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DEPARTMENT OF SOCIOLOGY
Agent-Based Modelling and
Microsimulation: Ne’er the Twain Shall
Meet?
Edmund Chattoe-Brown ([email protected])
http://www.le.ac.uk/sociology/staff/ecb18.html
Introduction
• Always a tricky business comparing
approaches “in general terms”: Your
mileage may vary as the Americans
put it.
• A number of concerns or questions
based around a simple example of
Agent-Based Modelling.
Agent-Based Simulation
• A very simple example: Not realistic but
the point will quickly become clear.
• Q: How do we explain urban residential
segregation between ethnic groups?
The Schelling model
• Agents live on a square grid so each site has eight neighbour sites.
• There are two “types” of agents (red and green) and some sites in the grid
are unoccupied. Initially agents and empty sites are distributed randomly.
• Each agent decides what to do in the same very simple way.
• Each agent has a preferred proportion (PP) of neighbours of its own kind
(0.5 PP means that you want at least half your neighbours to be your own
kind. Fractions are used so empty sites “don’t count” for satisfaction.)
• If an agent is in a position that satisfies its PP then it does nothing.
• If it is in a position that does not satisfy its PP then it moves to an
unoccupied position chosen at random.
• Each time period is defined to allow each agent (chosen in random order)
to “take a turn” at deciding and maybe moving.
Initial state
Two questions
• What is the smallest PP (between 0 and 1) that will produce clusters?
• What happens when the PP is 1?
Two (surprising?) answers
• PP about 0.3. People don’t have to be “xenophobic”
to generate residential clusters. If you had seen the
clusters in real data would you have “assumed”
xenophobia?
• As people get more “xenophobic”, clustering gets
“stronger” (clusters get more separate and have less
contact being “buffered” by empty sites) but at some
point, the clusters break down and with PP=1, the
system looks no different from the random starting
position.
Simple individuals but complex system
Individual Desires and Collective Outcomes
% Similar Achieved (Social)
120
100
80
60
% similar
% unhappy
40
20
0
0
50
100
-20
% Similar Wanted (Individual)
150
What about data?
• Individual data likely to be collected by
qualitative methods (ethnography, interviews,
perhaps experiments). This forms a testable set
of hypotheses.
• Aggregate data likely to be collected
quantitatively (surveys, GIS). The simulated
outcome of the individual actions is falsified
against similarity between simulated and real
data.
Important aspects
• No “fiddle factors” or “fitting”.
• No theory constructs.
• No “noise”.
• Simulation generates not just residential
clusters but other independent (?) patterns on
which it may be falsified like move histories,
behavioural clusters (on PP) and so on.
• Unambiguously causal claims.
Important cautions
• Degrees of fit?
• Not mistaking criticisms of the whole scientific
approach for criticisms of specific methods: If
each agent makes decisions in a unique way
then not just all modelling but all social science
must give up. Debate is about when (and to
what extent) different patterns exist to be found.
What about microsimulation?
• Very broadly speaking, social science seems to
divide into research on attributes (and their
relations: age, gender) and research on
practices (and their meanings). Microsimulation
leans towards the attribute approach.
• This can be seen not just in practices like
reweighting and uprating but also in processes
for “producing” data like matching/imputation.
“Evidence”
• Definition provided in Williamson Int. J.
Microsim, 1(1), 2007, p. 1.
• Worry: It isn’t the case that ABM and
microsimulation will naturally “meet in the
middle” because behaviours aren’t just another
“attribute” like gender or age. (In fact,
sociologists might argue that gender isn’t an
attribute either but a negotiated achievement.)
Avoiding missing the point
• Beyond a certain point there is no point in trying
to adjudicate definitively between different
methods. At best one can:
• Seek domains of application for different
approaches. (Most current methods don’t do
this, ABM included.) “Instructions on the can”.
• Explore consequences of particular methods.
• Recall constantly that each method is an “article
of faith”.
Concern 1: Explanation versus prediction
• Prediction is problematic in social science
because “pure” prediction may involve no
generalisation. Without explanation we can’t tell.
• Prediction gets limited credit when tuneable
parameters exist. Has a system “tuned” to
predict simply matched some output patterns
without tapping into underlying behaviour?
• ABM uses comparison (rather than straight
prediction) as its test of explanation.
Concern 2: Power and prediction
• In simple statistical models, the power of a test is
relatively well defined.
• In complex microsimulation models, it isn’t clear if the
quality of prediction relative to the quantity of data is
impressive or inevitable given the number of degrees
of freedom.
• This would be a problem for ABM too except that
predictive quality on a small number of “key” outputs
isn’t the test of the model. Ideally, the simulated data
should match all properties of the real data.
Concern 3: Exogeneity
• In econometrics, exogeneity is an empirically
determined property of variable systems.
• In ABM, the comparison requirement forces attention
onto what can “legitimately” be treated as external to
any given system. Getting it wrong means the model
stops delivering effective comparisons.
• Microsimulation appears to assume exogeneity, as
when it treats a demographic process as a trend which
will be “refitted” when ageing no longer works. Such
beliefs are not falsifiable but may be harmful.
Concern 4: Correlation and causation
• Under what circumstances should we assume,
for example, that missing data can be “filled in”
on the basis of attribute patterns in existing
data. It is done but can it be justified? If this (and
other things like it) are done without justification,
what do we do when prediction fails?
• By comparison with ABM, to what extent are
models calibrated (independent component
measurement) rather than jointly fitted?
Concern 5: Noise/randomness/error
• The importance of distinguishing “behavioural”
micro error (hand slipping) from “unmodelled”
randomness. Again, econometrics specifies
precisely the properties that noise/error terms
must have. Such effects can’t just be “thrown in”
like blur on an unflattering photograph.
• Does too much randomness (of the “wrong”
kind) allow one to predict anything?
Concern 6: Linearity
• As we can see from the Schelling example,
even very simple systems can be non-linear. In
these circumstances, there is a legitimate
concern about “adding up” analyses of attributes
which is broadly what microsimulation does.
• Can we “split up” the whole cloth of social
interaction along attribute lines and then expect
the components to “add back up” to sensible
outputs?
Concern 7: Behaviour
• Why “inherit” potentially problematic models, as
from economics for example?
• Sharper distinction needed between
“accounting” microsimulation and “behavioural”
microsimulation? In some sense AM is a
“purely” technical challenge. Can behaviour be
“bolted on” to a basically AM framework? (A
revisit of the earlier worry about whether
behaviour is “just another” attribute.)
Drawing these concerns together
• An individual based approach clearly ought to
be better than a highly aggregated one (ABM
and microsimulation agree on this).
• BUT how do we make sure (using some
combination of methodology and data) that
complex individual level models don’t end up
with too many degrees of freedom and pass the
prediction test illegitimately? ABM is evolving
ways to handle this issue. Is microsimulation?
Constructive suggestion 1
• We can use ABM to discover “how often” it is
safe to use what kinds of probabilistic models as
“reductions” (Hendry) of a Data Generating
Process.
• Unfortunately, even with ABM much simpler than
social behaviour is likely to be, the answer
seems to be, not very often.
Constructive suggestion 2
• There’s no reason, when “adding” behaviour to
microsimulation, not to add “proper” ABM
models. However, it is important to do this in a
way that doesn’t destroy the social (rather than
typically economic) assumptions built into them.
Constructive suggestion 3
• Microsimulation takes data much more seriously
than ABM does and this is admirable.
• Serious attention must be given to getting
“normal” ABM to track data, even approximately.
• Unfortunately, this does reveal a lot we really
don’t know. (Drunk and lamp-post story.)
• As long as ABM isn’t bolted awkwardly onto
microsimulation, it should be possible to get it to
do the sorts of things that make microsimulation
useful. (Politics!)
Conclusions
• The assumptions you don’t realise you are
making are the ones that will do you in!
• This discussion isn’t meant to imply that
ABM has no faults, it has many (and not
purely “technical” ones either) but that’s a
different talk!
Now read on?
• Journal of Artificial Societies and Social Simulation (JASSS):
• <http://jasss.soc.surrey.ac.uk/JASSS.html>
• simsoc (email discussion group for the social simulation community):
• <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=SIMSOC>
• Simulation for the Social Scientist, second edition, 2005, Gilbert and Troitzsch.
• Simulation Innovation, A Node (Part of ESRC National Centre for Research
Methods, conducting research, training and outreach in social simulation):
• http://www.simian.ac.uk, http://www.ncrm.ac.uk
• NetLogo (software used for these examples, free, works on Mac/PC/Unix and
comes with standard library of example programmes):
• <http://ccl.northwestern.edu/netlogo/>
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• I’d like to take these ideas on in collaboration with a
historian, with a view to funded research/a PhD
award.