Human Augmentation SIP

Annex A – Project methodology

Projection and scenarios

Due to the high number of key factors and the complexity of the scenarios a calculation
based on a consistency matrix^50 with subsequent clustering was performed. The risk
assessment and horizon scanning (RAHS) software, an in-house product of the future
analysis branch of the Bundeswehr Office for Defence Planning was used to conduct
this analysis. The calculation ensured that all the resulting raw (or initial) scenarios
were consistent (that is, free of contradiction). Each raw scenario is produced by using
one key factor projection. These raw scenarios were then grouped using a clustering
method similar to the one used to combine initial factors into blocks.^51 The percentage
of the relevant projection per key factor was calculated. For example, Table A.2 shows
the proportions (expressed as a percentage) of the projection used (from the respective
key factor) which were combined to produce the scenario ‘globalised world, national
tensions’.

Key factor Projection %

Health care

1A – Robocop through health care system

1B – Elysium

1C – Universal soldier

1D – Basar of Kyrion (Valerian)

0

64

36

0

Military needs

2A – Arms race

2B – Effective control

2C – Rogue nations

2D – Human performance enhancement mercenaries

90

0

0

10

Commercial

interest

3A – Market run

3B – Niche market

3C – Regulated market

3D – No market at all

0

100

0

0

System safety

4A – All inclusive safety

4B – Almost safe

4C – Almost safe with side effects

4D – At least no side effects

4E – Kamikaze safety

4F – Unsafe

3

72

10

7

4

4

Table A.2 – Section of the scenario cluster that became the ‘globalised world, national

tensions’ scenario

50 The consistency matrices were filled in by several people from all participating nations in parallel and
then consolidated. This approach should ensure that the previously mentioned limitations of the method
have been kept as low as possible.
51 By selecting certain parameters, the analyst has an influence on how many scenario clusters are
created during clustering. Experience is important to determine an optimal number of scenario clusters
without loss of information.