Transcript Modelling and Analysis B Finding our Inner Magicians

 Prediction: Modelling and Analysis B Finding our Inner Magicians
THE INVISIBLE WAND
Adaptive Co-Management
as an Emergent Strategy
in Complex Bio-Economic Systems
A Paper written by
Jack Ruitenbeek and Cynthia Cartier
Centre for International Forestry Research (CIFOR)
Occasional Paper No. 34, October 2001
Presentation by Rowan B. Martin
illustrated with René Magritte’s paintings
 Prediction: Modelling and Analysis B
Finding our Inner Magicians
Prediction is very difficult, especially if its about the future
Niels Bohr, 1920
No model exists for him who seeks
what he has never seen
Paul Eluard, 1930
Eluard met Magritte in
1929 and they became
lifelong friends. In 1936
Eluard published a
poem ‘René Magritte’
and in 1945 Magritte
illustrated a volume of
Eluard’s poems.
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Complex systems behave in a scale-dependent manner
The way in which they self-organise,
respond to shocks and generate surprises
presents a challenge for would-be modellers
Are the
models of
yesteryear up
to the task ?
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Conventional Economic Modelling
Most conventional economic models are deterministic

Given the data, these models produce
crisp predictions and confidence intervals
They provide insights on how systems function and
allow detection of factors which have the greatest
influence on outcomes F(x)=αx +βx +γx +δx
1
2
3
4
They are suitable for complicated systems but cannot address
the attributes of complex systems
T
V
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Most conventional models –
– assume partial equilibrium
Ceteris paribus – all other things being equal.
It is assumed that the changes taking place at a local scale do
not affect the overall external conditions governing the system
Such assumptions are violated in complex systems
– do not incorporate learning
– operate on inadequate time scales
and
– implicitly treat
the environment
as an external factor
τ
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Hybrid Economic Models . . . are first generation models
which link economic attributes to environmental factors
– they contribute to the understanding of complex systems . . .
– but do not directly describe
complete complex systems
– they rely to a large extent on the
judgement and experience of the modellers
In transferring such models from one system to another,
the predictive results are seldom as good as naïve models
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Modelling Frontiers
Models are now being developed which investigate
larger complex systems through simulation
However, these models –
– tend to become overly complex
– remain deterministic insofaras
they still represent a large number
of individual local relationships
– the types of surprises they generate are
usually less surprising than those encountered in real life
 Prediction: Modelling and Analysis – Finding our Inner Magicians
Rule-based Models
. . . are the ultimate in bottom-up structures which build upon
rules governing individual parts of systems. These models –
– do not rely on strict
rationality in their parts
– avoid the assumption of
invariant preferences
and allow adaptation
through learning
– are showing that
institutions may emerge from within systems
 Prediction:
Modelling and Analysis –
Finding our Inner Magicians
Bayesian Analysis
Bayes was a much-maligned
mathematician born in London in
1702 who established the basis
for many modern common
statistical methods. ‘Bayesian
statistics’ differ from most current
methods in that expectations are
adjusted as observations
accumulate in time.
Bayesian Analysis
For example,
a child may begin life
by assigning a 50% probability
that the sun will rise every morning
As time progresses and the event repeats itself each day,
he revises the probability upwards –
75%, 80%, 85% and so on
However, the probability never reaches certainty
Prediction:
Modelling and Analysis –
Finding our Inner Magicians
Bayesian methods are useful in
complex systems because fewer
observations are required
With conventional statistics,
by the time we establish that
the world is going to
hell in a handbasket,
it may be too late to
do anything about it
 Prediction: Modelling and Analysis – Finding our Inner Magicians
At present most modelling efforts We begin to look like conjurers
may be an ‘art aspiring to science’ .
. . or even ‘magic aspiring to art’
The task is to –
– observe and understand the
complex system as far as possible
– construct a model which helps to
further understand the dynamics
and adaptive capacity of that
complex system
– design a simplified version of the
model for decision-takers which
captures the main features