Modelling Human Factors in Combat Simulations: S....

Modelling Human Factors in Combat Simulations:

Fear as part of Combat21: Paper 1 ~

S. Trivella - CBSCS LW3

August 1998

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Modelling Human Factors in combat simulations

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Fear as part of Combat.? I

Abstract

This paper details work carried out within DLW (LW3) on the modelling of Human Factors (e.g. shock, surprise, fear etc.) within Combat models. It concentrates OR fear as a Human Factor, identifies its contributors and attempts to model its effect in a Systems Dynamic environment. The model is designed to be incorporated with a wargaming tool used within DLW, Combat21 (Concepts, Objectives, Manoeuvre in Battle in the 21" Century).

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EXECUTIVE SUMMARY

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This report has been written within the Directorate of Land Warfare (DLW (LW3)) with the aim of providing LW3 with the capability, using System Dynamics modelling, to investigate the impact of Human Factors on the outcome of battle.

This report initially discusses the need for the representation of Human Factors in combat models and simulations, and provides an overview of the combat model that is owned and used within DLW, Combat2l. Combat21 (Concepts, Objectives, Manoeuvre in Battle in the 2 1 '' Century) is a two sided land/air wargame based at the unit level. The current representation of Human Factors within C21 is fairly limited.

This report discusses and attempts to cany out the modelling of the Battle Winning Human Factor (l3WF) fear. Shock has also been looked at but is not detailed in this report. The report has been written whilst the models were under development, from the initial ideas stage, to formulating equations and the integrating with C2 1. Due to this, the way in which the models have been put together has been to use every influence possible to begin with and then to remove or change the factors that are not suitable for modelling in Combat21. Over the period of construction of the model it has been necessary to make 18 different versions of the fear model.

Studies carried out at the Defence Evaluation Research Agency (DERA) within the Centre for Human Sciences (CHS) on the feasibility of modelling fear identify a conceptual model which depicts several factors (i.e. battlefield factors, unit factors, individual factors, and leadership factors). These groupings have been taken and used to create separate groups to be used in a high level model as the basis for development.

A stand alone model has been developed (Fear2I) using the new fear groups and sub-factors within each one (e.g. enemy weapons, unit losses, terrain, shelling intensity, duration of engagements etc.). The outputs from the model are a modifier to the effectiveness of a unit, and a modifier to the defeathumender levels of units moving away from the normal fixed defeat percentage that is used in most combat models.

The future of Fear21 at this time is uncertain. Fear21 has fulfilled its design needs and is fully compatible with C2 1. It has only been time constraints within DLW that have prevented the model being incorporated. One consideration is the development of C21 that is currently under discussion.

Recommendations: The integration with the latest version of Combat21. This process can be carried out either within DLW or by Landair International. Due to the compatibility of the model the integration would be a fairly simple task.

Fear as part of Combat2 I

Testing to determine the effects on combat outcome. With the ability to control the minimum and maximum levels of fear used with simulation runs there is large scope for testing either previously run or new scenarios.

The verification of data inputs. As much of the data used within fear21 comes from militarylpsychoiogy judgement and from trends and pattems taken from previous studies there is a need for verification and more conclusive data.

The validation offear21 in conjunction with"the latest version of C21. Although fear can be used in C21 games without validation it is important forfear21 to be recognised as a required part of future wargaming and hopefully be the first of many HF that are incorporated into combat models.

Even though Fear21 has been developed with the intention of integration with C21, consideration should be given to adapting it for use in other combat models.

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List of contents

' 7 Abstract ................................................................................................. Executive Summary .................................................................................. 3 List of contents ........................................................................................ 5

Section 1: Introduction .............................................................................. 9

Section 2: Combat21 overview .................................................................... 11

Section 3: Factors affecting fear in combat ................................................... 15

Section 4: An initial model of the effects of fear ............................................... 23

Section 5: Developing the initial model ......................................................... 27

Section 6 : Evolving Fear21 ........................................................................ 35

Section 7: Fear21 .................................................................................... 41

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........................................................................................................... Section 8: Towards integration with Combat21 .............................................

Section 9: Conclusions and recommendations .................................................

References ........................................................ .................................... Abbreviations and glossary ..........................................................................

A: General description of of a Vensim view ....................................................... B: Initial model compatibility with Combat21 ...................................................

D: Possible equations for fear due to losses ....................................................... E: Type of equation used when considering climate/location ..................................

H: Fear21 model compatibility with Combat21 ................................................. Step by step guide to fear model changes .....................................................

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........................................................................................................... Annexes ................................................................................................

C: Equation and examples of calculation for previous performance ...........................

F: Training familiarity equation and combination with climate/location ..................... G: The equation and examples of the calculation for defeatkurrender levels ................

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Fear aspart of Combat21

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SECTION 1: INTRODUCTION

1.1.1 Human Factors are a difficult area, generally, for operational analysis. This is especially true of representing collective human performance. Psycholog!. can provide some insight and data (although mostly at the individual level). and trials can provide some data, but the essential lack of uniformity of human response to stimuli is extremely difficult to account for.’

1.1.2 This report discusses and attempts to carry out the modelling of the Battle Winning Human Factor (BWF) fear. Shock has also been looked at but is not detailed in this report. The report has been written whilst the models were under development. from the initial ideas stage, to formulating equations and the integrating with C2 1. Due to this, the way in which the models have been put together has been to use every influence possible to begin with and then to remove or change the factors that are not suitable for modelling in Combat21. Over the period of construction of the model it has been necessary to make 18 different versions of the fear model.

1.1.3 This paper briefly discusses the initial high level model and then concentrates on the final model’ (Fear21). A extended version of the paper that tracks the. model development from the very beginning exists but is, at the moment, purely being used as an audit trail and record of my work in this area within DLW (LW3).

1.1.4 From the outset my main aim was to look into the world of Human Factors (HF) and to examine the scope for representing them in combat models, primarily Combat2 1. This lead to a fair amount of report and paper reading from various resources, with most coming from the relevant areas within DERA. The work carried out by Mike Lamer for R.E.D Scientific’ was a good starting point as this was aimed directly at ’modelling HF in certain models, one of these being Combat21. By this time, parts of his work had already been implemented in Combat21. M e r looking at a wide range of other factors it was decided that 1 would look into ‘fear’, and in doing so try to take the work carried out by Paul Cawkhill, (CHS) DERA’, on the feasibility of modelling fear in combat one stage further. The main consideration for the fear was the inputs needed for the model and whether or not they could be obtained from Combat2 1.

As explained previously, the Human Factor models have mainly been based on work that has been carried out in the past on fear and shock and then taken further. This work has received constructive criticism and input throughout its development from a number of people and this will continue with Fear21. The modelling of human factors is a very difficult area to work in, as there are no set rules on the effects and influences of human factors, and people interpret and see things in different ways.

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When h s paper mentions the ‘Final Model’ this does not mean that the model bemg discussed does not need, or will not go through, any more development. It is simply stating that h s version of the model is developed as far as it can be, at the moment, due to time and resource constraints. From this point onwards, to avoid confusion of models, the final model will be referred to as Fear2 I.

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1.1.6

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1.2

The main aim of this work is to get Human Factors into Combat21. and hopefully links with other models, as previous studies have shown that a true representation of combat must include such factors. Moral Surprise is already in Combat2 1 and plays a big role in influencing other HF such as shock. When these three are full). represented in Combat21, other factors can be examined, with the possibility that they also be incorporated. More background work on surprise, fear, and shock at the unit level to develop more conclusive data is also needed.

At the end of my work I will not be able to say that any of these factors are truly represented in C21, combat itself can never be truly represented, and within a model such as C21 the complexity of HFs make it impossible, currently, to do so. What I hope to have done is to represent them in the best way that is possible, which will begin to fill an important gap in combat simulations.

Approach

The way in which I have tackled this, &d the layout of this report, is as follows:

An overview of C2 1. Brief background on the need for Human Factors and the organisations involved. Discussion of work carried out in previous studies and the main influences and inputs for this work. The creation of an initial model (framework) using Vensimo to be used for the basis of development. Discussion of the representation of the factors within the model and looking at improving the model and the modification of variables.+ The creation of Fear21 and the influence diagrams used. Testing and results from the model. Integration with C21. Examples of user screens that could be used within C21 to control and monitor any future HF modelling. The way ahead for this fear model and other HF modelling.

Many people will consider other factors that they feel should be included in fear modelling. Admittedly there are numerous factors that will influence a soldiers level of fear. The inclusion of the initial model and the discussion of the development will hopefully demonstrate how some of these have been dealt with through development and not simply ignored.

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SECTION 2: COAIBXTZ 1’ OVERC’IE\\’

2.1.1

2.1.2

2.1.3

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2.1.5

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2. I .7

COMBAT21’ (Concepts, Objectives, Manoeuvre in Battle in the 2!” CencL1n.l is a two sided (redhlue) landlair wargame. It is developed by Landair International L.td:’a and is owned by Sc (Land) and DLW. Currently each side controls around 30 unirs with the inclusion of Brigade HQs and Div HQs. The model in use at present is version 6.0 with the delivery of a new version expected by late 1998.

Along with the main C2It wargame there are a number of other models in what is known as the ’C21 family of models’. These include COMBAT NBC, COMBAT ARTY, COMBAT AVIATION, COMBAT MARITIME, COMBAT ENGINEER. and COMBAT LOGISTICS.

C21 is a Systems Dynamic model and is developed in the VensimO working environment. The model uses intluence diagrams and equations to produce either a simulation or an actual game. Vensime is also used to create the user screens (VenappTb‘ or VensimB Application) for the setup of the game and the monitoring and control of units throughout play. The use of such VenappTb‘ screens allows users to operate the model, without the need for training in VensimO per se. and to setup the model in a much shorter time.

VensimB! also has the ability to be used as an analysis tool: this allows any variable and subscript to be traced through a game, and the causes and effects to be analysed. These can then be displayed in many ways including line graphs, numeric tables, bar charts, causal graphs, and in the form of statistical data.

C2 1 also uses the Geographical Information Sohvare Maplnfo Professional& 4.1. This is for initial setup of forces, both red/blue intelligence screens. and the planning and input of routes for both sides. The abilitj. to have separate map displays allows C21 to be played as a closed game with the commanders (players) from each side only being able to see their own units and intelligence collected on the enem).. Fiz 1.2 shows examples of the output screens from C2 1 .

The current representation of Human Factors in C21 is fairly limited. The way in which combat is represented has many aspects that are suitable for Human Factors modelling. Units commit forces,* maintain reserves, and have defined orientation and coverage angles. Th&e assets led to the introduction into C2 I of Moral Surprise. The Moral Surprise uork is based on the R.E.D Scientific Report’ taking into account time. orientation. position, and threat surprise. This makes use of coverage angles. bearinss, expected engagements etc.

Further C21 is under consideration. If this proceeds then it may be possiblz to incorporate more Human Factors.

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illodelling Human Faciors in rombai srmirlations

Fig 2. I : User screens from C2 I

7. I .9 With such user screens as shown in Fig 2. I tlie user would be able to control the value ot'Human Factors parameters in C:! 1 games. Constants. lookups. and modifiers can all be altered from such screens allowing either new data IO be used or to run sensitivity analysis. The integration with C2 I will be considered later (Sec. 8).

*The tenns CObIB.4T2I. Combm2l. and C21 are used widely throughout this repon. .All of these we used to define the main wargame that is built by Land.4ir Intemntional Ltd. It is also the model with which these sepxace HF models have been desigwd to be integrated.

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2.1. IO C2 I is used within DLW not as a training tool or a planning tool but more as i! u.a>. in which to support force development and allow thoughts to be tricd and t t s i t d . With C21 as it currently exists scenarios and ideas can bs investigated in a quick and efficient way. The introduction of Human Factors to C21 Fvould create man\. advantages. It would not only dramatically improve- the abilitl, to investigate the importance of Human Factors in future doctrine, it would also bring Human Factors to the attention of a wider audience. This in tiim leads to a more realistic representation of combat, and gives the Military OA community more confidence in the results gained from such wargames and simulations.

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The need for the modelling of Human Factors

Seminal/Historical Analysis by the Defence Operational Analysis Establishment (DOAE), now recognised as the High Level Studies Department of the Centre for Defence Analysis (CDA (HLS)), showed the importance of factors such as shock and surprise in winning battles. These factors were not then represented i n operational analysis.. In spite of the importance of these factors: the OA cominunity has been slow to incorporate them. One reason for this is the acknowledged difficulty of modelling Human Factors. However, AD OA (Land), concluding that progress appeared to be slow, commissioned a study to be carried out.

2.1.2 The report T h e Application of Battle Winning Factors'' was carried out bq R.E.D Scientific Limited on behalf of ADOA (Land?. This report covered BWF such as physical and moral surprise, shock, air superiority and aggressive ground reconnaissance. It goes on to suggest mechanisms for representing these effects in three combat models one of which being C2 I . This Lvork came to thc attention of DLW and subsequently to the attention of Landair International and relevant pieces were then incorporated into C2 1. As C2 I has progressed. LW3 have worked with CDA (HLS) and CHS to try and ensure that Hirnian Factors are included in our future modelling tools.

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,tlodellmng Human Facrors m conibor srtnrilartons

k2}. .1_ ( Model bevolopment) . . . 1 { Human Factors )

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Fig 2.2: Organisations involved in C2 1. and the models' development of Human Factors.

2.1.3 The organisation intluence diagram [Fig 1.2) is a visual representation of the organisational links and drivers in the development of CZI and the Human Factors models.

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Fear as parr of Combat31

SECTION 3: FACTORS AFFECTING FEAR IN COMBAT

3.1.1 When takins into account the factors that affect the level of fear it is possible to split them into different categories. In previous studies the most significant factors that influence the level of fear, have been split into four categories':

1. Battlefield Factors 2. Individual Factors 3. Unit Factors 4. Leadership Factors

3.1.2 As C21 is an aggregate model, the 2"d category (individual factors) would prove difficult to model, and even parts of the other four would cause problems. Even if the factors were put into a model, the degree of influence of each factor would still be unknown, e.g. level of leadership and shelling intensity are both factors that effect levels of fear but to what extent is generally unknown. Past studies have shown that some factors could be ranked, but not all. The stochastic element is always prevalent; people act and respond in different ways to all situations. However, some of the factors will produce the same reaction in most soldiers, and therefore can be modelled.

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3.1.3 If the factors are to be modelled, one way of doing so is by taking a similar approach to that of the surprise work already included in C21. This is implemented on an index-based system, allowing surprise to be modelled on a 0 to 1 scale (0 being no surprise and 1 being total surprise). Lookup graphs are also widely used as these allow easy input of data and a clearer way to see the workings of the model. One of the outputs from the fear model will be a modifier to effectiveness as with the current C21 surprise module.

3.1.4 Going back to the four grouping of factors we can look at each one in more detail.

3.2 Battlefield Factors

I Level of noise Shelling intensity Perceived lethality of incoming fire Time of span of duty Level of cover Number of casualties Conventional weapons Isolated in battlefield

3.2.1 Level of noise

The list is not in any particular order and we start with level of noise. There have been several studies on how noise on the battlefield contributes to a soldier's fear. Different weapons make a wide range of noises and it has been shown that these can

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be ordered according to the fear that they induce. In retrospect. many people will say that silence is the most feared noise on the battlefield. The sense of not knowing. at least if you can hear firing a few miles away you are aware of the situation and your mind is occupied.

3.2.2 Shelling Intensity

Shelling intensity, rate of fire, or suppression bombardment, is known to induce both fear and shock, and if heavy enough has been known to reduce a unit's will to win enough to allow them to be defeated, even with minimal casualties.

3.2.3 Perceived lethality of enemy fire

Perceived lethality of incoming fire as well as perceived lethality of enemy units have been included in the model. Some might say that the most frightening weapons are the most lethal/dangerous ones. This is not the case; Stouffer3 carried out a questionnaire of over 700 men regarding their reactions to a variety of German weapons. The most frightening were rated as follows: 88" gun (48%); Dive- bomber (20%); Mortar (13%); Horizontal bomber (12%); and Light machine-gun (7%). The most dangerous weapons were also determined, these were rated as follows: 88" gun (62%); Mortar (1 7%); Light machine-gun (6%); Horizontal bomber (5%); and Dive-bomber (4%). If these two sets of results are compared, significantly large differences can be found e.g. the Dive-bomber was considered the most frightening by 20% of the men whereas only 4% rated it the most dangerous.

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3.2.4 If a similar survey was carried out today, weapons such as flame-throwers, FAEs (Fuel Air Explosives), and NBC attacks would be ranked very highly on the most frightening list and not always on the most dangerous/destructive list. Reasons for the discrepancies between the frightening and the dangerous lists can be attributed to a number of things. For example one of the commonest reasons for fearin; air attack had little or no relationship to the lethality of these weapons, but appear to be of purely 'psychological' character, and has possible links to noise, visual effect and powerlessness. In the case of flame-throwers and FAEs it appears to be more that soldiers are more frightened of losing their faces than they are of actually dying. A FAE might not kill you but is sure to inflict extremely severe pain and physical damage. This is also true with NBC attacks, while a bullet may kill you or put you in hospital for a period of time, the result of a NBC attack can cause you severe pain and discomfort for the rest of your life or lead to a slow and cruel death.

'By and large chemical and biological weapons produce relatively fewer physical than psychiatric casualties. They provide a good example of a weapon with a psychological impact greater than its real lethality. y 4


F'ear as part of Com ha12 i

3.2.5

3.2.6

During the Second World War Finan. studying US soldiers in Tunisia. found that the most frightening weapons were the ones that were either perceived as the most dangerous. likz the dive-bomber or fearful due to the noise the) created

A study of 120 US psychiatric casualties in the North African theatre in the Second World War showed that 116 (97%) of them feared shell fire the most; of these dive bombing accounted for 49 men (42%), artillery for 41 men (35%). and high level bombing 13 men ( 1 1%) . It is possible to say that this is correct for m>ost wars (the pattern is similar although data will vary), but it needs to be reviewed again. as more battle data becomes available. h American survey (details not known) came up with the graph below.

Weapons judged 'most frightenin-g' as a function of time in combat.

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Fig 3.1: Soldiers view on frightening weapons over time.

3.2.7 From the graph (Fig 3.1) it can be seen that. over a period of time. soldiers ears can change. This is quite often overlooked. This applies more to soldiers in their t?rst combat situation as they could fear a certain weapon more than another. but when faced with it, they might actually do not find it as frightening as they thought but now fear another weapon which they may not have feared before.

3.2.8 Another study looked at why men feared certain weapons. The reasons were split into six categories: accuracy, lack of warning. rapidity of fire. noise. no defence. and other. The results are shown in Fig (3.2).

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3.2.9

3.2.10

3.2. I 1

Accuracy Lack of warning Rapidity of fire Noise No defence Other

Modelling Human Factors in combat simiilations

Machine Dive High Mortar 88s gun bomber bomber 37 31 16 11 23 19 1 1 15 5 1 1 8 7 42 0 1 11 19 6 48 21 1 2 0 4 14 24 3 21 32 30

Time of Span of Duty

Again there is work to support the theory that the length of tour does have an effect on the level of fear experienced by people’. There has been research to suggest that different roles in battle will lead to different patterns in fear susceptibility over time as well the amount of combat experience, the types of combat experience, and the timing of combat experience. When designing the fear model this will have to be looked at more closely. If a relationship can be obtained, an initial simplistic way of modelling this would be a lookup graph with fear against time.

Level of cover

Level of cover was investigated in Tyler’s6 questionnaires of British soldiers who had been part of the peacekeeping forces (i.e., OOTW) in Bosnia and Herzegovina between 1992 and 1994. In the scenarios that he looked at, fear was highest in the scenarios with low level of cover as a characteristic, and fear was lowest in the scenarios where the level of cover was high. Cover can be looked at i n more detail by considering natural cover from terrain, cover from buildings, vehicles etc. The way in which this can be modelled in more detail later (Sec. 6.2).

Casualties

Numbers of casualties plays a major part in a soldier’s combat effectiveness. Knowing that your friends and colleagues are risking their lives and that you also could lose your own will invoke a level of fear. This level of fear is often regarded as a ’good level’, which can increase a soldier’s combat effectiveness. Actually seeing these casualties occur or even knowing of them will result in increased levels of fear. A survey of infantry veterans’ carried out in the Pacific theatre showed that observations of high casualties in one’s group, compiled with the death of one or more friends, was a very important determinant of combat fear. The knowledge of deaths ofpeople in other groups still leads to fear bu t not on the same scale. This will be considered when modelling these factors.


Fear asparr of Combat21

3.2.12

3.3

3.3.1

3.3.2

3.3.3

Isolation

The majority of people appear to be more susceptible to fear when alone’. Even experienced combat veterans, when isolated, performed badl?., and were far more inclined to surrender. Prolonged isolation had the effecr of reinforcins the soldiers’ fears and lessened their resistance’. In many instances the presence of another person increases the possibility of control. Even if the key person is himself unable to reduce the probability of an aversive outcome, his companion may be able to do so, or at least to contribute to reducing the odds. Besides the additional controllability offered by a companion, the presence of other people might have a directly inhibiting effect on the fear reaction itself.

Individual Factors

Confidence in own abilities Realistic training Previous combat experience 1

This fear model has always been aimed at the unit level; the introduction of individual factors that are more appropriate for an entity model can cause problems. It is not simply a case of ignoring them as they play a vital role in the modelling of fear.

Confidence in own abilities

A soldier’s confidence in himseltlherself to perform his role on the battlefield can lead to the failure/success, of not just himself, of him and his comrades7. Low self- confidence leads to high susceptibility to fear, which in tum can lead to a psychological breakdown’. In Stouffer’s survey3 of 1,700 infantry veterans in the Italian theatre of war, it was found that 70% of the soldiers had a negative reaction to seeing a comrade ‘crack up’. Over 50% of the 1,700 also said that it increased their own susceptibility to fear.

7,9,10 Realistic Training .

The theory that training is one of the most important parts of preparing soldiers for combat is probably as old as combat itself. The modification of training procedures in an attempt to gain more control over anticipated dangers might well prove to be of value in teaching people to cope with a range of dangerous jobs. Clinical research7 into the reduction of fear, points out the advantage to be gained from encouraging fearful peaple to practice coping with situations that contain the realistic dangers. It was also found that in a survey carried out on infantry troops in the Pacific Theatre, a positive correlation between their perceived adequacy of combat training and their observed fear reactions during combat was obtained.


Modelling Human Factors in combar simulations

Before During

3.3.4

3.3.5

3.3.6

3.4

3.4.1

3.4.2

2 13 71% 45 15%

In a comparable study carried out during the North African c a m p a i p in 1943. the troops themselves attached special importance to obtaining training under realistic battle conditions.

In a third study, carried out in the Italian Theatre, no less than 81 percent of the troops gave realistic combat training as the most important type of preparation to provide for new recruits’. There are theoretical ways of incorporating training (realistic) into combat models but their suitability and feasibility have not been considered in this study.

Previous combat experience.

Again this is a very individualistic factor, even more so then with training. There have been numerous studies carried out on the effect that previous combat has on soldiers”. 1 2 * 1 3 . The first of these carried out by Dollard’ who interviewed 300 combat veterans from the Spanish Civil War. A selection of the questions asked and the results obtained are shown below.

Q 1. Did you experience fear when going into your first action? (300 surveyed)

[ Yes I222 174% I

42. Were you more afraid before, during or after action?

Unit Factors and Level of leadership

Level of unit cohesiveness Level of unit moraie Level of trust in comrades Level of trust in commanding officer Level of leadership

Although ‘unit factors’ is exactly what we are dealing with in this fear model, we are still faced with problems. This section deals with morale, cohesion and trust at the unit level. This time it is not so much the problem of being too individualistic but that when working with the modelling of morale and cohesion definitions and influences become very ambiguous.

Many people, in a variety ways have defined both moraie and cohesion. A few of these definitions are given here:


Fear as part of Com bat2 I

The US army provides a definition for morale in its Field Manual on Leadership (FM 22-100 ~ 2 2 8 ) :

‘Morale is defined as the mental, emotional, and spiritual state of the individual. It is how he feels -happy, hopeful, confident, appreciated, worthless, sad, unrecognised, or depressed. ’

3.4.3 Earlier conceptions of morale, both military and industrial, included an element of mood or emotional state, but tied it closely to a goal-orientated group. Edward Munson (192 1) who organised the Morale Branch of the United States Army General Staff during World War I, defined morale as:

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‘the determination to succeed in the purpose for which the individual is trained, or for which the group exists’

3.4.4 In the study of the Second Scottish Rifles, Baynes (1961) devoted 16 pages to the definition of morale, which he called ‘the most important single factor in war.’ He concluded with his dictionary definition of morale as being:

‘A confident and resolute, willing, often self-sacrificing and courageous attitude of an individual to the hnctions or tasks demanded or expected of him by a group of which he is a part that is based upon such factors as pride in the achievements and aims of the group, faith in its leadership and ultimate success, a sense of fruitful participation in its work, and a devotion and loyalty to the other members of the group.’

3.4.5 Others have been more concise, for example:

‘the psychological forces within a combat group 12, 14 Grinker & Spiegel (1945) which impel its members to get into the fight’.

Leighton (1943)l4.l5 ‘the capacity of any group of people to pull together consistently for a common purpose. ’

Lord Moran (1945) of one’s capacity. ’

British Military Doctrine” (BMD) refers to morale on many occasions and emphasises its importance on the battlefield, ‘because success in war depends as much on moral as physical factors, morale is probably the single most important element of war. High morale fosters the offensive spirit and the will to win. It will inspire any army from the highest to the lowest ranks’.

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‘the ability to do a job under any circumstances to the limit 14, 16

3.4.6

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Mode Lling Human Factors in combat simulations

3.4.7 Cohesion is equally as definable. In ,everyday use of the word people would possibl!. use term cohesion to mean; solidarity, adherence. union, unit>., coherence. integration, tendency to remain united’* etc. As with morale BMD believe strongl!, that cohesion is an extremely valuable qualiry when it comes to winning wars. The!. define it as:

‘At its simplest, cohesion is unity. It is a quality that binds together constituent parts thereby providing resilience against dislocation and disruption. It minimises vulnerability to defeat in detail and the adverse effects of pre-emption. *’

3.4.8 Chief of Staff Edward Meyer (1982) defined cohesion as:

‘the binding together of soldiers in such a way as to sustain their will and commitment to each other, the unit and mission accomplishment, despite combat or mission s t r e ~ s . ” ~


Fear as pari of Conihar? I

SECTION 4: AN INITIAL MODEL OF THE EFFECTS OF FE.-\R

4.1.1 I have touched briefly on the inclusion of Moral Surprise in C21, which is a direct result of the R.E.D Scientific Report. The way in which this has been carried out lays down a good base for the development of the fear model. It was carried out usins a szirprise index based system and appropriate lookup tables. This is deemed the most suitable way in which to represent fear.

4.1.2 The main basis of th.is is to have various lookup tables to establish fear levels occurring from different factors, calculating an index value for fear. and- then using that to obtain a modifier to redhlue effectiveness. The index based system works on using fear on a.0-1 scale with 0 representing no fear at all and 1 being total maximum fear.

Fig 4.1: Vensim view' from initial model - Fear index system (red).

<=ME STEP> (Final f e a r index'value>

\ / <Suscept ib i l i ty t o fear due t o lenath

d

of du ty> increment red u n i t f e a r index

Fear Modifier I (3 1

r e d u n i t fear index

Modifier t o red effectiveness due t o fear rate red recove r s f r o m fear

4 K I

M I N I M U M FEAR MODIFIER A V E R A G E RED UNIT RECOVERY

TIME

4.1.3 To begin I had to construct the framework model, firstly on paper then in VensimG, with the necessary variables and all the factors mentioned in the previous section (i.e. cover, enemy fire, morale etc.). No equations were produced at this point; the framework model was only used as a starting point. Once all the associated influences were established it would be possible to start tying in equations and producing a working model.

t - he teiin 'view' comes fioin the software VensimcS. Because of h e way models are constructed inow than o n ~ visual representation is possible for the same logical model. The advantages of viewing the same model in a number of different ways are that you can emphasise parts of the structure. tailor die appearance to different standards? break up complicated models. and build up complexity as models develop. A n esplanation of the different types of variables and the rnethodoloky of VensitnQ views are detailed in h n e s A.

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~ . -

hlodelling Human Factors in combat siniulations

4. I .4

4.1.5

4.2

4.2.1

4.2.2

4.3

4.3. I

4.3.2

4.3.3

4.3.4

At this point the output from the model was set to be a modifier to effectiveness for both red and blue. The area of the model (view) that is used to calculate the modifier would not really change dramatically with model changes and an example of this is shown in Fig (4.1). A general description of Vensim views is detailed in Annes A.

The model in this basic format would actually work if values were manually entered for the fear appraisal (final fear index value). The main input for this part of the model is known as the 'fear appraisal' (shown in diagram,as 'final fear index value'). This is where the model developments will mainly occur. Behind this value we have the representation of all the factors that contribute to the level of fear experienced by a unit.

Incorporation of fear factors into model

In order to represent the factors mentioned in section 3.1 I have had to determine the suitability of each one. The possibility for the input being available from C2 1 (i.e. for fear from casualties the input would be casualty numbers, this needs to be available as an output from C21), and, if needed, which factors could be grouped together or ignored.

These'were initial ideas and not the final way in which these factors have been represented.

Battlefield Factors

Level of noise

There is no representation of 'combat noise' in C21 at the moment and it would be quite a large task to incorporate. Noise can be attributed to certain types of weapons and machinery and there is still a possibility of either having noise as a factor or combining it with other factors in the model.

S he1 I ing In tensity

Shelling intensity (rate of fire, suppression bombardment etc.) plays a big part in this model. There are good links to be made with C2 1 and this will integrate well.

Perceived Lethality

The main input for this factor will come either from a graph lookup.constructed in C21, or from a data table stored in an environment such as Microsoft Excel. Each unit would have its own data regarding the perceived lethality of enemy weapons and enemy units. This would also include situations such as AH attacks and minefields.

Time of span of duty

DGD&D/L W3/C BSCS/0898/00 I

Fear as par! of CombarZ I

Length of duty can easily be incorporated and the effect will probably be referenced fiom a lookup table. Whether it will be a modifier to the susceptibility 10 fear or used directly in the fear appraisal will be looked at throughout model development.

4.3.5 Level of cover

Cover is not used in C21 as such but there are definite links that can be made. In C21 the commander has the ability to change the posture of a unit and effectively have a unit ‘dug in’ (known as hold & defend). In this early stage of modelling there does not appear to be much more scope for influences to level of cover.

4.3.6 Number of Casualties

The amount of losses suffered by each unit is readily available in C21 as unit strengths play a large part in the model. These can also be used to calculate total force casualties. The relationship between unit casualties and force casualties will be discussed later.

4.3.7 Isolation

This is very much of an entity model factor. It would be virtually impossible to model in an aggregate model. One possible way of doing this would be to include it as a random event in combat (i.e. in every battle between 0 and x percent may be isolated in battle). The x could be set to a realistic figure but within the model there would be no scope for reducing or increasing, through combat methods, the percentage isolated. This could be linked in some way to enemy fire, however, this would create a stochastic event and C21 is purely a deterministic combat model.

4.4 Individual Factors

4.4.1 Confidence Realistic Training Combat Experience

As already stated these factors have no simple definitions and will prove difficult to equate and model. It is possible that these factors may have to be grouped under a joint heading.

As with most of the factors this will have to develop over time producing an improved representation in the final version.

4.4.2

4.5 Unit Factors

4.5.1. Level of Morale Level of cohesion


Modelling Human Faclors in com bar simulations

The earlier section (3.4) referring to unit factors outlined a wide ranse of definitions and contributors to the level of all these factors. Even after all the studies that have been carried out it would be foolish of to attempt to re-write the rule book and come up with a ’correct’ representation or formula. It has been suggested that these factors can be amalgamated and labelled with a different title.

4.5.2 Level of trust in comrades and commander

Trying to define these under a different heading has the potential to create a ’vicious circle’, as the new title would then suffer the same definition eni-ma. One solution for the other two factors in this section, trust in comrades and trust in commanding officer, would be to link them together within the next group; ‘Level of Leadership’. This would then relate past activities of the unit to a general probable unit state. Taking into account such factors as: number of previous engagements, previous success, length of time with or without action etc.

4.5.3 Level of Leadership

As mentioned in the previous section (Sec. 4.5.2) level of leadership can be linked together with the unit factors of morale and cohesion. It would not be suitable to try and model the level of leadership within a unit level model. The only way this can be represented is by using factors such as previous success to characterise the outcome of leadership decisions.


Fear as part of Coni bar2 I

SECTION 5 : DEVELOPING THE INITI.4L MODEL

5 . 1 . 1 The initial model was constructed with minimal thought into the nature of an!. lookups, and without limiting the model by only using variables that are available from C21. The segregation of these variables will have to be carried out at sonie stage, but in doing so now would dramatically reduce the number of influential factors that could be included. It is also far more sensible to include as many variables as possible to start with, as removing variables, or combining them, is preferred to adding at a later date. The need to combine or remove factors will be due to either lack of knowledge, but mainly due to the link with C21. It is felt that the best way to incorporate as many factors as possible is to again split them into related areas. These groups are not the same as the ones used earlier (battlefield, unit: individual etc.). There are 6 groups in total, 5 of them being used to calculate the fear appraisal and the other one, susceptibility due to time, beins used as a multiplier to the fear effect.

%

3.1.2 The five fear groups that are used in the.fear appraisal have been designed to include as many of the factors as possible. They are as follows:

Fear due to Enemy fire Fear due to Casualties Fear due to level of Cover Fear due to Level of morale and cohesion Fear due to Isolation

3.1.3 These six. main groups (fear influences) are all designed and built in their own VensimO viewstt where they are calculated independently from one another using the factors in their group. At the initial stage, the way in which these are represented and calculated is done in a very simplistic and basic way. The reason for this being that the factors and the level of representation in such an early model will no doubt need modification as development progresses

*The initial model is the framework model used as a basis for model development. Within Vensima it is kno\n as (.'.?I f W ) l . n i d f ,

The tenn 'view' comes from the software Vensima. Because of the way models are constructed more than one visual representation is possible for the same logical model. The advantages of viewing the same model in a number of different ways are that you can emphasise parts of the structure, tailor the appearance to different. standards, break up complicated models, and build up complexity as models develop.

t t

DG D& D/LW3/CBSCS/0898/00 I

Mode Iling Human Factors in combat sitnrilations

5 .Z Summary of groups

5.2.1 This next section briefly discusses the content and ideas for each section. This does not go into too much detail as so many changes were made after this model, and before Fear2I was produced. The diagrams included in each section are the influence diagrams taken from VensimB.

5.2.2 Enemy Fire

I

<PERCEIVED LETHALITY OF FIRE LOOKUP>

\ enemy weapon type

visual efkcts level of noise

Fig 5.1: Vensim view - Fear due to enemy Fire.

Fear due to enemy fire takes into account perceived lethality of fire, the rate of fire, the level of noise, visual effects and obviously the weapon type. As with most parts of the model the scale upon which everything is modelled is .on a 0 to 1 basis with 1 being the maximum level of fear possible, down to 0, no fear at all. The lethality value comes from a graphAookup table. Quite simply, each enemy unit is given a number, and when a particular unit comes under attack from that weapodunit the lookup is used to calculate the perceived lethality of that particular engagement. The noise value and the visual effect can be calculated in the same way, whereas the rate of fire varies the overall degree of fear due to enemy fire.

DGD& D/L W3/CB SCS/O898/00 I

Fear as part of Combat2 I

5.2.3 Casualties

I <FEAR RELATED TO TOTAL RED LOSSES LOOKLJ?'>

kar due to red Unit losses \

red unit losses

Fear due to red casualties I

Fig 5.2: Vensim view - Fear due to casualties.

This factor takes into account the losses (casualties) from within the unit in question and also the losses from the whole force. Both these factors are calculated from their independent graph lookups. Apart from the actual data used for the lookup tables, this factor is fairly simple to add in to C21. The influences, total losses, unit losses, and unit stren,oths are either already present in C21, or can easily be developed by creating new variables (e.g. total force losses are not used in C21, but can easily be obtained by mathematical manipulation of unit losses).

5.3.4 Level of cover

%iiem) weapon type> posture

<FEAR DUE TO LEVEL OF COVER LOOKUP>

level of cover

<FEAR DUE TO PERCIEVED CAPABILITY OF WEAPON LOOKUP>

I Fig 5.3: Vensim view - Fear due to level of cover.

This area tries to take into account fear due level of cover/exposure. Influential Factors will be whether the unit is dug in or not, vehicle types offering cover, type of


Modelling Hirman Factors in combat simulations

incoming fire etc. All of these can be combined to create a 'level of cover value' which will be the input to a lookup graph that calculates the fear value.

5.2.5 Mora le and Cohesion

F

ENEMY NATIONALITY AND CONDITION

/ FAITH TO CAUSE i morale and cohesion

number of previous enga, oements

-i- previous success

fear due to level ofnloralc. and cohesion f

<red iinit strength>

<FEAR DUE TO LEVEL OF MORALE AND COHESION LOOKUP>

; 5.4: Vensim view - Fear due to level of morale and cohesion.

The words morale and cohesion are probably not the best choice to describe this view, as there are so many factors that influence morale and cohesion it would be impossible to include even a fraction of them in such a model. The influences on this factor will certainly change with time, but at 'the moment it takes into account previous success, enemy nationality and condition, and the units faith to cause. Again ;he fear value is calculated from a &aph lookup.

5.2.6 Isolation

LOOKUP>

Fear dite to isolation

x value random h c t i o n

ig 5.5: Vensim view - Fear due to isolation.

At the moment the only way that isolation can be represented in C2 1, is to say that in every engagement up to a certain percentage of a unit may become isolated in the battlefield, and then using that percentage in a lookup table, calculate a suitable fear value. If isolation is to be represented this way it will be impossible for Red or Blue to influence their own or their opponents' 'fear due to isolation'.

DGD&D/LW3/CBSCS/0898/00 I

Fear as parr of Combat21

5.2.7 Length of duty

FEAR DUE TO LENGTH OF DUTY LOOKUP

Fig 5.6: Vensim view - Fear due to length of duty

Currently takes the length of time of duty and influences the susceptibility to fear as the campaign progresses. Later this might include previous engagements, and types of engagements, as exposure to different weapons and conditions will effect susceptibility to fear. This is also works on the basis of a lookup graph.

5.2.8 Fear index system

<TIME STEP> <Final f e a r index value>

\ / <Suscep t ib i l i t y t o fear d u e t o length

o f duty> increment red u n i t fear i n d e x

Fear Modifier I

red uni t fear index

Modifier t o red e f f e c t i v e n e s s due t o fear rate red r e c o v e r s f r o m fear

AVERAGE RED

TIME

\ M I N I M U M F E A R MODIFIER UNIT RECOVERY

Fig 5.7: Vensini view - Fear index system

The fear index system is where the fear appraisal is used to calculate a modifier to the effectiveness of a unit. The example that is shown in fig (5.7) is the influence diagram for Red units; an identical diagram exists for Blue. The average time for full recovery is used to calculate recovery rates as well as modifiers to control the output. Several modifiers are used to control the output of the model. The next section briefly talks about one of these with the rest being discussed in more detail later.

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5.2.9

5.2.10

5.3

5.3.1

5.3.2

5.3.3

5.4

5.4.1

5.4.2

5.4.3

5.4.5

Fear Modifier Constant

The fear modifier constant plays a very important role in the use of fear in C21. It controls the amount that fear can affect the performance of a unit throughout a simulation. If the constant is set to 0 fear will have no effect during the simulation, even if the fear values are all at maximum levels. Subsequently if the constant is set to 1, fear will be at its most effective.

The value of this will depend on the range of values that the model is able to produce. M e r further model development this can be determined.

Initial Testing and modifying

Once the model is fully programmed, the VensimB 3.0 software checks the model and the units involved (units not too important at this stage). Once this check has been passed, testing can start.

In these initial stages the model is checked for fatal mathematical and computational errors. As lookup tabledgraphs have had no real input at the moment it is necessary to check that all calculated variables are within their boundaries, non-negative, and that the fear index is bounded by its upper and lower confines.

Throughout testing the simulation can be run as a game, allowing for changes in certain variables to imitate an.actua1 game of C21. This allows users to concentrate on certain influences to see if different values produce the desired and correct result. This can be a long process and produces a lot of problem areas within the model that will need to be addressed.

Variable compatibility with C21

When a fear model is incorporated into C21 all the equations will need modifying. They will all need to be checked for possible errors, such as producing zero values that subsequently removes the whole fear element from the model.

The inputs into the fear module need to be determined, these will require either writing new equations, using existing C21 variables, or finding an alternative input that is already in C2 1 .

The introduction of subscripts to the model should not be a problem, but would need to be carehlly looked at, as they have not been used in the isolated model in order to save time.

The initial model compatibility table (Annex B) looks individually at all the variables used in the initial model. Displayed for each variable is; the type of variable, and its status with C2 1.


Fear aspart of Conibai2i

New variable - Solely designed for the fear models and does not appear in C2 1.

Lookup graphhable - New lookup graph.

I VensimB - Already appears in C21.

5.4.6 Under the heading 'Type', one of three definitions will appear. They, and their meanings, are as follows:

5.4.7 Three statements also define the status with C21. They are:

Inputs needs establishing

Fits with no alterations

Integral part of C21

5.4.8

5.4.9

5.4.10

5.4.1 1

- The variable has no immediate links with C21. Other new variables within C21 will be needed to gain necessary input information.

- Variable is fully compatible with C21 and will not require any manipulation of existing variables.

- The variable is an integral part of Vensim and is required for all models (i.e. Time).

As this was the initial model it was not too important whether or not the model was fully compatible. Even so, it was useful to use this information to give a rough idea of the progress of the model and the chances of incorporating into C2 1 .

Only nine of the 43 variables needed their inputs to be established. As the way in which these are defined and represented will undoubtedly change in later.versions, it would have been futile to try to create the link between the two models at such an early stage.

Within the list of nine variables that are not currently compatible, are variables such as: total red losses and length of duty. Even though they do not appear in C21, it would be very simple to create new equations using existing C21 variabtes to produce these necessary inputs. For example, total losses can simply be obtained from compiling all the data on each of the individual force units. Using basic equations the total force strength and total losses can be obtained.

Unfortunately not all of the incompatible variables can be dealt with in such a way. Many of the variables can be equated from existing variables, it is simply a case of finding suitable ones, and producing a method of manipulation to produce the required data.


---

Modelling Human Faciors m combai simulairons

Fig 5.9: Theoretical production of incompatible variables.

VARIABLE

5.4.12 This theoretical approach to solving the incompatible variables has a very promising way of looking at this problem. If variables could not be derived using this approach other methods would need to be looked at. As mentioned previously, there is no need to try and tackle this at this stage. When the model has reached a significant level of development, this process will need to be utilised.


6.1.1

6.1.2

6.1.3

6.2

6.2. I

6.2.2

, 6.2.3

6.2.4


SECTION 6: EVOLVXNG FEAR21

M e r some of the testing stage and talks with D. Rooney from CHS it was deemed necessary to make changes to the level of representation of the fear model. It has was decided that several of the factors that were used in the fear model were either insignificant, already covered under another section, or might not be fear inducing.

Detailed in the next section are the changes that were made over the development of the model up to the final model (Fear21).

The changes and representations discussed in this section are still not yet the finalised format, which follows in section 6 . In many places more than on’e method will be mentioned, this has been included to emphasise the array of different ideas and opinions that various people hold.

Fear due to Level of cover

Going back to the original model and ideas.

Factors Posture Vehicle protection Perceived capability

The best way to deal with posture within the fear model was to use it to represent a unit ‘dug in’, otherwise known as ‘hold and defend’. If a unit is dug in they are giving themselves the maximum amount of cover they can hope to have. This shifts all the induced fear onto fear due to enemy fire, casualties etc. Therefore, if the unit is dug in, the fear due to the level of cover will be zero.

In the new model, vehicle protection will be determined by a pre-set figure given to each unit. This will depend on the type of unit, and type and number of vehicles within that unit (i.e. a unit with mainly soft skin vehicles will have a low vehicle protection value whereas a tank battlegroup will have a fairly high value).

Terrain was considered as a factor, but initially was left out. It is now the view that the terrain type can be linked to level of cover. As C21 has a terrain grading system, which is primarily used to calculate movement rates on the ground and as a route planning influence, it can be assumed that the better going the terrain the less cover it offers. The ‘not so good going’ terrain will be made up of trees, rocks, hills etc. and should therefore offer more cover.

There are extreme cases where this would not be the case. This would occur in places such as a wide deep river running through an area of good going terrain. It would gain a sufficient rating for good cover but in reality would not offer any at all.



6.2.5 It was decided that perceived capability would be removed from the model, as was insignificant as a level of cover factor. It is true to say that the more ferocious and destructive that the weaponh i t a unit is facing, the better the level of cover the unit will feel it needs, but these characteristics are dealt with by ‘fear due to enemy fire‘. Using it as an influencing factor here would be counting its effect twice.

6.2.6 The element of surprise was added as an influence to the level of cover. The reason being that if a unit suffers a surprise attack and is not prepared for this engagement there will be a time delay in reaching a sufficient level of cover, unless the unit is already dug in (Hold and Defend). This will be represented in the following way: if a high level of surprise is achieved (separate variable) then the fear level will be at its greatest for a defined time to represent this time delay and then the fear value can be calculated in the usual way. This can be emulated in a simple way by using the FIXED-DELAY function within VensimB. This works by delaying the equation, or value, for the variable by a desired length of time and it then assigns a temporary value for that time period. Once this delay time has passed the fear due to level of cover can be calculated in the usual way.

6.2.7 For the model Fear21 the following factors will be used to represent fear due to level of cover in the way described above.

63

6.3.

Hold and defend

Terrain Surprise engagements Suitable lookup graph@)

Ve h iclehnit protect ion

Morale and Cohesion


Factors Enemy Nationality and Condition Faith to cause Previous success

6.3.2 As stated previously this factor group covers a very wide area. It was decided that the name of the section would remain the same and the term ‘morale and cohesion’ would be defined as the resulting effect of the factors included within this part of the model.

6.3.3 There have been a several studies carried out on nationality and condition with Rowland’’ producing ground breaking ideas obtained fiom Historical Analysis. Each nation can be assigned a nationality factor and then this is used to quantify their combat capability. It might be possible to take these values and use them as multipliers in the fear model.


Fear aspart of Cornbar21

6.3.4

6.3.5

6.3.7

6.3.8

6.4

6.4.1

Another possible way of using nationality factors would be to use the difference in values between blue and red (i.e. the impact would be apparent depending on the magnitude ofthe difference in nationality factors). This would have to be looked at in depth. If a good approximation to the correct representation cannot be found then it should be removed from the model.

The final thoughts on Nationality factors were a lot more conclusive. It was deemed necessary to remove them, as they were not used as part of the main C21 model. Therefore, they could not be used in fear calculations if their effects are not recognised throughout. If Nationality Factors were introduced to C2 1 at a later stage, then they can be looked at again.

In the perfect army no matter what type of war/scenario is being fought the soldiers involved would have 100% faith in their cause, this is very rarely the case. Faith to cause was to be a constant that is determined before a game, depending on the type of war and reasons behind it. As the same players will not always be used in C21, the interpretation of this value will differ from game to game. It was left in the model at this stage and can be removed simply by setting it to 1. The final thoughts on this factor are that its importance is too great to be left out. With ever increasing political and religious tension throughout the world, the need to represent ‘faith to cause’ of various nations and forces in varied situations needs to be recognised.

,

Previous success simply takes into account the current unit stren,$h and the number of previous engagements of a unit. The name will be changed to ‘previous performance’, as a high success value does not necessarily denote that a unit has actually had success.

Annex C contains the VensimB equation used and some worked examples.

For Fear21 the following factors will be used to represent fear due to level of morale and cohesion in the way described above. ,

Faith to cause Previous performance rating Suitable lookup graph@)

Length of duty

Fear due to length of duty would now be used as a measure of ‘susceptibility to fear’. A possible way in which to incorporate this will be to alter the fear modifier to effectiveness by means of a suitable lookup.

DGD&D/LW3/CBSCS/0898/001


6 -5

6.5.1

6.5.2

6.5.3

6.5.4

6.5.5

6.5.6

6.6

6.6.1

Fear due to enemy fire

Going back to the original model and ideas

Factors 0 Level of noise 0 Shelling intensity

Perceived lethality of incoming fire

Level of noise has received criticism as to whether soldiers really become fearful of noise or whether it is combined with the general enemy fire fear. There is data to support the fear inducing properties of noise (Para. 3.2.1), but in C21 it would not be possible to include any representation without radically changing the way C21 is.. modelled. Thus, the fear that is invoked by noise from enemy fire will be represented within the perceived lethality of enemy firehnit. Hence, noise would be removed from the model.

.

Shelling intensity will be kept in the model but would be referred to as ’rate of fire’ due to the link with C21.

Perceived lethality of incoming fire will be adapted to include fear due to the type of the engaging enemy unit and also weapons such as enemy mine fields. The information for each weapodunit will be stored in a data table or graph lookup.

With level of noise removed from the equation the only two factors influencing ‘fear due to enemy fire’ will be the weapon type and the rate of fire. This compares well with one of the main BWF, concentration of force. The input used for rate of fire can be taken from the suppression element in C2 1.

For Fear21 the following factors will be used to represent fear due to level of morale and cohesion in the way described above.

Rate offire

Suitable lookup graph(s) Perceived lethality of enemy firehnit

Fear due to isolation

It was proposed previously (Sec. 5.2.6) that in order to estimate the percentage of a unit that is isolated in the battlefield, it would be necessary to generate this using a random number method. To avoid this and to give a fairer representation, it will now be calculated using time. The length of time that a unit is under fire will determine the percentage isolated. The longer the time period, the higher the percentage isolated. A limit on the amount can be set.

DGD&D/LW3ICBSCS/0898/00 1

Fear aspart of Corn bat2 1

6.6.2

6.6.3

6.7

6.7.1

6.7.2

6.8

6.8.1

6.8.2

6.8.3

There has been continual difficulty in modelling the possible isolation within a unit This has lead to the introduction of the influences of ’duration of attack’ and ’suppression bombardment’. The variable ‘duration of attack’ is not from C2 1 it has been developed using existing variables. in order for this information to be obtainable from C21. This method of representation has arisen from, and compliments. work carried out showing that; depending on the intensity of fire and the duration of the attack, the greater these are, the percentage of a unit that is isolated, increases.

For Fear21 the following factors will be used to represent fear due to isolation, in the way described above.

Suppression bombardment Duration of attack Suitable lookup graph(s)

Fear due to casualties


Factors Unit losses Total losses

The way in which these two factors are to be combined needs to be considered carefully. There is no doubt that actual unit losses create more fear than whole force losses. One way would be to heavily weight the-fear due to unit losses part of the equation. Another idea is that when casualties within any one side reach a certain level, the casualties elsewhere have little or no effect at all. This has led to two types of possible equations that are discussed in more detail in Annex D.

Further development regarding Total Losses

The information available to units regarding levels of total losses would not be immediately available and not always reported in full. In order to compensate for this I have included in the model three new variables (two of which are constants) that constrain the flow of this information.

The first of these is ‘Reported Total Losses’. This works in a similar way to the surprise delay used in the level of cover calculations. It acts as a time delay for the reports of the losses to reach the unit. The period of time representing the delay is defined by the second new variable, DELAY ON REPORTED LOSSES. This is a constant and will be set to a default value.

The third new variable in this area, PERCENTAGE OF LOSSES REPORTED, is used to influence the amount of casualty information that is received by each unit. This works by simply altering the ‘reported losses’ variable by the designated

DGD&D/LW3/CBSCS/0898/001


percentage. The ability to alter such variables will be mentioned later (Integration with C21 (Sec. 8)).

For Fear21 the following factors will be used to represent fear due to casualties in the way described above:

6.8.4

0 Unit losses 0 Reported Total losses

Suitable l o o h p graph(s) 0 Relevant constraining variables


Fear as part of Cornbar2 I

SECTION 7: FEAR21

’ I

7.1.1

7.2

7.2.1

7.2.2

7 3

7.3.1

7.3.2

7.3.3

Apart from the five separate fear areas, changes have been made in other parts of the model. These have arisen from yet more evaluation of the model and talks with D Rooney .

The main changes have taken place in the following areas.

New fear evaluation screen Re-worked effectiveness equations Training/familiarity with EnvironmentlClimate

0 Modifier to defeat surrender levels Defeat flag

New fear evaluation screen

The appraisal of the main fear factors is now calculated on the ‘fear evaluation’ view within VensimB. This is purely so that analysis of this part is made easier, and also the equations for calculating the modifiers are a lot clearer. The equation used to develop the final fear value is, at the moment, depicted in various ways but is being looked at by D.Rooney and P.Cawkhil1 at CHS whom will hopefully find a fair representation.

The C21 variable ‘in contact flag’ has been included in the isolated model solely in order to inform the model of the state of the unit. This works as a switch by having one and zero to represent whether the unit is engagedunder fire or not.

Re-worked effectiveness equations

The view that deals with the calculation of the ‘modifier to effectiveness’ has been re-built fiom scratch. The general way it is represented is the same, but it now has more distinct variables and a few supplementary ones.

There is now a constant called the ‘MINIMUM FEAR VALUE’ which is included to represent the level of fear that is present even if a unit is not under attack. There is the possibility that this could be represented by a lookup graph because, as with overall fear effect, this changes over time. This can be set to zero if it is felt that it is not appropriate. This was originally brought into the model to represent ‘background fear’. There is also a ‘MAXIMUM FEAR VARIABLE’ which can be used to limit the amount that fear can be used to effect a units performance.

The advantage of having both of these variables is the ability to manipulate the range of fear values that a unit can experience. For example, if the ‘MAXIMUM FEAR VALUE’ is set to zero then, regardless of any of the fear influencing variables, fear will constantly be zero and have no.effect in the model. The same applies with the



7.3.4

7.4

7.4.1

7.4.2

7:5

7.5.1

7.5.2

7.6

7.6.1

‘MINIMUM FEAR VA.LUE’, if this is set to, say, 0.6, the unit will always, regardless of the fear influences, experience a fear level of at least 0.6.

It might seem that having control over these variables would be detrimental to the validity of the model. This is not so. Being able to manipulate these values will allow comparison runs to be played. A 3-day C21 game could be played without the use of fear (and any other HF if made part of C21) and then re-run, in a short space of time, using fear as part of the model. By changing-the upper and lower limits of fear, similar trials could be carried out to show the difference and the influence that the incorporation of Human Factors makes to combat models.

Average fear recovery time

The average time for a full recovery has so for been represented as a constant. It is felt that this variable should be used to effect a unit’s ability to cope with subsequent fearful experiences giving different recovery times for units depending on their status. The inputs to be used for this will include previous engagements and experience. Having the recovery time as constant leads to all units recovering in the same time span. When considering a unit that has sustained multiple engagements with enemy units, having a low performance rating and low unit strength it will have a considerably longer recovery time than a unit that has the opposite traits.

. ’

The way in which this will be tied into the model will be to use the ‘performance rating’ variable and a suitable lookup graph.

Training

The following sections discuss aspects of training and varied ways to represent them. They are in chronological order and represent the method of representation - . over the later models (versions 13-18).

Training as fear factor had been removed previously due to its unsuitability in C21, but after discussion with both military and human scientists it has been deemed too important, and ways of representing training within the models are now being looked at again. The way in which it is measured is the problem that needs tackling, but if it can be done it will be a valuable input in future HF modelling, as it is an important influence to so many. When a method of representation is decided upon it will be possible for each unit to be assigned a value at the start of the game in the normal setup screens. This will need to be controlled and will be looked at later.

Susceptibility to fear

There will be two more variables added as influences to susceptibility to fear, these will be training and climate/physical environment. The climate/physical environment influence takes into account scenarios where the battlefield is an unfamiliar


Fear as parr of Com bo2 I

environment. An example of this would be 'jungle warfare' This IS an extreme c a x but all types of scenario need to be considered i.e. desert. arctic erc

7.7 Climatemhysieal environment

~ ~

; 7.1: The principal extreme climate/physical environmental areas around the world.

UCLI: W t A I H E A AREAS W IHE VNHLC

.- . .... -. ... . . . , ....... . . - . . i: ' Y

ra=C LirE

; 7.2: Weather areas of the world.


Modelling Human Factors in combat sitnulations

7.7.1 Fig 7.1 and Fig 7.2 show the main areas around the world where the climate and physical environment factors need to be considered. As can be seen in the figures, climate/physical environment varies between places such as deserts, mountains, and jungles etc. and varying climates such as extreme cold, extreme heat, and humidity. The way in which it can be incorporated into the fear model is to raise the susceptibility to fear and the final fear value by various amounts depending on the specific situation. This is based on the theory that the fear models at present represents units fighting in areas of the world were they are highly trained and familiar with the environmental conditions. An example of the sort of equation that will be used can be seen in Annex E.

A

1 -

X

‘ear Effect

XO-

(

I

I I I I I 2 4 6 8 10 n

Scenario Value

Fig 7.3:.The effect of climate/physical environment.

7.7 .2

7.8

7.8.1

7.8.2

As shown in Fig 7.3 the climate/physical environment value has an extreme effect on the fear value as it approaches 1. This will rarely, if ever, occur and the value will probably normally be between 6 and 10.

Consideration of a units familiarity/training with the climate/physical environment

The climate/physical environment factor is a very important aspect in Human Factor models but the effect of the different climates and types of physical environment will be different for each unit. This will depend on the training that the unit has received and their familiarity with combat in such situations.

It is possible to adapt the equation so that a value for each unit can be calculated separately. The way in which this is done is to give each unit a value between 0 and

DGD&D/L W3/CBSCS/0898/00 I

Fear aspart of CotnbaCl

1. 0 being low traininglfamilianty and 1 being high. This scale can then be transformed into the ’n’ scale used on the above graph to calculate the effect on the specific unit. The input for the climaWenvironment traininglfamiliarity will be a user input but probably not as an actual numeric value but on an scale: very lo%, low, medium, highh, and very high. In doing this it will ensure more consistency, as the value 0.764 on the training scale does not equate to much!

7.8.3 In order to go from the low, medium etc. scale to the ‘n’ scale we assign a value to each level (i.e. medium = 0.50) and then use simple equations to transform i t (Annex F).

7.9 Defeat / Surrender levels

OEFEAT/SURRMDER <fear effect on effectiveness, MULTIPLIER LOOKUP,

< I N I T I A L UNIT OEFEAT LEVEL, \ / Defeat level nwltiplier

’I I

Fig 7.4: Vensini vieu - DefeadSurrender Level Modifier.

7.9. I

7.10

7.10.1

7.1 I

7.1 1 . 1

As well as the modifier produced from the model effecting a unit’s effectiveness it will also alter the defeavsurrender level. This is calculated in the model by using the resulting modifier in a lookup table and producing yet another modifier. This then varies the percentage of casualties needed to enable a defeat or surrender. Annex G displays the equation, and a couple of examples, of the calculation for the defeafhrrender levels.

Defeat FIag

This has been added to the model simply to be used in isolation. It has been placed in the defeat/surrender view in order to determine the point at which the unit is either defeated or has surrendered. This is simply calculated by setting it to zero, if at any point the unit strength is less than the defeat level, it takes the value of 1 (one).

Duration of enemy attack

Within C21 there is already the ability to calculate the amount of time that a unit is under direct fire. For ease of testing and for use in isolation this new variable counts the length of time that the unit is in contact and/or under fire.


Modelling Human Factors in combat simrilations

7.12

7.12.1

7.12.2

7.13

7.13.1

7.14

7.14.1

7.14.2

Inputs, Outputs, Units and Real World Values (R\W)

Throughout models it is important to make sure that the wide range of variables have measurable units associated with them, and that the range of values that are produced have some meaning in the real world. It is no use to just have a number to 3 decimal places somewhere between 0 and 1. The most important variables to be RWVs are the inputs and the outputs. It is preferred that all the variables in between of the inputs and outputs have units assigned to them, but these can be dimensionless.

Using RWV does not eliminate the use of the 0-1 scale values that have been using in the models; these can still be used as long as they can be changed into RWVs. In some cases, mainly variables that are user inputs, the 0-1 scale will not be transformed into a numerical RWVs, but represented in words (i.e. low medium high, true false, present absent etc.). When expressed as words it is necessary to clearly define the meanings to avoid them being ambiguous.

Fear22 compatibility with C21

From the Fear21 variable compatibility table (Annex H) it is clear that the fear model would have no variable compatibility errors if the model were to be integrated with C21. There are other issues to take into account such as the subscripts that would have to be used for each variable but this is a minor consideration in comparison.

Influence Diagrams (Fear22)

Figs 7.5 to Fig 7.13 show the influence diagrams used in Fear21. For people who are unfamiliar with the Vensim environment I have included a brief description of the way in which these influence diagrams work.

Apart from the boxed descriptions (yellow) in each diagram all words and phrases are variables used within the model. The arrow between them simply demonstrates a direct influence. The variables that appear with ’<>’ surrounding them (i.e. <fear due to enemy fire>) denotes that this variables appears elsewhere in the model. The blue variables are lookup graphdtables and any other variables that are in upper case are cons tan ts .


Fear as part of Cornbar? I

Influence Diagrams from Fear Model Fear21

I Caiculation of fear outputs,

recovery ttms and fear cotstrainis 6uscepTtbillly TO fear due To length o f duty> MAX FEAR EFFECT ON

\ UNIT EFFECTIVENESS

Max fear effect

<Fear evaluation) MINIMUM FEAR VALUE

overage fear recovery tim

<previous performnce rating> :'fi iOnT0CT flag>

RECOVERY LOOKUP

Fig 7.5 Vensim view - Fear effect calculation and its outputs.

F a r appraisal and trainiy/familiarity

<Fear due t o level of cover,

(fear due t o level of m r a l e and cohesion>

(Fear due t o enemy fire)

(Fear due t o tsolattan, n scenario value

\

TR A I N I N G / F A M I L I A R I l Y REGARDING

CLIM A TE/LOCA TION

<Fear due TO casualties:

I

Fig 7.6: Vensim view - Fear appraisal.


Modelling Human Factors in combat simtilations

Calculation of fear due t o casualties; considering unit and total losse~s as well as

report constraints u n i t strength

[Total losses\

Fear due t o casualties

{unit losses

Reported total losses

f \ fear due to unit losses

DELAY ON /Fear \ d r t o losses

f REPORTED LOSSES

/ <FEAR RELATED TO TOTAL LOSSES LOOKUP>

PERCENTAGE OF LOSSES REPORTED

/ (FEAR RELATED TO U N I T

LOSSSES LOOKUP>

Fig 7.7: Vensim view - Fear due to casualties.

Fear due t o enemy f i re; bombardment, weapon/uni t type, <PER C E1 VE D LETHA L I l Y

OF FIRE LOOKUP)

d Fear due t o enemy f i re

Suppression Bombar d m n t enemy weapon type

Fig 7.8: Vensim view - Fear due to enemy f i r e h i t .


Fear as part of Corn bar2 I

Fear due t o level o f Morale and 1 Cohesion; defined as Faith t o Cause,

J <unit strength> 1 / previous performnce rating

morale and cohesion fear due t o level of 1 A wraleand cohesion

FAITH TO CAUSE f <FEAR DUE TO LEVEL OF

MORALE AND COHESION LOOKUP>

Fig 7.9: Vensim view - Fear due to level of morale and cohesion.

I Fear due to isolation; calculates I (FEAR DUE TO percentage isolated using suppression

bombardmnt and duration o f attack 1 ISOLATION LOOKUP> I I \

/Fear due t o isolation of unit isolated

<in contact flag> / /

Durationof enemy attack

k

/ <Suppression Bombardmnt>

< ISOLATION DUE TO \ (TIME STEP> FIRE LOOKUP>

Fig 7.10 Vensim view - Fear due to isolation.

DGD& D/L W3/C BSCS/089 8/00 I


-. Fear due t o level of cover; considers I

f surprise

'<FEAR DUE TO LEVEL OF COVER LOOKUP>

terrain, surprise attacks, vehicldunit protection, and unit posture

Hold & defend i \ Fear due t o level of cover

level of cover

Grprise\ delaying ef fect

COVER TIME DELAY I

\vehicle protect ion DUE TO SURPRISE

Fig 7.1 1: Vensim view - Fear due to level of cover.

The susceptibility to fear due t o length o f duty o f the unit; either time during gam or tim during

game plus user defined number o f hours lengthof duty

Susceptibility to fear due t o length o f duty

<Ti w

SUSCEPTLBILITY TO FEAR DUE TO LENGTH OF DUTY LOOKUP

Fig 7.12: Vensim view - Susceptibility due to length of duty


Fear as part of Combur2I

I00 ~

-5 ,

>(I

..----

2,

,I

Modifier t o defea t / sur render levels

( I N I T I A L U N I T

f o r each unit; uses fear levels and lookup table

t o mdifier defeat levels D E F E A T LEVEL> ! \ Defeat level rmltiplier

( D E F E A T I S U R R E N D E R M U L T I P L I E R LOOKUP, (fa,. on

effectiveness,

defeat/surrender flag

l

Fig 7.13: Vensim view - DefeaUSurrender multiplier.

7.1 Testing and results

7.2.1 Although the fear model has not been incorporated into C21 at this stage testing in isolation has been possible. Numerous fictional scenarios have been designed and then the data for each variable manually inputted into the model.

The results gained from these appear to be promising and Figs (7.14) and (7.15) show examples of the output from Fear21 The impact of this will be seen more clearly when integration with CombaQl is complete.

7.2.2

DGD&D/LW3/CBSCS/0898/00 I Paze 5 I


0.6

0.45

0.3

0.15

0

Graph For fear effect

5 7.15: Graph showing the fear effect with clear engagement points and recovery periods.

. DGD&D/L W3/C BSCS/O 898/OO I

Fear as part of Coni baQ I

SECTION 8: TOWARDS INTEGR4TION WITH COivfBAT21

8.1.1 C21 is designed, developed and gamed using the modelling and analysis software VensimB DSS32 Version 1.62-8. The only other piece of software needed is MapInfo ProfessionalTh* 4.1, which is used to obtain the geographical part of the model.

8.1.2 C21 has been designed such that the user does not need to know too much about

Application) uses a model combined with a set of rules (code) for interacting with the model to allow easy access to the use and results. To the user the Venapp appears as a series of menus or a sequence of screens allowing him or her to use and analyse a Vensim model in a straightforward and meaningful way.

Vensim@. This is achieved by use of a VenappTM 40 . A Venapp (Vensim

Generally, the main purposes of a VenappTM are to:

Simplify scenario generation. '

Support interactive gaming. Provide on-line commentary on a model. Focus attention on specific aspects of a model.

Give non-Vensim users access to models.

8.1.4 Version 6.0 of C3 1 uses Venapps throughout gahing and for the main setup of each unit. Other variables, such as time constants can be changed using the Venapp screens, prior to a run commencing. If the fear model were to be an integrated part of C21 it would be useful to have the flexibility to see and manipulate the lookup tables and relevant constants.

8.1.5 Default values would always be loaded into the model as they are saved within each side's orbat files. It would then be possible to have games with controllable levels of fear, and run games with and without fear playing an active part.

8.1.6 I have designed several new Venapp screens to add to the current ones in the C2 1 model to demonstrate a possible way of carrying these ideas through. Fig (8.1) shows the basic user screen that is used to represent the blue orbat. Each unit on this orbat acts a button and takes you to another screen. Fig (8.2) displays the screen beneath the Battle group units. This screen carries setup details for each battlegroup, from the number and types of MBT within the unit, to their night fighting capability. The four drop-dqwn menus that appear in the bottom left comer of this screen are four of the variables used within the fear model/calculations. Two of these are shown in more detail in Fig (8.3).

8.1.7 The use of drop down menus ensures that correct and valid values are used. This prevents model errors and also makes the user aware of the range of appropriate 'values.



8.1.8 The list of variables for the TrainingFamiliarity input moves away from having the famiiiar 'zero to one' scale and introduces words to represent these. The model still reads these in as numeric values but having words instead of numbers on the user screens helps to define the usually perplexing scales. The scale used in this case is from 'Very low' right through to 'Very high'.

8.1.9 The minimum and maximum fear inputs incorporate both numerical values and worded alternatives. The scale used is again still zero to one but on this occasion the lower value and upper values of zero and one are replaced by 'No fear' and 'Full fear' respectively.

ig 8.1: Venapp examples - Blue orbat

DG D& D/L W3 /C B SCS/O 89 8/00 1

Fear as parr of CombaPI

Battle Grow Setuu

I.

I

TRAIN IN GlFAMILIARITY

MEDIUM

VERY HIGH HIGH

LOW VERY LOW

Maximum fear value

NO FEAR 7

...~ ............................................

FULL FEAR

L

Fig 8.3: Venapp examples - Fear variables drop down menus on BG setup screen.

8 I. I O Using wording for the limits on this scale clarifies the boundaries of the variable and gives more meaning to the numeric alternatives between the two extremes.



8.1 Human Factors menu

8.2.1 As part of the main user screens in C21 there is a button to take the user to an 'Advanced Gaming Setbp' screen. Here it is possible to extend previous games or load old runs. There has been a 'Human Factors Setup' Button added here to take the user to the screen shown in Fig (8.4).

. . . . , . . . . . Factors Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . ; (.. i ' \ . . . . . . . . . . . . . . . . . . . . . . . . , . .;, .:;::.;::,,:. . , .........

. . . . - " .

. *

. . , . . .- .: . . . . .

. . . . . . . . . . . . . . . . . . . ... -ur*ln.(U.dl&,"':'.: . . . . . . .

::., ,.

. . . ~

. . ...

Fig 8.4: Venapp examples - Human Factors menu

8.2.2 On this menu the advanced setup screens for fear, shock, and surprise for both blue and red can be found. I will discuss the shock and surprise implications later (Sec. 8.2.1 1). Linked to the 'advanced fear setup' button is fear setup screens for blue and red (Red fear setup shown in fig (8.5).

Contained in this screen are all the lookups that are used within the model and some of the constant variables that the user may wish to change.

8.2.3

DG D& D/L W 3 /C B S C S/O 89 810 0 1

Fear as parr of Combat7 I

8.2.4 The lookup tables displayed in this screen can be selected with the simplest of mouse clicks bringing the lookup table to the foreground of the screen (Fig 8.6).

c' r w m TON LO....

-I 1

ii' P W m n W UnLe9.L

ClrOLI-r my-3 I. 1 . u r * . l L '

m--, L.-..-y I . I t ! -=* LC

MU.

-1, 1_1 .ur~ -.--. ~ ~ - ~ . ~ . ~ b ~ - - ~ ~ r r . ~ . l r a ~ ~ ~ . - W U : i W an

Fig 8.6: Venapp examples - Lookup editing from within setup screen.

8.2.5 The user can then import values, manually input values, or even draw new points on the graph using the mouse. As with all the variab:is these graphs are set to a default setting and in normal game play would not be altered.

DGD&D/L W3/CBSCS/0898/00 1 Page 5 7


8.2.6 If a game were to be played primarily to investigate the effect of fear then this is when it would be useful to adjust these tables.

I

Fig 8.7: Venapp examples - fear setup constant variables

8.2.7 Fig (8.7) shows the variables that are contained on the same screen as the lookup tables. As with the drop-down menu variables that I have already mentioned, where possible, the values have been replaced by words. The variables shown here deal with time delays, length of previous duty, and casualty report percentages.

8.2.8 In the past, one common criticism of models such as C21 has been that the variables are not defined which leads to confusion. To prevent this from happening with this fear model, I have carried out the following:

8.2.9 Next to each variable that I have added you will see a 171 . This is used to take the user to a ‘variable explanation’ screen (Fig (8.8).

Each variable is listed and an explanation given, both for general information and to avoid confhion and misrepresentation of variables.

8.2.10


Explaination o f Fear Variables

I Current Len& of Duty Curet% length d duly allows the userto input a Ome. in hours, relabng to how long the unit has already been out an the ba"ield for.

Deab on Roportod Loeoeo

Dele/ on reported losses alows for communicabon D m e delqs on the battlefield Generalty this IS the amount of Dme it takes force casuahes to reach them

Cover Delav due to Sumrise

Cover delay due to surprise is the average lime it takes for a unit to gain sufiicienr c o w following a high lml surprise enemy attack

Percentane of Lossos Reported

Percentage of losses reponed is the percentage of total torce losses that the unit is informed of when final@ reported to them

g 8.8: Venapp examples - Variable explanation screen.

8.2.1 1 Back on the human Factors Menu screen there are buttons for both shock and surprise. Beneath the surprise button I have connected in the existing moral surprise section of C21 in the same way that fear has been carried out (Fig 8.9). This contains the lookups for orientation, time, and positional surprise as well as a few relevant constant variables. These surprise variables are also linked to their own explanation screen.

Red Surprise Setup

Orlrntstlon Surprlae Tablo

I / w Porlon-Surprlro Table

g 8.9: Venapp examples - Surprise setup screen.

8.2.12 At the time of this paper going to print fear had not been incorporated into C2 I, and therefore plays no part in any games played.' The reason for the Venapp screens

DG D& D/L W 3 /C BSCYO89 8/00 1

Modelling Human Factors in combat simitlations

being produced prior to the inclusion of the model is purely to demonstrate ho\\ Human Factors within C21 could be used and controlled.


Fear as part '0 f Coin bat-? I

9.1.1

9.1.2

9.1.3

SECTION 9: CONCLUSIONS AND RECOMMENDATIONS

There is no doubt that Human Factors work in the OA modelling world needs to continue. Due to the limited amount of data available at present people are reluctant to pursue work of this nature. Over time combat models will be designed to be more suitable for the modelling of Human Factors both at the individual level and at the unit level.

The future of Fear2I. at this time is uncertain. Fear21 has hlfilled its design needs and is fully compatible with C21. It has only been time constraints within DLW that have prevented the model being incorporated. One consideration is the development of C21 that is currently under discussion.

Recommendations: The integration with the latest version of Combat2 1. This process can be carried out either within DLW or by Landair International. Due to the compatibility of the model the integration would be a fairly simple task.

Testing to determine the effects on combat outcome. With the ability to control the minimum and maximum levels of fear used with simulation runs there is large scope for testing either previously run or new scenarios.

The verification of data inputs. As much of the data used within fear21 comes from militaqdpsychology judgement and from trends and patterns taken from previous studies there is a need for verification and more conclusive data.

The validation offear21 in conjunction with the latest version of C2 1. Although fear can be used in C21 games without validation it is important forfear21 to be recognised as a required part of future wargaming and hopefully be the first of many HF that are incorporated into combat models.

Even though Fear21 has been developed with the intention of integration with C2 1, consideration should be given to adapting it for use in other combat models.

DG D& D/L W 3/CB SC YO89 8/00 1 .


References

1 .

2.

3.

3 a.

4.

5 .

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

Lamer, M., Jones, C. and Peacocke, C. (1996). The Application of Battle Winning Factors to Combat Models: Final Report Version 1.0. R.E.D. Scientific Limited Ref. No. 0205-54 REP.

Cawkhill, P. (1997). Psychological Factors in Military Operational Analysis: The feasibility of Modelling Fear in Combat. Rep. no. DERA/CHS/HS3/TR97077/1 .O. DER4 UK.

Stouffer, S. A., Lumsdaine, M. H., Williams Jr, R. M., Brewster-Smith., M., Janis, I. L., Star, S . A., and Cottrel Jr, L. S . (1949). Studies in Social Psychology in World War 11. Volume 2. The American Soldier: Combat and its aftermath. Princeton, New Jersey, Princeton University Press.

LandAir International, Whiteparish, Salisbury

The Handbook of Military Psychology

Dollard, J. (1943). Fear in Battle. Institute for Human Relations, Yale University.

Tyler, S. J. (1995). The suppressive and morale effects of artillery bombardment: survey of fifty-four British soldiers. DRA rep. no. DRA/CHS/HS3/CR95022/OlY DERA, UK.

Rachman, S. J. Institute of Psychiatry (1978). Fear and Courage. W. H. Freeman and Company, San Francisco.

Shils, E. and Janowitz, M. (1948). “Cohesion and disintegration in the Wehrmacht in World War 11.” Public Opinion Quarterly, 12, 280-315.

Watson, J. and Rayner, R. (1920) Journal of Experimental Psychology.

Eysenck, H. J. (1976). Case Studies in Behaviour Therapy.

Lidz, T. (1 946) Psychiatry.

Grinker and Spiegel. (1945).

See for example the analysis by Bandura, A., Psychological Review (1977).

Manning Frederick J. Morale, Cohesion and Esprit de Corps. Division of Neuropsychiatry, Walter Reed Army Institute of Research, WDC USA.

Leighton (1943).


Fear as part of Coni bat2 I

16

17.

18.

19.

20.

21.

22.

23.

23.

25.

Lord Moran (1945). Taken from Reference 35

British Military Doctrine. Army Doctrine Publications - Volume 1 Operations (June 1994) DGD&D/18/34/46. Army Code Number 71565.

The Readers Digest Great Encyclopaedic Dictionary: Volume 1 (1961). Oxford University Press, London.

Meyer, Edward (1982). Chief of Staff US.

Rooney, D. (1 997). The Potential for Representing Combat Participation in Operational Analysis Models. Rep. no. DERA/CHS/HS3/CR970 1 111 .O. DERA UK.

Watson, P. (1978). War on the Mind. London, Hutchinson & Co.

VensimB Reference Manual. Ventana, Belmont Massachusetts USA.

Dupuy, Col. T. N. (1990). Understanding Defeat: How to recover from loss in battle to gain victory in war. NOVA Publications.

Dupuy, Col. T. N. (1987). Understanding War: History and theory of combat. Leo Cooper, London.

Rowland, D., Watson, D. and Stephens, A. (1994). National Variations and Combat Effectiveness Estimates for the Ex-Soviet Baltic States. DOAC Report R 9400.



Abbreviations and Glossary

.4D 0.A (Land) BMD BWF

CBSCS CD.4 (HLS) CHS Combat2 1

DCS (S&A) DER4 DGD&D DLW (LW3) DOAE Fear2 I

HF

HQ Initial model R.1 a p1 n fo

NBC Sc (Land) Venapp VensirnO

Assistant Director of Operational Analysis British Military Doctrine Battle Winning Factor(s)

College Based Sandwich Course Student Centre for Defence Analysis (Higher Level Studies)

Centre for Human Sciences

Concepts Objective Manoeuvre in Battle in the 2 1'' Century Deputy Chief Scientist (Scrutiny and Analysis) Defence Evaluation Research Agency Directorate General, Development and Doctrine Directorate Land Warfare (Land Warfare 3) Defence Operational Analysis Establishment Latest version of fear model designed to be incorporated with C2 1

Human Factor(s) Headquarters

The first fear model to be constructed. Used as a basis for development The Geographical software used in conjunction with VensimB to run

c 2 1

Nuclear Biological Chemical

Science (Land) Vensim Application. Used to create interactive C2 I user screens Systems Dynamics software used to create C2 1


Fear as pari of Coni bat2 I

Annexes

Annex A: General description of a Vensim View

As stated previously the term 'view' comes from the software VensimG. Because of the \yay models are constructed more than one visual representation is possible for the same logical model. The advantages of viewing the same model in a number of different ways are that you can emphasise parts of the structure, tailor the appearance to different standards, break up complicated models, and build up complexity as models develop.

< T I M E STEP> <Final fear index value>

\ / <Susceptibility t o fear due t o length

o f duty> increment red unit fear index

Fear Modifier I

Fig -41.1: Vensim view - Fear index system

Al. l

Al.2

.A 1.3

red unit fear index

Modifier t o red effectiveness due t o fear rate red recovers from fear

AVERAGE RED

TCME

B\ MINIMUM FEAR MODIFIER UNIT RECOVERY

Fig (Al . l ) shows a typical view from a Vensim model. The diagram is simply an influence diagram with the relevant factors having directional connecting arrows to demonstrate their influence. Behind each variable is an equation defining these relationships. Any variable that is enclosed by '<>' also appears elsewhere in the model.

Different coloured variables signiFy a range of properties; these are determined by the modeller and therefore vary model to model. In the fear model most variables are black, with the other variables either being blue, lookup graphs, or red, important fear contributors.

Variables that appear in capital letters are either constants or lookup graphs and variables appearing in boxes are simply being highlighted.


' Modelling Human Factors in combat simulations

Annex B: Initial model compatibility with Combat21

FEAR DUE TO LEVEL OF MORALE AND

Fear due to red losses

Fit in with no alterations

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Fear as part of Coni bar.? I

Annex C: Equation and examples of calculation for previous performance rating

C I . 1 The type of equation used to calculate previousesuccess (now previous psrfomiance) will be as follows:

Previous performance rating = (unit strength/100)-(0.5*(ZIDZ( 1 -(unit strengtNlOO),number of previous engagements)))

Example 1 Unit strength = 94 Previous engagements = 2 Performance rating = 0.925

Example.2 Unit strength = 44 Previous engagements = 2 Performance rating = 0.3

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x = 0.75 1 y = 0.2

Annex D: Possible equations for fear due to. losses

DI.l Equation Type1

z = 3 1 Fear = 0.6125

Fear = (zx + y)/(z + 1) x = fear due to unit losses y = fear due total red losses z = weighting

x = 0.5 1 y = 0.5

Example 1

z = 3

Example 4 x = 0.75 y = 0.2 z = 2 Example 5 x = 0.25 y = 0.85 z = 2 Example 6 x = 0.5 y = 0.5 z = 2

Example 2

Fear = 0.567 Fear = 0.45 Fear = 0.5

Example 7 x = 0.75 y = 0.2 Example 8 x = 0.25 y = 0.85 Example 9 x=O.5 , y = 0.5

Example 3

z = 4 Fear = 0.64 2 = 4 Fear = 0.37 2 = 4 Fear = 0.5

D1.2 In example 1, the fear due to unit losses is very high. The question can be asked: ' i f fear was that high due to unit losses, would the fact that there is low fear due total losses, result in the overall fear to be less than fear induced by the unit losses? - Probably not.

The weighting of x in these examples is arbitrarq. Examples 4-9 are the same equations but with different weightings.

D1.3

The results still produce possible figures, but not a validated representation.

Dl.4 Equat ion Type 2

6.7.6 Equation 2 is more biased to unit losses than type one. The way in which it works is ' that up until unit casualties reach a certain percentage the fear value takes into

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Fear as part of Cotii bar2 I

Example 10 Example 1 1 Example 12

account both unit losses and total losses. When the uni t casualties reach that percentage the fear value is calculated solely on unit losses.

x = 0.75 y = 0.2 2 = 2 Fear = 0.75 x = 0.25 y = 0.85 Z = 2 Fear = 0.45 s = 0.5 y = 0.5 2 = 2 Fear = 0.5

An example of a possible equation is

IF THEN ELSE (unit losses<x,(unit losses + total losses)/2, unit lossss)

6.7.7 An extension to this would be to incorporate equation type 1 into type 2 . Looking at example 1 , with low levels of unit Fear, it produces feasible fear values. By inserting this into%type 2 we should have an equation suitable For fear due to casualties.

The equation would be: IF THEN ELSE(unit losses<Z,(W.fear due to uni t losses+Fear-due to losses)/(w+ l),unit losses)

Z = level of unit casualties needed to ignore total losses W = weighting on unit losses

With the weightins set to 2, and the value Z set at 45 the Following results are produced:

These results appear to be a lot more appropriate. Therefore the equation for fear due to casualties will now be:

IF THEN ELSE(unit losses<20,(2,fear due to unit losses+fear due to losses)/( w+ 1 ),mi t losses).

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Annex E: Type of equation used when considering climatellocation

s = { 1 +(n- 1 )?co}/n n 2 1 Where s = fear level (with climate/physical environment) XO= fear level (without climate/physical environment) n = scenario assigned value (database)

As n 4 1 , x -1 As n -, 00, s 4 X(I

If n =1, s = I . This would result in maximum fear regardless of the other factors. This would be unrealistic. The values that n will take will be between 1.5 and 10 inclusive (1% n 510). The way in which this equation works is by taking the oriiinal fear level and calculates a step up value for it. This figure can never be lower than the original figure and never greater than 1 (one) (ma imum fear value).

.

6.15.3 The value of n for each particular scenario could be stored in a database allowing the physical environment to be a user input and the value n assigned automaticall).. Having a value of 1.5 would be quite an extreme case, with a fear level of 0.3 (s0=0.3) the value after considering the cIimate/physical environment (n=l.3) would give a fear value of0.767.

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Fear as part of Coni bar2 I

A n n e x F: Training/faniiliarity equation and combination with climatdlocation

n = ( 1 7tf+3)/2 Where n = input value for graph (equation) tf = trainingfamiliarity value

6.16.4

6.16.5

The hvo equations stated previously can be combined as n appears in the first equation and is solely defined in ths second equation. The resulting equation would be:

(2+( 17tf+ l)S")/( 17tf+3)

This will not be used in the model; it will be left as the hvo separate equations for clarity as the above combined equation has no real meaning. The equations on their own show clearly the \\-orkings and theory behind them.

Annex G : T h e equation and examples of the calculation for defeat/surrender levels

The equation used for the 'unit defeat level' is:

100-((.1 -INITIAL UNIT DEFEAT LEVEL)*(Defeat level multiplier/lOO))

Examples

1 . Fear'effect on effectiveness = 0.6 Initial defeat level = 40% Unit defeat level = 52%

2. Fear effect on effectiveness = 0.9 Initial defeat level = 40% Uni t defeat level = 79%

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Annex H : Fear21 model compatibility with Combat21

.A\'ER.AGE FEAR R E C O I ' E l ' T I J I E F.AITH TO C.AL'SE > l . C i FE.AR E F F E C T 0 3 I:SIT E F F E C T I I ' E S E S S JII5 lJIL .JI FE.AR l . .ALI-E C I i ma t e/ p h !si ca 1 e n 1.i ro n men t I : / !L'/ r ! . ' I I ' i c / ) I t l / I Q ;r )L)

Fear d u e to enem! fire Fear d u e to isolation. Fear d u e to level of co\.er Fear d u e to level of morale and cohesion Fear d u e to casualties Fear d u e to losses Fea r due to u n i t losses

i \.e n ess

ue

.\lorale and cohesion Z u m b e r of previous engagenicnts Percentuge of u n i t isolated P rev i o us pe r fo r m a n c e r a ti n g

( 'n i t losses 1' n i t s t re ng t 11 Reported total losses CO\'ER T I J I E DEL.A\' D I ' E TO S I ' R P R I S E ' Det'eilt level multiplier Defcut/Sur-render llag DEFE.AT/SC.RRESD ER JI1 'LTI P t l ER LOOKL'P Dela!. on reported losses ISITI.AL. [.SIT DEFEAT LEI 'EL 1 scenario va lue 1SOL.ATIO~ DC'E TO FIRE LOOKI'P PEKC:EXT.-\GE O F LOSSES; R E P O R T E D Surprise Surp r i se delil!.ing effect Time I ' 11 i r d e feil t l e \ . e l

.Si ij I/ I/.LJ.Y.Y i 0 / h f ) I I I /'I' / / 'c / / I / ;'I I I

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Feur as pari qi'C'otiihar.? I

SusceptibiliQ to fear d u e to length of d u Q

Total losses ! . . . . , : , _ ' I . . j , l f . . ) r ; . . .

FE.4R DI 'E TO ISOL.ATIO\ LO0K:I'P FE.4R Dl iE TO L E l ' E L OF C:Ol-ER L O 0 I i l : P FE.4R DC!E TO LEI 'EL OF 3IOR.ALE AXD C O H E S I O 3 L O O K I ' P FE.4R REL.4TED TO I.3'UIT LOSSSES L O O K l i P FE.4R REL.4TED TO TOT.41. LOSSES LOOKtyP PEKC:IE\'ED LETH.ALIT\. OF FIRE L 0 0 K I : P K E C:O I'E R\' LOO KC; P SL.SCEPTIBILIT\' TO FE.AR DC'E TO LENGTH OF D I T ) . LOOKL'P

L . ! ' r " , l , : . _ . . . I

I .

5 L .. I ,_ < - . _ ; / ! I ! :

FlS.AL TI3lE ISITI .4L TIXIE S . 4 l ' E P E R = TIJIE S T E P T14IE STEP = O X

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Annex 1: Step by step guide to fear model changes

i\ I od el cz 1J00 I

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C j 1 -F003

C2 l-FO04

C2 I-FO05

C2 I-FO06

C2 I-FO07

C21-FO08 .

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Changes Fear due to casualties equation Isolation Enemy fire

Shelling intensity = rate of fire Noise equation remove and lookup

ENEMY NATIONALITY = nationalit). input Faith to cause = 1 Previous success now equals:

(unit strength/lOO)-(( 1 -unit strength/l OO)/number of previous engagements) . .

previous success = previous performance ratins

Graph for time = .7<y< I FEAR MODIFIER CONSTANT = *fear due to length of time Fear due to length of time = susceptibility to fear due to time FEAR MODIFIER CONSTANT = fear modifier

Increment = -susceptibility. to fear Rewrote level of cover Enemy weapon type (Ctrl X) from level of cover view

Terrain added to level of cover Morale graph changed to e-2s (commented).

Nationality input = I Posture = hold & defend (comment) Terrain = 1 (comment about assumptions) Level of cover = If THEN ELSE(h&d=I, 1 ,ter*vehicle) (comment)

Changed all test to comic sans MS FEAR MODIFIER =fear modifier Removed effectiveness from suscept. as causing sim eqs. Nationality now takes on hidden modifier to effectiveness Changed first input on suscept graph to 0 not 0.326

Played around with the increment equation. Used MIN and MAX, MIN gives linear representation MAX gives large negative values.

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C2 I-FO010

C21-FO013

C2 I-FOO I4

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C2 I-FOO I6

C21-FOO I7

Feur us j

Made comparisons \vith suppression model and have changed equations accord i n g11,.

Constructed new vie\\. for overall fear index value and added in a minimum fear modifier to set lowest level ot' fear possible (can be zero).

Introduced Max to modifier in order to represent fear that is present from just being in battle, can be set to anything from zero to one. I f ' this is set to one. it will represent total fear constantl).

Played around with the increment and decided to use comparisons ivith suppress ion met hods.

First model using suppression method. Works well.

Introduction of MIN FEAR VALUE. Equations for most variables checked Talks with CHS concerning final fear value Time delay on information concerning total losses

Final fear value = fear appraisal value Introduction of climate/location and familiarity Main calculations uses Fear Evaluation Introduction of defeat/surrender levels

Surprise actual input for cover Surprise delaying effect used in cover Level of cover equation changed

Enemy weapon type now equals enemy unit type. Defined 'duration of enemy attack' in isolation view. Isolation lookup has changed. Percentage isolated = percentage of unit isolated

Removed unnecessary -gaming' variables in order to run test simulations. Isolation lookup extended to take account for longer engagements. Unit losses to total losses ratio now 3: 1 Swapped asis on recovery lookup. Moral? & cohesion lookup now opposite slope.

Enemy fire lookup changed. Still does not use correct values. Used the function ZIDZ in performance rating equation to prevent errors

and altered equation to prevent negative values.

7 1

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C2 I-FOO 18 Introduction of -defeat flag’ I 1 i

Duration of enemy attacks/engagements calculated and time under fire i

Enemy weapon type = addition of unit Vpe. M&C Lookup graph concave not convex Total losses : Unit losses = 1 :3

found to be in C2 I .

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Modelling Human Factors in Combat Simulations: S....

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