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Putting people into models Starting with qualitative models

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1 Putting people into models Starting with qualitative models
Ingrid van Putten CSIRO – Marine and Atmospheric research (Hobart- Australia) CSIRO Mathematics, Informatics, and Statistics

2 One of a number of modeling approaches
If you use it depends on What you want from model? (Understand, Predict, Modify) What can different types provide? (Generality, Precision, Realism) Don’t need much data QUALITATIVE MODELS Good for combining bio-physical and human domain – but philosophically – can we actually model humans? PRECISION GENERALITY REALISM Richard Levins 1966 PRECISION PRECISION GENERALITY REALISM GENERALITY REALISM STATISTICAL MODELS MECHANISTIC MODELS

3 Philosophical perspective Can we model human behaviour?
Behaviorism: probably…? Metaphysics: never…! Ivan Pavlov (1849 –1936) Burrhus Frederic Skinner (1904 – 1990) Aristotle Plato Behaviour shaped by response to environmental stimuli Human beings perceive, assess, decide, and act. Modellers need algorithms for each stage Human beings aren’t reducible to any description. Transcendental nature of ‘self’ and cognition. But how do we “observe” and interpret what human beings do

4 Assuming we can model human behaviour
How do we observed it? Inductive reasoning Deductive reasoning Based on observation Based on interpretation Inference of general principles or rules from specific facts Inference of specific facts from general principles or rules What’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 agents Formal logic compliant agents

5 Gather 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 …… 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

6 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.

7 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

8 Gather all information necessary for rational judgment
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

9 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.

10 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 Question People with higher numbers (e.g. 85) Group 1 60 to 120% higher payment offered for the goods by people with higher numbers People with lower numbers (e.g 20) Group 2

11 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.

12 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 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

13 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

14 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

15 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

16 Leonardo 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 144 Geometric or Exponential Increase

17 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"

18 Lotka-Volterra type equations describe the Darwinian evolution of a population density
Charles Darwin PREY PREDATOR Predator-Prey Alfred Lotka 1925 Vito Volterra 1926

19 -α1,2 -α1,2 +α2,1 +α2,1 Mathematical relationship Community Matrix
Lotka and Volterra Richard Levins 1966 Community Matrix Signed Digraph -α1,2 -α1,2 +α2,1 +α2,1 Levins 1968 Levins 1974

20 Qualitative modelling
Positive effect Negative effect Predator-Prey Competition Mutualism Amensalism Commensalism Self-Effect

21 Community Matrix 3 2 1 Due to interaction with 1 2 3 1. Small fish
+a21 -a22 -a23 +a32 -a33 Change in 2. Large fish 3 Fishery 3. Fishery Self effect 2 Large fish Community matrix - signs only Due to interaction with 1 Small fish 1 2 3 1. Small fish - + Change in 2. Large fish 3. Fishery Self effect

22 Additional 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 change Press perturbation: shift in parameter leading to new equilibrium Pulse perturbation: shock to population or variable leading to transient dynamics 1 Assess model stability (important for assessing the reliability of predictions) – if strong positive feedback system then unstable 2 Qualitative modelling can be used to identify data gaps and hypotheses for further investigation 3 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)

23 non-fishing based recreation
Australian example of qualitative model Connect climate change drivers, to marine environment and marine sectors (‘expert model’) Temperature Currents Wind Cyclones & storms Climate drivers + Sea level rise Rainfall - + 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

24 Build same model with community members
Temperature Wind Sea level rise Cyclones & storms non-fishing based recreation Traditional owners Renewable energy Other industrial use Currents Climate drivers Rainfall Emergent species Pests & diseases What did we learn? Incomplete understanding of the whole system Will help shape communication/education/information Retained species Marine environment (ecological groups) Ecosystem integrity Non-retained species Commercial fishing Aquaculture Marine sectors Charter fishing Marine tourism Recreational fishing

25 Commercial fishing activity
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

26 Climate is not only thing that drives fishing activity (fisher’s mental model of where it fits in)
Climate change Sea temperature Currents Quota ownership characteristics # 1 Bank lending rules Family fishing history Family quota ownership Pass quota down Retirement funding options/ alternatives Retained species Emergent species Quota trade characteristic # 2 Method of lease quota trade Fishing pressure Season Government TAC levels Variable cost Price of lease quota Admin. monitoring requirements Quota ownership Vessel ownership Vessel Size Fixed cost Fish abundance Exchange rate Imports Price of fish Harbour access channel sand build up Public works funding Access to harbour Diversification options Exploratory licence rules Govt dept resources Oil & gas industry development Age Alternative income earning options 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 Exploratory fishing # 4 Work opportunities # 3 Profitability Commercial fishing activity

27 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 (-) Mc J Mc A Juveniles Adults Live in shallow water Merluccius capensis Hakes model + (-) + Merluccius paradoxus Mp J Mp A Juveniles Adults Live in deep water + Yodzis 1998

28 Benguela Ecosystem - effects of seal cull on hakes
Merluccius capensis & Merluccius paradoxus model Hakes model + + (-) (-) + - + + Shallow Deep - - Punt 1997

29 Another example of qualitative model in fisheries
How QMs can address hypotheses regarding reduced banana prawn catch What happens when the model gets perturbed 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 In 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 were prawn 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.

30 Example of qualitative model in fisheries
Banana Prawn Subsystem P L P A Larvae Adults P J P m N3 N2 N1 Prawn food Prawns Predators Juveniles Maturing P J P A

31 Example of qualitative model in fisheries
Pr PL off-shore Nut Op PA PJ estuary Banana Prawn biological Subsystem Pm in-shore The 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.

32 Example of qualitative model in fisheries
CPUE Eff $ Rec (est) Rec (oce) Comcatch Human system Pr Pr Tur (est) Rain E PL Op PA Pr Rain L Sal Manhab Tur (oce) Nut PJ estuary Pm off-shore Nut in-shore Biological system Environment & habitat CSIRO Mathematics, Informatics, and Statistics – Jeff Dambacher

33 Example of qualitative model in fisheries
Rec (oce) Rec (est) CPUE Comcatch Recreational fishing system Commercial fishing economic system $ Pr Pr + PL Op - PA Pr ? Nut PJ estuary Pm off-shore Nut in-shore Biological system Environment & habitat Tur (est) Manhab Rain E Tur (oce) Rain L Sal PERTUBATION

34 Why use qualitative modelling?
1 Few data required – only need signs of the interactions Fish stocking Fish population Stocking of rivers with fish increases the abundance of fish Positive effect Birth rates Female educa-tion Negative effect Female education will decrease birthrates Police on the street will decrease the number of cars stole and if more cars get stolen this will increase police presence Policy present on street Cars stolen Reciprocal effects S. Metcalf, Murdoch University

35 Why 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). 2 Birth rates Female educa-tion Wealth Can investigate direct and indirect interactions and their effects on the dynamics of the system Direct interaction Indirect interaction 3 Qualitative models are excellent for producing with stakeholders (participatory modelling) 4

36 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

37 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 REALISM GENERALITY Richard Levins 1966 STATISTICAL MODELS MECHANISTIC MODELS

38 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)

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