Transcript 1 - IMBER
Putting people into models Starting with qualitative models Ingrid van Putten CSIRO – Marine and Atmospheric research (Hobart- Australia) CSIRO Mathematics, Informatics, and Statistics One of a number of modeling approaches If you use it depends on What can different types provide? (Generality, Precision, Realism) What you want from model? (Understand, Predict, Modify) Don’t need much data QUALITATIVE MODELS Good for combining biophysical and human domain – but philosophically – can we actually model humans? PRECISION GENERALITY REALISM Richard Levins 1966 PRECISION GENERALITY REALISM STATISTICAL MODELS PRECISION GENERALITY REALISM MECHANISTIC MODELS Philosophical perspective Can we model human behaviour? Behaviorism: probably…? Ivan Pavlov (1849 –1936) Burrhus Frederic Skinner (1904 – 1990) Behaviour shaped by response to environmental stimuli Human beings perceive, assess, decide, and act. Modellers need algorithms for each stage But how do we “observe” and interpret what human beings do Metaphysics: never…! Aristotle Plato Human beings aren’t reducible to any description. Transcendental nature of ‘self’ and cognition. Assuming we can model human behaviour How do we observed it? Inductive reasoning Based on observation Inference of general principles or rules from specific facts Deductive reasoning Based on interpretation Inference of specific facts from general principles or rules What’s next? “Causal black box” “Cognitive white box” Do we need to know what goes on in the cognitive box (the brain) when Formal logic Empirical heuristicsmodelling people? compliant agents based agents Do we need to know what goes on in the cognitive box when modeling the way people make decisions? Ask an economist …… Faced with a problem Gather all information necessary for rational judgement Uncertainty Make decision Person acts rationally in complete knowledge out of self-interest and the desire for wealth homo economicus Not much gain from knowing what goes on in the cognitive box Psychologists 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 judgement Uncertainty Heuristic (shortcut) Make decision 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 In some situations, heuristics lead to predictable biases and inconsistencies Gather all information necessary for rational judgment Uncertainty Heuristic (shortcut) BIAS Make decision 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 Loss aversion (endowment effect) – people demand much more to give up an object than they would be willing to pay to acquire it losing $100 affects your level of happiness much more than winning $100 Probability and belief biases Outcome bias – People overestimate small probabilities and underestimate large probabilities 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 Social biases False consensus bias – People tend to overestimate the degree to which others agree with them (Lichtenstein et al. 1978 Memory biases Consistency bias – people often incorrectly think past attitudes and behavior resemble present attitudes and behavior. How do we know cognitive biases happen? Do experiments with people to find out how they might behave in different situations Example of an experiment to establish cognitive bias Anchoring – the tendency of people to rely too heavily, or "anchor," on one trait or piece of information when making decisions Question Guess the percentage of African nations that are members of the United Nations Group 1 Was it more or less than 10% 25% on average Group 2 Was it more or less than 65% 45% on average Before the experiment Write down the last two digits of your social security number Consider whether you would pay this number of dollars for items value (e.g. wine, chocolate, computer equipment) with an unknown People with higher Group 1 60 to 120% higher payment numbers (e.g. 85) offered for the goods by people People with lower with higher numbers Group 2 numbers (e.g 20) Question Why do we care about cognitive biases? Raghu mentioned it – for instance climate change communication Things 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. 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 7% 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 years 15% are unsure if climate change is real 2011 paper on public perceptions of climate change by the CSIRO 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 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 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 Why 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 work 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 Introduction to qualitative modeling Systematically developed by Richard Levins (1966) Qualitative models are based on signed digraphs Sign Directed Graphs (Signed Digraphs) Predator-Prey A few historically significant scientific discoveries had to happen before qualitative modelling came along 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 89 144 Geometric or Exponential Increase Essay on the Principle of Population Populations Increase Geometrically (e r t ) Resources Increase Arithmetically (x + y) Thomas Malthus In 1798 (age 32) "The power of population is indefinitely greater than the power in the earth to produce subsistence for man" Lotka-Volterra type equations describe the Darwinian evolution of a population density Charles Darwin PREY dN1 N1 birth 1, 2 N 2 dt PREDATOR dN 2 N 2 2,1 N1 death dt Predator-Prey Alfred Lotka 1925 Vito Volterra 1926 Mathematical relationship dN1 N1 birth 1, 2 N 2 dt dN 2 N 2 2,1 N1 death dt Lotka and Volterra 1925-1926 Richard Levins 1966 Community Matrix 0 -α1,2 +α2,1 0 Levins 1968 Signed Digraph -α1,2 +α2,1 Levins 1974 Qualitative modelling Positive effect Negative effect Predator-Prey Competition Mutualism Amensalism Commensalism Self-Effect Change in Community Matrix 1. Small fish 2. Large fish 3. Fishery Due to interaction with 1 2 3 -a11 -a12 0 +a21 -a22 -a23 0 +a32 -a33 3 Fishery 2 Large fish 1 Small fish Self effect Change in Community matrix - signs only 1. Small fish Due to interaction with 1 2 3 0 2. Large fish + - - 3. Fishery 0 + Self effect What can qualitative modelling tell you – beside increases and decreases? 1 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 2 Assess model stability (important for assessing the reliability of predictions) – if strong positive feedback system then unstable 3 Qualitative modelling can be used to identify data gaps and hypotheses for further investigation Additional benefit of qualitative modelling Qualitative 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) Marine sectors Marine environment (ecological groups) Climate drivers Australian example of qualitative model Connect climate change drivers, to marine environment and marine sectors (‘expert model’) Temperature - Wind Cyclones & storms Other industrial use Renewable energy Currents + + Rainfall Emergent species Sea level rise Pests & diseases Retained species Non-retained species + Ecosystem integrity Commercial fishing Aquaculture Charter fishing Marine tourism Recreational fishing Traditional owners non-fishing based recreation Marine sectors Marine environment (ecological groups) Climate drivers Build same model with community members Temperature Wind Currents Rainfall Emergent species Retained species Cyclones & storms Sea level rise Pests & diseases What did we learn? Incomplete understanding of the whole system Will help shape communication/education/information Ecosystem Non-retained species integrity Commercial fishing Aquaculture Charter fishing Marine tourism Recreational fishing Traditional owners Other industrial use non-fishing based recreation Renewable energy The pathway by which the fishers thought climate change affected them (fisher’s mental model) Climate change Sea temperature Currents Retained species Emergent species Fish abundance Price of fish Profitability Commercial fishing activity Climate is not only thing that drives fishing activity (fisher’s mental model of where it fits in) Retirement funding options/ alternatives Vessel Size #1 Vessel ownership Family quota ownership Quota ownership Fixed cost Harbour access channel sand build up Sea temperature Family Currents Retained species fishing Quota ownership history characteristics Pass quota down Climate change Bank lending rules Emergent species Quota trade Method of lease quota characteristic Admin. monitoring requirements Fishing pressure trade #2 Season Price of lease quota Variable cost Fish abundance Imports Exchange rate Price of fish Exploratory licence rules Public works funding Government TAC levels Govt dept resources Age Oil & gas industry development Important to understand how these things fit together Diversification Alternative income Work Exploratory to Profitability options if Access we want to use policy to change the system improve it – earning options harbour opportunities fishing or make it more robust #4 Commercial fishing activity #3 Example of how Qualitative models can provide powerful insight when you want to implement policy to improve the system Benguela Ecosystem - effects of seal cull on hakes (-) Merluccius capensis Live in shallow water Hakes model + (-) + Mc J Juveniles + Mp J Juveniles Mc A Adults Mp A Adults Merluccius paradoxus Live in deep water Yodzis 1998 Benguela Ecosystem - effects of seal cull on hakes Merluccius capensis & Hakes model Merluccius paradoxus model + (-) + (-) + + + - Shallow Deep Punt 1997 Another example of qualitative model in fisheries How QMs can address hypotheses regarding reduced banana prawn catch What happens when the model gets perturbed • • • • • • Weipa region 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”. Example of qualitative model in fisheries Banana Prawn Subsystem PL PA Larvae Adults PJ Juveniles PJ Pm Predators N3 Prawns N2 Prawn food N1 Maturing PA Example of qualitative model in fisheries Pr Pr off-shore PL PA Op Pr Nut estuary PJ Pm Nut in-shore Banana Prawn biological Subsystem Example of qualitative model in fisheries Eff Rec (est) Rec (oce) CPUE Com $ catch Human system Pr Pr PL Op PA Pr Nut estuary PJ off-shore Pm Nut Tur (est) Man hab Biological system in-shore Rain Tur E (oce) Rain L Environment & habitat Sal CSIRO Mathematics, Informatics, and Statistics – Jeff Dambacher Example of qualitative model in fisheries E Rec (est) Rec (oce) CPUE Com Commercial fishing economic system Recreational fishing system $ catch Pr Pr + PL - Op PA Pr ? Nut estuary PJ Pm Nut Tur (est) Man hab Biological system in-shore Rain Tur E (oce) PERTUBATION 0 off-shore Rain L Environment & habitat Sal Why use qualitative modelling? 1 Few data required – only need signs of the interactions Positive effect Negative effect Reciprocal effects S. Metcalf, Murdoch University Fish stocking Fish populati on Stocking of rivers with fish increases the abundance of fish Birth rates Female education Female education will decrease birthrates Policy present on street Cars stolen Police on the street will decrease the number of cars stole and if more cars get stolen this will increase police presence Why use qualitative modelling? 2 Any type of interaction cay be included in qualitative models (biological populations, whole ecosystems, groups of people, economic variables, nutrients, social and demographic characteristics). Birth rates 3 Female education Can investigate direct and indirect interactions and their effects on the dynamics of the system Indirect interaction Direct interaction 4 Wealth Qualitative models are excellent for producing with stakeholders (participatory modelling) Why involve the community in the modeling exercise? (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: Stakeholder’s 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 Weaknesses of qualitative models 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 QUALITATIVE MODELS PRECISION GENERALITY REALISM PRECISION Richard Levins 1966 GENERALITY REALISM STATISTICAL MODELS PRECISION GENERALITY REALISM MECHANISTIC MODELS Approaches 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)