Presentation on theme: "CSIRO Mathematics, Informatics, and Statistics Putting people into models Starting with qualitative models Ingrid van Putten CSIRO – Marine and Atmospheric."— Presentation transcript:
CSIRO Mathematics, Informatics, and Statistics Putting people into models Starting with qualitative models Ingrid van Putten CSIRO – Marine and Atmospheric research (Hobart- Australia)
QUALITATIVE MODELS MECHANISTIC MODELS PRECISION REALISM GENERALITY STATISTICAL MODELS GENERALITY REALISM PRECISION REALISM GENERALITY Richard Levins 1966 What you want from model? (Understand, Predict, Modify) What can different types provide? (Generality, Precision, Realism) One of a number of modeling approaches If you use it depends on Good for combining bio- physical and human domain – but philosophically – can we actually model humans? Dont need much data
Philosophical perspective Can we model human behaviour? Metaphysics: never…! Behaviorism: probably…? Behaviour shaped by response to environmental stimuli Human beings perceive, assess, decide, and act. Modellers need algorithms for each stage Human beings arent reducible to any description. Transcendental nature of self and cognition. Ivan Pavlov (1849 –1936) AristotlePlato Burrhus Frederic Skinner (1904 – 1990) But how do we observe and interpret what human beings do
Do we need to know what goes on in the cognitive box (the brain) when modelling people? Assuming we can model human behaviour How do we observed it? Inductive reasoning Based on observation Whats next? Inference of general principles or rules from specific facts Deductive reasoning Based on interpretation Inference of specific facts from general principles or rules Cognitive white box Causal black box Empirical heuristics- based agents Formal logic compliant agents
Do we need to know what goes on in the cognitive box when modeling the way people make decisions? Ask an economist …… Uncertainty Faced with a problem Gather all information necessary for rational judgement Make decision homo economicus Person acts rationally in complete knowledge out of self-interest and the desire for wealth Not much gain from knowing what goes on in the cognitive box
Gather all information necessary for rational judgement Make decision Uncertainty Heuristic (shortcut) Amos Tversky and Daniel Kahneman (1972) 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) Psychologists say we do need to know about the cognitive box The rules explain how people make decisions, come to judgments, and solve problems The rules can be learned or hard-coded by evolutionary processes.
Cross fertilization between economics and psychology Behavioural economics Study the effects of social, cognitive, and emotional factors on economic decisions and resource allocation Concerned with the bounds of rationality of the economic agents
Gather all information necessary for rational judgment Make decision Uncertainty Heuristic (shortcut) In some situations, heuristics lead to predictable biases and inconsistencies BIAS In other words …… Behavioural rules in psychology work well under most circumstances, but in certain cases lead to systematic errors or cognitive biases
Some examples of cognitive biases Decision-making and behavioural biases Probability and belief biases Social biases Memory biases Loss aversion (endowment effect) – people demand much more to give up an object than they would be willing to pay to acquire it Outcome bias – People overestimate small probabilities and underestimate large probabilities Consistency bias – people often incorrectly think past attitudes and behavior resemble present attitudes and behavior. losing $100 affects your level of happiness much more than winning $100 Low 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 underestimated (Lichtenstein et al False consensus bias – People tend to overestimate the degree to which others agree with them
How do we know cognitive biases happen? Anchoring – the tendency of people to rely too heavily, or "anchor," on one trait or piece of information when making decisions Do experiments with people to find out how they might behave in different situations Guess the percentage of African nations that are members of the United Nations Was it more or less than 10% Was it more or less than 65% 45% on average 25% on average Before the experiment People with higher numbers (e.g. 85) 60 to 120% higher payment offered for the goods by people with higher numbers People with lower numbers (e.g 20) Consider whether you would pay this number of dollars for items value (e.g. wine, chocolate, computer equipment) with an unknown Question Group 2 Group 1 Group 2 Group 1 Write down the last two digits of your social security number Example of an experiment to establish cognitive bias
Things like confirmation bias which describes how people are more likely to search for or accept information that supports pre-conceived beliefs. Why do we care about cognitive biases? Raghu mentioned it – for instance climate change communication 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.
2011 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 bias We overestimate the prevalence of our personal opinions in society while we underestimate the prevalence of beliefs that conflict with our own 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 years 15% are unsure if climate change is real 7% of Australians believe that climate change isnt happening at all. That same 7% believe that almost 48% of the population agree with them.
Some 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 thats 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. CONFIRMATION BIAS- Believers search for or interpret information in a way that confirms their preconceptions AVAILABILITY BIAS - because believers think of it, the believers think it must be important."
Why 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 help As 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 that Qualitative 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 As Rashid said – economics can develop incentives to change behaviour - knowing about mental shortcuts people take in making decisions will help develop incentives that work Why is it useful to know about mental shortcuts that psychologists study (heuristics) when modelling human behaviour?
Systematically developed by Richard Levins (1966) Sign Directed Graphs (Signed Digraphs) Predator-Prey Introduction to qualitative modeling Qualitative models are based on signed digraphs A few historically significant scientific discoveries had to happen before qualitative modelling came along
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? Fibonacci number sequence: Fibonacci number sequence: Leonardo Fibonacci in 1202 (age 32) Geometric or Exponential Increase Liber Abaci (Book of Calculation)
Populations Increase Geometrically ( e r t ) Essay on the Principle of Population Thomas Malthus In 1798 (age 32) "The power of population is indefinitely greater than the power in the earth to produce subsistence for man" Resources Increase Arithmetically (x + y)
Alfred Lotka 1925 Vito Volterra 1926 PREY PREDATOR Predator-Prey Lotka-Volterra type equations describe the Darwinian evolution of a population density Charles Darwin
-α 1,2 0 +α 2,1 0 Levins 1968 Levins 1974 Community Matrix Signed Digraph Lotka and Volterra Mathematical relationship Richard Levins 1966
Community matrix - signs only Community Matrix a 11 -a a 21 -a 22 -a 23 0+a 32 -a 33 Change in 1. Small fish 2. Large fish 3. Fishery Due to interaction with 123 Fishery Large fish Small fish Change in 1. Small fish 2. Large fish 3. Fishery Due to interaction with 123 Self effect
Qualitative models can identify key drivers of change and predict the direction (+, -, 0) of response to change Press perturbation: shift in parameter leading to new equilibrium Pulse perturbation: shock to population or variable leading to transient dynamics 1 Qualitative modelling can be used to identify data gaps and hypotheses for further investigation 3 Qualitative models are relatively easy to produce with stakeholders (next step to building a conceptual model) What can qualitative modelling tell you – beside increases and decreases? …a very underrated tool in biology and social science (M.L. Cody 1985) Additional benefit of qualitative modelling Assess model stability (important for assessing the reliability of predictions) – if strong positive feedback system then unstable 2
Temperature Currents Rainfall Wind Sea level rise Cyclones & storms Climate drivers Pests & diseases Ecosystem integrity Retained species Emergent species Non-retained species Marine environment (ecological groups) non-fishing based recreation Commercial fishing Recreational fishing Marine tourism Charter fishing Traditional owners Aquaculture Renewable energy Other industrial use Marine sectors Australian example of qualitative model Connect climate change drivers, to marine environment and marine sectors (expert model)
Temperature Currents Rainfall Climate drivers Pests & diseases Ecosystem integrity Retained species Emergent species Non-retained species Marine environment (ecological groups) Commercial fishing Recreational fishing Marine tourism Charter fishing Aquaculture Marine sectors Wind Sea level rise Cyclones & storms non-fishing based recreation Traditional owners Renewable energy Other industrial use Build same model with community members What did we learn? Incomplete understanding of the whole system Will help shape communication/education/information
Commercial fishing activity Climate change Sea temperature Retained species Price of fish Profitability Currents Emergent species Fish abundance The pathway by which the fishers thought climate change affected them (fishers mental model)
Commercial fishing activity Method of lease quota trade Climate change Sea temperature Retained species Price of fish Profitability Oil & gas industry development Age Alternative income earning options Harbour access channel sand build up Public works funding Access to harbour Exchange rate Imports Variable cost Price of lease quota Admin. monitoring requirements Quota ownership Diversification options Exploratory licence rules Govt dept resources Vessel ownership Vessel Size Fixed cost Currents Emergent species Fish abundance Fishing pressure Season Government TAC levels Bank lending rules Family fishing history Family quota ownership Pass quota down Retirement funding options/ alternatives Quota ownership characteristics # 1 Quota trade characteristic # 2 Work opportunities # 3 Exploratory fishing # 4 Climate is not only thing that drives fishing activity (fishers mental model of where it fits in) Important to understand how these things fit together if we want to use policy to change the system - improve it – or make it more robust
Hakes model Merluccius capensis Yodzis 1998 Benguela Ecosystem - effects of seal cull on hakes (-)(-) (-)(-) + + (-) Example of how Qualitative models can provide powerful insight when you want to implement policy to improve the system Mc J Mc A JuvenilesAdults Live in shallow water Merluccius paradoxus Mp J Mp A JuvenilesAdults Live in deep water
Punt 1997 Shallow Deep Hakes model Merluccius capensis & Merluccius paradoxus model Benguela Ecosystem - effects of seal cull on hakes (-) (-)(-)(-)(-)
Reduced 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 region Another example of qualitative model in fisheries How QMs can address hypotheses regarding reduced banana prawn catch What happens when the model gets perturbed
Banana Prawn Subsystem P LP A P J P m Juveniles Adults Larvae Maturing P A P J N3 N2 N1 Prawn food Prawns Predators Example of qualitative model in fisheries
PL PA PJ Pm Nut Op Pr off-shore in-shore estuary Banana Prawn biological Subsystem Example of qualitative model in fisheries
Human system PL PA PJ Pm Pr Nut Op Pr off-shore in-shore estuary Example of qualitative model in fisheries Com catch CPUE Eff $ Environment & habitat Biological system Man hab Rain E Tur (est) Rec (est) Rec (oce) CSIRO Mathematics, Informatics, and Statistics – Jeff Dambacher Tur (oce) Rain L Sal
PL PA PJ Pm Pr Nut Op Pr off-shore in-shore estuary Example of qualitative model in fisheries Rec (est) Com catch CPUE E $ Tur (est) Man hab Rain E Rain L Sal Environment & habitat Biological system Commercial fishing economic system Recreational fishing system Rec (oce) Tur (oce) + - ? 0 PERTUBATION
Why use qualitative modelling? S. Metcalf, Murdoch University Few data required – only need signs of the interactions Reciprocal effects Negative effect Fish stocking Fish populati on Birth rates Female educa- tion Policy present on street Cars stolen Stocking of rivers with fish increases the abundance of fish Positive effect Police on the street will decrease the number of cars stole and if more cars get stolen this will increase police presence Female education will decrease birthrates 1
Can investigate direct and indirect interactions and their effects on the dynamics of the system Direct interaction Indirect interaction 3 Any type of interaction cay be included in qualitative models (biological populations, whole ecosystems, groups of people, economic variables, nutrients, social and demographic characteristics). 2 Birth rates Female educa- tion Wealth Why use qualitative modelling? Qualitative models are excellent for producing with stakeholders (participatory modelling) 4
(Participatory modeling) Stakeholders learn more about: How to structure and formulate their ideas Understand situation and possible options How to understand, discuss and cooperate with others Scientists learn more about: Stakeholders views and social behavior Ways of translating research into policy practice Policy makers benefit as legitimacy of models is enhanced Direct integration into the decision-making process Social and scientific validation Policy makers benefit from what the scientists and stakeholders have learned by developing the model together and from the legitimacy gained through this process Why involve the community in the modeling exercise?
QUALITATIVE MODELS MECHANISTIC MODELS STATISTICAL MODELS PRECISION REALISM GENERALITY REALISM PRECISION REALISM GENERALITY Richard Levins 1966 Omits small effects or large infrequent effects Functions often vaguely defined Loss of detail in space, time, and individual organisms Presumption of linearity and equilibrium Time lags not explicit Weaknesses of qualitative models
Approaches to Complexity Making the simple complicated is commonplace; making the complicated simple, awesomely simple, thats creativity. (Charles Mingus) Thanks to: Jeff Dambacher (CSIRO Mathematics, Informatics, and Statistics), Sarah Metcalf (Murdoch University), Pascal Perez (University of Wollongong)