context in environmental modelling– the room around the elephant

Context in Environmental Modelling - the room around the elephant, Bruce Edmonds, iEMSs, Leipzig, July 2012, slide 1

Context in Environmental Modelling– the room around the elephant

Bruce EdmondsCentre for Policy Modelling,

Manchester Metropolitan University


Acknowledgements

Many thanks to all those with whom I have discussed these ideas, including: Emma Norling, Nick Shryane, Jason Dykes, Scott Moss, those at

the Conference Series on “Modelling & Using Context”, the regulars at the Manchester Complexity

Seminar and those in the SCID Project.


Some Questions about Context

• How important is the context when modelling process/aspect/system X/Y/Z?

• How much can we ignore context…• …or, conversely, how much of the context

do we have to include within our models?• If we include context-dependency does that

stop us being scientific?• How can we square the context-dependency

of the observed/involved world with our models of that world?


Talk Outline

1. Context-dependency in the environment

2. Context-dependency in human behaviour

3. Some defensive responses to context-dependency

4. Some possible ways forward


Note on Talking about Context

• The word “context” is used in many different senses across different fields

• Somewhat of a “dustbin” concept resorted to when more immediate explanations fail (like the other “c-word”, complexity)

• Problematic to talk about, as it is not clear that “contexts” are usually identifiably distinct

• Mentioning “context” is often a signal for a more “humanities oriented” or “participatory/involved” approach and hence resisted by “scientists” who are seeking general laws


Ecological Context-DependencyPart 1:


Ecological Context

• A certain kind of environment might provide certain affordances/difficulties

• Organisms adapt to exploit these but also create new affordances/difficulties

• Migration between similar ecologies makes organisms ready to exploit each type available

• The organisms are only fully understandable in their ecological context – the web of other organisms and their environment


A (colourful!) Picture of the World

• Each square (patch) is a different, well-mixed location

• There are 15 kinds of location with individuals in each (4 bit string)

• Small stars are herbivores, circles those who have eaten another (the bigger the more it has eaten)

• Different colours indicate different species (not all species are visually distinguishable)


Brief (!) Model Outline

• Basic energy economy (life tax, 90% transference, reproduction at 3, birth at 1 etc.)

• Patches and organisms have a binary vector (lengths 4 and 100 respectively)

• Fixed 100x100 random matrix made at start that broadly determines…

• …who can eat who (or who extract energy from environment) determined by eater & eaten’s binary strings (sum of entries in matrix at rows and columns indicated by 1s)

• Slow processes of mutation, migration etc.


Simulation at (up to) Reference Point

CarnivoresAppear

First SuccessfulHerbivore

Simulation“Frozen”


From this point on…

50 times for each of 16 different “aspects” (as well as none, the base case)…• Reset world to this point• “Block” interaction on one of the dimensions (the

entries in the matrix indicated by 1s in that column/row number are not summed)

• Simulate the world for a further 100 ticks (with different random seed each time)

• Measure the genetic diversity of the population overall and by each niche type (average hamming distance between all distinct agents)


Affect of Blocking Different Aspects of Interaction (av. over 20 runs after 100 ticks, ±2SD)


Effect of Blocking Aspects of Interaction by Aspect

Base Case(no blocking)


Implications of Environmental Context-Dependency

• Whilst there are some underlying universals that affect the environment (water, genetics, energy…)

• What characterises “the” environment is that it is not singular but a complex, overlapping patchwork of different ecological contexts

• We can gain some understanding of what is happening within each context, but generic understandings across these can be weak


Context-Dependency in Human Behaviour

Part 2:


A (simplistic) illustration of context from the point of view of an actor


Situational Context

• The situation in which an event takes place• This is indefinitely extensive, it could include

anything relevant or coincident• The time and place specify it, but relevant

details might not be retrievable from this• It is almost universal to abstract to what is

relevant about these to a recognised type when communicating about this

• Thus the question “What was the context?” often effectively means “What about the situation do I need to know to understand?


Cognitive Context (CC)

• Many aspects of human cognition are context-dependent, including: memory, visual perception, choice making, reasoning, emotion, and language

• The brain somehow deals with situational context effectively, abstracting kinds of situations so relevant information can be easily and preferentially accessed

• The relevant correlate of the situational context will be called the cognitive context

• It is not known how the brain does this, and probably does this in a rich and complex way that might prevent easy labeling/reification of contexts


The Context Heuristic

• The kind of situation is recognised in a rich, fuzzy, complex and unconscious manner

• Knowledge, habits, norms etc. are learnt for that kind of situation and are retrieved for it

• Reasoning, learning, interaction happens with respect to the recognised kind of situation

• Context allows for the world to be dealt with by type of situation, and hence makes reasoning/learning etc. feasible

• It is a fallible heuristic…• …so why do we have this kind of cognition?


Social Intelligence Hypothesis

• Kummer, H., Daston, L., Gigerenzer, G. and Silk, J. (1997)

• The crucial evolutionary advantages that human intelligence gives are due to the social abilities it allows

• Explains specific abilities such as imitation, language, social norm instinct, lying, alliances, gossip, politics etc.

• Social intelligence is not a result of general intelligence, but at the core of human intelligence, “general” intelligence is a side-effect of social intelligence


An Evolutionary Perspective

Social intelligence implies that:• Groups of humans can develop their own

(sub)cultures of technologies, etc. (Boyd and Richerson 1985)

• These allow the group with their culture to inhabit a variety of ecological niches (e.g. the Kalahari, Polynesia) (Reader 1980)

• Thus humans, as a species, are able to survive catastrophes that effect different niches in different ways (specialisation)


Implications of SIH

• That different complex “cultures” of knowledge are significant

• An important part of those cultures is how to socially organise, behave, coordinate etc.

• One should expect different sets of social knowledge for different groups of people

• That these might not only be different in terms of content but imply different ways of coordinating, negotiating, cooperating etc.

• That these will relate as a complete “package” to a significant extent


Social Embedding

• Granovetter (1985) • Contrasts with the under- and over-socialised

models of behaviour• That the particular patterns of social

interactions between individuals matter• In other words, only looking at individual

behaviour or aggregate behaviour misses crucial aspects

• That the causes of behaviour might be spread throughout a society – “causal spread”

• Shown clearly in some simulation models


Illustration of Causal Complexity

Lines indicate causal link in behaviour, each box an agent (Edmonds 1999)


Implications of Social Embedding

• In many circumstances agents can learn to exploit the computation and knowledge in their society, rather than do it themselves (invest in what Warren Buffet invests in)

• Knowledge is often not explicit but is something learned – this takes time

• This is particularly true of social knowledge – studying guides as to living in a culture are not the same as living there for a time

• Social embedding means that human behaviour can not be understood well separate from its cultural context


The Social Co-Development of Shared Recognised Context

• Over time, due to their similarities, certain kinds of situation become recognised as similar by participants

• This facilitates the development of a set of shared habits, norms, knowledge, language etc. that is specific to the context

• The more this happens the more distinctive that kind of situation becomes and hence more recognisable by newcomers

• Eventually these may become institutionalised in terms of infranstructure, training etc. (e.g. how to behave in a lecture theatre)

• This co-development of context may be the reason for its social/evolutionary value


Implications of the Context-Dependency of Human Behaviour

• Behaviour of observed actors might change sharply across different social contexts

• The relevant behaviour, norms, kinds of interaction etc. might also change

• Social contexts are co-developed and changing• They may be different for different groups• Some kinds of social behaviour seem to be inherently

context-dependent (compliance)• It is unlikely that a lot of key social knowledge, norms,

behaviour etc. will be generic• Models that assume a cross-context engine of human

behaviour may be deeply misleading!


My Central Point

• Given the sharp context-dependency of both human behaviour and the environment…

• …how is it that a lot of our models use generic models of human behaviour and/or the environmental response?


Defensive ResponsesPart 3:


Some Possible Responses

• Its too difficult, I’ll ignore it• I am looking at the wider/more general picture, what is

common across contexts• I treat intra-context variation as random noise• I have included context, it is the variables a, b, c etc.

which vary with the context• I am acting within context only• I am only modelling a single context• It is not scientific • I need an analytic expression for my model• Use natural language/analogical models only• I don’t have enough data


Ignoring Context

• Much modelling happens with a single context in mind, in which case it can be ignored but only if– everyone is using the same idea of this context– there is no significant “leakage” of causation

from outside the background, that is the scope is wide enough to include all significant influencing factors

– The actors/organisms don’t deal with the same situation as different cognitive contexts


The “Simple is more General” Fallacy

• If one has a general model one can make it more specific (less general) by adding more processes/aspects…

• …in which case it can become more complex• However, the reverse is no true…• If one simplifies/abstracts then you don’t get a more

general model (well almost never)!– there may be no simpler model that is good enough for

your purpose– But, even if there is, you don’t know which aspects can

be safely omitted – if you remove an essential aspect if will be wrong everywhere (no generality)


Context-Dependency and Randomness

Lots of information lost if randomness used to “model” contextual variation


Scaling by Size

• Look at variance as system size increases…• Does variance as a proportion of size disappear?• In this case Law of large numbers does not apply• Simple examples:

• Kaneko (1990): parallel globally coupled chaotic processes• Edmonds (199?): scaling Brian Arthur’s “El Farol Bar” Model

Size

Variance(scaled by size)

Contextual variation

Model with random noise


Context-Dependencyand “Being Scientific”

• If the relevant context can be reliably indentified then…

• …context-dependency is not the same as subjectivity (even if there are a some hard cases that escape definition)

• Generality is nice if you can get it, but its no good pretending to have it if you can’t

• Science should adapt to what it wishes to understand, not the other way around

• It does mean (often) an acceptance that general/generic approaches are not useful


Analogical Thinking

• Humans are good at using analogies, relating an idea or example from one context to another in a rich, relevant and flexible manner – it is a powerful method of thought

• They build the mapping from the analogy to the a context “on the fly”, largely unconsciously

• The mappings are different each time an analogy is applied, thus not a reliable source of transmittable knowledge – each person might build a different mapping unless they inhabit the same context

• Many published models do not have an explicit mapping to a domain, but are used more as analogy

• This is sometimes hidden, so when a simulation (or analytic model) does not directly map to observations but to an idea which then applies as an analogy to the domain and not directly, this gives a spurious impression of generality


Some Ways ForwardPart 4:


Some ways forward

• Keeping the data and simply NOT summarising it (at least not prematurely)

• Data mining local patterns to detect commonality of multiple models/measurements across similar contexts

• More complex simulation models with context-dependent cognitive models

• Context-sensitive microsimulation models• Context-oriented visualisation techniques• Use of “mundane”, context-specific models of human behavior rather

than ambitious generic ones• Integrating personal/anecdotal accounts of behaviour – making use of

qualitative evidence• Not leaving the context(s) – acting within the normal sphere of shared

and relevant situations• Staging abstraction more gradually• Clusters of related models


Cleveland Heart Disease Data Set – the processed sub-set used

In processed sub-set:• 281 entries• 14 numeric or numerically coded attributes• Attribute 14 is the outcome (0, 1, 2, 3, 4)• Some attributes: age, sex, resting blood

pressure (trestpbs), cholesterol (chol), fasting blood sugar (fbs), maximum heart rate (thalach), number of major vessels (0-3) colored by flourosopy (ca)

• From the Machine Learning Repository


Fitting a Global Model (R=56%)

Num = -0.01*age + 0.17*sex + 0.20*cp + 0.00*trestbps + 0.10*restecg + -0.01*thalach + 0.23*exang + 0.18*oldpeak + 0.16*slope + 0.43*ca + 0.14*thal + -0.60 (+/- 0.83)


Looking for Clusters in HD Data Set (Start of Process)


Final Set of Clustered Solutions

• Final solution set after some time.

• Still complex but some structure is revealed

• Note presence of “fbs” despite not being globally correlated and that “chol” helped define the context space


A useful context is one that:– includes related models with different

goals/predictions but similar scope

Clusters of Model Scopes suggest a Context

M1 M2

M1

suggests a context


Basic Cognitive Model

• Rich, automatic, imprecise, messy cognitive context recognition using many inputs (including maybe internal ones)

• Crisp, costly, conscious, explicit cognitive processes using material indicated by cognitive context

Context Recognition

Context-Structured Memory

Reasoning/planning/belief

revision/etc.


Example – models in the cognition of a trading agent

700

750

800

850

900

950

750 850 950

Volume - past 5 periods

Vol

atili

ty -

pas

t 5

perio

ds


The model contents in snapshot of one trader

model-256 priceLastWeek [stock-4]

model-274 priceLastWeek [stock-5]

model-271 doneByLast [normTrader-5] [stock-4]

model-273 IDidLastTime [stock-2]

model-276 IDidLastTime [stock-5]

model-399

minus [divide

[priceLastWeek [stock-2]] [priceLastWeek [stock-5]]]

[times [priceLastWeek [stock-4]] [priceNow [stock-5]]]


0

5000

10000

15000

20000

25000

30000

0 100 200 300 400 500Time

To

tal V

alu

e o

f Ass

ets

Total Assets in a Typical Run

Black=context, White= non-context


Some Simulation Work addressing Context-Dependency in Cognition

• (Schlosser & al 2005) argue that reputation is context dependent

• (Edmonds & Norling 2007) looks at difference that context-dependent learning and reasoning makes in an artificial stock market

• (Andrighetto & al 2008) show context-dependent learning of norms is different form a generic method

• (Tykhonov & al 2008) argue that trust is context dependent


Conclusions

• Ignoring it and simply hoping it won’t matter is not an option (if we are serious about our project)

• There are ways forward to meaningfully make progress in dealing with context-dependency

• And some of these involve the integration of qualitative/in situ approaches with quantitative/formal modelling

• We will need a LOT more data both multi-dimensional and at a finer-granularity, but this is starting to come on stream

• Context seems to be an important factor impeding the integration of both: action-oriented and model-based approaches, as well as quantitative and qualitative approaches

• Please help


The End

Bruce Edmondshttp://bruce.edmonds.name

Centre for Policy Modelling http://cfpm.org

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