Transcript Sample CASOS Talk

Dynamic Network Analysis
for Organizational Management
Kathleen M. Carley
Carnegie Mellon University
[email protected]
Or – Harnessing
Complexity to Manage Teams,
and Organizations
412 268 6016
Center for Computational Analysis of Social and Organizational Systems
http://www.casos.cs.cmu.edu/
Organizational Adaptation and Agility
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Redundancy in access to personnel, resources, and
expertise
Congruence so that work is performed effectively
Cognitive Demand - Personnel fill multiple roles, and in
particular there are change agents with high cognitive
demand who coordinate others
Transactive memory
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Detailed and “lossy” shared
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so have high level of shared situation awareness
Organized as small world
DNA tools to measure and assess all of these
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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The Science of Dynamic Network Analysis
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The theory and design of dynamic networks among
diverse entities and the study of all phenomena
emerging from, enabled by, or constrained by such
networks.
Entities include both intelligent agents such as humans
or robots and artifacts such as events, resources and
locations.
Theory, models, metrics, evaluation
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Informal and Formal Structure
Multiple networks connect people
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Network to Meta-Networks
WHAT:
Tasks
Mission
HOW:
Expertise
Resources
Databases
WHO:
Personnel
Teams
It’s more
Suppliers
July 18, 2015
WHERE:
Offices
than just people
WHY:
Beliefs
Goals
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Illustrative Networks
High School Dating
Physicist Collaborations
Contagion of TB
Fresh Water Food Web
Sexual Contacts
The Internet
Email Profile
al Qaida 2004
Topic Network (Email)
The nodes and edges have attributes
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Yours, Mine and Ours
Joe: I think Sandy knows
design and is working on
the candy Y add. I wonder
if Bill could work on Soda
Y. They could do it
together.
Sandy: I know design but Joe
knows marketing. I need to
know what products are
bought with soda X so I can
finish the add. Joe should
know who knows this. I’m not
asking Bill – he’s a moron.
Perceptions of the network differ
… sometimes due to “mission”
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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NYC response network
And, the networks evolve or can be changed
9-11-2001
9-19-2001
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Network Based Approaches to
Organizational Modeling
Organizations can be represented as a meta-network linking actors,
knowledge, resources, tasks
• Using the meta-matrix we can assess …
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Vulnerabilities
Shared situation awareness
Transactive memory
Congruence
Meta-Network
Redundancy
Performance
Adaptability
Performance
Actors
Know
/Reso
Actors
Knowledge
/Resources
Tasks
Social
Network
Knowledge
Network
Attendance
Variable
Network
Information
Network
Needs
Network
Tasks
Precedence Fixed
Evolution occurs at different rates
Using Dynamic Network Analysis tools can be developed to assess
and simulate changes in organizations
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Where does the data come from?
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Minimally invasive data
capturing
Archival data and HR records
Online email or chat
Time cards and billing
information
Modification request (MR)
system
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Users, testers, developers
request changes
Bug fixes, new functionality
Version control system
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Actors
Knowledge
/Resources
Tasks
Actors
Know
/Reso
Tasks
Maintains all changes to all
files
Some set of changes
correspond to each MR
Has data about who made
change when
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Build
Network Text Mining
Texts
Assess
Change,
What if
Analysis –
Multi-agent
DNA
Analyze –
Statistics
SNA, DNA,
Link
Analysis
DyNet
Meta-Network
Extended Meta-Matrix Ontology
DyNetML
Table 1:
Name of
Individual
Abdul
Rahman
Yasin
Abu Abbas
Meta-matrix Entity
Agent
Knowledge
chemicals
Hussein
Hisham Al
Hussein
Resource
Task-Event O rganizati
on
chemicals
bomb,
Al Qaeda
World
Trade
Center
Dy ing,
Green
Berets
Achille
Lauro cruise
ship
hijackin
mastermindi
ng
school
p hone,
bomb
Abu Madja
p hone
Hamsiraji
Ali
p hone
Abdurajak
Janjalani
Hamsiraji
Ali
Muwafak
al-Ani
Jamal
M ohammad
Khalifa,
Osama
bin
Laden
Saddam
Hussein
Abu
Say y af,
Qaeda
Abu
Say y af,
Qaeda
Location
Role
Attribute
op erative
26-Feb-93
terrorist
p alestinian
1985
Iraq
Baghdad
M anila,
Zamboanga
second
secretary
Philip p ine
leader
Philip p ine
leader
Basilan
commander
2000
February
13,
2003,
October
3,
2002
Al
Al
1980s
brother-inlaw
$20,000
business
card
bomb
Abu
Say y af,
Iraqis
Philip p ines, terrorists,
M anila
dip lomat
Iraqi
1991
Unified Database(s)
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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From texts to networks:
process and methodology
Analyst: Coding Settings
That mobile phone also registered calls to Abu Madja and Hamsiraji Ali,
leaders of Abu Sayyaf, Al Qaeda's Philippine branch.
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Three Modes
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Content analysis
Semantic network
Meta-network
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ORA: From networks to patterns
ORA: a DNA statistical analysis tool for locating patterns and identifying vulnerabilities
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July 18, 2015
Organized by function not
measure
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E.g., Intelligence Report
Management Report
Import/Export tools
Linkage to mysql
Visualization components
Batch, web, thick-client
Can handle large 106
networks quickly
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Criticality - Nodes that Stand Out
Degree Centrality High
Betweenness and
in the know
not Degree
Cognitive
Demand
Task exclusivity
critical ability
emergent leader
connects groups
Eigenvector
central core
Betweenness
many paths
Resource
exclusivity
Knowledge
exclusivity
Mobilize
resources
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
Mobilize info
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Structure around core actors matters
Iris Mack
Similar for 2 others
Other 5 are in a
concentrated core
Structural, Pass
information
Complex knot, gets
things done
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Congruence & Team Design
Needs
Educational program to prevent disease spread in children
Infectious disease
Current Capabilities
child health
child health
Abe
July 18, 2015
publication
Educational program to prevent disease spread in children
Infectious disease
Tanya
education
Marta
education
publication
Jason
Vicki
Cadin
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Congruence for Top and Other Contributors
MR
Structure
IRC
Geography
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Congruence (avg.)
Congruence (avg.)
IRC
Release 1
Release 2
Release 3
Release 4
MR
Structure
Geography
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Release 1
Release 2
Release 3
Release 4
All are geographically congruent – location and work match
Structural congruence not needed for top – reporting and work match
Modification requests – assignment and expertise match
IRC – assignment and expertise match
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Management Report
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ORA:
Management
Report
Key individuals, resources, knowledge and tasks
Points of concern
General structure
Using Metrics Based on a Social and Managerial
Understanding of Complexity
to Assess Potential Strengths and Weaknesses
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Intelligence Report: Who’s Key ?
ORA:
Intelligence
Report
Metric
Node
Graphic
Connects
Critical role
groups
Hyram & Lynn Advertising
In the Know
Power
Ed
Ed
Ed
Critical
expertise
Betty - Gas
Critical access
Central core
E
mergent leader
Most Computationally
Complex
Metrics
NA
Ed
Abe
Theoretically
Interesting
Cognitively and Socially Not Sensible
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Challenge: Global Versus Local Constraints
Global – the whole network matters
Local – I know at most 3 degrees out
1) measures computationally less complex
2) derived groups more meaningful
3) needle in the haystack
4) ripple effects
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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ORA:
Sphere of
Influence
How to Influence
Node Type
Size
Percent
agent
11
+91%
knowledge
6
+50%
task
2
+40%
Most similar other –
Gwen
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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ORA:
Located
Groups
Locate Groups
Diego
Lynn
Betty
Abe
Carmen
Gwen
Frank
Ed
Kyle
Izzy
Hyram
Jenn
Group Members and Expertise
Abe Betty Carmen and Diego
Electricity, Gas, Land, Water, Mall, Indust. Park
Computational
Complexity
in Generating Fuzzy Groups
Ed Frank
Gwen and Lynn
Accounting,
Film, Mgmt,
Stocks, Advertising
Trails VS Network
Representation
Hyram Izzy Jenn and Kyle
Development, Land fill, Real estate, Sports
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Enron – Critical Actors are Interstitial
Week Skilling is named as CEO
Jeff
Skilling
Kenneth
Lay
Tanya
Jones
Veronica
Espinoza
Jeff
Dasovich
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Immediate Impact - Forecast
• What if ? Remove top 5 emergent leaders
• Change in performance
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ORA:
Immediate
Impact
Anticipated drop – -25%
Change in information diffusion
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Anticipated drop – -46%
New emergent leaders
1. 0.40 Abe
2. 0.31 Carmen
3. 0.31 Diego
Challenge: Linking to real timing of information flow
Challenge: Complexity inspired performance indicators
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Is Your Organization a Set of Silos?
Information flow in an extended
leadership team.
Each program area is a different
color.
Large nodes represent program
managers and smaller ones their
direct reports.
July 18, 2015
Same network without program
managers. Despite good intentions
and engaged leadership, the
equivalent of functional silos had
emerged underneath each of the
managers.
This was a problem because people
lower in the hierarchy needed to
connect across programs to provide
services for customers.
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Forecasting Using Multi-Agent Simulation
DyNet – What-If Analysis with the Near Term Impact Report
DyNet
Simulation of
dynamic networks
Visualizer
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Change in Reformism – Near Term Prediction
People
People
Relation
Social
Network
Knowledge
Knowledge
Network
Who knows who Who knows what
Knowledge
Relation
Information
Network
What informs what
Tasks
Relation
Tasks
Assignment
Network
Who does what
Needs Network
What knowledge is
needed to do that task
50
Precedence
Network
45
Which tasks must be
done before which
40
35
Baseline
30
25
Without Top
Conservative
20
Without Top Liberal
15
10
5
Communicate
Choose
Interaction
Partner
Learn
Change
Beliefs
0
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86
Decisions
Reposition
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Impact of isolation
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
ORA:
Near Term
Analysis
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Adaptivity and Performance
Amount of Individual Experience
1500
Successful adaptation
requires either:
Moderate levels of
training and little
structural change
1230
960
Performance
690
Or
Lots of change and
variable amount of
training
Improvement
185%
138%
92%
46%
< 46%
420
150
0
161
322
483
644
805
Amount of Change in Structure
Carley & Svoboda
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Summary
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Meta-Networks powerful representation of organizational
design
Tracking changes in meta-network enables
understanding and managing adaptation: forecasting,
and pre-assessment of changes
Dynamic re-assignment, re-training etc. enables creation
of agile and adaptive organization
In the human loop solution
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80% machine assessment
Increased allowance for human creativity and interpretation
Design for adaptability
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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CASOS Tools
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July 18, 2015
ORA – statistical toolkit for meta-matrix, identifies
vulnerabilities, key actors (including emergent
leaders), and network characteristics of groups,
teams, organizations, C2 – used with army battle
labs, risk estimation NASA
DyNetML – XML based interchange language for
relational data
AutoMap – Semi-automated text analysis
Social Insight – network visualization
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Agent-based Modeling for Dynamic Networks CASOS Complex Adaptive DNA Models
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Construct – MAS-DNA model for examining group
change under diverse cultural, social and
technological contexts
NetWatch – impact of data integration, sharing and
control on ability to detect evolving network
BioWar – city scale multi-agent network model of
response to weaponized biological and chemical
attacks
OrgAhead – multi-agent network model of evolving
organizational forms
DyNet – MAS-DNA model for examining change in
networked systems under uncertainty
MTE – MAS-DNA model for predicting action and
response in urban and state level settings
July 18, 2015
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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Yours, Mine and Ours
Sandy: Joe is dating
Monique. Their in
love. But I don’t see
what she see’s in
him.
July 18, 2015
Monique: I’m
dropping Joe.
Sandy seems his
type. Maybe Allie
can set me up with
Mike.
Allie: Too bad
Monique and Joe
are an item, I think
my friend Mike
would like her.
Copyright © 2007 Kathleen M. Carley, CASOS, ISR, SCS
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