Mundane Rationality as a basis for modelling and understanding behaviour within specific contexts

Mundane Rationality as a basis for modelling and understanding

behaviour within specific contexts

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University

Social Intelligence Hypothesis (SIH)

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

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

• This explains the prevalence of specific abilities such as: imitation, language, social norms, lying, alliances, gossip, politics etc.

• Social intelligence is not a result of general intelligence applied to social organisation, but the essential core of human intelligence

• in fact our “general” intelligence could be merely a side-effect of social intelligence

Mundane Rationality, Bruce Edmonds, NESS@ECCS, Vienna, September 2011, slide 2

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 very different ways of coordinating, negotiating, cooperating etc.

• That these will relate as a complete “package” to a significant extent, that has developed over time and passed down to new members


An Evolutionary Perspective

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

different, (sub)cultures of technologies, norms 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 composed of separate groups with different cultures and survival strategies, are able to survive catastrophes that effect different niches in different ways (specialisation)


Social Embedding (SE)

• Granovetter (1985) AJS 91 (3): 481-510• 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 modelsMundane Rationality, Bruce Edmonds, NESS@ECCS, Vienna, September 2011, slide 5

Illustration of Causal Complexity

Lines indicate causal link in behaviour over time, each box an agent’s talk or action decision (Edmonds 1999)


Implications of Social Embedding

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

• This 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

• Trying to make social knowledge explicit, rather than adapt to it may be infeasible due to the complexity of the social embedding

• Our personal networks of friends and colleagues becomes our extended social body


Context-dependency of Cognition

• “The” context is the situation of an event, but this is indefinitely extensive

• The brain somehow categorises and recognises different kinds of situation and preferentially gives access to knowledge on this basis, it is context-dependent

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


The Context Heuristic

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

• Knowledge, habits, expectations 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

• These learnt kinds of situation are socially co-developed in time becoming entrenched in society and passed down the generations

• For example: lectures, interviews, parties• Not a general heuristic, but one particularly suited to

the complexity of a socially constructed environment


The general idea for context-dependent intelligence

fuzzy but rich learning & recognition

of the context

precise reasoning &

belief update within context

Perception

Action


Clusters of Domain and Content make a Context

M1 M2

M1

Abstract to a context


Implications of Context-Dependency

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

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

• Social contexts might need to be co-developed, changing and sometimes instituted (e.g. a lecture)

• These may need to be different for different groups• Some kinds of social behaviour are necessarily context-

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

etc. will be generic and hence amenable to explicit programming


Development of Social Context

• The context-dependency of cognition has a very social use, the development of mutually recognised contexts– If a particular kind of situation is recognisable by participants– Then specific language, habits, behaviours, norms, etc. will start to

be developed for that situation– The more that happens, the more the particular situation will be

distinguishable• Over time, social contexts become institutionalised and easy

(for us as observers) to identify• E.g. Lectures, parties, interviews etc.• Indeed it seems we construct our world (buildings, etc.) to

facilitate our context-dependent intelligence• Affords identifiable opportunities to utilise the knowledge and

computation of others, promoting social embedding, which in turn lock people in to these facilities


“General” Intelligence

• Intelligence: the ability of an actor/agent to decide what to do to achieve its goals in its situation and given its knowledge

• This is involves learning what works in any particular situation where there are time and resource constraints (e.g. how to socially embed when appropriate)

• Given the No Free Lunch theorems, this means that there is no “best” strategy for all situations, but rather each strategy is suited for particular kinds of situation

• Humans do not possess a general intelligence, but one with particular biases and aptitudes, e.g. to facilitate social organisation

• Unlike computation, there is no “general” model of intelligence – any generalisation, meta-strategy, mix of strategies etc. with be a disadvantage in some situations

• Looking for a foundational model of intelligence (one that can be specialised for particular circumstances) is hopeless, and recalls the doomed “Hilbert Programme” in mathematics


Implications for Models of and Implementations of Intelligence• Whilst there are formal systems (some logics, set

theory, Turing Machines etc.) that are “complete” in theory– They all make some things easier to do and some

harder, thus having representational biases– It does not mean there is any general and effective

means of finding the right computation even if we know one exists

• Rather, the structure of any intelligence needs to be suited to its environment and goals

• In particular, the intelligence of a social agent needs to be suited to its social environment


Herbert Simon

• Administrative Behaviour (1947) observed how people actually behaved in context, and it was not in any ideal form of rationality, but rather a set of relatively simple, but specific procedures– “The human being striving for rationality and restricted

within the limits of his knowledge has developed some working procedures… These procedures consist in assuming that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited range of consequences.” (1967)

• Not the same as his later, and much copied, concept of “bounded rationality” which is just an hobbled version of an ideal rationality


An Illustration of Simon’s (1967) Rational Decision Making


Summary – Mundane Rationality

• There is no such thing as a general intelligence or rationality, even in theory

• All intelligence is biased towards its environment/problems Human intelligence is biased:– Its survival advantage was social and thus so is our intelligence– It uses a context-dependent heuristic: a mixture of context-

recognition and crisp, relatively simple beliefs/strategies within these contexts

• Patterns of behaviour can be very specific to the particular context, but might be quite simple once the right context is recognised

• These patterns will be highly socially influenced but in a way that is specific to the context (which might include the group)


Consequences for Representing Human Behaviour in our ModelsGiven all this there are three approaches:

1. Make simple specific models only within a particular context (“in vitro” modelling)

2. Identify the separate contexts and have a (at least somewhat) different model for each along with a mechanism for recognising when context switches

3. Allow agents to individually learn the specific behaviours in a context-dependent manner and socially situated manner (resulting in among other things social embedding)


1. Behaviour within a single context

• Models might be simple, but you don’t know a priori which are applicable in the context

• It might be that traditional models of rationality are OK in some contexts (especially when the context is designed for these models as in many experiments)

• But it might be even simpler than these• Asking participants, observing them, analysing

narrative accounts are good places to start• Downsides include: difficult to delineate scope

of context, specificity of resultsMundane Rationality, Bruce Edmonds, NESS@ECCS, Vienna, September 2011, slide 20

2. Multi-context specification

• Specify N contexts, and for each of these a behavioural model (as in last slide)

• But people may consider the same situation in different mental contexts, e.g. a lecture as a dramatic performance vs. a lecture as a job interview

• Eliciting when people mentally flip between contexts is hard as this is largely unconscious

• But can be done by presenting examples to participants within contexts

• Also can involve an exponential effort in NMundane Rationality, Bruce Edmonds, NESS@ECCS, Vienna, September 2011, slide 21

3. Agents individually learning Context and within context behaviours

• Allow the agents to themselves learn the appropriate contexts along side learning the appropriate beliefs, language, habits, norms etc. within those contexts

• In other words to allow the agents to become individually acculturated

• Requires a model of context-dependent learning and reasoning etc.

• It takes time!• Difficult for us (as researchers) might find it difficult

to understand the culture that results, since we are not acculturated


Model Structure

How the:• context-identification system (CIS), • the context-dependent memory (CDM), • the local learning algorithm (LL), • and Inference system (IS) could work together

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


700

750

800

850

900

950

750 850 950

Volume - past 5 periods

Vol

atili

ty -

pas

t 5

perio

dsSnapshot of (most frequent) model domains in one trader


The (simplified) model contents in snapshot of one trader

model-256 priceLastWeek [stock-4]


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

model-273 IDidLastTime [stock-2]


model-399

minus [divide

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

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


Some Simulation Work addressing Context-Dependency in Simulations• (Schlosser & al 2005) argue that reputation

is context dependent• (Edmonds & Norling 2007) looks at

difference that context-dependent learning and reasoning in an artificial stock market

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

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


Conclusion

• Generic models of how decisions are made are doomed, although one can pretend to oneself that the “variance” from a generic model is random (due to different decisions coming about in different individual mental contexts), this itself is an assumption with consequences

• One can (sometimes) use simple models for behaviour, but only within context

• A lot of “field work” needs to be done to identify strategies, habits, norms etc. within contexts

• A need for techniques to systematically and transparently analyse qualitative data to inform our models of within context behaviour


The End

Bruce Edmonds

http://bruce.edmonds.name

Centre for Policy Modelling

http://cfpm.org

Social Intelligence and Multi-Agent Systems, Bruce Edmonds, MALLOW 2010, Lyon, 31st August, slide 29

Comparison in an Artificial Stock MarketEnvironment:• Traders (n context, n not)• 1 Market maker: prices and deals: 5 stocks• Each fundamental (the interest given on owning

stocks) changes by a slow random walk• Traders buy and sell shares at current market price,

but do not have to do so (there is simple cash too)• Traders have memories, can observe actions of

others, index, etc.• Traders have a limited budget• Modelling ‘arms-race’• Actions change environment

Agent Cognitive Model

• A 2D memory space of volatility vs. volume because these are associated with the “feel” of a market

• Current values of volatility and volume determine a radius of considered models, the current best (evaluated over a past period) is chosen and used to determine next action

• Compared to agents with a similar algorithm but a “point” memory space (all models equally accessible)


Agent models

• Rich language of models for their expectations of future prices

• Including those that enable chartist, fundamentalist, personal imitation, calculations on past prices, averages, own last actions etc.

• Agents decide what to do, based on projections using their current best model, their budget, transaction costs, fundamental, actions of others etc.


The primitives allowed in models

• AND, averageDoneByLast, averageIndexOverLast, averageOccuredToStockLast, averageSaidByLast, boundedByMaxPrice, divide, dividendOf, doneByLast, F, greaterThan, IBoughtLastTime, IDidLastTime, indexLag, indexLastTime, indexNow, indexTrendOverLast, ISaidLastTime, ISoldLastTime, lagBoolean, lagNumeric, lastBoolean, lastNumeric, lessThan, maxHistoricalPrice, minus, myMoney, NOT, onAvIBoughtLastTime, onAvISoldLastTime, OR, plus, presentStockOf, priceDownOf, priceLastWeek, priceNow, priceUpOf, randomBoolean, randomIntegerUpTo, randomPrediction, saidByLast, T, times, totalStockValue, volumeLastTime; F, indexLastTime, indexNow, maxHistoricalPrice, myMoney, onAvIBoughtLastTime, onAvISoldLastTime, randomBoolean, randomPrediction, T, totalStockValue, volumeLastTime plus: the names of the traders, stocks and a random selection of constants


Model Structure

How the:• context-identification system (CIS), • the context-dependent memory (CDM), • the local learning algorithm (LL), • and Inference system (IS) could work together


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

A CDM Learning and Update Algorithm


Some Space of Characteristics

D

p

2.1

3.7

0.9

2.2


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 Single Run

Black=context, White= non-context


700

750

800

850

900

950

750 850 950

Volume - past 5 periods

Vol

atili

ty -

pas

t 5

perio

dsSnapshot of model domains in one trader


The (simplified) model contents in snapshot of one trader



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



model-399

minus [divide

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

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

Conclusion of Example

• In the stock market example, nothing always works better, since other agents adapt to any winning strategy by a few

• The advantages of embedding or not-embedding are mixed here

• but contexts: recognised market “moods” did co-develop in certain periods

• There was a complex “lock-in” between the individual abilities and the organisation, one not easily predicted before experimentation

• Taking inspiration here from an observed system for how to program agents and how they relate for that system


About the model• 1D space for trait (or location) with tolerance following

Riolo et al. (2001) Nature, 411:441-443• 2 essential foodstuffs, each needs learning to extract via

matching a pattern with a model• Individuals adapt their models (one for each foodstuff)

that allows them to try and extract each foodstuff• Share food and models with those within their tolerance

– Food if they have excess– Model if (a) their model is fitter or (b) they are older

• Depending on nutrition levels they can reproduce (with possible mutation) die etc.

• Two kinds of model – suitable fuzzy model or unsuitable exact expression

• Derived from a model of symbiosis/specialisation (Edmonds 2006)

Evolutionary Models of Information Transmission in Human Societies, Bruce Edmonds, Darwin09, UIB, Mallorca, Nov. 2009, slide 39

Traits with Tolerance


Range of tag values

Tolerance value

Exact, not forced to specialise, model acceptance on fitness


Exact, forced to specialise , model acceptance on fitness


At time 100

At time 100

Fuzzy, not forced to specialise, noisy transmission, model acceptance on fitness


Fuzzy, not forced to specialise, acceptance of models by age


Second example of learning models to predict heart disease


seru

m c

hol

est

ero

l

resting blood pressure

Only models with more than two instances shown

Mundane Rationality as a basis for modelling and understanding behaviour within specific contexts

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