Information, Knowledge Management & Coordination Systems:

Complex Systems Approach

By Professor Liaquat Hossain

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Globally networked risks

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• 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)

Networks and Information Flow

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• 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.

Complex Network Science: New Educational and Research Paradigm

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• 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.

Why do we need to use Complex Systems Approach?

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• 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.

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

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Innovation using Complex Systems and Social Networks

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Questions guiding my research for the past 10 years

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• 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.

Organic and Networked organizations are like

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Parts fit in many ways Organic Networkeduild

Network as organising model

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

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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

Num

ber o

f Pap

ers Pu

blishe

d

Internet

Social Capital

Terrorism

Urban &CommunityFamily, Kinship& FriendshipOrganisations

Delinquincy

Diffusion

Social Support

Infection &DiseasesHealth

Flow of information that supports different systems

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Network principles applied to social, biological, innovation, transport, market, computer and other systems

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Measure Social Implications

Betweeness Control

Degree Activity

Closeness Independence

EgoEgo

Network principles applied to social, biological, innovation, transport, market, computer and other systems

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The role of centrality Consequences of Density

Strengths of Ties Networks with different efficiency

Predicting Hidden Links (Hossain, et al., 2012)

› Predicted core network of providers extracted from real data with customers around them

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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.

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

Adaptation,

Outcome t2

..

.

Context t2

Context t1

Network t1

Network t2

Network t2

Network t3

Learning t2

Adaptation,Learning t1

Intervention

Evaluation of actors’ fitness

Network

Structure

Network

StructureTie

Formation

Selection

Variation (Adaptation)

Retention

Node Structure (t1)

Network Topology

(t1)

Dynamics of Networks

Dyn

amic

s on

Net

wor

ks

Attachment rules

Node Structure (t2)

Network Topology

(t2)

Attachment rules

Node Structure (tn)

Network Topology

(tn)

Attachment rules

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

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

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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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Networks & Coordination in Crisis: Innovation & Learning(Hossain,& Kuti, 2010)

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Micro-LevelActor Network of combining agencies

Macro-LevelOrganizational network combining all agencies

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

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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

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Emerging Networksnot 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

Country Fire Servic

es

Metropolit

an Fire Brigades

Land Manage

ment Agencie

s

State Emergency Agencies Incide

nt Controller

Operation

Logistics

Planning

Ground

Personnel

Air Operation

Police

First Aid

Local

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)

Kilmore Coordination Network Evolution (Abbasi & Hossain, 2013)

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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

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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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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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Protocol for capturing qualitative & quantitative network data (Hossain, Bedir & Crawford, 2013)

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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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Inter-organizational disease outbreaks coordination

› Inbound case definition Communication › Cases inbound communication

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WHO

Federal Chief Health Officer

CDU:

NSW Chief Health Officer/ NSW - HSFAC

HNE

HNE - HSFAC

Inbound Monitoring

HSFACEOC

PHEOC

Sentinel indicator

GPsPHREDDS Inpatient

flow systemAdmits to

ICUWork 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

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

DirectorD+C

DirectorMental Health

7 Hospital in HNE

Total 37 ED

EDs

Hospital Clusters

EDs

Mental Hospital

Org1

Outbound Case communication

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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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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