Command and General Staff

College, and the War College. This topic is highly controversial (and provocative).

9. For more on framing effects, see Erving Goffman, Frame Analysis (Cambridge:

Harvard University Press, 1974).

10. FM 6-0 (Washington, DC: GPO), 6-116.

11. We are examining individual heuristics as identiﬁed in behavioral econom-

ics, not social heuristics (how a culture appraises a situation). The effect of social

inﬂuences on decision making is a topic beyond the scope of this paper. However,

a merging of individual and social inﬂuences is proposed in Mark Granovetter,

“Economic Action and Social Structure: The Problem of Embeddedness,” The

American Journal of Sociology 91, no. 3 (1985), 481-510.

12. See Daniel Kahneman and Amos Tversky, “Judgment under Uncertainty:

Heuristics and Biases” Science 185 (1974), 1124-31; Daniel Kahneman and Amos

Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica 47,

no. 2 (1979), 263-92; and Choice, Values, and Frames, ed. Daniel Kahneman and

Amos Tversky (New York: Cambridge University Press, 2000).

13. These assumptions are not critical for this analysis of unconscious decision

making heuristics. Viewed from a sociological perspective, we could potentially relax

these assumptions and examine the complex interplay of unconscious organizational

inﬂuences on decision making. This would be an interesting topic for future research.

14. In spite of experimental and real world tests, behavioral economics is not

without critics. For more, see Mikhail Myagkov and Charles R. Plott, “Exchange

Economies and Loss Exposure: Experiments Exploring Prospect Theory and

Competitive Equilibria in Market Economics,” American Economic Review 87, no.

5 (1997): 801-28.

15. These heuristics and their attendant biases are previewed in Judgment under

Uncertainty: Heuristics and Biases, ed. Daniel Kahneman and Amos Tversky (New

York: Cambridge University Press, 1982), 1-20.

16. Professor Christopher Paparone suggests that one might call these ref-

erences a search for metaphors. For more, see Christopher R. Paparone, “On

Metaphors We Are Led By,” Military Review 88, no. 6 (2008): 55-64.

17. Unless one is to believe the superstitious notion of a Soldier with the unlucky

distinction of being a “bullet-magnet.”

18. Kahneman and Tversky write, “Continued preoccupation with an outcome

may increase its availability, and hence its perceived likelihood. People are pre-

occupied with highly desirable outcomes, such as winning the sweepstakes, or

highly undesirable outcome, such as an airplane crash. Consequently, availability

provides a mechanism by which occurrences of extreme utility (or disutility) may

appear more likely than they actually are,” Judgment under Uncertainty: Heuris-

tics and Biases, ed. Daniel Kahneman and Amos Tversky (New York: Cambridge

University Press, 1982), 178.

19. Commander, U.S. 5th Fleet Public Affairs, “USS Hartford and USS New

Orleans Arrive in Port Bahrain,” 21 March 2009, story number: NNS090321-03,

<http://www.navy.mil/search/display.asp?story_id=43630>.

20. See Nassim N. Taleb, The Black Swan: The Impact of the Highly Improbable

(Random House, 2007).

21. We see this same type of phenomenon occurring in the sale of insurance.

People use the last accident or disaster as an upper limit on what is possible for

the future; therefore, they only insure up to this level.

22. The assumption made was that all real estate market ﬂuctuations are local.

At the national level (or systemic-level), the local markets would never fall at the

same time. In fact, this is what occurred.

23. Nassim N. Taleb, <http://www.fooledbyrandomness.com/imbeciles.htm>.

24. See Thaddeus Holt, The Deceivers: Allied Military Deception in the Second

World War (New York: Scribner, 2004), 39-40.

25. One must be careful using historical examples. The study of military history

potentially exposes us to availability-related biases. We do all that reading to learn

what has worked and what hasn’t worked in the past, yet this source of professional

knowledge can tether us to speciﬁc courses of action. If we apply lessons from

the past that are incorrectly suited for the problems of today, then we may sow the

seeds of disaster. Military history is useful for informing our understanding of the

problem, but we must be cautious not to let history inappropriately guide our actions.

26. Mark S. Mayzner and Margaret Tresselt, “Tables of single-letter and bigram

frequency counts for various word-length and letter position combinations,” Psy-

chonomic Monograph Supplements, 1965, no. 1, 13-32.

27. Although I generalize about mental search sets, it is important to acknowl-

edge that some personality types may exhibit parallel thought processes. We might

ﬁnd this capacity in “creative” people, such as painters, musicians, and architects.

28. I am indebted to Professor Christopher Paparone for this insight. Also see

Deborah A Stone, Policy Paradox: The Art of Political Decision Making, 2d Ed.

(New York: W.W. Norton, 2001).

29. See Daniel Kahneman and Amos Tversky, “Judgment under Uncertainty:

Heuristics and Biases” Science 185 (1974): 1124-31.

30. See John T. Fishel, “Operation Uphold Democracy: Old Principles, New Real-

ities,” Military Review 77, no. 4 (1997): 22-30, and Robert F. Baumann, “Operations

Uphold Democracy: Power Under Control,” Military Review 77, no. 4 (1997): 13-21.

31. In light of this potential bias, we may want to re-evaluate the allocation of

our budget resources. Which contribute more to combat effectiveness—dollars

spent on technical systems that enhance situational awareness, or dollars spent

on realistic, tough training?

32. In technical terms, correlation is a measure of covariance, which is a measure

of the linear dependence between two random variables. It does not imply causality.

For example, people carrying umbrellas are positively correlated with the possibility

of rain, yet carrying umbrellas does not cause it to rain.

33. See Loren J. Chapman and Jean P. Chapman, “Genesis of popular but

erroneous psychodiagnostic observations,” Journal of Abnormal Psychology 72

(1967): 193-204; Loren J. Chapman and Jean P. Chapman, “Illusory correlation as

an obstacle to the use of valid psychodiagnostic,” Journal of Abnormal Psychology

74 (1969); and Dennis L. Jennings, Teresa M. Amabile, and Lee Ross, “Informal

covariation assessment: Data-based versus theory-based judgments,” in Judg-

ment under Uncertainty: Heuristics and Biases, ed. Daniel Kahneman, and Amos

Tversky (Cambridge, 1982).

34. Richard J. Heuer, Psychology of Intelligence Analysis (Center for the Study

of Intelligence, 1999), 144-45.

35. Irving L. Janis, Groupthink: Psychological Studies of Policy Decisions and

Fiascoes, 2d ed. (Boston, MA: Houghton Mifﬂin, 1982). I am indebted to Major

Robert Meine, U.S. Army, for his comments on this article. He noted that the Army

is particularly vulnerable to the effects of groupthink given our rank structure, defer-

ence to authority, and organizational structure.

36. Heuer, ch. 8. The military has named this process “red teaming.”

37. This problem was a variation of Kahneman and Tversky’s famous taxicab

experiment in Judgment under Uncertainty: Heuristics and Biases, ed. Daniel

Kahneman and Amos Tversky (New York, Cambridge University Press, 1982),

156-57. It is similar to a quiz I gave during my Game Theory class at West Point.

38. Mathematically, this problem can be solved using Bayesian inference.

39. Some may feel that the lieutenant should err on the side of caution—assume

the man is an insurgent until proven otherwise. This may save the lives of soldiers.

However, in the broader context, this approach most deﬁnitely will increase the

innocent man’s sympathy for the insurgency (as well as his family’s). In fact, he

and his kin may begin to actively support or join the insurgency.

40. For more, see Maya Bar-Hillel, “The base-rate fallacy in probability

judgments.” Acta Psychologica 44 (1980): 211-33; Maya Bar-Hillel, “Studies of

Representativeness,” in Judgment under uncertainty: Heuristics and biases, ed.

Daniel Kahneman, Paul Slovic, and Amos Tversky (New York: Cambridge, 1982);

and Kahneman and Tversky, “Evidential impact of base rates” in Judgment under

Uncertainty: Heuristics and Biases, ed. Daniel Kahneman, Paul Slovic, and Amos

Tversky (New York: Cambridge, 1982).

41. See the hospital example in Daniel Kahneman and Amos Tversky, “Sub-

jective probability: A judgment of representativeness.” Cognitive Psychology 3

(1972): 430-54.

42. See the coin example in Daniel Kahneman and Amos Tversky, “Subjective

probability: A judgment of representativeness,” Cognitive Psychology 3 (1972):

430-54.

43. 0.5*0.5*0.5*0.5*0.5*0.5 = 0.015625 or 1.56 percent.

NOTES

70 Mission Command  MILITARY REVIEW

44. I am indebted to MAJ Nick Ayers, U.S. Army, for his explanation of tank gun-

nery training.

45. Judgment under Uncertainty: Heuristics and Biases, ed. Daniel Kahneman and

Amos Tversky, (New York: Cambridge University Press, 1982), 10.

46. For a complete description, see Holt, 31-32.

47. See <http://www.globalsecurity.org/military/ops/iraqi_freedom.htm>.

48. For this simple example, we assume independence of events. However, most

of these events are conditional on the success of other events; therefore, Bayesian

analysis may be more appropriate. The point of the example is that people do not

usually think even in terms of simple independent probability, let alone more complex

conditional probability.

49. 0.75*0.75*0.75*0.75*0.75*0.75 = 0.1779 or 17.79 percent.

50. See <http://www.globalsecurity.org/military/systems/ground/iaaps.htm>.

51. 0.95*0.95*0.95*0.95*0.95 = 0.77 = 77 percent. To be equivalent to the M1 tank,

each APS component would have to have a success rate above 95 percent (actual

answer is greater than 95.64 percent).

52. This problem is relatively simple to analyze when the probabilities involve

objective engineering data. They become much harder when we consider the subjective

probabilities found in social situations.

53. 1-0.77 = 0.23 = 23 percent

54. Judgment under Uncertainty: Heuristics and Biases, ed. Daniel Kahneman and

Amos Tversky, (New York: Cambridge University Press, 1982), 16.

55. Bayesian inferential techniques may be appropriate tools for overcoming

anchoring; however, they take time to model and understand.

56. FM 3.0, Operations (Washington, DC: GPO, 27 February 2008), 5-11.

57. See Christopher R. Paparone and George Reed, “The Reﬂective Military Practitio-

ner: How Military Professionals Think in Action,” Military Review 88, no. 2 (2008): 66-77.

58. Ibid., 74.

Written by the combined faculty of the Combat Studies Institute (1998,

U.S. Army Command and General Staff College Press), a collection of

24 essays that analyze various combat engagements and military lead-

ers throughout history—from Gustavus Adolphus in the seventeenth

century to Hamburger Hill in Vietnam—illustrating the concept we now

call Mission Command.

Edited by Adela Frame and James A. Lussier (2000, U.S. Army Command

and General Staff College Press), a collection of 66 essays from ﬁeld-grade

and general ofﬁcers utilizing what we now call Mission Command during

training exercises, mostly at the National Training Center. These essays

describe their thoughts, their actions, their successes, and their mistakes.

Written by John J. McGrath (2006, CSI Press), uses case studies from

ancient times through operations in Iraq to discuss the historical develop-

ment of technology and personal technique that has led to our current abil-

ity to apply Mission Command in today’s mobile military forces.

Written by Colonel Richard D. Hooker, Jr. (2012, CSI Press), a collec-

tion of 10 essays on battles from the last 150 years, offering concise

descriptions and analyses that focus on command and maneuver. The

insights focus on why successful leaders are able to gain decisive ad-

vantages over their enemies, even from a position of disadvantage.

COMBAT STUDIES INSTITUTE

Fort Leavenworth, KS

Crossing the Line of Departure: Battle Command on

the Move, A Historical Perspective

66 Stories of Battle Command

Wrath of Achilles: Essays on Command in Battle

Studies in Battle Command

All these titles are available in electronic form at: http://usacac.army.mil/cac2/CSI/RandPTeam.asp.

Limited hard copies are available to Department of Defense employees by contacting Mr. Kendall Gott at 913-684-2138 or

usarmy[email protected]