Information, Knowledge Management & Coordination Systems: Complex Systems Approach


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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

Published in: Education, Business, Technology
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Information, Knowledge Management & Coordination Systems: Complex Systems Approach

  1. 1. Information, Knowledge Management& Coordination Systems:Complex Systems ApproachBy Professor Liaquat Hossain
  2. 2. Globally networked risks2• Societies and organisations needbetter ways to respond to sudden riskthat may emerge from multiple sourceswhich are interconnected andinterdependent (Helbing, 2013:Globally networked risks andhow to respond, Nature, 2 May, 51-59)
  3. 3. Networks and Information Flow3• Observations of interaction networks in life, engineering,and the physical sciences suggest that the key functionalproperties 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 designsperform better than others in each of these respects.
  4. 4. Complex Network Science: New Educational and Research Paradigm4• The solution to complex issues requires a holistic educational and researchdelivery, which would cross the boundaries of social, economical, physical,agricultural, media and communications, environmental, engineering as wellas 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 Iactually combine social science and natural science approaches in a moreprofound sense to explore information flow in different systems.• The outcome of my research agenda will provide a fundamental theoreticaland empirical basis for cross fertilization of robust network models across thephysical, life, socio-economic and computational science.
  5. 5. Why do we need to use Complex Systems Approach?5• Complex systems advocate that real-world systems aremade up from a large number of interacting components.• this leads to complex behavior, which is difficult tounderstand, predict and manage;• Show emergence (behavior that is more than a sum of theparts of the system alone) and self-organisation (there is noexternal controller).• It contributes to improvements in areas such as the internet,innovation and diffusion process, sustainability, air traffic andtransport control, power systems, robotics, disease outbreaks,irrigation and land management, security, manufacturing andfinance, as well as ecology and biology.
  6. 6. But control or mechanistic view advocates“One of the most basic problems of modern management is thatthe mechanical way of thinking is so ingrained in our everydayconceptions of organization that it is often difficult to organize inany other way” (Source: Morgan, 1986, p. 14)From Kazys Varnelis, Triple CanopyWrong Way The organisation as a MachineMax Weber: 1864–1920)Org model of the Industrial eraMachine Bureaucracy
  7. 7. Innovation using Complex Systems and SocialNetworks7
  8. 8. Questions guiding my research for the past 10 years8• Investigating whether there is a relationship between social networks, maintenanceof the networks through ICT and its impact on performance outcomes for innovationprocess, oragnisational effectiveness in stable and adversarial situations;• Understand the formation and adaptation of hierarchical, non hierarchical, emergingand self organized structures to explore organizational learning, innovation anddiffusion so that we can begin to characterise the types of adaptation process aslearning through feedback;• Investigating formation and adaptation of coordinated response network involvingmulti-organisational and -jurisdictional structures leading to innovative way to designmulti agency crisis response system;• Support, equip, and enabling the ad-hoc networks (or open and user basedinnovation system) of affected communities and other supporting organizations tofunction effectively in crisis situations;• Role and implications of ad-hoc networks in sharing local knowledge about theaffected areas so that warnings and intervention processes for coordination can beeffective.
  9. 9. Organic and Networked organizations are like9Parts fit in many ways Organic NetworkeduildNetwork as organising model
  10. 10. Therefore, we can unpack the complexity of organisationsand organising› A set of actors & links between those actors› The study of relationships between people› Focus on measuring the interactions to determinespecific outcomes› Allows for a prediction or forecast based onnetwork behaviour› Insight into how and why information travels› Insight into relationships and the quality andnecessity of ties100123456781954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 20002002 2004YearNumberofPapersPublishedInternetSocial CapitalTerrorismUrban &CommunityFamily,Kinship& FriendshipOrganisationsDelinquincyDiffusionSocial SupportInfection &DiseasesHealth
  11. 11. Flow of information that supports different systems11
  12. 12. Network principles applied to social, biological, innovation,transport, market, computer and other systems12Measure SocialImplicationsBetweeness ControlDegree ActivityCloseness IndependenceEgoEgo
  13. 13. Network principles applied to social, biological, innovation,transport, market, computer and other systems13The role of centrality Consequences of DensityStrengths of Ties Networks with different efficiency
  14. 14. Predicting Hidden Links (Hossain, et al., 2012)› Predicted core network of providers extracted fromreal data with customers around them14
  15. 15. Predicting Links in Health Systems (Hossain, et al., 2013)15› Using ICD codes related to obesity fromhealth insurance data suggest nearly2500 obese people averaging $5k totalin treatments, peaking at 30-40k/patientresulting in grand total of $12.7m of fullprocedure cost.› Interestingly majority (75%+) of thepatients are female. Median of the firsttreatment was at the age of 42.› Difference between the age of patientand the age when they had the firsttreatment is about 3-6 years.› Extended the base data set to familymembers under the same policy, weincluded over 7400 members, covering850+ postcodes and 100+ hospitals.› The graph is about 4% of the datavisualized based on 30 postcodes fromthe vicinity of Sydney central. It can beseen how obese people are connectedto the hospitals, their family membersand location. The link weights are thedollar values, the more thick and redthey are the larger. Obese nodes are inred, the others are blue.
  16. 16. Generic Networks models developed and appliedin different settings16EmergenceChanging external environmentComplex Adaptive BehaviorInformation IN Information OUTSimple self organized local relationshipsInformation IN Information OUTPositive feedback Negative feedbackInterventionAdaptation,Outcomet2...Contextt2Contextt1Networkt1Networkt2Networkt2Networkt3Learning t2Adaptation,Learning t1InterventionEvaluation ofactors’ fitnessNetworkStructureNetworkStructureTie FormationSelectionVariation (Adaptation)RetentionNodeStructure (t1)NetworkTopology(t1)Dynamics of NetworksDynamicsonNetworksAttachment rulesNodeStructure (t2)NetworkTopology(t2)Attachment rulesNodeStructure (tn)NetworkTopology(tn)Attachment rules
  17. 17. Examples of Application Domains that I have beenworking for past 10 years17
  18. 18. Innovative Design of Monitoring behaviour (Natrajan and Hossain, 2004)18
  19. 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 = 4419
  20. 20. Networks & Coordination in Crisis: Innovation & Learning(Hossain,& Kuti, 2010)20Micro-LevelActor Network ofcombining agenciesMacro-LevelOrganizational networkcombining all agencies
  21. 21. Organisational clique analysis and macro-level cross-agency clusters (Hossain,& Kuti, 2010)21
  22. 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) burntBlack Saturday bushfires in Australia22Emerging Networksnot only different organizations(agencies) need to cooperate properlyinternally (intra-team & inter-team)but also they have to cooperate withother organizations (inter-organizational) We wanted to understand what thebreakdowns are (from a network analysisperspective, there is a need to Evaluate which types of node failureshave high level of impact oncoordination performance which will lead to develop a betterpredicting model for understanding therate of node failure and attack.IMT (IncidentManagementTeams)Multi-EmergencyAgenciesCountry FireServicesMetropolitan FireBrigadesLandManagementAgenciesStateEmergencyAgenciesIncidentControllerOperationLogisticsPlanningGroundPersonnelAirOperationPoliceFirstAidLocal
  23. 23. Rural Fire Coordination Network (Abbasi & Hossain, 2013)23Murphy: IC1Kreltszheim: IC2Creek: RDO (RECC)Arandt: DIC1Court: Tanker1 CrewDixon: DGOGrant: DDO (DSE Manager)
  24. 24. Kilmore Coordination Network Evolution (Abbasi & Hossain, 2013)24
  25. 25. Dynamics of disease outbreaks coordination (Bedir,Hossain & Crawford, 2011; 2012)› The Absence of unified approach results in differentmanagement and coordination approaches leadingto high variability of infection rates; hence mortalityand morbidity rates.› H109 infection in NSW) indicates that even withinthe same state there were large discrepancies withinthe same states with sometimes similardemographics (by June 17- 2009)› H1N109 infection rates in Australiaby June 17- 200925
  26. 26. Dynamics of disease outbreaks coordination (Bedir,Hossain & Crawford, 2011; 2012)› Therefore, we need to coordinatebetween multiple agenciesdynamically in order to interveneand contain dynamic form ofdisease outbreaks in an evolvingenvironmentModelling challenges of disease outbreak coordination26
  27. 27. Modelling challenges of disease outbreak coordination› Informal coordination is an importantfacet of emerging coordination whichis often ignored in coordinationresearch› It capitalizes on the existingcoordination channels to circumventtheir complications, inefficiencies oreven their inaccuracies.› It can be defined as “ whenindividuals or organisations establishcommunication networks outside thestandard coordination structure to“get things done” (Baker 1981; Han1983)”27
  28. 28. Protocol for capturing qualitative & quantitative network data(Hossain, Bedir & Crawford, 2013)28
  29. 29. Results of Inter-organizational disease outbreakscoordination (Hossain, Bedir & Crawford, 2013)› Organizationsinvolved and theircharacteristics› Organizationallinks› Links’ initiation› Links’ intensity› Links’ direction› Links’ timeline› Links’ purpose29
  30. 30. Inter-organizational disease outbreaks coordination› Inbound case definition Communication › Cases inbound communication30WHOFederalChiefHealthOfficerCDU:NSW ChiefHealthOfficer/NSW-HSFACHNEHNE-HSFACInbound MonitoringHSFACEOCPHEOCSentinelindicatorGPsPHREDDSInpatientflow systemAdmits toICUWork forcemonitoringConfirmedcases viaSWABSPHREDDS: Public HealthRespiratory EmergencyDepartment Data System.SWABS: Sample taking system.LAG LAG LAG LEAD LEADFront line
  31. 31. Inter-organizational disease outbreaks coordination› Outbound Informal communication31State Public HealthUnitCase definition outboundcommunication structureHSFAC DCODCO: Director of clinical Operations.DA: Director of Acute.ED: Emergency DepartmentOrg1 dotted to indicate that it operated at later stageduring the communication process.DirectorAcuteDirectorD+CDirectorMentalHealth7 Hospitalin HNETotal 37 EDEDsHospital ClustersEDsMentalHospitalOrg1Outbound Case communication
  32. 32. Current research projects1. RIMS: Robust Information Management Systemsfor Coordinated Response to Crisis;2. BISoN: A Biologically-Inspired Social Network forCoordinated and Adaptive Emergency Response;3. Computational Behavioural Modelling of MarketsSystems;4. CIMS: Innovation and Learning in CoordinatedInterventions for Mental Health Systems;5. H1N1 and SARS Outbreaks: multi-organisationalcoordinated surveillance and response;6. CrisNet for Zoonotic and Foodbrone Outbreaks:Socio-technical Crisis Information Networks forDisease Outbreaks Coordination;7. Behavioral Network Dynamics for understandingNutrition, Epidemiology and Immunity;8. Social networks and health promotion:Harnessing social networks to enhance theeffectiveness of peer counselling› $1 million funding from Australian Capital MarketsCRC-Commonwealth Research Centre and HCF-Hospital Contribution Fund to develop predictivemodels for understanding future market systemsunder crisis.› $6.5M in competitively basic research funding (EUFP 7 Framework, ARC Discovery, CRCs andARDA Advanced Research Development Agencyin the US).› Submitted 2 major collaborative research grantsunder EU FP7 framework.- COST-action: Communication and Information SystemsTechnology in European Emergency Management- H.E.L.P Health Emergency Learning and Planning› I am the founding Editor-in-Chief of SpringerInternational Journal “Crisis Communications”32
  33. 33. Possible Links with Education and Research› Management- Engineering Knowledge ManagementResearch- Design, Engineering and Innovation- Industrial Dynamics and Strategy- Sustainability Research- Climate Change and SustainableDevelopment- Climate resilient developmentBusiness- 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 socialnetworks; systems biology for exploring organisational andcommunity resilience networks› Transport: complex modelling of transport networks• In my research, I use methods and analytical techniques from mathematical sociology (i.e., social networksanalysis), social anthropology (i.e., interview and field studies) and computer science (i.e., informationvisualization, graph theoretic approaches and data mining techniques such as clustering);• Using this transdisciplinary approach, I explore innovation, knowledge management and coordinationsystems in distributed and complex setting for understanding distributed work groups, organizational andindividual performance and knowledge sharing and management support process for innovation and learning33