copyright © 2007 tennenbaum institute. all rights reserved. knowledge and skills for enterprise...

Copyright © 2007 Tennenbaum Institute. All rights reserved.

Knowledge and Skills for Enterprise Transformation.Knowledge and Skills for Enterprise Transformation.

Complex SystemsComplex SystemsCharacteristics & Sense MakingCharacteristics & Sense Making

William B. RouseWilliam B. Rouse

Knowledge and Skills for Enterprise Transformation. 2


OverviewOverview• Complexity & Complex SystemsComplexity & Complex Systems

– DefinitionsDefinitions– IntentionsIntentions– ViewsViews

• Healthcare DeliveryHealthcare Delivery– Stakeholders & IssuesStakeholders & Issues– Disease DetectionDisease Detection– Complex Adaptive SystemsComplex Adaptive Systems– Market ComplexityMarket Complexity– Health AdvisorHealth Advisor

• Sense MakingSense Making– Value PhilosophyValue Philosophy– Organizational BehaviorsOrganizational Behaviors– Situation AssessmentSituation Assessment

• SummarySummary


ComplexityComplexity• The intrinsic amount of resources, for instance, memory, The intrinsic amount of resources, for instance, memory,

time, messages, etc., need to solve a problem or time, messages, etc., need to solve a problem or execute an algorithm (NIST, 2004)execute an algorithm (NIST, 2004)

• How long it would take, or how much capacity would be How long it would take, or how much capacity would be required, at a minimum, for a standard universal required, at a minimum, for a standard universal computer to perform a particular task (Gell-Mann, 1995)computer to perform a particular task (Gell-Mann, 1995)

• The length of a concise description of a set of an entity’s The length of a concise description of a set of an entity’s regularities (Gell-Mann, 1995)regularities (Gell-Mann, 1995)

• The essence of complexity is the elaboration of highly The essence of complexity is the elaboration of highly structured communication, computing, and control structured communication, computing, and control networks that also create barriers to cascading failure networks that also create barriers to cascading failure events (Carlson & Doyle, 1995)events (Carlson & Doyle, 1995)


Complex SystemsComplex Systems• System: A group or combination of interrelated, interdependent, or

interacting elements that form a collective entity. Elements may include physical, behavioral, or symbolic entities. Elements may interact physically, mathematically, and/or by exchange of information. Systems tend to have purposes, although in some cases the observer ascribes such purposes.

• Complex System: A system whose perceived complicated behaviors can be attributed to one or more of the following characteristics: large numbers of elements, large numbers of relationships among elements, nonlinear and discontinuous relationships, and uncertain characteristics of elements and relationships. Complexity is perceived because apparent complexity can decrease with learning.


Complexity = f (Intentions)Complexity = f (Intentions)

System (S)Input (U) Output (Y)

IntentionIntention ExampleExample

ClassificationClassification ““It’s an instance of type S.”It’s an instance of type S.”

ExplanationExplanation ““It’s type S because ….” It’s type S because ….”

PredictionPrediction ““It’s future output will be Y.” It’s future output will be Y.”

ControlControl ““If input is U, it’s output will be Y.” If input is U, it’s output will be Y.”

DetectionDetection ““It’s output is not Y, but should be.” It’s output is not Y, but should be.”

DiagnosisDiagnosis ““It’s output is not Y because …” It’s output is not Y because …”


Views of Complex SystemsViews of Complex Systems

• Hierarchical MappingsHierarchical Mappings• Uncertain State EquationsUncertain State Equations• Discontinuous, Nonlinear MechanismsDiscontinuous, Nonlinear Mechanisms• Autonomous AgentsAutonomous Agents


Hierarchical MappingsHierarchical Mappings• Systems engineering = Processes for designing, developing, Systems engineering = Processes for designing, developing,

deploying, and sustaining complex systemsdeploying, and sustaining complex systems• Hierarchical decomposition of a very complicated design task into Hierarchical decomposition of a very complicated design task into

component taskscomponent tasks• Management of the execution of these tasks and integration of task Management of the execution of these tasks and integration of task

outcomesoutcomes• Complexity typically due to large numbers of interacting elementsComplexity typically due to large numbers of interacting elements• A large number of reasonably straightforward tasks whose outcomes A large number of reasonably straightforward tasks whose outcomes

will flow together to create a successful complex systemwill flow together to create a successful complex system• Appropriate resolution of multi-attribute tradeoffs across multiple Appropriate resolution of multi-attribute tradeoffs across multiple

stakeholdersstakeholders• Complexity managed by dividing and conquering itComplexity managed by dividing and conquering it


State EquationsState Equations• Systems engineering = Design of mechanisms whereby the “state” Systems engineering = Design of mechanisms whereby the “state”

evolves affecting system response and stabilityevolves affecting system response and stability• Central design issue is nature of appropriate feedback mechanisms Central design issue is nature of appropriate feedback mechanisms

for controlling system statefor controlling system state• Observability and controllability are key constructs; optimization of Observability and controllability are key constructs; optimization of

control often an overriding goalcontrol often an overriding goal• Inabilities to fully specify state-transition mechanisms & Inabilities to fully specify state-transition mechanisms &

uncertainties limit formulation to constrained optimality.uncertainties limit formulation to constrained optimality.• Formal depiction and manipulation of mechanisms underlying Formal depiction and manipulation of mechanisms underlying

complex behaviors seldom “scale up”complex behaviors seldom “scale up”• Complexity due to large numbers of state variables and significant Complexity due to large numbers of state variables and significant

levels of uncertaintylevels of uncertainty• Pursuit of optimal control solutions often made possible by Pursuit of optimal control solutions often made possible by

assumptions of linearityassumptions of linearity


Nonlinear MechanismsNonlinear Mechanisms• Simple underlying phenomena yield complex behaviors for systems Simple underlying phenomena yield complex behaviors for systems

with very few elements, perhaps even just one element with with very few elements, perhaps even just one element with particular interaction termsparticular interaction terms

• Nonlinear and/or discontinuous nature of the elements lead to Nonlinear and/or discontinuous nature of the elements lead to behaviors labeled as catastrophes, chaos, etc.behaviors labeled as catastrophes, chaos, etc.

• Systems that appear simple can produce very complex behaviors; Systems that appear simple can produce very complex behaviors; complex phenomena may be attributable to simple mechanisms.complex phenomena may be attributable to simple mechanisms.

• Complexity due to departures from our expectations of continuous, Complexity due to departures from our expectations of continuous, linear phenomenalinear phenomena

• Understand complexity by exploring underlying mechanisms which Understand complexity by exploring underlying mechanisms which may lead to design solutions.may lead to design solutions.

• Formal systems approaches tend to flounder when addressing Formal systems approaches tend to flounder when addressing fairly small numbers of nonlinear mechanismsfairly small numbers of nonlinear mechanisms


Autonomous AgentsAutonomous Agents• Composition of large numbers of simple behaviors into overall Composition of large numbers of simple behaviors into overall

system behaviors that exhibit hallmark characteristics of complex system behaviors that exhibit hallmark characteristics of complex systemssystems

• Simple behaviors created by autonomous “agents” acting Simple behaviors created by autonomous “agents” acting independently in pursuit of their individual goalsindependently in pursuit of their individual goals

• Reactions of agents to each other’s behaviors result in emergent Reactions of agents to each other’s behaviors result in emergent phenomena that could not have been predicted by dissecting phenomena that could not have been predicted by dissecting individual agents.individual agents.

• Understanding the nature of incentives, motivations, and Understanding the nature of incentives, motivations, and prohibitions that will influence individual agents to contribute to prohibitions that will influence individual agents to contribute to creating desirable collective behaviorscreating desirable collective behaviors

• Understanding and managing complexity are experimental rather Understanding and managing complexity are experimental rather than axiomatic undertakingsthan axiomatic undertakings

• Many things can be demonstrated but few can be provenMany things can be demonstrated but few can be proven


Contrasting ViewsContrasting ViewsNo. No. ViewView ApproachApproach FocusFocus

11 Hierarchical Hierarchical MappingsMappings

Design decompositionDesign decomposition Engineering solutionsEngineering solutions

22 State EquationsState Equations Axiomatic derivationAxiomatic derivation Control performanceControl performance

33 Nonlinear Nonlinear MechanismsMechanisms

Behavior demonstrationBehavior demonstration Basis of complexityBasis of complexity

44 Autonomous agentsAutonomous agents Empirical assessmentEmpirical assessment Emergent behaviorsEmergent behaviors


An ExampleAn Example• Effects of turbulent flow on aerodynamic behavior Effects of turbulent flow on aerodynamic behavior

and vehicle performance in high-density trafficand vehicle performance in high-density traffic– View No. 1 for designing the vehicleView No. 1 for designing the vehicle– View No. 2 to explore vehicle dynamicsView No. 2 to explore vehicle dynamics– View No. 3 to model turbulenceView No. 3 to model turbulence– View No. 4 to understand traffic effectsView No. 4 to understand traffic effects

• Problem, e.g., poor vehicle handling qualities vs. Problem, e.g., poor vehicle handling qualities vs. traffic congestion problemstraffic congestion problems


Healthcare DeliveryHealthcare Delivery• Stakeholders & IssuesStakeholders & Issues• Disease DetectionDisease Detection• Complex Adaptive SystemsComplex Adaptive Systems• Market ComplexityMarket Complexity• Health AdvisorHealth Advisor


Stakeholders & InterestsStakeholders & InterestsStakeholderStakeholder Risk Mgt.Risk Mgt. PreventionPrevention DetectionDetection TreatmentTreatment

PublicPublic e.g., Buy Insurancee.g., Buy Insurance e.g., Stop e.g., Stop SmokingSmoking

e.g., Get e.g., Get ScreenedScreened

Delivery SysDelivery Sys PhysiciansPhysicians Physicians & Physicians & HospitalsHospitals

GovernmentGovernment Medicare, Medicare, Medicaid, Medicaid, CongressCongress

NIH, CDC, DoD, et al.NIH, CDC, DoD, et al.

Non-ProfitsNon-Profits American Cancer Society, American Heart American Cancer Society, American Heart Association, et al.Association, et al.

AcademiaAcademia Business SchoolsBusiness Schools Basic Science Basic Science DisciplinesDisciplines

Technology & Technology & Medical SchoolsMedical Schools

Medical Medical SchoolsSchools

BusinessBusiness Employers, Employers, Insurance Insurance

Companies, HMOsCompanies, HMOs

Guidant, Guidant, Medtronic, et al.Medtronic, et al.

Lilly, Merck, Lilly, Merck, Pfizer, et al.Pfizer, et al.


Disease DetectionDisease DetectionCosts

CoveredPublic

AwarenessPublic

ReadinessScreeningAvailable

ScreeningEffective

PublicCommunication

PublicEducation

PhysicianEducation

ConsumerAdvocacy

MedicalResearch

$$$ $ $Public, Delivery System, Government, Non-Profits, Academia, Business


LEAPFROG

AMA CMSS

AOA

LCME

AOA-COPT

JCAHO

AAFP

ACGME

FSMB

ABMS

AARPNBGH

ACCME


Complex Adaptive SystemsComplex Adaptive Systems• They are They are nonlinear, dynamicnonlinear, dynamic and do not inherently reach fixed equilibrium points. and do not inherently reach fixed equilibrium points.

The resulting system behaviors may appear to be random or chaotic. The resulting system behaviors may appear to be random or chaotic.• They are composed of They are composed of independent agentsindependent agents whose behavior can be described as whose behavior can be described as

based on physical, psychological, or social rules, rather than being completely based on physical, psychological, or social rules, rather than being completely dictated by the dynamics of the system.dictated by the dynamics of the system.

• Agents' needs or desires, reflected in their rules, are not homogeneous and, Agents' needs or desires, reflected in their rules, are not homogeneous and, therefore, their therefore, their goals and behaviors are likely to conflictgoals and behaviors are likely to conflict -- these conflicts or -- these conflicts or competitions tend to lead agents to adapt to each other's behaviors.competitions tend to lead agents to adapt to each other's behaviors.

• Agents are Agents are intelligent, learnintelligent, learn as they experiment and gain experience, and as they experiment and gain experience, and change behaviors accordingly. Thus, overall systems behavior inherently change behaviors accordingly. Thus, overall systems behavior inherently changes over time.changes over time.

• Adaptation and learning tends to result in Adaptation and learning tends to result in self-organizingself-organizing and patterns of and patterns of behavior that emerge rather than being designed into the system. The nature of behavior that emerge rather than being designed into the system. The nature of such emergent behaviors may range from valuable innovations to unfortunate such emergent behaviors may range from valuable innovations to unfortunate accidents.accidents.

• There is There is no single point(s) of controlno single point(s) of control – systems behaviors are often – systems behaviors are often unpredictable and uncontrollable, and no one is "in charge." Consequently, the unpredictable and uncontrollable, and no one is "in charge." Consequently, the behaviors of complex adaptive systems usually can be influenced more than behaviors of complex adaptive systems usually can be influenced more than they can be controlled.they can be controlled.


Pharmaceuticals HealthWholesalers

HealthProviders

Consumers

Pharmacy

MedicalEquipment

OtherEquipment

HealthInsurance

Government &Policy Makers

R&D Laboratories


Market Complexity

0

5

10

15

20

25

30

35

Aero Auto Health Retail Telecom

Market

Co

mp

lex

ity

(B

its

)

Consumer

Total


Health AdvisorHealth Advisor


CONSUMER No. 1• Health State Provider Choice• Benefits Provider Choice• Information Provider Choice



CONSUMER No. M• Health State Provider Choice• Benefits Provider Choice• Information Provider Choice

WORLD• Economy, e.g., Recession Decreased Sales• Politics, e.g., War Increased Acute Health• Environment, e.g., Pollution Increased Chronic Health

PROVIDER No. 1• Patients Outcomes• Patients Claims• Claims Revenue



PROVIDER No. N• Patients Outcomes• Patients Claims• Claims Revenue

BENEFITS• Family Coverage, e.g., Employee %• Medical Coverage, e.g., 80/20%• Out-of-Pocket, e.g., Co-Pay• Wellness Coverage

INFORMATION• Provider Prices• Provider Performance• Symptoms Diagnosis• Diagnosis Treatment• Patient Health Record• Wellness Programs Health

HEALTH• Probability of Chronic Problems• Probability of Acute Problems

CHOICES• Coverage Yes/No• Provider 1, 2, 3 or N

EMPLOYER• Sales Jobs• Sales Benefits• Rates Benefits

INSURER• Benefits Revenue• Claims Costs• Profits Rates


OrgSim ArchitectureOrgSim ArchitectureFacilitation, e.g., Training, Advising, Guiding

User Interface, e.g., Large Screens, Voice, Gestures

Organizational Story, e.g., Aging Population

Characters, e.g., Patients, Doctors, Vendors

World Model, e.g., Hospital, City, Economy

Distributed Simulation Software

Hardware, e.g., Computers, Networks


Sense MakingSense Making

• Value PhilosophyValue Philosophy

• Organizational BehaviorsOrganizational Behaviors

• Situation AssessmentSituation Assessment


Value PhilosophyValue Philosophy

• Value focuses on organizational outputs Value focuses on organizational outputs (or outcomes), rather than inputs.(or outcomes), rather than inputs.

• Value relates to benefits of outcomes, Value relates to benefits of outcomes, rather than outcomes themselves.rather than outcomes themselves.

• Value implies relevant, usable, and Value implies relevant, usable, and useful outcomes.useful outcomes.


Organizational BehaviorsOrganizational Behaviors

Traditional SystemTraditional System Complex SystemComplex System

RolesRoles ManagementManagement LeadershipLeadership

MethodsMethods Command & ControlCommand & Control Incentives & InhibitionsIncentives & Inhibitions

MeasurementMeasurement ActivitiesActivities OutcomesOutcomes

FocusFocus EfficiencyEfficiency AgilityAgility

RelationshipsRelationships ContractualContractual Personal CommitmentsPersonal Commitments

NetworkNetwork HierarchyHierarchy HeterarchyHeterarchy

DesignDesign Organizational DesignOrganizational Design Self OrganizationSelf Organization


Situation AssessmentSituation Assessment• System StateSystem State

– Current and projected value flowsCurrent and projected value flows– Current and projected problemsCurrent and projected problems

• System PerformanceSystem Performance– Current and projected value, costs & value/costCurrent and projected value, costs & value/cost– Current and projected options for contingenciesCurrent and projected options for contingencies

• System StakeholdersSystem Stakeholders– Involvement of each stakeholder groupInvolvement of each stakeholder group– Performance of each stakeholder groupPerformance of each stakeholder group

• Information SystemsInformation Systems– Measurement, modeling & display of system stateMeasurement, modeling & display of system state– Agile “What If?” experimentation & adaptationAgile “What If?” experimentation & adaptation


SummarySummary• Complexity & Complex SystemsComplexity & Complex Systems

– DefinitionsDefinitions– IntentionsIntentions– ViewsViews

• Healthcare DeliveryHealthcare Delivery– Stakeholders & IssuesStakeholders & Issues– Disease DetectionDisease Detection– Complex Adaptive SystemsComplex Adaptive Systems– Market ComplexityMarket Complexity– Health AdvisorHealth Advisor

• Sense MakingSense Making– Value PhilosophyValue Philosophy– Organizational BehaviorsOrganizational Behaviors– Situation AssessmentSituation Assessment

copyright © 2007 tennenbaum institute. all rights reserved. knowledge and skills for enterprise...

Documents

enterprise transformation

complex systems system

essence of complexity

apparent complexity

rouse slide

large numbers of elements

particular task gellmann

entitys regularities