1. Cranfield Universityb (UK)
UNCLASSIFIED
Challenges and Opportunities in Applying Bayesian Network Modelling to Strategic Force Design
Thang Caoa, Andrew Couttsa, Ben Pietscha, Dermot Blumsona and Adam Zagoreckib Defence Science & Technology Groupa (Australia)
Cranfield Universityb (UK)

2. Outline Problem Statement and Motivation
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Outline
Problem Statement and Motivation
Modelling approach for evaluating force design options
Qualitative, quantitative and Bayesian Networks
DeMorgan qualitative Bayesian Network model
Causal Strength Logic (CAST)
Challenges
Future work and discussion

3. Strategic force design problem statement and motivation
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Strategic force design problem statement and motivation
Scale – whole of force to tactical unit design
Temporal – now to the next 30 years
Uncertainty – environment, technology, force effectiveness ect
Complexity – cause and effect relationships
Evaluation – compare design options
Methodology – qualitative, quantitative methodology or both
Motivation: Developing a modelling framework to address above problems

4. Example: US Army Design Methodology
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Example: US Army Design Methodology
“methodology for applying critical and creative thinking to understand, visualise, and describe unfamiliar problems and approaches to solving them”, Department of the Army. (2008). TRADOC Pamphlet , Commander’s Appreciation and Campaign Design, Fort Monroe, VA: Headquarters, United States Army
“systemic rather than reductionist, and qualitative rather than quantitative” The Art of Design, Military Review; Ryan, A. J., Schifferle, P., Stewart, M., Butler-Smith, A., Schmidt, M., Rochelle, J. & Webb, G. (2010).
Figure 5. Original visualisation of the U.S. Army Design Methodology.

5. Systemic Design Influence Diagram for force design
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Systemic Design Influence Diagram for force design

6. Force design evaluation model hierarchy
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Force design evaluation model hierarchy

7. Qualitative vs quantitative evaluation of options
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Qualitative vs quantitative evaluation of options
For qualitative evaluation of options:
Accounts for strategic factors influencing selection
Enables complex details within an overall expert assessment
Less preparation or agreement between stakeholders on methods and data
Against qualitative evaluation of options:
Subjective – Difficult for aggregation and repeatability
Slow – Difficult to conduct rapid ‘What if’ analysis
Opaque – Assessments may not provide full detail justification for result
Synthesis qualitative and quantitative
Support qualitative analysis with rapid structured/quantitative options comparison
Provide transparent explanation for assessments within the decision system
Propose Bayesian Network (BN) as a methodology to achieve this

8. Building a BN Model for evaluating force options
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Building a BN Model for evaluating force options
BNs are a practical modeling tool – but efficient model building techniques are needed
Possible approaches to building models:
Use human expert(s)
Directly from data
Combination of expert’s knowledge and data
Challenges:
Capture complex relationship graphically
Large number of probabilities required to specify conditional probability tables – growth exponentially – difficult for elicitation
Adapting elicitation for elites and non-technical experts

9. Reduce size and elicitation of CPTs for qualitative problem
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Reduce size and elicitation of CPTs for qualitative problem
There are at least three different approaches to the problem:
Independence of causal influences models (ICI)
assume some model of interactions between causes and the effect
Context specific independence (CSI)
exploit and encode efficiently independences (symmetries) in CPTs
Bayesian Network Interpolation Model (Cain’s elicitation calculator)
Exploit interpolation and parent interaction property to reduce the number of elicitations
There is no formal classification in the literature and there are examples that do not strictly fit any of the three approaches

10. Qualitative BNM from DeMorgan’s Law
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Qualitative BNM from DeMorgan’s Law
BN DeMorgan Model
Assume binary states and ICI
Modelling four types of cause-effect relationship: Cause, Barrier, Requirements and Inhibitor (16 Boolean functions)
Derive conditional probabilities from linearly growth elicited CPT
Model both positive and negative influence
Pros: Easy to interact with experts, good probabilistic semantics, model unobservable cause-effect, linearly growth elicited CPT
Cons: Binary states, require ICI assumption
Example: Red to green colour indicates low to high probability

11. Qualitative BN from Causal Strength (CAST) Logic
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Qualitative BN from Causal Strength (CAST) Logic
CAST Algorithm
Aggregate positive causal strengths
Aggregate negative causal strengths
Combine positive and negative causal strengths, and
Derive conditional probabilities
Pros: Easy to interact with experts, proven successful with many US military applications
Cons: Binary states, lack of probabilistic semantics, require ICI assumption
Example: Red to green colour indicates low to high probability

12. UNCLASSIFIED
Prototype: Land force design qualitative Bayesian network model (DeMorgan)
Red to green colour indicates low to high probability

13. Proposed force design evaluation framework
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Proposed force design evaluation framework
Qualitative Bayesian Network Model (QBNM) for evaluating high level force design
Apply QBNM (DeMorgan or CAST)for rapidly evaluating strategic factors
Support qualitative analysis with rapid structured/quantitative options comparison
Extending QBNM to full CPT BNM with multiple states
Extending BNM to multiple states
Applying interpolation algorithm for CPT gap-filling and allowing interaction between parent nodes .
Provide transparent decision model through Bayesian Decision Theory

14. Conclusion & Future Work
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Conclusion & Future Work
The proposed framework addressed:
Uncertainty, complexity, evaluation and methodology
Scale – on high level force design only
Temporal – need to extend to Dynamic Bayesian Network Model (DBNM)
Future work
Methodology and model validation (applied to force design problem only)
Extend DeMorgan and CAST models to multiple states
Investigate the applicability of CSI model
Develop BN meta-model from simulation for modelling tactical unit force design
Extend to DBNM for evaluating temporal force design
Collaboration

15. UNCLASSIFIED
Questions ?