Personal understanding and publically useful knowledge in Social Simulation

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

Personal Understanding and Publically Useful Knowledge in Social Simulation

Bruce EdmondsCentre for Policy Modelling,

Manchester Metropolitan University


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


Motivation and Some Preparatory Philosophy

Part 1:


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


Personal KnowledgePart 2:


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


Public KnowledgePart 3:


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


Some ComplicationsPart 4:


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.


Sharing Different Kinds of ThingsPart 5:


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


Some Examples and ConclusionPart 6:


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 WeisbuchHow 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.


The End

Bruce Edmondshttp://bruce.edmonds.name

Centre for Policy Modelling http://cfpm.org

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Personal understanding and publically useful knowledge in Social Simulation