DEPARTMENT OF SOCIOLOGY

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DEPARTMENT OF SOCIOLOGY
Agent-Based Modelling and Microsimulation Neer
the Twain Shall Meet? Edmund Chattoe-Brown
(ecb18_at_le.ac.uk) http//www.le.ac.uk/sociology/sta
ff/ecb18.html
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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.

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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?

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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 dont
  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.

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Initial state
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Two questions

• What is the smallest PP (between 0 and 1) that
  will produce clusters?
• What happens when the PP is 1?

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Two (surprising?) answers

• PP about 0.3. People dont 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
  PP1, the system looks no different from the
  random starting position.

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Simple individuals but complex system
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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.

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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.

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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.

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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.

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Evidence

• Definition provided in Williamson Int. J.
  Microsim, 1(1), 2007, p. 1.
• Worry It isnt the case that ABM and
  microsimulation will naturally meet in the
  middle because behaviours arent just another
  attribute like gender or age. (In fact,
  sociologists might argue that gender isnt an
  attribute either but a negotiated achievement.)

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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 dont do this,
  ABM included.) Instructions on the can.
• Explore consequences of particular methods.
• Recall constantly that each method is an article
  of faith.

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Concern 1 Explanation versus prediction

• Prediction is problematic in social science
  because pure prediction may involve no
  generalisation. Without explanation we cant
  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.

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Concern 2 Power and prediction

• In simple statistical models, the power of a test
  is relatively well defined.
• In complex microsimulation models, it isnt 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 isnt the test of the model. Ideally, the
  simulated data should match all properties of the
  real data.

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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.

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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?

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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 cant just be thrown in
  like blur on an unflattering photograph.
• Does too much randomness (of the wrong kind)
  allow one to predict anything?

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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?

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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.)

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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 dont end up with too many degrees
  of freedom and pass the prediction test
  illegitimately? ABM is evolving ways to handle
  this issue. Is microsimulation?

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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.

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Constructive suggestion 2

• Theres no reason, when adding behaviour to
  microsimulation, not to add proper ABM models.
  However, it is important to do this in a way that
  doesnt destroy the social (rather than typically
  economic) assumptions built into them.

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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
  dont know. (Drunk and lamp-post story.)
• As long as ABM isnt bolted awkwardly onto
  microsimulation, it should be possible to get it
  to do the sorts of things that make
  microsimulation useful. (Politics!)

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Conclusions

• The assumptions you dont realise you are making
  are the ones that will do you in!
• This discussion isnt meant to imply that ABM has
  no faults, it has many (and not purely
  technical ones either) but thats a different
  talk!

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Now read on?

• Journal of Artificial Societies and Social
  Simulation (JASSS)
• lthttp//jasss.soc.surrey.ac.uk/JASSS.htmlgt
• simsoc (email discussion group for the social
  simulation community)
• lthttps//www.jiscmail.ac.uk/cgi-bin/webadmin?A0SI
  MSOCgt
• 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)
• lthttp//ccl.northwestern.edu/netlogo/gt

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• Id like to take these ideas on in collaboration
  with a historian, with a view to funded
  research/a PhD award.