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Walter L Perry

James Moffat

Prepared for the United Kingdom Ministry of Defense

Information Sharing Among Military

Headquarters

The Effects on Decisionmaking

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1 Command and control systems—United States 2 United States—Armed

Forces—Communication systems 3 Military art and science—United States—

Decision making 4 United States—Armed Forces—Headquarters I Moffat, James, 1948– II Title.

Preface

New concepts such as network-centric operations and distributed anddecentralised command and control have been suggested as techno-logically enabled replacements for platform-centric operations and forcentralised command and control in military operations But asattractive as these innovations may seem, they must be tested beforeadoption This report assesses the effects of collaboration across alter-native information network structures in carrying out a time-criticaltask, identifies the benefits and costs of local collaboration, and looks

at how ‘information overload’ affects a system

A joint US/UK study team conducted the research described inthis report In the United States, the research was carried out withinRAND Europe and the International Security and Defense PolicyCenter of the RAND National Security Research Division, whichconducts research for the US Department of Defense, allied foreigngovernments, the intelligence community, and foundations In theUnited Kingdom, the Defence Science and Technology Laboratory(Dstl) directed the work and participated in the research effort Dstl

is the centre of scientific excellence for the Ministry of Defence, with

a mission to ensure that the UK armed forces and government aresupported with in-house scientific advice RAND has been granted alicence from the Controller of Her Britannic Majesty’s StationeryOffice to publish the Crown Copyright material included in thisreport

This report will be of interest to military planners, operators,and personnel charged with assessing the effects of alternative infor-

mation network structures, processing facilities, and disseminationprocedures Planners contemplating the use of network-centric pro-cesses to achieve military objectives can use the methods described inthe report to evaluate alternative structures and processes Informa-tion technologists can assess the contribution of each alternative tothe decisionmaker’s knowledge prior to taking a decision The ulti-mate goal is to develop tools that will allow operators to quicklyevaluate plans for their level of situational awareness.

For more information on the RAND International Security andDefense Policy Center, contact the director, James Dobbins He can

be reached by email at James_Dobbins@rand.org; by phone at 393-0411, extension 5134; or by mail at RAND Corporation, 1200South Hayes Street, Arlington, VA, 22202-5050 More informationabout RAND is available at www.rand.org

Contents

Preface iii

Figures ix

Tables xi

Summary xiii

Acknowledgments xxxi

Abbreviations and Glossary of Terms xxxiii

CHAPTER ONE Introduction 1

Objective 1

The Information Superiority Reference Model 2

Research Approach 4

Organisation of This Report 6

CHAPTER TWO Decisions in a Network 7

The Decision Model 8

Estimators 10

A Networked Decision Model 10

Clusters 12

Partitioning 13

Requirements for a Model of the Process 14

Framing 15

Shared Awareness and Clustering 15

A Simple Logistics Example 16

CHAPTER THREE

Representing Uncertainty 19

Decisions 19

A Multivariate Normal Model 20

Knowledge from Entropy 21

Knowledge 22

The Effects of Knowledge 23

More General Models 24

Multi-Attribute Assessment 26

Simple Additive Weights Method 27

Weighted Product Method 28

Precedence Weighting 29

Mutual Information 31

Relative Entropy 32

Mutual Information 33

Cruise Missile Type and Speed 33

Entropy and Mutual Information 35

Summing Up 37

CHAPTER FOUR The Effects of Collaboration 39

Knowledge 39

Bias 40

Precision 40

Precision and Entropy 41

Estimating Local Knowledge 42

Precision and Knowledge in the Logistics Example 42

Accuracy 45

Accuracy in the Logistics Example 48

The Effects of Bias, Precision, and Accuracy on Knowledge 50

Completeness 51

Information ‘Ageing’ 53

Time Lapse 53

Updating 54

Measuring the Overall Effect of Cluster Collaboration 56

Contents vii

CHAPTER FIVE The Effects of Complexity 61

Complex Networks 61

What Is Complexity? 62

Plecticity 64

Accessing Information 64

Distance and Connectivity 66

Network Redundancy 71

Unneeded Information 73

The Combined Effects 73

The Benefits of Redundancy 74

Combining the Benefits 77

The Costs of Information Within a Cluster 79

Costs of Unneeded Information 80

Costs of Redundant but Needed Information 80

Combining the Costs of Information for a Cluster 83

Combining Costs and Benefits 84

Overall Network Performance 85

Summing Up 86

CHAPTER SIX Conclusion 87

APPENDIX A The Rapid Planning Process 91

B Information Entropy 105

C Application to a Logistics Network 111

Bibiliography 119

Figures

S.1 The Information Superiority Reference Model xvi

S.2 Overall Network Plecticity xxiv

1.1 The Information Superiority Reference Model 3

2.1 Decisionmaker’s Conceptual Space and Stored Situations 9

2.2 Network of Decisionmaking Elements 11

2.3 Networked Sustainment Decisions 17

5.1 Three Simple Connectivity Assessments 69

5.2 Connectivity Assessments with More Than One Source Node 70

5.3 Node-Centric View of Information 72

5.4 Overall Network Plecticity 74

5.5 The Effect of
i and
i on the Benefits of Redundancy 76

5.6 Cost of Unneeded Information 81

5.7 The Costs of Redundancy for
i=1 82

5.8 The Costs of Redundancy for  i =
6 82

A.1 Stage 1: Observation Analysis and Parameter Estimation 93

A.2 Stage 2: Situation Assessment 97

A.3 Stage 3: Pattern Matching and Course of Action Selection 100

A.4 CLARION+ Screen Image of Land-Air Interaction 102

A.5 Rapid Planning Type II Mixture Model Depiction 103

C.1 Assessing the Effects of Information Sharing on Combat Effectiveness 112

C.2 A Supply-Driven Information Network: Case S 113

C.3 A Demand-Driven Information Network with No Information Sharing: Case D1 114

C.4 A Demand-Driven Information Network with Information

Sharing: Case D2 115 C.5 Overall Network Knowledge 117 C.6 Collaboration-Based Knowledge 117

Tables

3.1 Precedent Weight Assessment 31 3.2 Joint Probability Mass Function for Speed and Missile Type 34 A.1 Initial Situation Assessment Matrix 99

Summary

New information technologies introduced into military operationsprovide the impetus to explore alternative operating procedures andcommand structures New concepts such as network-centric opera-tions and distributed and decentralised command and control havebeen suggested as technologically enabled replacements for platform-centric operations and for centralised command and control Asattractive as these innovations seem, it is important that militaryplanners responsibly test these concepts before their adoption To dothis, models, simulations, exercises, and experiments are necessary toallow proper scientific analysis based on the development of boththeory and experiment

The primary objective of this work is to propose a theoreticalmethod to assess the effects of information gathering and collabora-tion across an information network on a group of local decision-making elements (parts of, or a complete, headquarters) The effect ismeasured in terms of the reduction in uncertainty about the informa-tion elements deemed critical to the decisions to be taken

Our approach brings together two sets of ideas, which have beendeveloped thus far from two rather different perspectives The first ofthese sets is the Rapid Planning Process, developed as part of a project

on command and control in operational analysis models within the

UK Ministry of Defence Corporate Research Programme It is a struct for representation of the decisionmaking of military com-manders working within stressful and fast-changing circumstances.The second set of ideas comes from the work on modelling the effects

con-of network-centric warfare, carried out recently by the RANDCorporation for the US Navy We assess the effects of collaborationacross alternative information network structures in prosecuting atime-critical task using a spreadsheet model We quantify the benefitsand costs of local collaboration using a relationship based on

information entropy as a measure of local network knowledge We also

examine the effects of complexity and information overload caused bysuch collaboration

Decisions in a Network

New technologies are enabling militaries to leverage informationsuperiority by integrating improved command and control capabili-ties with weapon systems and forces through a network-centricinformation environment The result is a significant improvement inawareness, shared awareness, and collaboration These improvements

in turn affect the quality of the decisionmaking process and the sion itself, which ultimately lead to actions that change the battle-space

deci-In this report, we focus on the quality of the decisions, or theplanned outcome, rather than on whether or not the desired effect iseventually achieved

We note that decisions are made based on the information able from three sources: information that is resident at the decisionnode; information from collection assets and information processingfacilities elsewhere in the network; and information from other localdecisionmakers with whom the decision nodes are connected andwith whom they share information

avail-Rapid Planning Process

In most cases, decisionmakers must make decisions without fullunderstanding of the values of the critical information elementsneeded to support the decisions The decision taken depends on thecurrent values of the critical information elements, which are depen-dent on the scenario This dependency is modelled using the Rapid

Summary xv

Planning Process The critical information elements map out thecommander’s conceptual space In the basic formulation of the RapidPlanning Process, a dynamic linear model is used to represent thedecisionmaker’s understanding of the values of these factors overtime This understanding is then compared with one or more of thefixed patterns within the commander’s conceptual space, leading to adecision

A probabilistic information entropy model is used to representthe uncertainty associated with the critical information elementsneeded for the decision Ideally, through the Rapid Planning Process,additional information from collection assets or from collaboratingelements in the network serves to reduce uncertainty and thereforeincrease knowledge

Knowledge

We are principally concerned with the information and cognitivedomains, as depicted in Figure S.1 The domains of the informationsuperiority reference model divide the command and control cycleinto relatively distinct segments for ease of analysis Their descriptionincludes the entities resident in the domain, the procedures per-formed and the products produced there, and the relationshipsamong the domains

Information derived from sensors or other information ing resides in the information domain This information is trans-formed into awareness and knowledge in the cognitive domain andforms the basis of decisionmaking Our metrics quantify this processthrough the use of information entropy and knowledge measures.Information sharing among nodes ideally tends to lower infor-mation entropy (and hence increase knowledge) partly because of thebuildup of correlations among the critical information elements That

gather-is, information can be gained about one critical information element(e.g., missile type) from another (e.g., missile speed) Such cross cou-pling is a key aspect for consideration, and we use conditional en-tropy to capture these relationships

expectations, and concerns

Situational awareness, shared situational awareness, collaboration, and decisionmaking

Information domain

Data collection, fusion to produce the CROP, dissemination of

the CROP, and sensor tasking

Physical domain

Ground truth: entities, systems, intentions, plans, and physical

activities

Collected data

Sensor tasking

Knowledge derived from entropy is a quantity that reflects thedegree to which the local decisionmaker understands the values of theinformation elements It is represented as a number between 0 and 1,with the former representing ‘no understanding’ and the latter repre-senting ‘perfect understanding’ From this knowledge, decision-makers can assess whether or not they are in their ‘comfort zone’—that is, whether the values of the key information elements supportthe decision they wish to take (such as one to launch the next attackmission)

Summary xvii

Effects of Collaboration

Networks provide an opportunity for participating entities to shareinformation as part of a collaborative process.1 Here we focus on thesynergistic effects of collaboration that improve the quantity (thecompleteness of our information) and the quality (its precision andaccuracy) of the information needed to take decisions We model thenetwork as the combination of clusters of entities and represent eachentity by a node A cluster consisting of a single node is taken to bethe degenerate case Each such cluster consists of a set of entities,

which have full shared awareness Full shared awareness means that all

entities in the cluster agree on the set of information elements andtheir values at any given time

Estimators

Through observations of the battlespace, sensors and other tion sources generate estimates for the information elements deemedcritical to the decision The uncertainty associated with the informa-tion elements is expressed in terms of probability distributions, themeans of which are estimates of the ground-truth values Because themean of a probability distribution is a parameter of the distribution,

informa-we turn to parameter estimation theory to assess the quality of theinformation available to the decisionmaker and examine how thequality of the estimates contributes to knowledge

• Bias: Bias in an estimate is error introduced by systematic

distor-tions An unbiased estimator is one for which its statisticalexpectation is the true value of the estimated parameter That is,the expected value of the estimate of the parameter, µ^, is thetrue value of the parameter, µ The bias in the estimate is there-fore the degree to which this is not true

• Precision: The variation in estimates of the critical information

elements can occur in a purely random way Random errors

1 Collaboration in this context is taken to be a process in which operational entities actively

share information while working together towards a common goal.

affect the precision of the estimates reported because theyincrease the variance of the distribution of the estimated infor-mation element In general, precision is defined to be the degree

to which estimates of the critical information element or ments are close together.2 Bias and precision, therefore, areindependent—that is, biased estimates may or may not be pre-cise

ele-Precision and Entropy

The amount of information available in a probability density is ured in terms of information entropy, denoted H(x) Informationentropy is always a function of the distribution variance, and there-fore we use it as the basis for developing a knowledge function Forexample, the bivariate normal distribution is H(x, y) = log |
|, where

meas-
is the covariance matrix From this, we create a precision-basedknowledge function as3

pro-reflects the level of understanding within a cluster of decisionmakers.

For the simple case of two collaborating decisionmakers (i.e.,two nodes of the network forming a cluster) who share two pieces ofinformation with a multivariate normal distribution, the change inknowledge is given by

2 This is a commonly accepted definition Ayyub and McCuen (1997, p 191) define sion as ‘the ability of an estimator to provide repeated estimates that are very close together’.

preci-A similar definition can be found in Pecht (1995).

3 Actually, the exact entropy value for the bivariate normal case is

H x, y( )= log 2
e( )2

 However, because we are concerned about the relative entropy, we use the simpler version, which we refer to as ‘relative entropy’.

informa-]= b2 +  2

, where
2 is thevariance of µ ^ The MSE is an extremely useful metric because itincludes both accuracy in the total and precision as a component Inestimating ground truth, the bias accounts for nonrandom errors andthe precision accounts for random errors

We illustrate by continuing with the bivariate normal case Weassume that Bayesian updating is used to refine the location estimatebased on the arriving reports Bayesian updating is not always un-biased, and therefore we introduce systemic error In this case, thebias is the Euclidean distance between the Bayesian estimate and theground-truth value:

The Effects of Bias, Precision, and Accuracy on Knowledge

We now account for bias, precision, and hence accuracy in theknowledge function by replacing the distribution variance with theMSE, or the accuracy measure D(x, y) in the knowledge function.Therefore, for the multivariate normal case, we get a modified knowl-edge function of the form:4

Completeness

In addition to precision and accuracy, collaboration also affects thecompleteness of the critical information elements available within acluster For the entire network, we assume there are a maximum of N

critical information elements For a given cluster, the total numberrequired is C
N However, at a given time, t, only n
C might beavailable If waiting for additional reports is not possible, a decision-maker would be required to take a decision without benefit of com-plete information Depending on his experience and other contextualinformation, the decisionmaker may be able to infer some likely lessreliable value for the missing information For now, we assume that ifthe value of an information element is missing, the value of com-pleteness at cluster i is

4 The subscript M denotes knowledge derived from the MSE.

Information Freshness

A final consideration when assessing uncertainty is that of freshness.The information arriving at a decision node consists of reports con-cerning one or more of the critical information elements necessary totake a decision Both precision and accuracy depend on the jointprobability density function that reflects the uncertainty in ourknowledge of the ground-truth fixed pattern at a decision node.These reports are used to update the joint probability distribution ofthe information elements and hence the probability of correctness ofeach of the fixed patterns in the local decisionmaker’s conceptualspace

We have selected Bayesian updating as the method for ing reports from various sources and sensors All things being equal,

combin-we desire to give more combin-weight to more recent reports, which requiresthat we reevaluate all available, valid reports at the time a decision is

to be taken A time-lapse estimate, 0 
1, is used to determine therate of information decay so that old information is given less weightthan current information

Measuring the Overall Effect of Cluster Collaboration

Finally, we combine the currency-adjusted precision and accuracyknowledge function with completeness to arrive at a single metric toassess the effects of collaboration across the cluster The ideal case iswhen we have full completeness, i.e., X t (n) = X t (C )=1, and theknowledge shared across the cluster is fully accurate, K M (x)=1.Unfortunately, this ideal is seldom, if ever, achieved Consequently,

we require a construct that gauges the degree to which accuracy, ascalculated here, and completeness contribute to knowledge.

In general, when X t (n) is small, the knowledge function shouldalso be small One way to reflect this behaviour is to replace the MSE

in the entropy calculation with

), we get

K( )x =1
b2+ 2

n(bmax2 + max2 )

for the univariate normal case.5

Up to this point, we have captured the effects of collaborationamong decision nodes within a cluster on knowledge The measuredeffects of information sharing through collaboration are accuracy andcompleteness For the most part, these effects are dynamical, becausethey vary with the quality and quantity of reports received and pro-cessed over time Missing from this analysis so far is an assessment of

5 The
subscript in this case refers to knowledge based on both the MSE and completeness.

Summary xxiii

the systemic effects of the network structure—that is, the effects thatare more static Next, we take up such measures of network com-plexity and combine them with the collaborative effects to arrive at asingle measure of network performance and its effect on decision-making

Effects of Structural Complexity

All networks exhibit complexity to a greater or lesser degree Militarycommand and control systems operating in a network-centric envi-ronment also exhibit complex behaviour The challenge is under-standing exactly what the complexity is, what its effects are, and how

to quantify these effects We note that there are both good and badeffects of complexity Unfortunately, the term ‘complexity’ has anegative connotation; therefore, we have adopted Murray Gell-Mann’s more neutral term, ‘plecticity’

In this context, plecticity refers to the ability of a connected set of

actors to act synergistically via the connectivity between them Thismeasure is intended to take into account the fact that there may beconstraints, due to technical or procedural limitations, on how nodescan constructively connect to other nodes; that is, a node’s connec-tivity can add costs as well as benefits to the cluster A measure ofplecticity should account for the value of the cluster’s ability to gleaninformation from throughout the network to fulfil its particular func-tions, include a means for measuring the value of information redun-dancy, and reflect a cost to network effectiveness if nodes are over-whelmed

For networks with inadequate clustering, as with excessiveclustering—flows 1 and 3, respectively, in Figure S.2—we wouldexpect low plecticity scores The goal is to configure the informationflow over a network with established link connectivity so as to maxi-mise plecticity as measured in the terms discussed above and as illus-trated by flow 2 in the figure

Benefits = high Costs = high Plecticity = low

Adequate information flow

Excessive information flow

Accessing Information

The metric developed for completeness earlier is simply a ratio ofcounts: available required information elements to total requiredinformation elements No attempt is made to assess the degree to

which we can really expect to receive the information element, i.e., the

degree to which the network allows the cluster to access information

in the network A metric that does so is the ratio of the aggregateexpected degree of critical information access to the total number ofrequired information elements Such a metric accounts for the uncer-tainties associated with retrieving needed information

We thus replace the binary accounting for information ments, with a connectivity score based on a distance function thatrecognises the cost imposed by the path the information must takethrough the network to arrive at the node requiring it

ele-For any information element, a l, we are interested in the test path from source node to destination node, d l
1, howevercalculated The restriction that the path distances always exceed 1.0accounts for the fact that, for connectivity to exist at all, at least onelink must exist between source and destination The case in which nolinks exist implies an infinitely long path resulting in 0 connectivity.The quantity, d l, represents the expense incurred by moving infor-mation element a l from source to destination The associated con-nectivity value is calculated as

we consider only the loss of nodes We create a depletion vector, L l,whose elements consist of the connectivity values for informationelement a l, with each of the path nodes removed in turn The vector

Ll then represents the vulnerability of the path and, as such,expresses the degree of uncertainty associated with retrieving informa-tion element a l from network sources The adjusted connectivity forinformation element a l from network sources to a single destination

Benefits of Network Redundancy

Network redundancy focuses on the reliability of the network; itsability to deliver information in the face of node loss; system outages;inefficient operating procedures; or some combination of all theseelements At the same time, a network can deliver excessive informa-tion, thus causing delays because of the time and resources required

to process all of it Consequently, network redundancy can be both acost and a benefit of the network information flow

Needed information can be provided to a cluster from multiplesources If the value of the information will change over time, we canexpect multiple reports from each source These multiple reportsrequire combining in some way as previously discussed under col-laboration Whatever method is used, the degree to which the reportscontribute to estimates close to ground truth and to a narrowing ofthe distribution variance, a benefit will accrue to the cluster because

of redundancy Recall that the total number of required informationelements across the whole network is N; the number critical to a clus-ter is C, where C
N; and the number of these available within thecluster is n, where n
C If we let the vector
=[
1 ,
2,
,
C]T

represent the aggregate value of reports received for each requiredinformation element ( a1,a2,
,aC) from P =[ p1, p2,
, pC]T sources,then we can construct a suitable normalised aggregate metric, R(
),as

R( ) =1
1

n ie
i i
1( )

i=1 C

The combined benefit of information redundancy information

to the cluster, based on the conditional dependency between bility and redundancy, is

where
> 1 is a constant that ensures a nonzero denominator and

bounded between 0 and 1

Costs of Information Overload

At the same time, a network can deliver excessive information Themore sources of required information and the more frequent thereporting, the longer it takes for the cluster to get a coherent view ofthe situation That is, it takes time to process information, which may

or may not contribute to improving the quality of the estimates Thisexcess is referred to as ‘information overload’ In addition, some ofthe sources may provide disconfirming evidence The value of thedisconfirming evidence can be good or bad, depending on the degree

to which it reflects ground truth Disconfirming evidence requirestime to evaluate and therefore may increase uncertainty and decreasethe quality of the estimates Finally, it is also possible that raw datamay be processed before being sent, thus arriving at the cluster astime-stamped information with the time at which the processingended This possibility introduces an artificial latency that contributes

to uncertainty

The supply of unneeded information to a cluster has an diate negative impact, because it must be processed or, at a mini-mum, interferes with the receipt of needed information However, asmore unneeded information is supplied, its impact is reduced Thus,

imme-a good function to model this behimme-aviour is the exponentiimme-al

so that the marginal cost of an additional source of information is

greater than the previous source At some further point, this cost thenlevels off so that the marginal costs are minimal This behaviour isbest described using a logistics response function for each informationelement shared within the cluster For simplicity, we express thecombined costs of oversupply of needed information as a simple sum,

where
i and
i are shaping parameters

In considering the overall costs for the cluster, a balance is struckbetween costs of needed and unneeded information We use a simpleweighted linear sum of the two components of information overload,

or O[U (m),G(P)] = U (m) + (1
)G(P), where 0 
 1, as a relativeweight parameter

Redundancy-Based Plecticity

The next step is to combine the costs and benefits of plecticity for acluster associated with the mission at hand For each cluster in thenetwork, the measure of network plecticity, C(B,O), is calculated asfollows:

C B,O( )= B R [ ( )X k( ) ] [1
O U m[ ( ),G P( ) ] ]

Network Performance

The last step is to combine the redundancy-based plecticity with thebenefits of collaboration across all the clusters of the network Ourcollaboration metric quantifies the effects of information sharingacross a cluster on information completeness and accuracy, whereasplecticity measures the positive and negative effects of redundantinformation and the degree of information access The former assessesthe dynamic nature of the operation conducted on the network; the

Summary xxix

latter measures the effects of the underlying network structure and istherefore systemic All the dependencies among the several compo-nents of collaboration and plecticity are not generally well under-stood However, we know that high-quality performance requiresgood cluster knowledge and the means to share it and that scores ineither category are penalised by deficiencies in the other Therefore,the measure of total network performance is taken to be


,K( N)= i=1 L [C i( )B,O K i ,]i,

where i=1 L
i =1 and L is the number of clusters

For values of (
,KN) close to 1.0, the network is performingwell by producing the information required to take decisions withineach of the clusters when required However, this is not the wholestory The next step is to assess how well the combat mission isaccomplished As important as good decisions are, good combat out-comes are the ultimate measure of the value of network-centric opera-tions An example application shows how these approaches can becombined The mathematical approach is used to filter out preferrednetwork and clustering assumptions, which are then tested in asimulation environment This allows the development of bothnetwork-based Measures of Command and Control Effectiveness andhigher-level Measures of Force Effectiveness

Acknowledgments

The authors wish to express their gratitude to several individuals whoprovided guidance and assistance to the project In the United King-dom, we thank Lynda Sharp and Tim Gardener (Dstl), LorraineDodd (Qinetiq), and Christopher Watson (BAE Systems) for theirtechnical support and continued interest in the application of themethodologies presented in this report We also thank the Controller

of Her Britannic Majesty’s Stationery Office for granting permission

to publish the Crown Copyright material contained in the text, cially the discussion of the Rapid Planning Process found in Appen-dix A At RAND, we thank Gina Kingston, a visiting fellow from theDefence Science and Technology Organisation, Australia, for herhelpful comments and early review of the text, and Tom Sullivan forhis suggestions concerning the use of the mean squared error toinform knowledge We also thank R J Briggs for his assistance indeveloping some of the code that was used to apply the concepts.Christopher Pernin (previously on secondment to Dstl) provided theanalysis in Appendix C and contributed extensively to developing theCollaboration Metric Model used to illustrate the value of the met-rics We also thank former RAND colleague Jimmie McEver andRoger Forder at Dstl for their careful reviews of this work Theircomments and suggestions strengthened the finished report Finally,

espe-we thank Robin Davis for her assistance in preparing this documentfor publication

Abbreviations and Glossary of Terms

Accuracy The degree to which information agrees with

ground truth

Awareness A realisation of the current situation

Bias Error in an estimate introduced by systematic

distortions

intelligence, surveillance, and reconnaissanceCEC Cooperative Engagement Capability; a capability

that combines data from all platforms in anoperation and allows the combined data toproduce a better shared CROP

Cluster A set of network nodes possessing full shared

awareness

Collaboration A process in which operational entities actively

share information while working together towards

a common goalComplexity The condition of having several interrelated parts

in a network with several interrelated operationalentities Kolmogorov definition: The length of theshortest binary program needed to compute astring of data; the minimal description lengthConceptual space The conceptual space of a commander is the space

defined by the values of his critical informationrequirements

CROP common relevant operating picture; a view of the

battlespace shared by all friendly forces

Full shared

awareness

A set of network nodes that (1) share information,(2) agree on the same set of critical informationelements, and (3) agree on the current values ofthe agreed critical information elements

Information

entropy

A measure of the average amount of information

in a probability distribution (also referred to asShannon entropy)

Information

superiority

The ability to collect, process, and disseminateinformation as needed; anticipate changes in theenemy’s information needs; and deny the enemythe ability to do the same

IPB intelligence preparation of the battlefield

Knowledge Accumulated and processed information wherein

conclusions are drawn from patternsLogically

connected nodes

Nodes with a communication path between them

Abbreviations and Glossary of Terms xxxv

Metrics Mathematical expressions that evaluate both the

relative effect of alternatives and the degree towhich one is better or worse than anotherMLRS Regt Multiple-Launch Rocket System Regiment

MSE mean square error; a measure of the accuracy of an

estimate It is the sum of the bias and theprecision of the estimate

Mutual

information

The amount of information gained about random

variable X based on information gained about dependent variable Y

Physically

connected nodes

Nodes with a communications link between them

Plecticity The ability of a connected set of actors to operate

synergistically via the connectivity among themPrecedence

weighting

A multi-attribute decisionmaking method

Precision The degree to which multiple observations are

realisations

Introduction

New information technologies introduced into military operationsprovide the impetus to explore alternative operating procedures andcommand structures New concepts such as network-centric opera-tions and distributed and decentralised command and control havebeen suggested as technologically enabled replacements for platform-centric operations and centralised command and control As attrac-tive as these innovations may seem, it is important that military plan-ners responsibly test these concepts before their adoption To do this,models, simulations, exercises, and experiments are necessary

Objective

The major objective of this work is to produce a method to assess theeffects of information gathering and sharing across an informationnetwork on the quality of decisions taken by a group of local deci-sionmaking elements (parts of, or a complete, headquarters) Theeffect is measured in terms of the reduction in uncertainty about theinformation elements deemed critical to the decisions to be taken atthese local decisionmaking elements We are thus assuming that theset of information elements necessary to produce a local conceptualpicture of the battlespace is known.1 The issue here is the degree of

1 Other experimentally based research work in the United Kingdom is considering what these factors are in different scenarios.

confidence with which they are known, as measured by the local sionmaking element’s level of knowledge.

deci-The term ‘knowledge’ has several meanings, and therefore it isimportant that, at the outset, we define what it means in the context

of the decisionmaking processes described in this work Formally, we

define knowledge to be accumulated and processed information

wherein conclusions are drawn from patterns Information elementsaccumulated over time form patterns that can be matched to knownpatterns The more reports confirming a given pattern, the less uncer-tainty remains and the more knowledge is gained

The Information Superiority Reference Model

In terms of the categorisation developed by Alberts et al (2001), we

are representing the flow of information about the physical domain around the network in the information domain and its effect (in terms

of knowledge, situation assessment, shared awareness, and

decision-making) in the cognitive domain These concepts are embodied in the

information superiority reference model depicted in Figure 1.1

Infor-mation superiority is a term used to express the ability of one side in aconflict to impose its will over the other based on superior informa-tion collection, processing, and dissemination capabilities Formally,

we define information superiority to be the ability to collect, process,

and disseminate information as needed; anticipate changes in theenemy’s information needs; and deny the enemy the ability to do thesame

Both sides in a conflict generally have different perceptions of a

single reality, referred to as the situation Figure 1.1 shows how the

three domains contribute to this perception We list the major ties performed in each of the domains in each of the boxes Thephysical domain is where reality, or ground truth, resides In addition

activi-to physical objects, such as weapon systems, terrain features, andsensors, this domain also contains intangibles, such as enemy intent,plans, and current and projected activities A complete assessment ofthe situation will contain estimates about each