Bruce Edmonds discusses the potential for "post-truth drift" in social simulation, where models are presented in a way that prioritizes impact over truth. He identifies several ways this can occur: 1) not clearly stating a model's purpose; 2) overly relying on assumptions of simplicity without evidence; 3) over reliance on existing theories without empirical validation; 4) using analogical reasoning to claim false generality; and 5) using ambiguous language. Edmonds advocates clearly communicating a model's limitations and intended purpose to avoid misrepresentation.

1. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 1
The Post-Truth Drift in Social Simulation
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

2. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 2
A Few Words about Language
• Only some of language involves descriptions of
the world – are anything to do with truth
• Other parts include “speech acts” (Searle 1969)
which are designed to effect change on the world
around (mostly via other actors around us)
• In the late 20th century the post-modernist critique
(Derida etc.) pointed out that one has to take into
account the power relations behind any text
• We now live in an age where the political impact
of statements often seems to trump a strict
adherence to the truth

3. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 3
the “Post Truth” age
• “Post-truth” was nominated as their word of the
year by the Oxford Dictionaries in 2016
• It is a kind of attitude to statements where their
truth is considered less important than its impact
• It is not about lying or telling untruths, but a lack of
concern about truth
• Traditionally “Science” is supposed to have strong
norms against communication that is deceptive
• I am worried about this kind of problem in social
simulation

4. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 4
The situation within social
simulation
• Our subject matter is incredibly complex,
combining: cognition, social phenomena,
ecological factors, institutions etc. etc.
• But the technique has obvious potential
application to many policy issues
• There are pressures to claim substantive progress
to grant funders and to get published
• Hence there is the motivation to allow others to
think our models are more useful than their
construction and the evidence warrants

5. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 5
The “Hype Curve”
Time
Interest/Uptake/Reputation
2. The “In Thing”

6. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 6
Cautionary Tales
• The book “Limits to Growth” (Meadows et al 1972)
illustrated some of the possible problems of
unlimited growth using a simple system dynamics
model. But the model was not empirically
grounded and attacked.
• Models of stocks apparently showed the health of
the Maine Cod fisheries right up to their collapse
• Economic models (-; enough said ;-)
Claiming too much could result in long-term
disillusionment with our field and ABM

7. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 7
This Talk…
Looks at a number of ways in which a drift towards
deceptive client-facing language is occurring:
1. Not being clear about the purpose of our models
2. Wishful thinking about the difficulty of our subject
matter
3. Over reliance on pure “theory”
4. Fooling ourselves with analogical thinking
5. The comfort of weasel words

8. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 8
1
Not being clear about the purpose
of our models

9. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 9
Different tools for different jobs
• A good tool is well designed for its purpose
• Each model is just such a tool
• However, there are many alternative models for
every target so that we do not know what model is
good for what purpose and what target
• Our models needs to be justified with respect to a
clearly stated purpose
• If a model have more than one purpose it should
be justified with respect to each separately

10. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 10
Some Modelling Purposes
• Predictive
• Explanatory
• Theoretical Explanation
• Analogical
• Illustration
• Description
• As a Participatory Tool
• Entertainment
• Identifying knowledge gaps
• etc.

11. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 11
Motivation for Prediction
• If you can reliably predict something about the
world, this is undeniably useful…
• ...even if you do not know why your model
predicts (e.g. a black-box model)!
• But it has also become the ‘gold standard’ of
science…
• ...becuase (unlike many of the other purposes) it
is difficult to fudge or fool yourself about – if its
wrong this is obvious.

12. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 12
Predictive modelling
Target system
Initial
Conditions
Outcomes
Predictive Model
Model
set-up
Model
results

13. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 13
Examples
• The gas laws (temperature is proportional to
pressure at the same volume etc.) predict future
measurements on a gas without any indication of
why this works
• Nate Silver’s team tries to predict the outcome of
sports events and elections using computational
models. These are usually probabilistic
predictions and the wider predicted distribution of
outcomes is displayed (http://fivethirtyeight.com)

14. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 14
Motivation for Explanatory
• When one wants to understand why or how
something happens
• One makes a simulation with the mechanisms
one wants and then shows that the results fit the
observed data
• The intricate workings of the simulation runs
support an explanation of the outcomes in terms
of those mechanisms

15. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 15
Explanatory modelling
Mechanisms
Model
processes
Model
results
Outcomes
Model
Target System
Outcomes are explained
by the processes

16. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 16
Examples of Explanatory Models
• The model of a gas with atoms randomly bumping
around explains what happens in a gas (but does
not directly predict the values)
• Lansing & Kramer’s (1993) model of water
distribution in Bali, explained how the system of
water temples acted to enforce social norms and
a complicated series of negotiations

17. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 17
A Confusion:
predictive vs. explanatory
• Both are empirical but are frequently confused
• Different development approaches are needed for
each and each has different dangers
• Friedman (1953) argued that economic models do
not have to mimic the observed micro-processes
just predict the global outcomes…
• ...but then it became usual to divide data into in-
sample and out-sample – condition on the first
and then “predict” the second
• But these then fail to be either predictive or
explanatory models!

18. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 18
Motivation for Analogical Modelling
• Provides a ‘way of thinking about’ stuff
• The model is not (directly) about anything
observed, but about ideas (which, in turn, may or
may not relate to something observed)
• It can suggest new insights or new future
directions for research
• We need analogies to help us think about what to
do (e.g. what and how to model)

19. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 19
An illustration of analogical use of a
model
Target system 1
Model
Informal Ideas
Target system 2

20. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 20
Theory Exposition
• If one has a system of equations, sometimes one
can analytically solve the equations to get a
general solution
• When this is not possible (almost all complicated
systems) we can calculate specific examples – to
simulate it!
• We aim to sufficiently explore the whole space of
behaviour to understand a particular set of
abstract mechanisms
• No empirical link, so you can not conclude
anything about the real world

21. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 21
Summary of Modelling Purposes

22. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 22
Some common confusions
• Firstly in many publications researchers do not
make their model purpose clear
• So the model is hard to judge properly
• Some have simply not thought about it!
Some common confusions:
• Theory  Analogy
• Illustration  Explanation
• Explanation  Prediction

23. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 23
2
Wishful thinking about the difficulty
of our subject matter

24. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 24
The “Medawar” Zone
From (Grimm et al. 2005)
on the “optimal” complexity
of models.
This cites (Loehle 1990)
which argues for a
pragmatic choice on which
problems one tackles as a
researcher, following
(Medawar 1967)
This does not say anything about what kind of model is
optimal for any particular phenomena but is about a pragmatic
choice by researchers as to what problems one chooses

25. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 25
Complication and complexity
Diagram from (Sun et
al 2016) explaining
how an increase in
complication may
result in a decrease in
complexity after a
certain level
But no good reason for
this is presented
Complicatedness of model structure
Complexityofmodelbehaviour
The complexity of model behaviour may be more difficult to
perceive when it gets complicated, but it still exists

26. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 26
Human Limitations
• There will always be limitations on how much we
can perceive, understand, check simulations, run
simulations etc.
• These should simply be honestly declared
• But we should not pretend that any kind of
simplicity is a priori more suited to some
phenomena
• We just do not know how complex/simple an
adequate model needs to be for most social
phenomena, because all sorts of aspects of our
social reality might be needed in any case

27. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 27
Possible underlying assumptions
Unsound assumptions
• That a simpler model will be more general
• That a simple model will be approximately right
and the accuracy gets better the more relevant
aspects one includes
Justifiable reasons
• Complex models are hard to understand (but
there are techniques to help with this)
• I only have XX months to do this in (but then
maybe you should not have attempted to tackle
this, rather than tackle it badly)

28. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 28
3
Over reliance on pure “theory”

29. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 29
Using Existing Theory
Assuming or testing existing theory make the job of
the modeller very much easier, for example to:
• compare the possible consequences of Theory A
vs. Theory B
• assume a certain theory to construct a simulation
• explore the consequences of an existing theory
• construct a meta-theory to understand
commonalities/differences between a set of
existing theories
These are useful but…

30. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 30
… this presumes …
• That these theories are serious candidates for
explaining their phenomena
• The only way one can tell this is if they have
substantial (and usually multiple independent)
empirical support
• Mere consistency with other theories does not
indicate reliability, since a cluster of theories might
have been developed by researchers under the
same (non-empirical) influences

31. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 31
Disadvantages of theory
• Neat clear theories are more attractive than
messy ones, so they tend to bias one’s view more
(Kuhnian Spectacles)
• People sometimes want theory because they
crave generality, but generality is something that
has to be won, you can’t make your model/theory
more general just by wishing it so
• Yes, some theory, is unavoidable in the building
of any simulation, but this does not have to be
‘high’ theory, but can be a more mundane, theory
(e.g. grounded in qualitative observation)

32. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 32
4
Fooling ourselves with analogical
thinking

33. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 33
Common Sense understanding
Intuitive understanding expressed in normal
language
Observations of the system of concern
Common-SenseComparison

34. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 34
Scientific Understanding
Intuitive understanding expressed in normal
language
Observations of the system of concern
Data obtained by measuring the
system
Models of the processes in the
system
ScientificComparisons

35. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 35
Analogical understanding
Intuitive understanding expressed in normal
language
Observations of the system of concern
Models of the processes in the
system
Common-SenseComparison

36. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 36
The Uses of Analogical Thinking
• Analogical thinking is probably deeply engrained
in the way we think
• It is a very useful way of gaining some guidelines
for what to think about novel situations
• And thus can provide new hypotheses
• It is helpful in the personal sphere, informing and
guiding our thinking, but it is rarely something that
is helpful to share publically

37. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 37
An illustration of analogical use of a
model
• With analogy, the mapping from model to
phenomena is not well defined, but re-created
(on-the-fly) each time
Target system 1
Model
Informal Ideas
Target system 2

38. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 38
Disadvantages of Analogical Use
• It does not provide reliable information!
• Just because you can think of some phenomena
in some way does not make it true
• But the way humans are expert at inventing ways
to fit an analogy to anything, it gives an illusion of
generality
• That is, such a model feels as if it could be very
general independently of any evidence
• It is no indication of predictive or explanatory
success

39. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 39
5
The comfort of weasel words

40. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 40
From intermediate goals…
• Given the difficulty of our ultimate task, it is
natural to propose, aim for and accept
intermediate goals
• Within the field, with other modellers this is not so
bad, since most people understand this
• (as long as you ensure newcomers understand
the low-status nature of these goals)
• But when talking to others, outside the field, then
we have to be FAR more careful so they
understand clearly what has been achieved (or
not)

41. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 41
…to weasel words
• There is a temptation to fudge achievement in the
language we use.
• Not lie exactly, but allow deceptive language to
grow in use, e.g.
– “prediction” where this means just an internal
calculation to the model and not about the world
– “what if” analysis, where this just means trying different
experiments with a model, whilst others think this is a
conditional prediction (if A is true then B will happen)
• Often when others will think we are saying
something more impressive than it actually is

42. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 42
Some danger signs in a paper or
presentation
• The purpose of the model is unclear or lies
vaguely several goals, so the paper is more
difficult to judge (e.g. a mix of theoretical results
and analogical interpretation)
• The strength of conclusions about the observed
world is not in line with its evidential grounding
• What happens in the model and what happens in
the world are conflated in the language used
• The work proceeds by confirmation from or
consistency with other work/theory
• Critique of the work is made deliberately hard

43. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 43
My fear…
…is that the following saying becomes widespread:
“Lies, damned lies, statistics and
agent-based modelling”

44. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 44
Conclusions: some pleas
Please….
1. Be crystal clear about the purpose of your model,
justify one purpose at a time
2. Accept normal human limitations, but don’t create or
use theoretical-sounding excuses for this, just be
honest about them
3. Suspect all theory, especially if it makes your job a
lot easier or gives one false comfort
4. If analogical, don’t conclude anything about the
world, probably keep this to oneself until the ideas
have been proved in other ways
5. Be careful with your language when talking to others,
it may corrode trust in the longer term

45. The Post-Truth Drift in Social Simulation, Bruce Edmonds, Social Simulation Conference, Dublin, September 2017. slide 45
The End
Centre for Policy Modelling: http://cfpm.org
Bruce Edmonds: http://bruce.edmonds.name
A version of these slides are at: http://slideshare.net/BruceEdmonds
The paper is available at: http://cfpm.org/discussionpapers/195
Different modelling purposes: http://cfpm.org/discussionpapers/192