Using Quantitative Analysis in Support of Military Intelligence P. Dobias, P. Eles DRDC CORA J. Schroden CNA J. Wanliss Presbyterian College 28th International Symposium on Military Operational Research 29 Aug-2 Sep 2011, UK
Outline • Context • Data sources/considerations • Traditional methods – Trends – Seasonality – Forecasting violence – Assessing enemy • Fractal nature of conflicts – Implication of data structure – Multi-fractal forecasting – Current status of research 2
Context • Providing information to enable mission planning: – Enemy intent/capabilities – Terrain/Environment – Human terrain, culture, social structure • How to conduct assessment in the environment characterized by: – Lack of cultural/social/tribal/religious understanding – Insufficient sources of varying reliability – Incoherent and mutually competing enemy groups 3
Data sources Afghanistan India Pakistan 800 kWh per capita per annum 700 • Demographics • Economics 600 500 400 300 200 Afghan Central Many NGO 100 0 Statistical Office sources provide 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 collects and info such as International Grain Council Kabul Herat Kandahar Jellalabad 0.90 0.80 disseminates variety wheat/sheep 0.70 0.60 US$/kg 0.50 of population stats prices or power 0.40 0.30 0.20 usage 0.10 0.00 Jan-06 Jul-06 Oct-06 Jan-07 Jul-07 Oct-07 Jan-08 Jul-08 Oct-08 Jan-09 Jul-09 Oct-09 Jan-10 Jul-10 Oct-10 Apr-06 Apr-07 Apr-08 Apr-09 Apr-10 • Polling • Violence Metrics According to some estimates Afg is Collected by security forces, it is the most polled country in the world. one of the most reliable data Kabul group, NGO’s, ISAF, all sources around. Most data is stored conduct polls asking a variety of in CIDNE (replaced JOIIS in 2010) questions Tentative Polling 90 80 70 60 Score 50 40 Q1 30 Q2 20 10 0 2006 2007 2008 2009 2010 2011 4 Year
Concerns about data • “One - of” reportings – Some organization collects data; process not repeated – Impossible to produce trends • Changes in collection methodology and timing – Incoherent and internally inconsistent data – Trends of limited validity • Lack of continuity – Discontinued collection – Data gaps – Limited usefulness of trends • Multiple, often conflicting sources • Parallel data storage – All mil data should be in CIDNE – Number of authoritative spreadsheets containing specific info – Difficult correlating of various data 5
Trends in violence • Strong seasonality – Peaks in July-August – Lowest in December-January – Dips in April due to poppy season • Long-term increase • Concentrated along Ring- Road (populated areas) – Most violence in South and East 6
Seasonal decomposition • Seasonality in Afghanistan • Methodology Multiplicative model X = T x S – Average X over one season – X /< X > provides raw seasonality, – Annual cycle, difference over 50% is used to obtain S – Must be considered when analyzing – T = X / S for each point changes • Long-term trend • Assessment – Identification of recurrent patterns Can be used to correlate with factors that do not have seasonal – Identification of long-term trend components – Correlations with other factors (friendly activity, weather anomalies) – Deviations from the trend – Implications for the future activities 7
Use of violent data • Understanding enemy • Forecasting and risk assessment – What is the enemy’s intent? – What are the enemy’s – What violence levels are capabilities? expected? – How does the enemy – Management of resources allocate resources? (medical, materiel, personnel) – What is the enemy’s – Based on assumption that refit/resupply cycle? historical trend can be projected to the future – How does the enemy adapt – Usually encapsulates some to our OPS? relationship between violence • Limited value if used and other factors (e.g. troop alone; needs numbers, major events) supplementary info sources and qualitative analysis 8
Assessment of Insurgency • What is the state of • What are the insurgent insurgency? resources? – What are their capabilities, – How are they distributed? intent, morale? – Origin of resources • Model and Indicators (local/external) • Violence as indicator – Developing a model of insurgency to identify – Particular event categories indicators – Distinguish between – Combination of violence dedicated and opportunist categories: fighters • Effectiveness – Indication of insurgent focus • Particular attack categories and intent • Ratios of particular categories • Target – Supported by other sources 9
Forecasting • Deterministic vs. • Assumptions: stochastic model – Past connection – What are other between violence and a uncertainties? factor X will hold – Is the nature of – Seasonality will remain randomness known? the same – Are the trials – Behaviour of factor X independent? – Is the statistical distribution known or can it be inferred? 10
Fractal Structure of Violence • Power-law • Temporal, Spatial, Event-based – Fractal nature of the data is reflected in the characteristics power law distributions • Persistence – A result of the memory in the system (the numbers of events at various times not independent) – Implies criticality or near-criticality 11
Multi-fractal forecasting • Identify “trigger” threshold – Binary approach (below/above threshold) – Time between crossing • Enable short term threshold (waiting time) forecast: – Exploits universality of – More efficient resource scaling and persistence allocation – Expectation management – Consequence management 12
Ongoing activities and future plans • Fractal Properties of • Multi-Fractal Irregular Warfare Forecasting – Revisit scaling – Revisit persistence of properties for extended expanded data sets data sets – Test thresholding – Revisit intermittency algorithms and persistence – Test multi-fractal – Agent-based modeling forecasting on limited of small to large scale data sets combat – Test predictive power – Identifying key drivers and validate on real of fractal behaviour data 13
Conclusions • Quantitative analysis can provide a different perspective and 5 additional insights into the enemy 4 • It cannot be a standalone activity 3 and needs to be supplemented by qualitative assessments 2 • Simple, conventional methods 1 can provide insights directing 0 further analysis • Advanced methods can capitalize on the internal dynamics of conflicts as complex systems 14
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