The Sociality of Context

The Sociality of Context, Bruce Edmonds, Social.Path, Surrey, June 2014, slide 1

The Sociality of Context

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University


Talk Outline

1. A bit about Context

2. Context-Dependency in Cognition

3. Context-Dependency and Society

4. An Architecture for Cognitive Context

5. Learning/Identifying Context


Context

• “Context” is used in many different senses across different fields

• The senses and concepts herein come from discussions and papers presented at the international series of conferences on “Modelling and Using Context”

• Somewhat of a “dustbin” concept resorted to when more immediate explanations fail (like “complexity”!)

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


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 deals with situational context effectively, abstracting kinds of situations so relevant information can be 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?


An Illustration of the Heuristic

Model

Factor 1

Factor 2

Factor n

Factor n+1Factor n+2

Etc.

Inferences/predictionsModel

Factor 1

Factor 2

Factor n

Factor n+1Factor n+2

Etc.

Outcomes

Foregroundfeatures

Later recognition

1. Learning Situation 2. Application Situation

Possible abstraction to a ‘context’


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, skill, habit etc. 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


Co-Development of Shared Context

• Over time certain kinds of situation get recognised as similar by individuals

• These individuals may further distinguish it as a specific context

• Knowledge is remembered and stored associated with that context

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

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

• This self-reinforcing loop can entrench the context and ensure its persistence and identifiability over time


Social Context

• Heavily entrenched contexts may become institutionalised in terms of infrastructure, training etc. (e.g. how to behave in lectures)

• Thus easily distinguishable and widely recognised

• Socially entrenched contexts can be explictly labelled and talked about

• This reification allows the contexts to be further entrenched as it might be demarked and institutionally protected


Social Advantages of an Ability to (Co)Recognise Context

If (broadly and roughly) the same contexts are recognised by different people then…• Communication is easier – assumptions

about situation likely to be shared• Terms specific to that kind of situation can

be developed focusing on the foreground• Different ways of coordinating can evolve

for each different kind of situation, allowing the kind of coordination to be suitable for the situation


Some Ways in Which Dealing with Context is Avoided by Researchers

• Some seek to only consider behaviour that is general (i.e. across context), treating the unexplained variance as noise

• Some retreat to only considering behaviour within very specific contexts

• Some treat context-specific behaviour as if it is “just more detail”, and so keep to abstract representations to avoid this

• Others simply ignore it and hope that more generic representations will do


Basic Cognitive Model

• Rich, automatic, messy, unconscious context recognition using many inputs (including internal ones) – ML

• Crisp, conscious, explicit processes using material indicated by cognitive context: reasoning and learning – AI

Context Recognition

Context-Structured Memory

Reasoning/Planning

Belief Revision/Learnin

g


Context-Dependency Makes Learning and Reasoning Feasible

• Context allows us to progress beyond the ‘loose’ loop of:repeat learn/update beliefs deduce intentions, plans and actionsuntil finished

• to a more integrated loop of:repeat repeat recognise/learn/choose context induce/adapt/update beliefs in that context deduce predictions etc. within that context until predictions are consistent… and actions/plans can be determined plan & actuntil finished


context identification

CIS

CDM

inputs from enviroment or problem

actions and/or plans

negative feedback if wrong context

feedback on under- or over-determination

within context update or learning

cognitive context

LL

IS within context

reasoning

An Implemented Cognitive Architecture

Local Learning algorithm

Context-Identification

System

Context-Dependent Memory

Inference system


Example ABM using this Architecture

• Artificial Stock Market with market maker and trading costs• Several traded stocks and cash• Fundamentals in form of slow random walking dividend

rate for each stock• Agents learn models to predict strategies using a rich GP-

like language• Models evaluated over recent time period, then best

selected, effect of actions predicted and action selected• Memory a space of market volume vs. volatility where

models are ‘placed’ as learnt• Context agents additionally select a region of memory

according to current conditions and learn/evaluate/select models according to the currently region of memory

• Other agents the same with the whole memory selected


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


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]]]


Other Useful Loops

• If there is not enough knowledge to determine action, expand the context scope

• If action is over-determined then narrow the scope

context identification

CIS

CDM

inputs from enviroment or problem

actions and/or plans

negative feedback if wrong context

feedback on under- or over-determination

within context update or learning

cognitive context

LL

IS within context

reasoning


Learning/Recognising Context

• Context seems to be recognised in a rich, unconscious and “fuzzy” manner

• Using both external (how others behave) and internal (emotions, goals) cues

• Some social contexts will have labels but others only indirectly inferable

• However, social context should be identifiable by clusters of behaviours, norms, habits etc. all having the same conditions of occurence


• A possible context is where models that fit different kinds of behaviour have a similar scope

• You know you are in the wrong context if many aspects of your knowledge fail simultaneously

Clusters of Conditions of Application suggest a Context

M1 M2

M1

suggests a context

Spa

ce o

f po

ssib

le

cond

ition

s


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


So What?

• Human behaviour might be more regular when situations are divided into contexts

• Identifying similar context may gives clues as to when regularities can be generalised

• Context-sensitivity allows different kinds of coordination for different kinds of situation

• Big Data may allow for clues to context• Accepting the complication of context may facilitate

a better interface with the social sciences• It may allow for better human-machine interfaces

(ones that are less socially inept)


The End

Bruce Edmondshttp://bruce.edmonds.name

Centre for Policy Modelling http://cfpm.org

These slides will be uploaded to:

http://slideshare.net/BruceEmonds

Education

The Sociality of Context