Personal understanding and publically useful knowledge in Social Simulation

Personal Understanding and Publically Useful
Knowledge in Social Simulation

Bruce Edmonds
Centre for Policy Modelling,
Manchester Metropolitan University

Personal Understanding and Publically Useful Knowledge in Social Simulation, Bruce Edmonds, ESSA Sum Sch, Toulouse, July 2012, slide 1

Some “stylized facts” about PhD
Students (and other new researchers)
• They are very keen to tell people all the
details of what they have done
• They often come across some text/person
which transforms their way of thinking
• They often want to convince the world of
their new conceptual framework or
methodology
• Their language can be very bound up with
their “home” research group, sometimes to
the extent that others find it hard to
understand them

This Talk will…

• …attempt to explain why these occur
• Point out some mistakes and confusions that
some researchers make
• To make a distinction between personal and
“public” knowledge
• Appreciate the value of non-communicable
knowledge
• To encourage you to think about what you
communicate to other researchers and how
• …and thus help you to have a greater impact
in terms of reporting your research


Outline of Talk

1. Introduction: Motivation and Some
Preparatory Philosophy
2. Personal Knowledge
3. Public Knowledge
4. Some Complications
5. Sharing Different Kinds of Things
6. Some Examples


Part 1:

Motivation and Some Preparatory
Philosophy


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)

SIH In academic life

• Different communities of practice develop
within different academic groups
• There will be a core of (often implict)
practices, styles, techniques, assumptions
that define that group and hence are (pretty
much) not alterable
• Other items (often explict) will be the focus
of debate within the community such as
models, data sets, explicit hypotheses etc.

Kuhn and scientific revolutions

• Kuhn (1962)
• Observed that science often progresses in
terms of fairly sudden revolutions rather
than via a gradual build up of knowledge
• “Revolutionary science” involves a change
in paradigm
• In between revolutions: “normal science”
• Effect of “theoretical spectacles” where data
is selected dependent on paradigm
• Different paradigms are incommensurable

Example: Continental Drift

• From Kuhn (1962)
• That the earth’s crust was composed of huge
“floating” plates that slowly moved was
resisted by established researchers for a long
time until the weight of evidence became
overwhelming and a sufficient of (mostly
younger) researchers had adopted this (Kuhn’s
“revolutionary science”)
• In contrast, ideas/results that are compatible
with existing ideas can be incrementally added
with relative ease (“normal science”)

Explanatory Coherence

• Thagard (1989)
• People choose whether to believe something
depending on how it would affect the
coherency of the (augmented) network of
(relevant) beliefs

Growing an internal “Ecology” of
Ideas
• As the network of ideas, associations and
knowledge grows and is pruned a stable
structure appears
• This is (usually) resistant to ideas that are
incoherent with what is already there
• Asking others to change whole parts of the
structure is unlikely to be accepted easily
• Ideas that strengthen the existing structure
(giving it more coherence) are more likely
to be accepted but only if it delivers a lot

Theoretical Spectacles

• Kuhn (1962): when we have adopted a theory
we tend to filter what we see:
– We notice aspects of data/observations that agree
with it or are explained by it
– We don’t notice (or explain away) anything that
does not fit with the theory
• We see the world “through” the present theory
• This effect is even stronger with agent-based
simulation models, because:
– they are readily interperable in terms of them
– the act of playing with a model over a period of
time involves you in the model and its construction

The Tethered Goat

In terms of ideas and assumptions, people are
like a tethered goat, they can wander a little way
from what they were taught but not far

The Key Question (for this talk)

What kinds of thing are usefully
communicated?

In other words, of all the knowledge-related
things one researcher has (e.g. ideas,
conceptual frameworks, models, proofs,
methodologies, data sets, case studies etc.)
what is worth telling other researchers about
(in presentations, papers etc.) and how?

The Sad Fact

Just because it is interesting and/or important
to you, does not mean it will be to others

Or to put it in a positive light…
Some things that are not useful and/or
meaningful to persuade others about, are
vitally important to us individually and can be
the powerhouse behind a creative academic
career

Part 2:

Personal Knowledge


Polyani and Personal Knowledge

• Polanyi (1974)
• Some kinds of knowledge are not explicitly
communicable…
• rather they are primarily passed on by doing
things together…
• involving action, observation and a close
feedback between people


Some Examples of Personal
Knowledge

• How to ride a bicycle
• What is socially acceptable at informal
occasions
• In what circumstances to use particular kinds
of language
• How to be a physicist/sociologist/biologist etc.
• How to talk
• What (most) words mean
• How to think

How to Simulate

• Although one can learn many supporting aspects of
social simulation, such as
– What the commands mean
– How to run programs, make graphs
– How to analyse results
– Particular algorithms
• A lot that is crucial about simulation is learning by
doing in a community of others, including:
– The style of doing social simulation
– What makes a good/interesting simulation
– How different simulation mechanisms work together
– How to relate/interpret simulation results to the
observed world


“Bridging Rules”

• Cartwright (1980)
• Makes a distinction between:
– Explanatory Laws – why things happen
– Phenomenological Laws – which literally fit the
data
• And pointed out that often these were different
• For example the gas laws, and the atomic
model of what is happening in a gas
• The connections between these were often left
implicit, as defined by a community of practice,
even in “mature” sciences – a kind of personal
knowledge

“Bridging” in Social Simulation

• The details of the simulation run provides a possible
“explanation” of the results but is very complex (which is
the point of doing it!)
• The simulator will develop an intuition and rules of
thumb…
• Which may be made explicit in terms of a hypothesis
about how/why the simulation gives the results it does
• Some simulations concentrate on making this
explanation as clear as possible but leave the
connection with what is observed as an analogy
• Others concentrate on connecting the specification and
results with evidence but leave any comprehensible
explanation vague or implicit – e.g. only implied by
graphs of results and textual description
• But really we need these linked, but this is rarely explicit

Conceptual Frameworks

• A set of inter-related ideas that a person (or
occasionally a research group) uses to understand
what is happening or what they are doing
• Can be the engine behind a stream of productive
research
• But is basically personal, only transmittable by a
longer-term interaction…
• And even then each person constructs their own
version internally
• Which is important in terms of developing different
approaches when the problem/environment
changes

Part 3:

Public Knowledge


Popper and Feyerabend

• Feyerabend (1975): It does not matter how
one gets hypotheses (indeed one should
not constrain this processes)
• Popper (1965): but these become publically
useful (i.e. something akin to knowledge) if
what is communicated is:
1. Possible to show it is wrong (which includes
being well sufficiently well-defined)
2. and people have ample chance to do so


The Sociality of Precise Entities

• Precisely defined entities (simulations, data sets,
hypotheses, proofs, algorithms) can be transmitted
faithfully
• That is, they survive distant transmission without error
• This allows for a very social process in science, where
different people can consider the same entities, which
provide a precise common reference
• And (possibly) allow a collective exploration of variations
of these (or uses of them)
• Which might result in a true evolution of these entities
• This does not mean that interpretations, discussions,
understanding of these are not important…
• …but that these might change with fashions, needs,
politics etc. over a shorter time frame


Transmitting Ideas via Precise
Entities

• Often, the most effective way to get an idea
accepted, is to transmit an associated precise
entity, such as a simulation
• You cannot control how the associated ideas
will be interpreted
• But at least this is re-interpreted from the
precise entity each time by each person rather
than being a re-interpretation of an
interpretation of an… etc.
• The precise entities often persist long after the
associated interpretations have lost potency

Part 4:

Some Complications


Context

• Humans unconsciously and automatically
learn to recognise/categorise kinds of
situation and then preferentially allows
access to memory based on this
• This means that what is relevant to a kind of
situation automatically comes to mind and
makes “foreground” conscious thought
feasible
• (with the exception of socially instituted
contexts) contexts are not usefully reifiable

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


Consequences of the Context-
Dependency of Human Thought
• Much academic explicit thought relies on the reliable,
but implicit, co-recognition of the appropriate context
• Making as much of the context assumptions explicit as
possible is an important part of being more scientific
• But it is impossible to do 100%
• Much social science keeps descriptions within context,
maintaining its qualitative richness, but this is less
“social” in terms of the collaborative processes of
science
• Thinking from within different cognitive contexts is a
creative and productive method
• But not itself usefully communicable (in possible
contrast to the results of that thought)


Thinking Analogically

• In an analogy the referential mappings to the domain of application are
flexibly created in a creative fashion each time
• Thus there is a part (“the analogy”) that is applied to a different
context, but…
• … its meaning (mapping of analogy to in-context references) is
different each time it is applied
• We are so adapt at applying ideas in an analogical fashion that we are
often unaware of the process
• The analogy may be transmitted in formal form (as a simulation or an
explicit hypothesis)
• Analogies give an impression of generality because the form may be
the same but, in fact, the mappings are different each time.
• This contrasts with “scientific” applications where what the different
parts of the model refer to are specified explicitly
• Analogical thinking is powerful in developing personal understanding,
but is different from actually modelling social phenomena
• Some simulations are used only as a computational analogy (e.g.
evolution of cooperation)

Inseparability of Ideas and Message

• Quite a lot of separation between ideas and the way
these are communicated, but this is never total.
• All communication does rely (eventually) on implicit
shared understanding (e.g. natural language, how to
make computational devices etc.) but, if this can be
relied upon, then expressions using this can be
effectively formal – such as an explicit hypothesis – and
hence used to effectively communicate precise
elements…
• ..but it also means that even precise entities carry with it
implicit “flavours/conations/assumptions/messages”
• …and that the effective communication of one kind of
entity often is facilitated by the communication of
associated entities.


Part 5:

Sharing Different Kinds of Things


Simulation Models

• Simulation models are effectively not just
the code of the simulation, but to be
meaningful, need a cluster of other things
– A set of results to help give an understanding of
what the simulation does
– Explicit hypotheses about it
– A variety of descriptions at different levels
about the simulation code
– A description of how this simulation is currently
being interpreted in terms the meaning of its
parts in terms of what is being modelled

Theories

• Theories are not simple, despite the
impression that might be given in papers
etc. (Giere 1988)
• But rather, like simulations, have different
aspects, including:
– Its formal expression
– exemplar precise formulations of this (models)
– the meaning of its terms


Data Sets

• These can be very persuasive but are not
often shared
• There are now websites to do this with, but
there are issues of confidentiality, license
conditions etc.
• Helpful to be accompanied with descriptions
of:
– Context of data
– Summaries of it, graphs tables
– An example of its use
– Maybe some hypotheses it seems to
confirm/disconfirm


Ideas

• Single ideas are somewhat difficult to
transmit
• As explained, people are not open to new
ideas, unless they provide added value in
terms of making their thought systems more
coherent or in terms of what it allows them
to do
• Again, ideas travel better with precise
exemplars

Conceptual Frameworks

• Trying to communicate a whole framework is
very VERY difficult
• Not only because the elements of this rely on
their relation to all the other elements and so is
hard to communicate bit by bit
• It is hard to “think outside the box” from within
such a framework and make your thoughts
understandable by others
• Often explicit exemplars that come out of the
framework are more effective “ambassadors”
for the framework

Methodologies

• Almost always, a mixture of precise and
implicit knowledge parts
• Similar to a conceptual framework, in that
you have to “live” it, so that one learns it
within a community that follows it, in
addition to those parts that are explicitly
described (statistics, SNA methods, etc.)
• Often what people do is primary and
rationales of why are subsequent to this,
which it makes difficult to “convert” others

Visions

• This is a general story, a motivating narrative
that outlines what could come to pass
• This seems to “travel” relatively well as
narratives seem to be innate in humans
• However these tend to be transitory and highly
related to fashion in academic and funding
circles
• Also one cannot control the interpretation of
these narratives, once “released” they are out
of your control

Part 6:

Some Examples and Conclusion


Axtell & Epstein’s Growing Artificial
Societies

• They had huge difficulty in getting their
simulation work published in social science
and economic journals
• The book was successful far more in terms of
the simulations demonstrated than in the ideas
in the book
• As a result of the exemplars of what (even
simple) simulations could achieve this enabled
an interest that sparked exploration of
alternatives to analytic-dominated approaches

Axtell et al. Aligning Simulation Models

• Argued simple points – that replication is
important but also surprisingly hard
• But persuasive in terms of a series of
examples of actually doing it
• Motivation for others to do it is that it can
allow the attack on the interpretation of
simulation models by revealing hidden
assumptions (as in Edmonds & Hales 2003)


Deffuant, Amblard and Weisbuch
How Can Extremism Prevail?

• Showed a simple model, that others could
easily re-implement and play with
• Had some explicit hypotheses
• And lots of results, with extensive
illustration, exploration and analysis
• The intended interpretation only played a
small part in this


Barreteau, Bousquet & Attonaty Role-
Playing Games for Opening the Black Box of
Multi-Agent Systems

• Is also illustrated with an extensive case
study to convince that the approach is not
only possible but enables researcher to do
things not possible elsewhere
• Primarily cited as an exemplar of the
participatory approach and of
interdisciplinary research work
• Showed a new approach (to most) and
what one can achieve using it

Edmonds and Moss KISS vs KIDS

• A conceptual paper…
• But, the “KIDS” idea was taken up as a
label primarily by those that were already
making complex evidence-based
simulations
• Probably nobody who believes in simple
(KISS) models was convinced
• The body of relevant KIDS simulations and
their usefullness will ultimately determine its
take up

Many “PhD” papers I review…

I don’t want to name these but many I see
tend to…
• Concentrate on what they did in terms of
the specification of their simulations etc.
• Have a certain “zeal” in wanting to explain
their personal conceptual framework
• Have a relative lack of results
Of course, many more mature researchers do
this too!

Conclusion

• The aim of this talk is to make you more aware
of the impact of academic social life on your
activities and research
• In particular, to motivate you to think about
what you try to communicate in formal ways
(theses, papers, conference presentations)
and what can only come into play in extended
interactions (discussions, collaborations) with
others and as a personal treasure of ideas,
intuitions etc.
• For example, telling others what you have
done in great detail or trying to convince others
of your personal conceptual framework

References
• Axtell, R., R. Axelrod, et al. (1996). "Aligning Simulation Models: A Case Study and Results." Computational
and Mathematical Organization Theory 1: 123-141.
• Barreteau, B., Bousquet, F. and Attonaty, J-M. (2001) 'Role-Playing Games for Opening the Black Box of
Multi-Agent Systems: Method and Lessons of Its Application to Senegal River Valley Irrigated Systems’,
Journal of Artificial Societies and Social Simulation, 4(2):5 (http://jasss.soc.surrey.ac.uk/4/2/5.html)
• Boyd and Richerson (1985) Culture and the evolutionary process, University of Chicago Press.
• Cartwright, N. (1983) How the Laws of Physics Lie, Oxford University Press.
• Deffuant, G. Amblard, F. and Weisbuch, G. (2002) How Can Extremism Prevail? a Study Based on the
Relative Agreement Interaction Model, Journal of Artificial Societies and Social Simulation, 5(4):1
(http://jasss.soc.surrey.ac.uk/5/4/1.html)
• Edmonds, B. and Hales, D. (2003) Replication, Replication and Replication - Some Hard Lessons from
Model Alignment. Journal of Artificial Societies and Social Simulation 6(4)
(http://jasss.soc.surrey.ac.uk/6/4/11.html)
• Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P.
Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence,
3415:130–144. (http://cfpm.org/cpmrep132.html)
• Epstein, J. M. and Axtell, R., (1996) Growing Artificial Societies: Social Science from the Bottom Up, MIT
Press.
• Feyerabend, P. (1975) Against Method. New Left Books.
• Giere, R. (1988) Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.
• Kuhn, T (1962) The Structure of Scientific Revolutions, University of Chicago Press.
• Kummer, H., Daston, L., Gigerenzer, G., & Silk, J. (1997). The social intelligence hypothesis. In P. Weingart,
P. Richerson, S. D. Mitchell & S. Maasen (Eds.), Human by nature: Between biology and the social
sciences. Hillsdale, NJ: Erlbaum.
• Polanyi, M. (1974) Personal Knowledge: Towards a Post-Critical Philosophy, University of Chicago Press.
• Popper, K. R. (1979) Objective Knowledge: An Evolutionary Approach. Oxford University Press.
• Reader, J. (1990) 'Man on Earth'. Penguin Books.
• Thagard, P. (1989) Explanatory coherence, Behavioral and Brain Sciences, 12:435-502.


Ad. for a workshop 5/6 Sept!
The End

Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org


Personal understanding and publically useful knowledge in Social Simulation

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (20)

Similar to Personal understanding and publically useful knowledge in Social Simulation

Similar to Personal understanding and publically useful knowledge in Social Simulation (20)

More from Bruce Edmonds

More from Bruce Edmonds (20)

Recently uploaded

Recently uploaded (20)

Personal understanding and publically useful knowledge in Social Simulation

Editor's Notes