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K   Z Gxaxa1 ?"   G T Click to edit Master title style! !@  Z$YHxaxa1 ? % H RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  Z,aH1?t b Dstl 2001 ( 2 2  Bh   ZqH1?]i ~(Dstl is part of the Ministry of Defence (( 2 2) Bh )LB   c $1?V<   ZHxaxa1?a x*0 Z2  hh88<  c $ ? Jp*:T80___PPT10. dstltemplatev10  (  j  s *1 ?   H<  c $ ? X(=^80___PPT10. 0 I(      `Hxaxa1?)  kNotes Z ff22   `PHxaxa1?>  ~ Page * Z  ff22<  c $ ? X(=^80___PPT10.0^N0 ph@ (  d  <A1?@   T,Hxaxa1 ?. H    THxaxa1 ? p@ H 0<  c $ ? Bh*:T80___PPT10. 00u^N0 `z(    Z ]1?^q XOctober 01, 2001  Bh    T ]xaxa1 ?7  ]    T<]xaxa1 ?pP`  ] H  0޽h ? Jp*:T80___PPT10.@7 00u^N0  z(     ZT6]1?^q XOctober 01, 2001  Bh    T0[]xaxa1 ?  ]    T+]xaxa1 ?P5:  ] H  0޽h ? Jp*:T80___PPT10.( 0^N0 ((  ( (  T`x]xaxa1 ?  ]  (  T]xaxa1 ? % ] H ( 0޽h ? Jp*:T80___PPT10.0 0^N0 0(  0 0  Tp]xaxa1 ?  ]  0  Tt]xaxa1 ? % ] H 0 0޽h ? Jp*:T80___PPT10.'8 0^N0  8(  8 8 Z]1?^q XOctober 01, 2001  Bh  8  `t^xaxa1?k ~Combat Degradation Ex. 1   hh88d 8 <A1?@H 8 0޽h ? Jp*:T80___PPT10.`B> 00u^N0 @z(  @ @ Z/^1?^q XOctober 01, 2001  Bh  @  T=^xaxa1 ?  ^  @  TTC^xaxa1 ?j  ^ H @ 0޽h ? Jp*:T80___PPT10.`wJ 0^N0  H(  H H  TW^xaxa1 ?0p ^  H  T<{^xaxa1 ?p  ^ H H 0޽h ? Jp*:T80___PPT10.P 0^N0 "@P(  P P  T^xaxa1 ? |  ^  P BA 1?X $0>0H P 0޽h ? Jp*:T80___PPT10.pV  0^N0 `X(  X X  T|^xaxa1 ?0p ^  X  T^xaxa1 ?p  ^ H X 0޽h ? Jp*:T80___PPT10.жm  0^N0 `n(  ` ` NA 1?8PXX $0>0 `  T*oxaxa1 ?0p o  `  T/oxaxa1 ?p  o H ` 0޽h ? Jp*:T80___PPT10.Wu  0^N0 h(  h h  THNoxaxa1 ?0p o  h  T,doxaxa1 ?p  o H h 0޽h ? Jp*:T80___PPT10.p  0^N0 pt(  p p  Tyoxaxa1 ?  o  p  T4oxaxa1 ? % o  p Zoxaxa1? ! R QUESTIONS? 2 $ H p 0޽h ? Jp*:T80___PPT10.H0 XPP(  d  c $1 ?    HD  ZHxaxa1 ? P  H |Attention Grabber: I am here today to talk with you about HA with the emphasis on Analysis and OA, rather than historical research or survey s Intro; My name is Dan Tilley and I am part of the HA team at Dstl Policy and capability studies dept. I will be giving you a quick introduction to what historical analysis is and showing you a quick example of HA and some of the simple statistical techniques we use to provide useful advice to our customers. This will then be followed with a slightly more in-depth example of more recent work from my colleague Jakie Offord  ?H  0޽h ? X(=^80___PPT10. F0 pV(  d  c $1 ?    ]d  <1?  ^ F  Z 7]xaxa1? ] TGo through slide Point 1: OR or OA is about giving the best advice now rather than a rigorous complete answer too late. Where possible though HA does try to include as much rigour in it s work as possible. Point 2: HA tends to look at the bigger picture of the battle not at the individual component, I.e. at the whole battle not at how just one person performed or how one tank performed. Point 3: Point 4: We tend to employ historians for data collection and where possible we do try to check that the validity of the data sources. Point 5: nothing to add. Stress HA not about creating, interpreting historical narrative per se; about using historical data as a resource0 Z2  ff22H  0޽h ? X(=^80___PPT10. "0 $-(  $d $ c $1 ?    ] $ Z`w]xaxa1 ? @9  ] Go through Slide Point 1: Have looked at a diverse range of factors and relationships in conflict such as: Sub-Bullets Point 2: This is obviously important when planning a mission, to make sure operation is feasible and to make sure you have enough men and logistics supplies. Point 3: Drivers include, air superiority, shock, surprise etc. Point 4: Really verification rather than validation as such.  @H $ 0޽h ? X(=^80___PPT10.+(0 ,8(  ,d , c $1 ?    ] , Z]xaxa1 ? @  ] HA uses a number of simplistic statistical techniques to show trends in data and show the results are robust Go through slide Point 1: To avoid samples being biased care must be taken over the design of data collection and any samples that are collected need to be caveated to show whether they come from a reliable source or if no data could be derived from the sources. Point 2: Significance tests, were used during the early Combat Degradation work to test samples and hypothesis. Point 3: Used to look at the association of two variables/factors Ex.Combat Degradation, Influence in Coalitions and Breakthrough and Manoeuvre study to show the relationship between difference factors which lead to breakthrough and success in conflict. Point 4: Frequency Graphs-Used to show the Frequency of UK Involvement in UN Operations Point 5: Including Genetic Algorithms in the Influence in Coalitions study, which Al Morley will be Talking with you about in the next talk  H , 0޽h ? X(=^80___PPT10.30 XP4(  4d 4 c $1 ?    ]D 4 ZH]xaxa1 ? @  ] |Go through slide: Go through assumptions that are made Depending on time may actually go through simple equations on board }H 4 0޽h ? X(=^80___PPT10. 5;0 <(  <d < c $1 ?    ^ < Z^xaxa1 ? @  ^ f@Go through slide Plot of Attack Casualties per defender by Force Ratio (Riflemen Only) for both real combat and trials. Blue Line shows Combat Effectiveness in Trials Red Line shows combat effectiveness in actual warfare Shows clearly the degradation of combat effectiveness in real combat compared with live trials AH < 0޽h ? X(=^80___PPT10. jG0 D'(  Dd D c $1 ?    ^ D ZV^xaxa1 ? @9  ^ Point 1: Knowing this factor means that planning future missions and simulations of forthcoming conflicts can now be slightly more realistic.   @H D 0޽h ? X(=^80___PPT10.MA0 0LQ(  Ld L c $1 ?    ^ L Z^xaxa1 ? @  ^ The HA work on the counter terrorism overseas (CTO) project had two main aims: Point 1:To draw any conclusions possible from investigating high level data on campaigns for instance, government type, economic complexity, outcome. Point 2:To validate work going on in other areas of the CTO project, namely Network Analysis and Psychosocial Analsysis Point 3: Data on thirty terrorist/insurgent campaigns was collected. The campaigns chosen were post WWII campaigns by States and State Security Forces against terrorist and or insurgent groups This excluded campaigns including use of terror by states against sub-populations of the state and those of terror by sub-populations of the state against other sub-populations of the state.  H L 0޽h ? X(=^80___PPT10. S0 jbPT(  Td T c $1 ?    ^V T Z>^xaxa1 ? @  ^ ` Shows that PUF s SSyF have a greater chance of winning a campaign than any other state. I.e. in a ratio of 6:1 (even for this limited sample this is statistical significant) & H T 0޽h ? X(=^80___PPT10."iP 0 p\`(  \d \ c $1 ?    ^ \ ZD^xaxa1 ? @  ^ In validating the taxonomies derived in the network analysis and psychosocial analysis strands, the HA team were looking to see whether they were sufficient to classify all of the campaigns in the data set and of high enough resolution to usefully differentiate between the cases. The network analysis taxonomy concentrated on trying to identify the different network structures present in TIGs. In order to validate the Network Analysis work, each campaign was classified according to whether they had: hierarchical or decentralised structure; cellular or non-cellular structure; external sponsorship or external support; and various other factors: Some of the results of this classification are shown on the slide. The distribution of the campaigns considered within these attributes suggested classification was both exhaustive and of sufficient resolution. The psychosocial analysis work concentrated on trying to identify the different motivational factors present in TIGs. Each campaign was classified according to which high level attributes it displayed in terms of its idealism, agenda and cause. Tthe options were Global or regional idealism; revolutionary or apocalyptic agenda; material gain, single issue, political, religious or political/religious cause It was found that 26 of the 30 cases considered fell into the Regional Idealism, Revolutionary Agenda and Political cause category suggesting that although the taxonomy was exhaustive, it did not provide particularly good resolution with our data set. H \ 0޽h ? X(=^80___PPT10.p 0 >6d(  dd d c $1 ?    o* d Zp9oxaxa1 ? @  o b1. Clearly, Ideological fundamentalism is the most popular primary motivation this almost always involved communist, maoist or marxist elements. Overall, Nationalism is the most frequent motivation, however, it s more often a secondary or tertiary motivation as opposed to a primary. 2. Where Separatism was a primary motivation it was always accompanied by a secondary motivation, is 86% of cases, this was nationalism. Similarly, nationalism as a primary motivation was always accompanied by a secondary motivation and in 67% of cases this was sectionalism 2H d 0޽h ? X(=^80___PPT10.   0 l(  ld l c $1 ?    ov l Zmoxaxa1 ? @  o So finally, to summarise the findings of this HA work we can say that: The two most significant conclusions from the general investigation of the data were that: Point 1:Richer, democratic states are more likely t o succeed against terrorist threat Point 2: Insurgent campaigns seem to closely follow periods of radical political change whereas less violent, smaller terrorist and civil violence campaigns are much more likely to occur at least 10 years after any major political unrest. In general this work work was very well received and has paved the way for further investigation of terrorist groups using the HA methodology. Point 3: It fulfilled it s aim to validate the network and psychosocial analysis flagging up that the high level psychosocial taxonomy of motivations lacks resolution and providing evidence to support the other taxonomies. XH l 0޽h ? X(=^80___PPT10. 0 d\t(  td t c $1 ?    oP t Zoxaxa1 ? @  o This slide we just show at the end to give audience idea of the difference work HA undertakes and then we ask if there are any questions H t 0޽h ? X(=^80___PPT10.Ujx\{pv&@xuwyHJB. 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