Forecasting accuracy
Posted: Thu Apr 02, 1998 9:41 am
My point was that you cannot forecast the future with any degree of
accuracy especially when you are talking about 10 or more years ahead.
Yes you may be able to correctly forecast a few months ahead.
We can begin to understand the behaviour of systems of TODAY where we
have good tools to manage complexity. The future is another story. What
do we know about incipient discontinuities? Are our paradigms
sufficiently adaptive to deal with situations that have yet to manifest
themselves? In an increasingly global society, are we not prisoners of
our own world views much of the time? Social and political dimensions to
model building are often excluded or at best ill defined with resultant
consequences. Then we have to deal with how different people, even
within the same community (unless they are overcome with group think)
interpret their worlds.
Take the Year 2000 problem. Some airlines plan to ground their aircraft
on the magical day. Why? The uncertainty of managing complexity is too
high. Lives are at stake.
If the UK Treasury gets it wrong at worst lifestyle is at stake. Also
how do you know whose forecasts to believe? Who decides this? The UK is
not alone. Every country has the same problem with Treasury models.
Models that ignore globalization are likely to be less useful. Was it
the Conant-Ashby theorem that stated that a regulator of the system
needs a good model of the system? For a SD model (or any other model) to
be a good model of the systems it must reflect the behaviour/working of
the system against a specified purpose. If the purpose is predictive
with a *high degree of accuracy for a decade or more ahead* then I
believe that the modeller has unreasonable expectations of SD (or any
other modelling approach).
Scenario planning (yes, SD models are an excellent tool to support this)
gives a reasonable way of trying to understand the impact of possible
futures but does not try to predict the future. This is the way to
tackle such problems. And forget about confidence limits/accuracy etc.
What about having to take decisions for long term projects? I accept
that managers and politicians have to take decisions but as always there
is are risks involved. That is where risk assessment comes in. Once a
long term project has started we better be aware of feedback loops
that negate the rational of the original decision and be prepared to
cut our losses.
Tony Gill phone: +44 (0)1295 812262
Phrontis Limited
Beacon House fax: +44 (0)1295 812511
Horn Hill Road
Adderbury email: TonyGill@phrontis.com
Banbury
OXON. OX17 3EU URL: http://www.phrontis.com/
U.K.
accuracy especially when you are talking about 10 or more years ahead.
Yes you may be able to correctly forecast a few months ahead.
We can begin to understand the behaviour of systems of TODAY where we
have good tools to manage complexity. The future is another story. What
do we know about incipient discontinuities? Are our paradigms
sufficiently adaptive to deal with situations that have yet to manifest
themselves? In an increasingly global society, are we not prisoners of
our own world views much of the time? Social and political dimensions to
model building are often excluded or at best ill defined with resultant
consequences. Then we have to deal with how different people, even
within the same community (unless they are overcome with group think)
interpret their worlds.
Take the Year 2000 problem. Some airlines plan to ground their aircraft
on the magical day. Why? The uncertainty of managing complexity is too
high. Lives are at stake.
If the UK Treasury gets it wrong at worst lifestyle is at stake. Also
how do you know whose forecasts to believe? Who decides this? The UK is
not alone. Every country has the same problem with Treasury models.
Models that ignore globalization are likely to be less useful. Was it
the Conant-Ashby theorem that stated that a regulator of the system
needs a good model of the system? For a SD model (or any other model) to
be a good model of the systems it must reflect the behaviour/working of
the system against a specified purpose. If the purpose is predictive
with a *high degree of accuracy for a decade or more ahead* then I
believe that the modeller has unreasonable expectations of SD (or any
other modelling approach).
Scenario planning (yes, SD models are an excellent tool to support this)
gives a reasonable way of trying to understand the impact of possible
futures but does not try to predict the future. This is the way to
tackle such problems. And forget about confidence limits/accuracy etc.
What about having to take decisions for long term projects? I accept
that managers and politicians have to take decisions but as always there
is are risks involved. That is where risk assessment comes in. Once a
long term project has started we better be aware of feedback loops
that negate the rational of the original decision and be prepared to
cut our losses.
Tony Gill phone: +44 (0)1295 812262
Phrontis Limited
Beacon House fax: +44 (0)1295 812511
Horn Hill Road
Adderbury email: TonyGill@phrontis.com
Banbury
OXON. OX17 3EU URL: http://www.phrontis.com/
U.K.