The document discusses the complexity of massively interacting systems, focusing on how human behavior and social interactions create interconnected networks that are influenced by various factors, including geography and individual agency. It highlights the significance of big data in understanding these interactions and notes the challenges of decision-making in such intricate environments. Additionally, it emphasizes the need for new analytical frameworks that integrate human expertise and cognitive systems to navigate and interpret the vast amounts of data generated by these systems.

1. Massively Interacting Systems:
Thinking & deciding in the age of Big Data
Chris Barrett
Scientific Director
Virginia Bioinformatics Institute

2. Part 1

3. What is interaction? What’s the issue?
• A finite undirected graph Y
• A sequence of local maps
• An ordering of the vertex set of Y
[FY,p] = P Fp(i)

4. Hypergraph

5. “Genuine” social entities & interactions
“ .. [usual causal] hierarchy collapses when
causality crosses across units and
levels….human behavior in social setting is
interdependent …. although … not a new
insight, social life is interdependent in …
spatial forms – things “go together” in and
across distinct places …. which might be better
described as neighborhood causal
processes…”
Robert J. Sampson, The Great American City, 2012

6. What interacts in an evolving city?
• People are entities that have
purposes, needs, capacities and interact
• Neighborhoods are entities that have
purposes, needs, capacities and interact, “have their
own logic and causality.”
• Causes, causal interactions, occur across “normal”
causal boundaries
– People interact with people and neighborhoods
– Neighborhoods interact with neighborhoods and people
– E.g., self selection bias, extra neighborhood proximity
processes etc are within and among network processes
that do not supervene one another.

7. This is not entirely unique to
neighborhood selection
• Traffic and transportation
• Motives/goals, activities, transport resource, transport
infrastructure, resource competition, form and function of
infrastructure, traffic, communicated dynamics, time
delays, goal failure/success etc…. loop and evolve
• Genetic predisposition, homophily, family and
peer mimicry, other social functionalities affect
• Success, variously
• Suicide
• Smoking
• Obesity
• Healthful behaviors ……., etc.

8. In fact, it is seen in biology
• Suzuki, et al, 2003
• The pigmentation control gene Fox1 is defective in a
mutant mouse and shuts down the normal process by
which pigmentation patterns are stabilized in the skin/hair
of the mouse.
• It creates moving waves of color striping
• This gene normally can produce all solid, spot and striped
patterns by simply activating at a particular times in
embryonic development; the morphology of the embryo at
that time determines the pattern created by the gene
• In the mutant case, the continued malfunction, given the
most recent color morphology, generates a new pattern,
and so on.

9. Traveling Stripes- Suzuki
These dynamics are the same kind
as in Belousov-Zhabotinskii reaction
of nonlinear waves in excitable
physical media

10. Are they are the same causal class?
• The Chemical Basis of Morphogenesis, Turing
1951
• Usually, diffusion processes (local
communication) stabilizes in a mixed system, but
under “exicitable” media conditions, structure
appears and evolves
• It is seen in physical chemistry and excitable
media
• It is, essentially, a rewrite computational THEORY
of interaction that is just now being really
discovered

11. Beyond traditional genomics
morphology
morphology
gene
Maybe

12. Beyond traditional social modeling
Social
context
Social
context
individual
behavior
Definitely

13. And even in physical systems
• Chemical morphogenesis, B-Z dynamics

14. The “inside-outside” problem is a
related issue of non-supervenance
• What is an organ?
– Biome
• What is in an organism, what is outside of it?
• What/ where is a thought?
– Extended mind
– Distributed algorithmic accounts of causes
• What is an agent?
– Is agency necessarily encapsulated?
– Driver behavior
• What is an urban agent?

15. Part 2

16. Where does Big Data come from?
Metric, declarative, procedural sources & integration

17. What is Big?
• The world's technological per-capita capacity
to store information has roughly doubled
every 40 months since the 1980s; as of
2012, every day 2.5 quintillion (2.5×1018)
bytes of data were created [stored].
Wikipedia, “Big Data”, August 2012

18. Unstructured data growing

19. Storage shortfall: need synthesis

20. Virtualized storage is exponential; not
enough.

21. Unit price down, total investment up

22. In genomic sequencing, bp/$ is down
exponentially

23. Moore’s Law is not enough

24. New post-genomic reality: The cost drivers
have shifted to analytical computing

25. Data creation, deletion & storage
• We will know what - of all that data - it is
possible to “forget” only when we know how
to summarize what is possible
• That’s a big analytical problem as we will see
• Use of graph theory and graphical dynamical
systems (networks) is essential
– Computationally very intensive

26. Part 3

27. Who cares?

28. Massively Interacting Systems
• These things produce branching processes
• Sometimes they are periodic, sometimes they are not
• They do not explore the entire possible state space (all
morphologies are not expressed)
• So even with the immense amount of underlying data
necessary, decision analysis must produce infinitely more
• This complicates measurement as well as theory making in
sense of acceptable explanations of observations
• It makes observed, metric, data; declarative data and
procedural information all essential
• It is the effects of processes of composition of complex
interactions that ultimately generates so much data, both
measured and synthesized.

29. Q: How can we support human
analytical capabilities in this situation?

30. The end of the great man theories
of….. decision making
• Many stakeholder synthetic information
• The analysis environment is not separated from “the
world”
• An entirely new interaction medium will create NEW
REALITIES
• The approach must involve human expertise and
context, it must be a cognitive augmentation system
• It must involve distributed, social cognition
• It must follow context & allow information deletion
• It will change scientific process and assumptions

31. People are interconnected properties
Age 26 26 7
Income $27k $16k $0
Status worker worker student
Automobile

32. Extra-household connectivities also influence/
reflect motives, activities and behavior
Office Links Jill Shawn
Friendship
John Links
Joe Mar
y
Ron Family Jane Tim
Links

33. Interdependent motives & activity structures of
individuals underlie observable behavior

34. Unencapsulated Agency:
The Inside /outside problem

35. Built, functional, locational structure defines where
activities occur and influences movement/comms
• Synthetic activity locations, such as homes,
are placed with probability proportional to
location geo-functional weights:
(type: home location – # people, cost, etc.)
California
Illinois

36. Bipartite map of people with activities onto
appropriate locations with functional capacities
Motivated People Activity- appropriate Locations
Vertex attributes: Vertex attributes:
 age  Coordinates
 household size  Type
 gender
 income
Edge attributes:
 activity type: shop, work, school
 (start time 1, end time 1)
 (start time 2, end time 2)

37. Many social contact networks:
this one is physical proximity X duration

38. Example: large scale socio-physical interaction
• Attack in Washington DC
– NPS1, a 2006-based unclassified study scenario with
lots of people publishing and even putting lectures on
YouTube
• Basically we wanted to know if there really might
be significant social behavior options in the
immediate aftermath that could be imagined &
that might have long term influence
• Disaggregate, detailed socially-coupled
simulation used combined with physical
modeling

39. Technical Perspective: Socially-coupled
systems
• Massively interacting systems generating arbitrarily much data
• Want general, re-usable, approach. Many examples:
transport, facebook, biosystems, economic systems
• Generally, the topic of HPC based data-centric methods, network
science/ network dynamics are central
• Socially-coupled systems display a lack of symmetries => problems
for usual dimensional reduction approaches
• Systems are huge, details matter
• Detailed disaggregate modeling, appropriate abstractions, novel
HPC simulation methods & statistical approaches are necessary
• Necessary source information is diverse, including process
knowledge
• Totally different view of decision analysis necessary

40. A: Synthetic decision informatics for
large, complex socially coupled systems

41. Contextual Synthetic Information
• The information platform is the interaction
medium
• The only way to really deal with the massively
interactive, branching—thus extreme data—
world.

42. Part 3.1

43. Physical Event 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

44. Time Damage to power network and long
0:00
term power outage area
• Probability of damage to individual substations
Aggregated outage area
• / / : High/medium/low: probability of damage
• 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.

45. Time
0:00 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

46. Time
0:00 Building Collapse Distribution

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

48. Part 3.2

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

50. Time
+0:00 to +0:10
Transportation load comparison
Blue - Higher load in No Restoration case
Purple - Higher load in Partial Restoration case

51. Composite behavior differences w & w/o early restored comms

52. CIIMS Avatars automatically create realistic individual behaviors
through large scale interaction, local machine intelligence
New timeline feature:
Scenario displays details
connected to timeline
New use of
timeline:
detailed analysis
of
interdependent
individual
behaviors

53. Interdependent, contextual, intentional individual avatar behaviors
induce social level effects w/o scripting

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

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

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

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

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

59. 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

60. Socio-technical influences on individual behavior
• If communication is provided earlier and contact made, less panic
unstructured behavior, more sheltering, less searching, etc.
• There are hundreds of thousands of these avatars and many
different specific motivations, or perhaps, different complex
contextual embodiments of similar generic motivations
• The composite effect on many things, including exposure to injury
cannot be always be calculated in aggregate in particular scenarios
from data obtained elsewhere.
• Supporting problem evolution and the extreme importance of
sparse sequential analysis is a major conclusion of this study.
• The 1st 72 hrs is not the same problem as what follows. Saturated
performance from initial behavioral models as situation evolves.
• These methods do more than better answer a given question:

61. Section 3.3

62. Data Intensive Computing Resources
Module Wall
Compute Time Compute Time
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
*Summary over all iterations r1413
Data Initial Dynamic (1 run) Complete Design (20 2M individuals, 2
cells, 30 replicates) weeks, full design
Database 3.55 GB 27 GB 25TB 250TB
Disk 1.16 GB 15 GB 20TB 175TB

63. Thanks