How to assess deception in a complex environment Paul R. Syms, Dstl LBSD Fort Halstead 26 ISMOR Bishops Waltham, 14 September 2009 Dstl/CP37862

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

© Dstl 20091 September 2009 Dstl is part of the

Ministry of DefenceUK UNCLASSIFIED

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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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Illusion(cannot see)

Self-deception(delusion)

Self-induced

Deception(intentional)

Misrepresentation(unintentional)

Defining deception – Whaley (1982)

Pluperception(accurately seen)

Other-induced

Misperception(wrongly seen)

Perception

• A typology of perception



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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

Direction

Collection

Analysis

Dissemination

IRs, PIRs

DataInformation

IntelligenceCueing

Target signatures

Analyst training

Sensor capabilities

CommandRFIs



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Direction

Collection

Analysis

Dissemination

IRs, PIRs

DataInformation

IntelligenceCueing

Target signatures

Analyst training

Sensor capabilities

CommandRFIs

Intelligence cycle: real world

Disinformation, decoys

Confirmatory bias

Culture,Expectations,Time pressures

Lack of vulnerabilities

Queuing

Imperfect

overload

“Yes, Sir!”

Personality

ECM

The ‘long screwdriver’

Complexitywarning!

Clutter



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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?

Documents

How to assess deception in a complex environment Paul R. Syms, Dstl LBSD Fort Halstead 26 ISMOR Bishops Waltham, 14 September 2009 Dstl/CP37862