Panel VI - Creating a Scientific Base for Resiliency – the Road to HFA2

1. Network Dynamics &
Simulation Science Laboratory
Policy Informatics at Societal Scale:
Massively Interactive Socially-Coupled Systems
Christopher L. Barrett
Scientific Director
Virginia Bioinformatics Institute
Virginia Tech

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Simulation Science Laboratory
Policy informatics
• Must be responsive to actual evidential policy making
– Principles, standards, objectives
– Processes and procedures
– Measurement of performance wrt objectives
• End of monolithic models of these complex systems
• End of simplistic ideas about prediction
• End of the “great man theories” of decision making
– Sociality in reasoning processes is not an abstraction now
• Embedded, pervasive computing and information networks
• Costs drivers have shifted from data to analytics

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Simulation Science Laboratory
PI in complex systems essentially change them
• Co-evolution and branching are at the heart of the real world of
big data
– Margin trading example of co-evolution
– “Arbitrage law” drives branching
– Taditional data sources, no matter how big will always be “measure
zero”
• Viable ICT approaches:
– replace positivist prediction paradigms with abductive, counter
factual/fictive, evidence-driven, systems
– are inherently privacy preserving
– Delivers problems to computing and deploys pervasive computing

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Simulation Science Laboratory
Is this necessary? Is it possible to “package”?
• We’ll look at:
– How to think about this
– Tools, methods, resources
– Rationale
• What is the necessary R&D program?
– What are the theoretical and practical issues?
• Relevance to policy making problems and organizations

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Simulation Science Laboratory
Intro to Synthetic Socio-technical information
• Start by using surveys and other individual information

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Simulation Science Laboratory
Add relevant individual behavior
• Attached to every synthetic individual
• Connects individual properties to plans and to joint plan
information

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Simulation Science Laboratory
Project onto activity locations (geographic & virtual)
Edge labels
• activity type: shop, work, school
• (start time 1, end time 1)
• (start time 2, end time 2)
Location Vertex:
• (x,y,z)
• land use .
• Business type
People Vertex:
• age
• household size
• gender
• income ..
• Demographically match schedules
• Assign appropriate locations by
activity and distance
• Determine duration of interaction
• Generate social network

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Simulation Science Laboratory
Produce synthetic data libraries & networks
• “Megapolitan” Regional networks
• Interaction with built socio-
technical infrastructures
• Methodology Advances
– Software scale to national scope
– Graph library to calculate graph
measures of large networks
Simulate
Composed
Interactions

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Simulation Science Laboratory
Sources of information
• Social media sources
• Existing and new crowd sourcing & embedded pervasive
sources
• Micro surveys
• Aggregators
• Conventional sources
• Enterprise information
• Biological information in detail
• Medical information…….etc

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Properties of synthetic information
• Synthetic information is inherently:
– Privacy preserving, yet
– Extremely granular
– Very large
– Dynamic
– Customizable by product lines
– Reusable and modifiable
• HPC and pervasive computation-oriented
– Changes how HPC must be delivered
– Emphasizes data services and synthesis, not modeled prediction

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Simulation Science Laboratory
Tools and Methods
•

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Simulation Science Laboratory
Synthetic information environments:
Big data synthesizers
creates and enables

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Simulation Science Laboratory
User & context–driven
Structured and Unstructured Data Sources
in the context of a query…

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Simulation Science Laboratory
Overview
Structured and Unstructured Data Sources
and transforms them…

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Simulation Science Laboratory
Very large synthetic information libraries
Structured and Unstructured Data Sources…into

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Simulation Science Laboratory
Example: Train a “reach back” response system
• Use decision analytics platform and crowd source
interface to create training environment
– Stakeholder integration
– Complex scenario
– Diverse component interactions with user
– Maintain non-specialist, application focus
– Use leading edge HPC and pervasive computing tools and
methods
• This is an introductory movie for the students

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Simulation Science Laboratory

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Simulation Science Laboratory
Motivation: Large scale interaction problems

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Simulation Science Laboratory
Individual behaviors and populations
• Socially-coupled systems involve people, their behaviors
and their environments
• They co-evolve and branch
• Behavior is structured by individual biological state,
cognitive state, individual motivations, perception and
situational reasoning, economic and social reasoning,
strategies and plans, technological and environmental
properties, functionalities and constraints, etc
• What matters?

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Simulation Science Laboratory
Consider what is involved in urban mobility

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Simulation Science Laboratory
How socially-coupled systems compose

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Simulation Science Laboratory
Composition: Wireless interference among vehicles

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Simulation Science Laboratory
The size of the problem: person to country
• From individuals: their state, motivations, activities and
• From locations: their functionalities, services, constraints,
supply chains, etc
• Individuals and related groups are defined
• Order 107 to roughly order 1010 interacting elements (now)

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Simulation Science Laboratory

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Simulation Science Laboratory

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Simulation Science Laboratory

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Simulation Science Laboratory

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Simulation Science Laboratory

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Simulation Science Laboratory

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Simulation Science Laboratory
Composed dynamics and behavior:
disease, individuals, populations, interregional travel, health care system

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Distribution of day of first arrival of disease

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Simulation Science Laboratory
Reporting of Adenovirus variant

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Simulation Science Laboratory
Day of overwhelmed hospital treatment resources

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Simulation Science Laboratory
Infrastructure catastrophe example

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Physical disaster in a social context
• Event put “on top of” a
normally functioning day’s
population dynamics
• National Planning Scenario 1
• Unannounced detonation
• Time: 11:15 EDT
• Date: May 15, 2006

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Damage to power network and long
term power outage area
• Probability of damage to individual substations
• / / : High/medium/low: probability of damage
Aggregated outage area
• Long-term outage area devised by geographically relating the location of substations in the city with
the blast damage zones.
• Loss of a substation has a much more widespread impact on provided power to the customers.
Time
0:00

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Simulation Science Laboratory
Infrastructure: initial laydown
• Positions and demographic identities of individual
synthetic people in the DC region were calculated at
the time of detonation.
• Street addresses mapped to geo-functional data
• Persons traveling to destinations were placed
outside on transportation networks –walk, roadway,
metro, bus.
• Power outage, damage, collapse, rubble, blast temp,
radiation dose rate assigned to each location and
transportation network node
Built Infrastructure
Power Outages
Position of People
Time
0:00

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Building Collapse DistributionTime
0:00

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Damage to transportation networks
• Red: completely damaged
• Orange: highly damage; reduced travel speed
• Green: medium damage
• Blue: light damage
• White: No damage
Walk network
Road
Time
0:00

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No communication – green
Partial Communication Restoration – Blue
First 29 hours
Social-behavioral Event in a Physical Context

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Composite behavior differences w & w/o early restored comms

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Simulation Science Laboratory
Aggregate behavioral details & exposure to injury
• Each individuals' daily or event context- driven activities take them inside and
outside periodically, the details affect their injury level at the time of, as well as
after, the blast.
• Injury traversing rubble
• Delay of access to care, etc
Outdoors Indoors

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Simulation Science Laboratory
Transportation load comparison
Time
+0:00 to +0:10
Blue - Higher load in No Restoration case
Purple - Higher load in Partial Restoration case

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Simulation Science Laboratory
Interdependent, contextual, intentional individual avatar behaviors
induce social level effects w/o scripting

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A drama in machine intelligence: Reuniting a family after the disaster
Cliff
• Father
• +0:00 - At work
• Uninjured
Clair and Denise
• Mother and infant daughter
• +0:00 - Home
• Both uninjured
Theo
• Son
• +0:00 Daycare
• Uninjured

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Initial Panic
Cliff
• +0:00 – Panics, abandon’s
car, heads to nearest hospital
• Exposed to 0.4cGy first 50
minutes
Clair and Denise
• +0:00 – Shelter at home
• Repeatedly calls 911
• Both exposed to 10cGy first
10 minutes
Theo
• +0:10 – Workers bring
children to nearby building for
shelter
• No exposure

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Calls finally go through
Cliff
• +3:00 – Call to Clair successful
• Stops panicking and finds
shelter
• +3:10 – Call to Theo (i.e.,
daycare worker) successful
Clair and Denise
• +3:05 - Evacuate City
• Doesn’t know where Theo is
Theo
• Continues shelter in Daycare

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Family Reconstitution
Cliff
• 44:30 Leaves shelter
Theo
• Remains at daycare

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Evacuation
Cliff
• +45:00 – Arrives at
daycare
• Evacuates city with
Theo

50. Network Dynamics &
Simulation Science Laboratory
Data Intensive Computing Resources
Compute TimeModule Wall
Time
Compute Time
Transportation 13.75 hr 8911 hr 648 cores
Behavior 3.92 hr 397 hr 96 cores
Communication 9.53 hr 9.53 hr
Health 4.3 hr 4.3 hr
Infrastructure 1.4 hr 1.4 hr
Data Initial Dynamic (1 run) Complete Design
(20 cells, 30
replicates)
2M individuals, 2
weeks, full design
Database 3.55 GB 27 GB 25TB 250TB
Disk 1.16 GB 15 GB 20TB 175TB
*Summary over all iterations r1413

51. Network Dynamics &
Simulation Science Laboratory
thanks