Transcript Modelling D&D, Part 1: Progress to Date

How to assess deception in
a complex environment
Paul R. Syms, Dstl LBSD Fort Halstead
26 ISMOR
Bishops Waltham, 1–4 September 2009
Dstl/CP37862
Contents
• Introduction: why try to model deception?
• Examples of deception from history
• Psychology of deception
• Assessing deception in OA models
– modelling the intelligence cycle in an imperfect world
• Applying cognitive modelling and historical analysis
• Summary and conclusions
• References and questions
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Why try to model deception?
• Because it happens!
– it is one of the best ways of achieving surprise in war …
– surprise is a driver of success in battle (Rotte & Schmidt, 2003)
• To promote awareness, to avoid falling victim to it
• To use deception to achieve military goals
– including own force protection
• To assess the value of investing in CCD/D&D
– training and CCD equipments
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Examples of deception from history
• 1250 BC: The Trojan Horse
– played to superstitions, and poor Trojan Opsec
– included a feigned withdrawal to complete the scenario
• 1781: Yorktown … or was it to be New York?
– dummy American positions supported by unexpected manoeuvre
– also Megiddo (1918), Alamein (1942), D-Day (1944), Iraq (1991)
– each played to the enemy’s expectations
• 1999: Serbian CCD blunted NATO airpower in Kosovo
– NATO killed 28 AFVs, 17 guns, in campaign of 38,000 sorties
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Where does deception occur?
• At the sensor/platform level?
• At the command level?
• Or in the mind of the decision-maker?
– at any or all command level(s)
– concluded by Whaley (1982); accepted by all authorities
• Conclusion: we must model the decision-maker (DM)
– and all relevant inputs to their decision, e.g. sensors
– with appropriate detail in their environment
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Defining deception – Whaley (1982)
• A typology of perception
Perception
Misperception
(wrongly seen)
Other-induced
Deception
(intentional)
Pluperception
(accurately seen)
Self-induced
Misrepresentation
(unintentional)
Self-deception
(delusion)
Illusion
(cannot see)
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Whaley’s structure of deception
• Six strategies for deception:
– dissimulation: hiding the real, and simulation: showing the unreal
– each by 3 means: hiding, disguising and distracting
– resulting in six basic types of deception
• May be neat and correct … but is it useful for modelling?
– only if it leads to patterns that can be exploited
– e.g. generalizing P(success) over one class of deception
– or patterns regarding the sustainability of the deception
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Psychology of deception
• Lots of work on this … by psychologists
– studies are largely qualitative, and outside the military context
– ‘gullibility’ has not been quantified for established psychometrics
• Deception plays on target expectations
– reinforced by confirmatory bias, i.e. not seeking negative data
• Deceptions can exploit cultural features of the target …
– discussed by Sheppard et al. (2000); Fidock (2002)
– but can also fail because of these (if C2 ignores ISTAR input)
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Why current tools are inadequate
• Current models centre on ISTAR or the attrition battle
– link from deception through C3 to outcome is weak
• Deception is poorly understood
• Current models typically evaluate P(success)
– whereas deception largely arises from C3ISTAR failures
• Deception exploits environmental clutter
– most models ‘subsume’ clutter into their data (or ignore it)
• Data on deception are scarce to non-existent
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Key modelling issues
• Camouflage is poorly modelled in OA studies
– older STA models ignored shadow, camouflage and clutter
– field trials did not support OA needs
• Concealment is poorly modelled in OA studies
– most terrain and LoS models are inadequate
– high-level OA models don’t account for enemy use of terrain
• Deception is inherently hard to model
– complex, high cost and risk to include it in OA studies
– lack of understanding, lack of data
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Must focus on intelligence cycle
Command
RFIs
Direction
Intelligence
IRs, PIRs
Cueing
Dissemination
Collection
Information
Analysis
Target
signatures
Sensor
capabilities
Data
Analyst
training
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Intelligence cycle: real world
Personality
Command
The ‘long
RFIs
screwdriver’
Intelligence
“Yes, Sir!”
Confirmatory bias
Direction
Queuing
Cueing
Dissemination
Disinformation,
decoys
IRs, PIRs
Complexity
warning!
Collection
ECM
Imperfect
Information
Culture,
Expectations,
Time pressures
Data
overload
Analysis
Lack of
Analyst
training
Target
signatures
Clutter
Sensor
capabilities
vulnerabilities
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A role for cognitive modelling?
• Currently aims to analyse a single decision-maker (DM)
• Could be extended to DM groups
– more complexity: a DM group is not the sum of its participants
– cultural effects such as ‘group-think’
• Bayesian belief networks (e.g. Stech & Elsaesser, 2007)
– “what is the probability of A being true, given B?”
– widely used for automated diagnostics, medical etc.
• Dempster-Shafer inference (e.g. Dean et al., 2005)
– DMs’ beliefs represented as changing ‘confidence masses’
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Applying cognitive modelling
• CCD/D&D is a more complex question than CID
– must represent competing Blue and Red C3 processes
– decisions are between the main options for each side
• e.g. attack left flank, centre or right flank
• Blue choosing convoy routes, Red choosing ambushes
• Must include other elements of the appreciation
– feasibility of routes, terrain, etc.
– rapidly leads to a ‘combinatorial explosion’ of options
• Still needs link from C3 model to battle outcome
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A role for historical analysis?
• Appropriate to study the human factors in deception
• Two possible applications of HA:
• 1. A catalogue of deceptions
– to correlate factors and circumstances with P(success)
– requires ‘slow time’ collection – harder to search deliberately
• 2. Assess value of deception in historical battles
– probit analysis, as used by Rotte & Schmidt (2003)
– Linked to Rowland’s surprise? US Helmbold HA database?
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Summary
• CCD/D&D can be a battle-winning phenomenon
– deception happens in the mind of the deceived
– therefore understanding its psychology is key
• Requires the most complex form of C3 model
– incorporating human fallibility, imperfect comms within an HQ ...
– current models take too much information from ‘ground truth’
• First step must be to model the decision-maker
– the technique of cognitive modelling shows promise
• HA may improve understanding and data provision
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Sources and references
DEAN D.F., HYND K.S., MISTRY B., VINCENT A. & SYMS P.R. (2005) ‘A new technique to address
CID and IFF studies’ Paper presented to 22 International Symposium on Military Operational Research,
Bishop’s Waltham, Hants., 30 August – 2 September 2005; DSTL/CP16723
FIDOCK J.J.T. (2002) ‘Building representations of organizational behaviour in models of military
headquarters’ DSTL/CR03842/1.0
HATHERLEY A.G.C. (2000) ‘Why were NATO air operations so ineffective against Serbian land forces
in Kosovo, and what lessons can be learned from this?’ Cranfield University/RMCS Shrivenham 6
Military Studies MA Course dissertation R/00/1112
ROTTE R. & SCHMIDT C.M. (2003) ‘On the production of victory: empirical determinants of battlefield
success in modern war’ Defence and Peace Economics 14(3): 175–192
SHEPPARD C., MATHIESON G.L. & CORRIE N. (2000) ‘Human Decision-Making in OA: Knowledge
Requirements and Guideline Structure’ DERA/CDA/SA/CR000070/1.0
SYMS P.R. (2005) ‘Quantifying CCD effectiveness: measuring the unmeasurable?’ J. Defence Science
10(1): 18–26; DSTL/JA13058
WHALEY B. (1982) ‘Toward a General Theory of Deception’ J. Strategic Studies 5(1): 178–192
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Questions?
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