Date: 4 Jun 2013 Time: 12:45pm - 2:00pm Venue: Room 101, Runme Shaw Building, The University of Hong Kong Speakers: Professor Liaquat Hossain, University of Sydney ------------------------------------ http://www.cite.hku.hk/news.php?id=502&category=conference

1. Information, Knowledge Management
& Coordination Systems:
Complex Systems Approach
By Professor Liaquat Hossain

2. Globally networked risks
2
• Societies and organisations need
better ways to respond to sudden risk
that may emerge from multiple sources
which are interconnected and
interdependent (Helbing, 2013:Globally networked risks and
how to respond, Nature, 2 May, 51-59)

3. Networks and Information Flow
3
• Observations of interaction networks in life, engineering,
and the physical sciences suggest that the key functional
properties of these networks are:
• the flow of information they can support,
• the robustness of the flow to node failure, and
• the efficiency of the network
• Studies have also shown that certain network designs
perform better than others in each of these respects.

4. Complex Network Science: New Educational and Research Paradigm
4
• The solution to complex issues requires a holistic educational and research
delivery, which would cross the boundaries of social, economical, physical,
agricultural, media and communications, environmental, engineering as well
as medical and mental health systems disciplines.
• My ambition goes beyond simply being transdisciplinary in the sense of e.g.
combining sociology, political science and computational social sciences, but I
actually combine social science and natural science approaches in a more
profound sense to explore information flow in different systems.
• The outcome of my research agenda will provide a fundamental theoretical
and empirical basis for cross fertilization of robust network models across the
physical, life, socio-economic and computational science.

5. Why do we need to use Complex Systems Approach?
5
• Complex systems advocate that real-world systems are
made up from a large number of interacting components.
• this leads to complex behavior, which is difficult to
understand, predict and manage;
• Show emergence (behavior that is more than a sum of the
parts of the system alone) and self-organisation (there is no
external controller).
• It contributes to improvements in areas such as the internet,
innovation and diffusion process, sustainability, air traffic and
transport control, power systems, robotics, disease outbreaks,
irrigation and land management, security, manufacturing and
finance, as well as ecology and biology.

6. But control or mechanistic view advocates
“One of the most basic problems of modern management is that
the mechanical way of thinking is so ingrained in our everyday
conceptions of organization that it is often difficult to organize in
any other way” (Source: Morgan, 1986, p. 14)
From Kazys Varnelis, Triple Canopy
Wrong Way The organisation as a Machine
Max Weber: 1864–1920)
Org model of the Industrial era
Machine Bureaucracy

7. Innovation using Complex Systems and Social
Networks
7

8. Questions guiding my research for the past 10 years
8
• Investigating whether there is a relationship between social networks, maintenance
of the networks through ICT and its impact on performance outcomes for innovation
process, oragnisational effectiveness in stable and adversarial situations;
• Understand the formation and adaptation of hierarchical, non hierarchical, emerging
and self organized structures to explore organizational learning, innovation and
diffusion so that we can begin to characterise the types of adaptation process as
learning through feedback;
• Investigating formation and adaptation of coordinated response network involving
multi-organisational and -jurisdictional structures leading to innovative way to design
multi agency crisis response system;
• Support, equip, and enabling the ad-hoc networks (or open and user based
innovation system) of affected communities and other supporting organizations to
function effectively in crisis situations;
• Role and implications of ad-hoc networks in sharing local knowledge about the
affected areas so that warnings and intervention processes for coordination can be
effective.

9. Organic and Networked organizations are like
9
Parts fit in many ways Organic Networkeduild
Network as organising model

10. Therefore, we can unpack the complexity of organisations
and organising
› A set of actors & links between those actors
› The study of relationships between people
› Focus on measuring the interactions to determine
specific outcomes
› Allows for a prediction or forecast based on
network behaviour
› Insight into how and why information travels
› Insight into relationships and the quality and
necessity of ties
10
0
1
2
3
4
5
6
7
8
1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 20002002 2004
Year
NumberofPapersPublished
Internet
Social Capital
Terrorism
Urban &
Community
Family,Kinship
& Friendship
Organisations
Delinquincy
Diffusion
Social Support
Infection &
Diseases
Health

11. Flow of information that supports different systems
11

12. Network principles applied to social, biological, innovation,
transport, market, computer and other systems
12
Measure Social
Implications
Betweeness Control
Degree Activity
Closeness Independence
EgoEgo

13. Network principles applied to social, biological, innovation,
transport, market, computer and other systems
13
The role of centrality Consequences of Density
Strengths of Ties Networks with different efficiency

14. Predicting Hidden Links (Hossain, et al., 2012)
› Predicted core network of providers extracted from
real data with customers around them
14

15. Predicting Links in Health Systems (Hossain, et al., 2013)
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› Using ICD codes related to obesity from
health insurance data suggest nearly
2500 obese people averaging $5k total
in treatments, peaking at 30-40k/patient
resulting in grand total of $12.7m of full
procedure cost.
› Interestingly majority (75%+) of the
patients are female. Median of the first
treatment was at the age of 42.
› Difference between the age of patient
and the age when they had the first
treatment is about 3-6 years.
› Extended the base data set to family
members under the same policy, we
included over 7400 members, covering
850+ postcodes and 100+ hospitals.
› The graph is about 4% of the data
visualized based on 30 postcodes from
the vicinity of Sydney central. It can be
seen how obese people are connected
to the hospitals, their family members
and location. The link weights are the
dollar values, the more thick and red
they are the larger. Obese nodes are in
red, the others are blue.

16. Generic Networks models developed and applied
in different settings
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Emergence
Changing external environment
Complex Adaptive Behavior
Information IN Information OUT
Simple self organized local relationships
Information IN Information OUT
Positive feedback Negative feedback
Intervention
Adaptati
on,
Outcome
t2
..
.
Context
t2
Context
t1
Network
t1
Network
t2
Network
t2
Network
t3
Learning t2
Adaptati
on,Learning t1
Interventi
on
Evaluation of
actors’ fitness
Network
Structure
Network
Structure
Tie Formation
Selection
Variation (Adaptation)
Retentio
n
Node
Structure (t1)
Network
Topology
(t1)
Dynamics of Networks
DynamicsonNetworks
Attachment rules
Node
Structure (t2)
Network
Topology
(t2)
Attachment rules
Node
Structure (tn)
Network
Topology
(tn)
Attachment rules

17. Examples of Application Domains that I have been
working for past 10 years
17

18. Innovative Design of Monitoring behaviour (Natrajan and Hossain, 2004)
18

19. Networks & Coordination (Hossain, 2008; Hossain & Wu, 2009)
› 712 employees extracted who sent emails within the Dahbol Project scope
› Using coordination sentence and phrase keyword extraction, 173 employees demonstrated coordination
› Coordination scores minimum = 3, maximum = 244, average = 44
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20. Networks & Coordination in Crisis: Innovation & Learning
(Hossain,& Kuti, 2010)
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Micro-Level
Actor Network of
combining agencies
Macro-Level
Organizational network
combining all agencies

21. Organisational clique analysis and macro-level cross-
agency clusters (Hossain,& Kuti, 2010)
21

22. Learning from Emergency Response Network
- 173 people died
- 414 people were injured
- 7,562 people displaced
- Over 3,500 structures destroyed
- 450,000 ha (1,100,000 acres) burnt
Black Saturday bushfires in Australia
22
Emerging Networks
not only different organizations
(agencies) need to cooperate properly
internally (intra-team & inter-team)
but also they have to cooperate with
other organizations (inter-
organizational)
 We wanted to understand what the
breakdowns are (from a network analysis
perspective, there is a need to
 Evaluate which types of node failures
have high level of impact on
coordination performance
 which will lead to develop a better
predicting model for understanding the
rate of node failure and attack.
IMT (Incident
Management
Teams)
Multi-
Emergency
Agencies
Count
ry Fire
Servic
es
Metropolit
an Fire
Brigades
Land
Manage
ment
Agencie
s
State
Emergenc
y
Agencies
Incide
nt
Contr
oller
Opera
tion
Logist
ics
Planni
ng
Groun
d
Perso
nnel
Air
Opera
tion
Poli
ce
First
Aid
Loca
l

23. Rural Fire Coordination Network (Abbasi & Hossain, 2013)
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Murphy: IC1
Kreltszheim: IC2
Creek: RDO (RECC)
Arandt: DIC1
Court: Tanker1 Crew
Dixon: DGO
Grant: DDO (DSE Manager)

24. Kilmore Coordination Network Evolution (Abbasi & Hossain, 2013)
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25. Dynamics of disease outbreaks coordination (Bedir,
Hossain & Crawford, 2011; 2012)
› The Absence of unified approach results in different
management and coordination approaches leading
to high variability of infection rates; hence mortality
and morbidity rates.
› H109 infection in NSW) indicates that even within
the same state there were large discrepancies within
the same states with sometimes similar
demographics (by June 17- 2009)
› H1N109 infection rates in Australia
by June 17- 2009
25

26. Dynamics of disease outbreaks coordination (Bedir,
Hossain & Crawford, 2011; 2012)
› Therefore, we need to coordinate
between multiple agencies
dynamically in order to intervene
and contain dynamic form of
disease outbreaks in an evolving
environment
Modelling challenges of disease outbreak coordination
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27. Modelling challenges of disease outbreak coordination
› Informal coordination is an important
facet of emerging coordination which
is often ignored in coordination
research
› It capitalizes on the existing
coordination channels to circumvent
their complications, inefficiencies or
even their inaccuracies.
› It can be defined as “ when
individuals or organisations establish
communication networks outside the
standard coordination structure to
“get things done” (Baker 1981; Han
1983)”
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28. Protocol for capturing qualitative & quantitative network data
(Hossain, Bedir & Crawford, 2013)
28

29. Results of Inter-organizational disease outbreaks
coordination (Hossain, Bedir & Crawford, 2013)
› Organizations
involved and their
characteristics
› Organizational
links
› Links’ initiation
› Links’ intensity
› Links’ direction
› Links’ timeline
› Links’ purpose
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30. Inter-organizational disease outbreaks coordination
› Inbound case definition Communication › Cases inbound communication
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WHO
FederalChiefHealthOfficer
CDU:
NSW ChiefHealthOfficer/NSW-HSFAC
HNE
HNE-HSFAC
Inbound Monitoring
HSFAC
EOC
PHEOC
Sentinel
indicator
GPs
PHREDDS
Inpatient
flow system
Admits to
ICU
Work force
monitoring
Confirmed
cases via
SWABS
PHREDDS: Public Health
Respiratory Emergency
Department Data System.
SWABS: Sample taking system.
LAG LAG LAG LEAD LEAD
Front line

31. Inter-organizational disease outbreaks coordination
› Outbound Informal communication
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State Public Health
Unit
Case definition outbound
communication structure
HSFAC DCO
DCO: Director of clinical Operations.
DA: Director of Acute.
ED: Emergency Department
Org1 dotted to indicate that it operated at later stage
during the communication process.
Director
Acute
Director
D+C
Director
Mental
Health
7 Hospital
in HNE
Total 37 ED
EDs
Hospital Clusters
EDs
Mental
Hospital
Org1
Outbound Case communication

32. Current research projects
1. RIMS: Robust Information Management Systems
for Coordinated Response to Crisis;
2. BISoN: A Biologically-Inspired Social Network for
Coordinated and Adaptive Emergency Response;
3. Computational Behavioural Modelling of Markets
Systems;
4. CIMS: Innovation and Learning in Coordinated
Interventions for Mental Health Systems;
5. H1N1 and SARS Outbreaks: multi-organisational
coordinated surveillance and response;
6. CrisNet for Zoonotic and Foodbrone Outbreaks:
Socio-technical Crisis Information Networks for
Disease Outbreaks Coordination;
7. Behavioral Network Dynamics for understanding
Nutrition, Epidemiology and Immunity;
8. Social networks and health promotion:
Harnessing social networks to enhance the
effectiveness of peer counselling
› $1 million funding from Australian Capital Markets
CRC-Commonwealth Research Centre and HCF-
Hospital Contribution Fund to develop predictive
models for understanding future market systems
under crisis.
› $6.5M in competitively basic research funding (EU
FP 7 Framework, ARC Discovery, CRCs and
ARDA Advanced Research Development Agency
in the US).
› Submitted 2 major collaborative research grants
under EU FP7 framework.
- COST-action: Communication and Information Systems
Technology in European Emergency Management
- H.E.L.P Health Emergency Learning and Planning
› I am the founding Editor-in-Chief of Springer
International Journal “Crisis Communications”
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33. Possible Links with Education and Research
› Management
- Engineering Knowledge Management
Research
- Design, Engineering and Innovation
- Industrial Dynamics and Strategy
- Sustainability Research
- Climate Change and Sustainable
Development
- Climate resilient development
Business
- Innovation Management
› Environment: Sustainable use of (natural, physical and cyber)
infrastructure/resources
› Food: Innovation and leaning in sustainable food production;
coordination of foodbrone outbreaks
› Informatics: Bio-security, Cognitive Systems; SW Engineering
› Veterinary: coordination of zoonotic outbreaks
› Systems Biology: Biological optimisation model for social
networks; systems biology for exploring organisational and
community resilience networks
› Transport: complex modelling of transport networks
• In my research, I use methods and analytical techniques from mathematical sociology (i.e., social networks
analysis), social anthropology (i.e., interview and field studies) and computer science (i.e., information
visualization, graph theoretic approaches and data mining techniques such as clustering);
• Using this transdisciplinary approach, I explore innovation, knowledge management and coordination
systems in distributed and complex setting for understanding distributed work groups, organizational and
individual performance and knowledge sharing and management support process for innovation and learning
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