the scandal of generic models in the social sciences

The Scandal of Generic Models in the Social Sciences, Bruce Edmonds, Gronigen, , April 2013, slide 1

The Scandal ofGeneric Models in the Social Science

Bruce Edmonds

Centre for Policy Modelling,Manchester Metropolitan University


Outline of Talk

1. The Main Point

2. A Bit About Context

3. Social Roots of Cognitive Context

4. Defensive Responses

5. Some Ways Forward


The Main Point(in four slides)

Part 1:


The Elephant in the Room

• Many aspects of human cognition are known to be highly context-sensitive, including: memory, preferences, language, visual perception, reasoning and emotion

• There is a mountain of qualitative research that has documented instances where a specific context is essential to understanding the observed behaviour

• Simple observation and introspection tells us that behaviour in different kinds of situation is not only different but decided on in different ways (e.g. in a lecture and a football game)


However despite this…

• Almost all formal models of human behaviour (mathematical, logical or computational) are generic – they do not exhibit this sharp context-dependency

• Another stream of models (models fitted to or tested against data) consider a single model (at a time) against a set of data that derives from many different contexts

• This seems to me to be a case of massive “wishful thinking”


Qualitative research…

• Does take context seriously in a way, but has (largely) retreated to description within specific (and described) contexts

• Knowledge is only useful if it is to some extent applicable in a new situation (even if only slightly new)

• Analogical reasoning can use knowledge from one context to be projected upon another, and this can give insights (interesting hypotheses) but not reliable knowledge

• This kind of research often avoids responsibility for the application of knowlnecessarily in a different situation to where it was observededge gained from its studies () whilst implying it is somehow useful


Context

To summarise:• We have one set of researchers who are ignoring context,

optimistically hoping to find general patterns even though they must know context must be crucial in many cases

• Another set of researchers refuse to look at what is general across contexts or how contexts might systematically affect behaviour

• Not many are seriously trying to study social context itself – how it works, what regularities there are, how to identify it, how to model its impact

• Social context is central to human behaviour but effectively not researched much


The Point of the Talk is…

1. To look at the role context (again!)

2. To look at (and argue against) some of the arguments that are raised to justify ignoring context

3. To persuade you to take context seriously, not just as an “add-on”

4. To suggest some positive ways forward

5. To (hopefully!) motivate you to join this project and help contribute methods that will help us seriously tackle context


A Bit About ContextPart 2:


The Difficulty of 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


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


Implications of the Context Heuristic

• One would expect whole clusters of norms, habits, expectations, knowledge, language, ways of making decisions etc. etc. to be associated with a context

• Although we may use various proxies for context (place in network, physical location, occurrence of co-occurring words etc.) kinds of situation are recognised in an information rich manner, often with quite subtle social signals

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


Social Roots of Cognitive ContextPart 3:


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


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 Social Roots of Context-Dependency

• 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!


Some Defensive ResponsesPart 3:


Some Possible Responses

• Its too difficult, I’ll continue to ignore it• I am only 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/features 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• We can achieve generality through simpler models• I need an analytic expression for my model• Use natural language/analogical models only• I don’t have enough data


Background – wishful thinking and the resistance to change

• Everyone (me included) has developed certain core habits/skills and is loth to abandon these, since it is a lot of effort

• There is also a tendency to shape our opinions/arguments to be coherent with our goals (rather than the other way around)

• We should not assume that the world we study (the social world) is organised for our benefit (as academics studying it)

• Thus I ask you to resist the impulse to react against “yet another difficulty” – the difficulty is real and exists even if we ignore it


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 not 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

What appears to be random may in fact be due to variation of context


Using Randomness as a Proxy for Contextual Variation

• It is completely unsurprising that many factors will be significantly correlated with many outcomes in a multi-context situation

• It is also unsurprising that the explanatory level of such exercises are unimpressive

• The correlation may be due to a strong correlation in only one kind of context and, indeed, mask anti-correlations in others

• Using randomness as a proxy for contextual variation simply discards a lot of the information in the phenomena – it amounts to ignoring evidence


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 identified 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• Integrating personal/anecdotal accounts of behaviour –

making use of qualitative evidence with its context• Not leaving the context(s) – acting within the normal sphere of

shared and relevant situations• Staging abstraction more gradually• Clusters of related models covering different contexts


Data-mining Rich Data

• Rich Data is data that:– Encodes many different aspects of the

event/situation/actor– Is over time (not one-shot) and reasonably

frequent– Is not merely lots of data if this is thin (e.g.

second-by-second coordinates of where a person is)

• The first stage is to look for “Local Patterns” – patterns that hold for a part of the data along with their scope


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 may suggest a Context

M1 M2

M1

suggests a context


Need for a Meta-Clustering Algorithm

Item Type

Time of Day

ShopLocation

Suggestsa Context


Adding Context-Dependency into Simulation Models

• Wander has argued for a needs-oriented cognitive architecture for the agents in a simulation

• Here the idea is to allow the agents in a simulation to learn/reason/decide in a context dependent fashion

• This does not affect the method of each of these, but does change what knowledge, expectations etc. the methods act upon


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: an artificial stock market

• Traders decide which of several stocks to hold, or to keep cash

• Fundamentals are the dividend rates which follow slow random walks

• Agents evolve and evaluate possible strategies by what their wealth would be (including all aspects like transaction costs) of using these over a past time period

• Some agents can be given a context-dependent memory of strategies, others a universal store of strategies


Example – models in the cognition of a trading agent

700

750

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750 850 950

Volume - past 5 periods

Vol

atili

ty -

pas

t 5

perio

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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


Lessons from Stock Market Example

• Traders with a context-dependent memory learned to recognise certain market conditions and develop/select strategies specifically for these

• Though the context heuristic was not always better – e.g. if the market was very turbulent then it is better to act fast and adaptively

• In any case “context traders” acted in a different kind of pattern to “generic traders”

• The presence of “context traders” changed how the whole market reacted


Integrating Aspects of Qualitative Evidence into Formal Models

• Identifying kinds of context (those over which we might expect some regularity in terms of shared norms, expectations etc.) might allow suggestions from qualitative evidence to be incorporated into agent-based models

• For example by providing the repertoire of possible strategies in the context which are decided between

• This could greatly enrich agent-based models allowing in some of the social “mess” we observe

• However this requires new methods to analyse narrative evidence in a context-depenent manner


Analysing Narrative Evidence in a Context-Sensitive Manner

• It is hard since people assume context, it is usually left implicit, indeed people are often not aware of the context they are assuming

• However…– Socially entrenched contexts can be identified– When giving examples (could you imagine doing that in

situation X) people are relatively good at recognising when the context is wrong

– Our intuitions are a good starting point, as long as we are aware they might be wrong

• No well-developed methods – this needs further research


Conclusions – Prospects for Researching Context• 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

• Ignoring context seems to be impeding the integration of both: action-oriented and model-based approaches, as well as quantitative and qualitative approaches

• Please help


Acknowledgements

Many thanks to all those with whom I have discussed these ideas, including…

Emma Norling, Nick Shryane, Jason Dykes,Scott Moss, Wander Yager, Cathy Urquhart;

those at the Conference Series on “Modelling & Using Context”;

the regulars at the Manchester Complexity Seminar; those in the SCID Project;

and the participants of the recent Lorentz workshop on “Formal Methods for an Informal World”.


The End

Bruce Edmondshttp://bruce.edmonds.name

Centre for Policy Modelling http://cfpm.org

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