policy making using modelling in a complex world

Policy Making using Modelling in a Complex world, Bruce Edmonds, CECAN, London, 20th July 2016. slide 1

Policy Making using Modelling in a Complex world

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University


or… towards what might be in a ‘Cyan Book’(halfway between the aqua and green books)


IntroductionPart 0


Simple systems…

… may be complicated but behave in predictable ways, allowing them to be represented by models...• where one can use them to numerically forecast• where uncertainty can be analytically estimated• where one can get rough estimates cheaply, and

better estimates with increasing investment• which one can sensibly plan and execute ot

systematically• where there is a basically one right way of doing it• so that one can fully understand the model


A double pendulum

Even with only two bits of wood the result can be complex

An Example Video is at: http://www.youtube.com/watch?v=czLIj-4suOk

A trace of the motion:


The Main Point of the Talk…

…is that complex systems need to be dealt with in a different way to that of simple systems......not only modelled in a different way using different techniques but also how models about complex systems are used in any policy development process needs to change.• Complex modelling will be increasingly important as

we try to develop better policies and deal with complex and fast moving situations

• But it can not be ‘business as usual’ – just doing better modelling with the same modeller–policy actor relationship will not work well


Structure of the (rest of the) Talk

1. A bit about modelling context, purposes and tensions

2. Some of the underlying assumptions and habits that need to change

3. An example model – Stefano Picascia’s Modelling of the Housing Rental Market

4. Some suggestions as to ways forward


Modelling, its context, purposes and tensions

Part 1


Some of the Context

• An increasingly professionalised coterie of skilled modellers within government (GORS etc.)

• Leading to new standards for modelling and analysis within government – the ‘Aqua Book’ – which lays down very sensible guidelines for model development and quality assurance

• Within the context of the ‘Green Book’ – a framework for policy development and evaluation

• Along side a set of academic and other experts increasingly willing to be involved in helping government model and analyse complex stuff


Different modelling purposes

Models can be used for a wide variety of different purposes, and these impact upon the kind of techniques needed and its difficulties, e.g.• Forecasting – predicting unknown (e.g. future)

situations and outcomes• Explanation – understanding how known outcomes

might have come about• Theoretical Exploration – understanding a complex

model by exploring some of its properties and behaviours

• Analogy – using a model as a way of thinking about something else


A picture of modelling

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Model Scope

• The scope of a model is the conditions under which it is useful for its planned purpose

• Whilst this is implicit and stable for many simple systems, this is not the case for many complex ones

• Thus trying to make scope explicit is important, and these relate to model assumptions

• A process not modelled (and hence outside its scope) can overwhelm the results…

• ..but in complex systems internal processes of change can also emerge, and some of these can be usefully modelled (but only in more complex ways)


Possible modelling trade-offs

• Some desiderata for models: validity, formality, simplicity and generality

• these are difficult to obtain simultaneously (for complex systems)

• there is some sort of complicated trade-off between them (for each modelling exercise)

simplicity

generality

validity

formality

AnalogySolvableMathematicalModel

Data

What Policy ActorsWant


Some underlying assumptions and habits

Part 2


The Green Book

• Basically a formulation of cost-benefit analysis• As if an economist had written a manual for policy

actors in how to think (i.e. as their theory states)• It does have some useful advice on generating

and considering alternatives and trying to judge uncertainty

But does assume that one can:1. list the main alternative options2. forecast the results of these3. put meaningful numerical values on these


Quantification

• Implicit throughout the Green Book approach• Makes life much easier for policy actors• Especially when asked to justify an approach• But can be more misleading than helpful because it

gives a false impression of accuracy• And implicitly leads to a focus on the measurable and

that things will ‘average out’ etc.• Was a limitation of purely mathematical approaches,

but computer simulation does not have to be focused on these aspects

• Collecting qualitatively different possible outcomes might be much more useful


No gradual approximation, but scope-limited usefulness

It is often assumed that as time and effort increase the accuracy of the results improve, but this is not the case with complex systems and modelsRather in order for the outcomes to be within scope enough iterative development has to occurBefore this the results are worse than nothing

Time and cost

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That (real, complex) systems ever get near an equilibrium• From economics, there is an obsession with equilibrium in

much modelling• Where, even when it is known equilibria are not observed,

they are assumed in order to forecast• Most macro economic models (and many cost-based

planning models) do this• In complex systems, even when they are in near-

equilibrium state, they can move away from this• Good when things are not changing very much, almost

useless for turning points or when structural change is occurring

• (See forthcoming open-access book “Non-Equilibrium Social Science”)


The Aqua Book

• Is a standard for the development and quality assurance of modelling/analysis for policy

• Has a lot of very sensible advice (I wish that my academic colleagues had similar standards)

• It distinguishes different roles involved in the modelling process, which clarify the chain of command/assurance and recommends extensive and frequent communication between them

• However, it only talks about the responsibilities of the modelling team, as if modelling can be an ‘on demand service’ for policy actors

• Mostly it is written for the cases where the model or situation is basically simple

• See my recent review in JASSS (jasss.surrey.ac.uk)


That different models should agree

• Sometimes policy actors complain that different models of the same phenomena disagree

• But this is inevitable where different models are taking different approaches and making different assumptions – the results are relative to these

• One can try and force models to agree, but in the process one eliminates the variety of modelling assumptions (in return for an illusion of certainty)

• In risk-analyses one wants variety so that it is more likely that future possibilities can be anticipated


Planning and Managing Modelling

• In a simple case one can apply an approach (as described in the Aqua book) where one carefully plans, manages and evaluates models

• As if this was like building a bridge!• But in complex cases complications about what

needs to be included or not requires a more iterative approach…

• ...where models are repeatedly built for a purpose and the lessons learnt as you go along...

• Becuase the difficulties can not be predicted in complex cases!


Compartmentalism

• That some problems can be separated into smaller sub-problems which can be modelled more simply

• Not true in many complex cases, where the scope of modelling is dependent on having enough of the key processes represented

• Sometimes several different modelling approaches with different (but overlapping) assumptions can be more helpful

• Just fiddling, incrementally expanding an existing (and failing) model will probably not help here


Instrumental Institutionalisation

• Even a wrong or clearly inadequate model can be a useful part of a policy development process if the model is flexible enough

• Because when used repeatedly its limitations and oddities can be fixed with institutional ‘kludges’

• Becoming something more like a consistent ‘base line’ from which policy can be debated

• e.g. the standard transport models• But as these are instutionalised, they are increasingly difficult to

change because the other policy processes have adapted to it• Resulting in a situation similar to the apocryphal ‘slowly boiled

frog’ story – at each time the motivation to change is less than the perceived advantage of doing so, in which case wait for a crisis to occur!


An Example: A Model of the Rental Housing Market

Part 3


The model

• By Stefano Picascia, an PhD student of mine, now at Sienna University, Italy

• Is an agent-based simulation that represents both tenants and developers co-adapting

• Is geographically based with tenants making decisions as where to move to based on location as well as quality of housing and price

• Developers put in captial to build/rennovate housing for tenants

• Rents are determined by the quality and prices of surrounding housing


The Manchester Case

Waves of price changes can spread Can have different outcomes each time it is runHas also been applied to London and Beirut

Video of model running is at: http://www.youtube.com/watch?v=PtYTtkPrACM


Average prices in a run


Different Sectors of the City in a run


What it does and does not tell us

In the model (which is the private rental sector only):• That change is fundamentally internally driven as

well as due to outside events• Price oscillations are endemic to the system• That some regions of cities will be stuck as low

quality housing for long periods of time• The very high price regions stay that way• That under certain conditions sudden

‘gentrification’ may occur to some degree• For poorer districts decline is gradual and

continual between any such periods


Some ways forward and conclusions for complex cases

Part 4


From Probabilistic to Possibilistic

• When outcomes can not be sensibly forecast…• And especially numerically forecast…• …where even probability zones or 90% bounds

are misleading• Then moving to an approach that models and

understand (more of) underlying processes...• ...in terms of the different kinds of outcome might

be much more informative• Each outcome tagged with its own assumptions

and scopes (if they differ)


From Forecasting to Risk Analysis

• However much one might like forecasting, often it is simply not possible…

• ...let alone in a way such that the outcomes from different options can be compared!

• Predicting outcomes can be more misleading than helpful• Rather it may be more approapriate to use models for risk

analysis – finding all the ways a policy might go wrong (or right!)

• Techniques are available to help discover and understand how endogenous processes might result in different future possibilities

• Which can then inform the design of ‘early warning’ monitors giving the most immediate feedback to policy makers


Informing the adaptive ‘driving’ of policy• Complex models are no good for policy makers!• Because they have to make decisions on grounds they

understand and know the reliability of• They can not (and should not) delegate this to ‘experts’

and their inscrutable models• Rather modellers should use their modelling to understand

the key emergent kinds of outcome• To inform:

– the consideration of these kinds of outcome– the design of appropriate data visalisations– the design of ‘earl warning indicators

• …So that policy can adapt to changing trends and events as quickly and fluidly as possible


Conclusions

• Modelling of complex phenomena is not cheap or quick and requires iterative development

• It will not forecast the impact of potential policies or events, but can anticipate possible future outcomes in a way intuition can not

• There will always be a ‘scope’ – a set of conditions/assumptions a model depends upon

• But a good model can repay its investment in terms of cost and improving people’s lives many, many times over


Summary

It is no good wishing that the world or modelling is simple and trying to ‘force’ it to

be so, one has to adapt to suit reality…

…this includes how models and modelling are used by the policy process


The End

The Centre for Policy Modelling: http://cfpm.org

These slides will be available at: http://slideshare.net/BruceEdmonds

Stefano’s work was developed under this project, funded by the EPSRC, grant number EP/H02171X