Context-dependency, risk analysis and policy modelling

Context-dependency, risk analysis
and policy modelling

Bruce Edmonds
Centre for Policy Modelling,
Manchester Metropolitan University

Context-dependency, risk analysis and policy modelling, Bruce Edmonds, CRW@ECCS ’2012, Brussels, September 2012, slide 1

Part 1:

the Problem


“A large amount of research is pertinent to
public policy. But a seemingly tiny amount of
that knowledge makes it through to the policy
world. This is a frustration not only for the
research community but also for the policy
community…” (Chris Tyler 2010)


The “gap” between the worlds of
research and that of policy
• There is often a mismatch between the wishes of
policy advisors/makers and academic researchers
in terms of the kind of conclusion desired.
• Those in the policy world seem to wish for a
relatively simple “best guess” as to which of a
limited number of options might be most desirable
(possibly accompanied by a supporting narrative).
• Those in the research community might be all-too
aware of the difficulties of the issues under
consideration and so wish to be much more
cautious in their conclusions, and wish to include
special cases, caveats and complexities.


Some Bad Solutions

• The policy world can try to „force‟ researchers to
produce simplistic answers as to which of a limited
number of options is the best (to gain credibility for
a policy or even defray future blame).
• Researchers essentially „con‟ the commissioners of
research, claiming that they will answer all the
questions that are specified, but re-defining the
goals/terms as they go along or „discovering‟
obstacles to satisfying the policy makers/advisors
• The researcher and/or the policy advisor convince
themselves that the research does in fact give a
definitive, simple and possibly general answer and
rush to implement the policy indicated


The “gap” between the worlds of
research and that of policy

• There are many reasons behind these very
different views…
• …including different: kinds of goal,
motivations, training, strategies, language,
habits and constraints
• This talk will focus on a particular difficulty,
that caused by the context-dependency of
human socio-political behaviour


Part 2:

An Analysis of a Part of the Problem:
Context-Dependency


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


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, 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 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 use or reason with their
own knowledge
• That is perceiving and acting through their
network of social relationships
• Social embedding means that human
behaviour can not be understood well separate
from the surrounding social context
• This is not a problem for normal human action
as human cognition automatically (and
unconsciously) adapts to context


Researchers’ Responses to Context

A number of responses:
• Only do research within a single context,
resisting any generalisation
• Only use discursive, natural language
approaches where context is implicitly dealt
with (but also mostly hidden)
• Try to see what (inevitably weaker
knowledge) is general across the various
contexts in what is being studied


Policy responses to Context

• Wishing to know what is the “best guess” as
to which policy is best for most stakeholders
• Using their detailed and informed intuition
as to what the best policy is and looking for
a more “scientific” justification for it
• Wanting a general narrative that “explains”
(in comprehensible terms) why a certain
policy direction is desirable or which can be
used to build a consensus around


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


Context-Dependency
and “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

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


A Dilemma

• KISS: Models that are simple enough to understand
and check (rigour) are difficult to directly relate to
both macro data and micro evidence (lack of
relevance)
• KIDS: Models that capture the critical aspects of
social interaction (relevance) will be too complex
and slow to understand and thoroughly check (lack
of rigour)

• But we need both rigour and relevance
• Mature science connects empirical fit and
explanation from micro-level (explanatory and
phenomenological models)


What does not work…

• Inventing a model which only relates macro-
level variables, not taking into account the
social structures, using randomness to
“cover” the variation across contexts (might
as well use a straight line projection or
random number generator!)
• Presenting a policy maker/advisor with a
“warts and all” model/account that is so
complicated it is hard to work out a
coherent policy response

Part 3:

Some Suggestions


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

The End

Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org
Manchester Metropolitan University
http://www.business.mmu.ac.uk


Context-dependency, risk analysis and policy modelling

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