Causal Loop Diagram - the Department of Computer Science!

Consider A → B• We are reasoning here about causal influences– The changes on B caused by changes in A• This is not merely an associational relationship• This should not merely be a matter of definition• Notion of “Increase”• Must Clearly Distinguish– “if X were to INCREASE, would Y increase or decreasecompared to what it would have otherwise been ”?• “if X were to INCREASE, would Y increase ordecrease over time”?– i.e. “if X were to INCREASE, would Y rise or fall over time”?

Causal Pathways• We can reason about the influence of onevariable and another variable by examining thesigns along their causal pathway• Two negatives (whether adjacent or not) will actto reverse each other– Consider A → - B → - C• An increase to A leads B to be less than it otherwisewould have been• B being lower than it otherwise would have been causesC to be higher than it otherwise would have been• (compared to what it otherwise would havebeen)

Tips• Variables will often be noun phrases• Variables should be at least ordinal• Links should have unambiguous polarity• Indicate pronounced delays• Avoid mega-diagrams• Label loops• Distinguish perceived and actual situation• Incorporate targets of balancing loops• Try to stick to planar graphs• Diagrams describe causal not casual factors!

Ambiguous Link• Ambiguous Link: Sometimes +, sometimes -Food intakeEnergy Surplus+Rate of WeightGain• Replace this by disaggregating causalpathways by showing multiple linksFood intake+Calories Taken in+++Basal Metabolism Calories Burrt-Energy Surplus+Rate of WeightGain

Example 2• Ambiguous Link: Sometimes +, sometimes–Overtime• Replace this by disaggregating causalpathways by showing multiple links+Work Accomplishedper DayOvertime++FatigueGreater Incorporation ofOutside Tasks at Work- Efficiency+ Work Accomplished+ per Day+More TimeWorking

Example 3• Ambiguous Link: Sometimes +, sometimes -Proportion of Fatin FoodsCalories Ingested• Replace this by disaggregating causalpathways by showing multiple linksroportion of Fatin Foods++SatietyDiet CaloricDensity-Amount of FoodEaten++Calories Ingested

Feedback Loops• Loops in a causal loop diagram indicatefeedback in the system being represented– Qualitatively speaking, this indicates that agiven change kicks off a set of changes thatcascade through other factors so as to eitheramplify (“reinforce”) or push back against(“damp”, “balance”) the original change• Loop classification: product of signs inloop (best to trace through conceptually)– Balancing loop: Product of signs negative– Reinforcing loop: Product of signs positive

Example Vicious/Virtuous Cycles• Positive (reinforcing) feedback can lead toextremely rapid changes in situation+# of Infectives# New Infections+Weight Perceivedas Normal+Individual TargetWeight+++CustomersWord ofMouth Sales+Prevalence ofObesity++Prevalence ofGDMPrevalence ofMacrosomic Infants++Mean Weight inPopulation+# of ActivatedMemory Cells# of ClonalExpansions+

Example “Balancing Loops”• Balancing loops tend to be self-regulating# New Infections+-# of SusceptiblesAdaptation++PolicyReevaluationPolicyEffectiveness--Food IngestedHunger+Mistakes-+Learning fromMistakes

Best Practice:Incorporating Thresholds• Balancing loops tend to be self-regulating+PolicyReevaluationTreshold for PolicyDissatisfaction to Lead toActionFood IngestedThreshold Hunger toMotivate EatingPolicy Adaptation+PolicyEffectiveness--+Treshold for PolicyDissatisfaction to Lead toActionThreshold Hunger toHunger

Best Practice:Indicating (Pronounced) Delays• Balancing loops tend to be self-regulating+PolicyReevaluationThreshold Hunger toMotivate EatingPolicy Adaptation+PolicyEffectivenessTreshold for PolicyDissatisfaction to Lead toAction--Food IngestedHunger+

Elaborating Causal LoopsCreation of Nutritionand Exercise Programs+-Prevalence ofObesity+Prevalence ofGDM+Study of Obesity++Prevalence ofMacrosomic Infants# of Infectives++# New Infections+-# of Susceptibles

Classic FeedbacksSusceptibles -+Contacts ofSusceptibles withInfectives++ New InfectionsInfectives

Broadening the Model BoundariesSusceptibles-+Contacts ofSusceptibles withInfectives++InfectivesNew + Infections+People Presentingfor TreatmentWaiting Times-+Health Care Staff

Example Vicious/Virtuous Cycles• Positive (reinforcing) feedback can lead toextremely rapid changes in situationExisting UsersNumber of Connections toMusic Download Server++New UsersDiscovering Site+Likelihood of Cross Listingand Listing on SearchEngines+Length of Time PerDownload+Likelihood of User StartingMultiple SimultaneousDownloads-Confusing Code++Word ofMouth Sales++CustomersEase of Understandingwhere to Make a Change-ConfusingAdditions

Elaborating Causal Loops+Length of Time PerDownloadNumber of Connections toMusic Download Server++Likelihood of User StartingMultiple SimultaneousDownloads+Length of Time PerDownload+Number of Connections toMusic Download Server+Users AbandoningDownload in Frustration+Likelihood of User StartingMultiple SimultaneousDownloads-

More Elaborate DiagramsKaranfil, 2008

More Elaborate Diagrams 2LaVallee& Osgood, 2008

Causal Loop Structure :Dynamic Implications• Each loop in a causal loop diagram isassociated with qualitative dynamicbehavior• Most Common Single-Loop Modes ofDynamic Behavior– Exponential growth– Goal Seeking Adjustment– Oscillation• When composed, get novel behaviors dueto shifting loop dominance– Behaviour of system more than sum of parts

CL Dynamics: Exponential Growth(First Order Reinforcing Loop)• Example++CustomersWord ofMouth Sales+Site Popularity++Likelihood of Cross Listingand Listing on SearchEnginesFrom Tsai• Dynamic implications20,00015,000Graph for Stock10,0005,00000 10 20 30 40 50 60 70 80 90 100Time (Month)Stock : Current

CL Dynamics: Goal Seeking(Balancing Loop)• Example:-PotentialCustomers-• Dynamic behaviorWord ofMouth Sales+From TsaiGraph for Inventory10075502500 10 20 30 40 50 60 70 80 90 100Time (Month)Inventory : Current

CL Dynamics: Oscillation(Balancing Loop with Delay)• Causal StructureDesiresInventory+Inventory+ -ProducingStarts-FinishingProduction+• Dynamic Behavior:6,857demand vs. productionFrom Tsai3,1420 30Time (year)demand : Oscilproducing : Osciltons/yeartons/year

Growth and Plateau• Loop structure:– Reinforcing Loop– Balancing LoopCustomers• Dynamic Behavior:+Word ofMouth Sales++100,000--PotentialCustomers+Existing Users+Graph for CustomerNew UsersDiscovering Site+Likelihood of Cross Listingand Listing on SearchEngines+Internet Users Yet toDiscover Site-75,00050,000From Tsai25,00000 10 20 30 40 50 60 70 80 90 100Time (Month)Customer : Current

Complexities & RegularitiesDepartment of ComputerScience

Measles & Mumps in SKDepartment of ComputerScience

Example: STIs

Three STIs: Test Volume vs CaseCounts

TB Saskatchewan’s War on “WhitePlague”

Cases and Contact Tracing6000500040003000Contacts ExaminedIncident Cases2000100001900 1920 1940 1960 1980 2000 2020

Contact Tracing Effort per Case

Broadening the Model Boundaries:Endogenous Recovery DelaySusceptibles-+Contacts ofSusceptibles withInfectives++InfectivesNew + Infections+People Presentingfor TreatmentWaiting Times-+Health Care Staff

Common Phenomena In Complex Systems• Counter-intuitive behaviour(Often fb interactions)• Snowballing: When things go bad, they often go verybad very quickly– “Vicious cycles” lead to “cascading” of problems(Due to positive feedback)– “Path dependence”: Different starting points canlead to divergence in project progress(Due to positive feedback interacting w/ mult.negative fb)• Policy resistance: Situation can be unexpectedlydifficult to change(Typically due to negative feedbacks that resistchange)

Examples of Policy Resistance– Cutting cigarette tar levels reduces cessation– Cutting cigarette nicotine levels leads to compensatorysmoking– Targeted anti-tobacco interventions lead to equallytargeted coupon programs by tobacco industry– Charging for supplies for diabetics as cost-cuttingmeasure leads to higher overall costs due to reduced selfmanagement,faster disease progression, higher demandfor dialysis & transplants– ARVs prolong lives of HIV carriers, but lead to resurgentHIV epidemic due to lower risk perception– “Saving money” by understaffing STI clinics, leads to longtreatment wait, greater risk of transmission by infectives&bigger epidemics– Antibiotic overuse worsens pathogen resistance– Antilock breaks lead to more risky driving– Natural feedback: Intergenerational “Vicious Cycles”

Examples of Policy ResistanceImage Source:Larson, G.The Far Side Series– Cutting cigarette tar levels reduces cessation– Cutting cigarette nicotine levels leads to compensatorysmoking– Targeted anti-tobacco interventions lead to equallytargeted coupon programs by tobacco industry– Charging for supplies for diabetics as cost-cuttingmeasure leads to higher overall costs due to reduced selfmanagement,faster disease progression, higher demandfor dialysis & transplants– ARVs prolong lives of HIV carriers, but lead to resurgentHIV epidemic due to lower risk perception– “Saving money” by understaffing STI clinics, leads to longtreatment wait, greater risk of transmission by infectives&bigger epidemics– Antibiotic overuse worsens pathogen resistance– Antilock breaks lead to more risky driving– Natural feedback: Intergenerational “Vicious Cycles”

Slides Adapted from External SourceRedacted from Public PDF forCopyright Reasons

Issues with Causal LoopDiagrams• Unclear variables• Diagrams can become very large• Confusion regarding polarity• Non-causal relationship• Conservation not captured• Behavior not always same as archetype• Unclear paths/Missing causal factors• Missing links• Asymmetry in direction of change

Unclear VariablesVariables Lacking ClearPolarity• Gender• Ethnicity• ShapeOften categorical & nonordinalImplicit Polarity• Population (size)• Revenue (amount of)• Sound, Color (more of)• Socioeconomic status(more of)• Ask whether “more X” is– Meaningful– Unambiguous

Unclear Links• Causal loop diagrams should make clearthe causal pathway one has in mind• One of the most common problems incausal loop diagrams is showing a linkwithout the meaning being clear– Often there are many possible pathways, anddistinguishing them can help make thediagram much clearer

Refining a Diagram• It takes time to arrive at an acceptablediagram• Some of the biggest investments lie in– Figuring out the appropriate variables to use– Illustrating the different pathways– Refining the names of the variables

Very Large Diagramshttp://kim.foresight.gov.uk/Obesity/Obesity.htmlStill useful for getting “big picture”identifying where research “fits in”, research gaps

Polarity• A → + B Does not mean that if A risesthen B will rise over time– Just says that B will be higher than it wouldotherwise have been– B may still be declining over time – but ishigher than it otherwise would have been• A → - B Does not mean that if A rises thenB will decline over time– Just says that B will be lower than it wouldotherwise have been– B may still be risingover time – but is higherthan it otherwise would have been

Reminder– An arrow with a positive sign (+): “allelse remaining equal, an increase(decrease) in the first variable increases(decreases) the second variable above(below) what it would otherwise havebeen.”– An arrow with a negative sign (-): “allelse remaining equal, an increase(decrease) in the first variable decreases(increases) the second variable below(above) what it otherwise would havebeen.”

Critical: Notion of “Increase”• Must Clearly Distinguish• Correct Interpretation: “if X were to INCREASE, wouldY increase or decrease compared to what it would haveotherwise been”?• Different notion: “if X were to INCREASE, would Yincrease or decreaseover time”?i.e. “if X were to INCREASE, would Y rise or fall overtime”?

Artifactual Loop

Artifactual Loop 2

Artifactual Loop 3

State of the System: Stocks(Levels, State Variables)• Stocks (Levels) represent accumulations– These capture the “state of the system”– Mathematically, we will call these “statevariables”• These can be measured at one instant intime• Stocks are only changed by changes to theflows into & out of them– There are no inputs that immediately changestocks

Examples of Stocks• Water in a tub orreservoir• People of different types– { Susceptible,infective, immune}people– Pregnant women– Women between theage of x and y– High-risk individuals• Healthcare workers• Medicine in stocks• Money in bank account• CO 2 in atmosphere• Blood sugar• Stored Energy• Degree of belief in X• Stockpiled vaccines• Goods in a warehouse• Beds in an emergencyroom• Owned vehicles

Changes to State: Flows (“Fluxes”)• These are always associated with rates• If these flow out of or into a stock thatkeeps track of things of type X, the ratesare measured in X/Unit Time (e.g.person/year)• Typically measure by accumulating peopleover a period of time– E.g. Incidence Rates is calculated byaccumulating people over a year

Examples of Flows• Inflow or outflow of abathtub (litres/minute)• Rate of infection (e.g.people/month)• Rate of recovery• Rate of Mortality (e.g.people/year)• Rate of Births (e.g.babies/year)• Rate of treatment(people/day)• Rate of caloricconsumption• Rate of pregnancies(pregnancies/month)• Reactivation Rate (#of TB casessreactivating per unittime)• Revenue ($/month)• Spending rate($/month)• Power (Watts)• Rate of energyexpenditure• Vehicle sales

Flows 2• May be measured by totalling up over a periodof time and dividing by the time• We can ask conceptually about the rate at anygiven point – and may change over time• When speaking about “Rates” for flows, wealways mean something measured as X/UnitTime (also called a rate of change per time)– Not all things called “rates” are flows• Exchange rate• Rate of return

Key Component: Stock & FlowFlow+StockFlowStock

109.5Flow Impact on StockFlow2,0001,500Stock91,0008.550080 10 20 30 40 50 60 70 80 90 100Time (Month)Flow : Current00 10 20 30 40 50 60 70 80 90 100Time (Month)Stock : CurrentImpact of Lowering Flow (Rate) to 5/Month?2,000Stock1,5001,00050000 10 20 30 40 50 60 70 80 90 100Time (Month)Stock : Stock and Flow AlternativeStock : Current

Loops & Stocks• Causation does not effect big changeinstantaneously– Loops are not instantaneous• Stocks only change by changes to the flowsinto & out of them– There are no inputs that immediately changestocks• All causal loops must involve at least onestock!

Delayed Impact

System Structure Diagrams• Semi-quantitative models• Combine causal loops diagram elementswith stock & flow structure• Clearly distinguish stocks & flows• If complete, all loops will go “through astock”– Loop goes into the flow of a stock (as onevariable in the diagram)– Loop comes comes out of stock (as nextvariable in diagram)

Slides Adapted from External SourceRedacted from Public PDF forCopyright Reasons