Presentation on theme: "Putting people into models Starting with qualitative models"— Presentation transcript:
1Putting people into models Starting with qualitative models Ingrid van PuttenCSIRO – Marine and Atmospheric research (Hobart- Australia)CSIRO Mathematics, Informatics, and Statistics
2One of a number of modeling approaches If you use it depends onWhat you want from model?(Understand, Predict, Modify)What can different types provide?(Generality, Precision, Realism)Don’t need much dataQUALITATIVE MODELSGood for combining bio-physical and human domain – but philosophically – can we actually model humans?PRECISIONGENERALITYREALISMRichard Levins1966PRECISIONPRECISIONGENERALITYREALISMGENERALITYREALISMSTATISTICAL MODELSMECHANISTIC MODELS
3Philosophical perspective Can we model human behaviour? Behaviorism: probably…?Metaphysics: never…!Ivan Pavlov(1849 –1936)Burrhus Frederic Skinner (1904 – 1990)AristotlePlatoBehaviour shaped by response to environmental stimuliHuman beings perceive, assess, decide, and act.Modellers need algorithms for each stageHuman beings aren’t reducible to any description.Transcendental nature of ‘self’ and cognition.But how do we “observe” and interpret what human beings do
4Assuming we can model human behaviour How do we observed it?Inductive reasoningDeductive reasoningBased on observationBased on interpretationInference of general principles or rules from specific factsInference of specific facts from general principles or rulesWhat’s next?Do we need to know what goes on in the cognitive box (the brain) when modelling people?“Cognitive white box”“Causal black box”Empirical heuristics- based agentsFormal logic compliant agents
5Gather all information necessary for rational judgement Do we need to know what goes on in the cognitive box when modeling the way people make decisions?Ask an economist ……UncertaintyFaced with a problemGather all information necessary for rational judgementMake decisionhomo economicusPerson acts rationally in complete knowledge out of self-interest and the desire for wealthNot much gain from knowing what goes on in the cognitive box
6Psychologists say we do need to know about the cognitive box When people are faced with a complicated judgment or decision, they often simplify the task by relying on heuristics, or general rules of thumb (shortcuts)Amos Tversky and Daniel Kahneman (1972)Gather all information necessary for rational judgementUncertaintyHeuristic(shortcut)Make decisionThe rules explain how people make decisions, come to judgments, and solve problemsThe rules can be learned or hard-coded by evolutionary processes.
7Cross fertilization between economics and psychology Behavioural economicsStudy the effects of social, cognitive, and emotional factors on economic decisions and resource allocationConcerned with the bounds of rationality of the economic agents
8Gather all information necessary for rational judgment In some situations, heuristics lead to predictable biases and inconsistenciesGather all information necessary for rational judgmentUncertaintyHeuristic(shortcut)BIASMake decisionIn other words ……Behavioural rules in psychology work well under most circumstances, but in certain cases lead to systematic errors or cognitive biases
9Some examples of cognitive biases Decision-making and behavioural biasesLoss aversion (endowment effect) – people demand much more to give up an object than they would be willing to pay to acquire itlosing $100 affects your level of happiness much more than winning $100Probability and belief biasesOutcome bias – People overestimate small probabilities and underestimate large probabilitiesLow frequency events (such as smallpox, poisoning, and botulism) are overestimated (by a factor of 10), while high frequency events (such as stomach cancer, stroke, and heart disease) are underestimatedSocial biasesFalse consensus bias – People tend to overestimate the degree to which others agree with them(Lichtenstein et al. 1978Memory biasesConsistency bias – people often incorrectly think past attitudes and behavior resemble present attitudes and behavior.
10How do we know cognitive biases happen? Do experiments with people to find out how they might behave in different situationsExample of an experiment to establish cognitive biasAnchoring – the tendency of people to rely too heavily, or "anchor," on one trait or piece of information when making decisionsQuestionGuess the percentage of African nations that are members of the United NationsGroup 1Was it more or less than 10%25% on averageGroup 2Was it more or less than 65%45% on averageBefore the experimentWrite down the last two digits of your social security numberConsider whether you would pay this number of dollars for items value (e.g. wine, chocolate, computer equipment) with an unknownQuestionPeople with higher numbers (e.g. 85)Group 160 to 120% higher payment offered for the goods by people with higher numbersPeople with lower numbers (e.g 20)Group 2
11Why do we care about cognitive biases? Raghu mentioned it – for instance climate change communicationThings like confirmation bias which describes how people are more likely to search for or accept information that supports pre-conceived beliefs.Google search histories illustrated this:Believers will tend to use search terms“climate change proof”disbelievers terms such as“climate change myth”.Both believers and disbelievers are presented with search results that support their original belief.
122011 paper on public perceptions of climate change by the CSIRO Not only do we look for information that confirms our preconceived ideas but we also believe that everyone else believes the same as us?False-consensus biasWe overestimate the prevalence of our personal opinions in society while we underestimate the prevalence of beliefs that conflict with our own7% of Australians believe that climate change isn’t happening at all.That same 7% believe that almost 48% of the population agree with them.78% believe climate change is real.- 63% believe that climate change is already happening;- 15% believe that climate change will happen in the next 30 years15% are unsure if climate change is real2011 paper on public perceptions of climate change by the CSIRO
13Some of the biases Skeptics accuse Believers of OVERCONFIDENCE - in the predictions of their computer models.ILLUSION OF CONTROL - Believers think that human reductions of greenhouse gases will make a large enough contribution to reduce global warming, but Skeptics think that’s an illusion.LOSS AVERSION - Skeptics claim Believers overestimate the costs of warming (compared to the benefits).BANDWAGON EFFECT the tendency of Believers to believe climate change is happening because many other people believe the same.AVAILABILITY BIAS - “because believers think of it, the believers think it must be important."CONFIRMATION BIAS- Believers search for or interpret information in a way that confirms their preconceptions
14Why is it useful to know about cognitive biases As Raghu said – we can change peoples mental models - knowing about (both sceptics and believers) cognitive bias will helpWhy is it useful to know about mental shortcuts that psychologists study (heuristics) when modelling human behaviour?As Rashid said – economics can develop incentives to change behaviour - knowing about mental shortcuts people take in making decisions will help develop incentives that workAs Eileen said – we need coupled models to go into the future - knowing as much realistic information about the way we make decisions will be central to thatQualitative modelling is one of a number of approaches to couple human to bio-physical systems- Not data hungry- Intuitively simply- can follow easily from conceptual modelling- can be developed with the people represented in the model
15Introduction to qualitative modeling Systematically developed by Richard Levins (1966)Qualitative models are based on signed digraphsSign Directed Graphs (Signed Digraphs)Predator-PreyA few historically significant scientific discoveries had to happen before qualitative modelling came along
16Leonardo Fibonacci in 1202 (age 32) Liber Abaci (Book of Calculation)“A certain man put a pair of rabbits in a place surrounded on all sides by a wall. How many pairs of rabbits can be produced from that pair in a year if it is supposed that every month each pair begets a new pair which from the second month on becomes productive?”Leonardo Fibonacci in 1202 (age 32)Fibonacci number sequence:1 1 2 3 5 8 13 21 34 55 89 144Geometric or Exponential Increase
17Essay on the Principle of Population Populations Increase Geometrically (e r t )Resources Increase Arithmetically (x + y)Thomas MalthusIn 1798 (age 32)"The power of population is indefinitely greater than the power in the earth to produce subsistence for man"
18Lotka-Volterra type equations describe the Darwinian evolution of a population density Charles DarwinPREYPREDATORPredator-PreyAlfred Lotka1925Vito Volterra1926
19-α1,2 -α1,2 +α2,1 +α2,1 Mathematical relationship Community Matrix Lotka and VolterraRichard Levins1966Community MatrixSigned Digraph-α1,2-α1,2+α2,1+α2,1Levins 1968Levins 1974
21Community Matrix 3 2 1 Due to interaction with 1 2 3 1. Small fish +a21-a22-a23+a32-a33Change in2. Large fish3Fishery3. FisherySelf effect2Large fishCommunity matrix - signs onlyDue to interaction with1Small fish1231. Small fish-+Change in2. Large fish3. FisherySelf effect
22Additional benefit of qualitative modelling What can qualitative modelling tell you – beside increases and decreases?Qualitative models can identify key drivers of change and predict the direction (+, - , 0) of response to changePress perturbation: shift in parameter leading to new equilibriumPulse perturbation: shock to population or variable leading to transient dynamics1Assess model stability (important for assessing the reliability of predictions) – if strong positive feedback system then unstable2Qualitative modelling can be used to identify data gaps and hypotheses for further investigation3Additional benefit of qualitative modellingQualitative models are relatively easy to produce with stakeholders (next step to building a conceptual model)“…a very underrated tool in biology and social science” (M.L. Cody 1985)
23non-fishing based recreation Australian example of qualitative model Connect climate change drivers, to marine environment and marine sectors(‘expert model’)TemperatureCurrentsWindCyclones & stormsClimate drivers+Sea level riseRainfall-+Pests & diseasesEcosystem integrityRetained speciesEmergent speciesNon-retained speciesMarine environment (ecological groups)+non-fishing based recreationCommercial fishingRecreational fishingMarine tourismCharter fishingTraditional ownersAquacultureRenewable energyOther industrial useMarine sectors
24Build same model with community members TemperatureWindSea level riseCyclones & stormsnon-fishing based recreationTraditional ownersRenewable energyOther industrial useCurrentsClimate driversRainfallEmergent speciesPests & diseasesWhat did we learn?Incomplete understanding of the whole systemWill help shape communication/education/informationRetained speciesMarine environment (ecological groups)Ecosystem integrityNon-retained speciesCommercial fishingAquacultureMarine sectorsCharter fishingMarine tourismRecreational fishing
25Commercial fishing activity The pathway by which the fishers thought climate change affected them (fisher’s mental model)Climate changeSea temperatureCurrentsRetained speciesEmergent speciesFish abundancePrice of fishProfitabilityCommercial fishing activity
26Climate is not only thing that drives fishing activity (fisher’s mental model of where it fits in) Climate changeSea temperatureCurrentsQuota ownership characteristics# 1Bank lending rulesFamily fishing historyFamily quota ownershipPass quota downRetirement funding options/ alternativesRetained speciesEmergent speciesQuota trade characteristic# 2Method of lease quota tradeFishing pressureSeasonGovernment TAC levelsVariable costPrice of lease quotaAdmin. monitoring requirementsQuota ownershipVessel ownershipVessel SizeFixed costFish abundanceExchange rateImportsPrice of fishHarbour access channel sand build upPublic works fundingAccess to harbourDiversification optionsExploratory licence rulesGovt dept resourcesOil & gas industry developmentAgeAlternative income earning optionsImportant to understand how these things fit togetherif we want to use policy to change the system - improve it –or make it more robustExploratory fishing# 4Work opportunities# 3ProfitabilityCommercial fishing activity
27Example of how Qualitative models can provide powerful insight when you want to implement policy to improve the systemBenguela Ecosystem - effects of seal cull on hakes(-)Mc JMc AJuvenilesAdultsLive in shallow waterMerluccius capensisHakes model+(-)+Merluccius paradoxusMp JMp AJuvenilesAdultsLive in deep water+Yodzis 1998
28Benguela Ecosystem - effects of seal cull on hakes Merluccius capensis &Merluccius paradoxus modelHakes model++(-)(-)+-++ShallowDeep--Punt 1997
29Another example of qualitative model in fisheries How QMs can address hypotheses regarding reduced banana prawn catchWhat happens when the model gets perturbedReduced banana prawn abundance from recruitment overfishing,Reduced banana prawn abundance from change in environment,Reduced banana prawn abundance from pollution.Reduced fishing effort in Weipa.Reduced catchability from prawns remaining inshore,Reduced catchability from reduced aggregation or “balls”.Weipa regionIn the far north of Australia there is an active prawn fishing industry.This industry was experiencing reduced catch (like in many different fisheries).The aim of QM building a model was to see what might be causing the decline in the catch.There were a number of hypotheses as to why the catch was declining.The main ones wereprawn recruitment has collapsed due to over-fishing;recruitment has collapsed due to a change in the prawn’s environment;adult banana prawns are still present, but fishers can no longer effectively find or catch them.
30Example of qualitative model in fisheries Banana Prawn SubsystemP LP ALarvaeAdultsP JP mN3N2N1Prawn foodPrawnsPredatorsJuvenilesMaturingP JP A
31Example of qualitative model in fisheries PrPLoff-shoreNutOpPAPJestuaryBanana Prawn biological SubsystemPmin-shoreThe different life stage of the prawns take place in different parts of the marine system.The adults and the larvae are off-shore but the juveniles more into the estuarine system.During the maturation process the prawns move to in-shore areas after which they become adults and can be found in the off short areas again.This completes the cycle.At each life stage there are different predators (pr) and food sources they depend on (nutrients). There are also competitors for these same nutrients that the adult prawns depend on.This QM thus shows the biological system.
32Example of qualitative model in fisheries CPUEEff$Rec (est)Rec(oce)ComcatchHuman systemPrPrTur (est)Rain EPLOpPAPrRain LSalManhabTur (oce)NutPJestuaryPmoff-shoreNutin-shoreBiological systemEnvironment & habitatCSIRO Mathematics, Informatics, and Statistics – Jeff Dambacher
33Example of qualitative model in fisheries Rec(oce)Rec (est)CPUEComcatchRecreational fishing systemCommercial fishingeconomic system$PrPr+PLOp-PAPr?NutPJestuaryPmoff-shoreNutin-shoreBiological systemEnvironment & habitatTur (est)ManhabRain ETur (oce)Rain LSalPERTUBATION
34Why use qualitative modelling? 1Few data required – only need signs of the interactionsFish stockingFish populationStocking of rivers with fish increases the abundance of fishPositive effectBirth ratesFemale educa-tionNegative effectFemale education will decrease birthratesPolice on the street will decrease the number of cars stole and if more cars get stolen this will increase police presencePolicy present on streetCars stolenReciprocal effectsS. Metcalf, Murdoch University
35Why use qualitative modelling? Any type of interaction cay be included in qualitative models (biological populations, whole ecosystems, groups of people, economic variables, nutrients, social and demographic characteristics).2Birth ratesFemale educa-tionWealthCan investigate direct and indirect interactions and their effects on the dynamics of the systemDirect interactionIndirect interaction3Qualitative models are excellent for producing with stakeholders (participatory modelling)4
36Why involve the community in the modeling exercise? (Participatory modeling)Stakeholders learn more about:How to structure and formulate their ideasUnderstand situation and possible optionsHow to understand, discuss and cooperate with othersScientists learn more about:Stakeholder’s views and social behaviorWays of translating research into policy practicePolicy makers benefit as legitimacy of models is enhancedDirect integration into the decision-making processSocial and scientific validationPolicy makers benefit from what the scientists and stakeholders have learned by developing the model together and from the legitimacy gained through this process
37Weaknesses of qualitative models Omits small effects or large infrequent effectsFunctions often vaguely definedLoss of detail in space, time, and individual organismsPresumption of linearity and equilibriumTime lags not explicitQUALITATIVE MODELSPRECISIONREALISMGENERALITYRichard Levins1966STATISTICAL MODELSMECHANISTIC MODELS
38Approaches to Complexity “Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that’s creativity.” (Charles Mingus)Thanks to:Jeff Dambacher (CSIRO Mathematics, Informatics, and Statistics),Sarah Metcalf (Murdoch University),Pascal Perez (University of Wollongong)