Filed under: Politics | Tags: acmd, advisory council on the misuse of drugs, alan johnson, david nutt, drugs policy, evidence based policy making, expert advice, technocracy
The recent sacking of David Nutt – formerly head of the Advisory Council on the Misuse of Drugs (ACMD) – for giving scientific advice that showed the stupidity of the government’s drugs policy, suggests taking a look at the role of expert advice in policy.
The problem is that the government can have an unstated policy of accepting expert advice when it suits them and rejecting it when it doesn’t. Such a policy is ideal for the government, because if the advice fits what they wanted to do anyway, they can claim that they are supported by evidence, and if it contradicts them they can in most cases easily shrug it off by claiming (correctly) that the point of expert advice is not that it should define policy, but that it should be taken into account as part of wider considerations, and that in this case, blah blah… The policy is equivalent in outcome to having no expert advice, but in some cases looks better. Alan Johnson’s statement in his letter to Nutt was extraordinary in tacitly recognising this:
I cannot have public confusion between scientific advice and policy and have therefore lost confidence in your ability to advise me as chair of the ACMD.
It is understandable then that other members of the ACMD are resigning, although it is not entirely clear how principled this stance is when everyone on that council must have known beforehand that they were helping to legitimate highly irrational policies. (I don’t want to be too critical though, maybe the strategy of working within a faulty system can do some good.)
So where does this leave the issue of expert advice? Can it play a useful role and if so, how? One possible way out of the problem above would be for the government to create advisory groups and commit itself to following their advice whatever it might be. There are various problems with this though. Firstly, it is subject to manipulation by selection of the members of the group. Secondly, it’s not clear that it would even work – Tony Blair stated the reason to go to war with Iraq “must be according to the United Nations mandate on Weapons of Mass Destruction”, but changed his mind when that mandate disappeared.
But a third and deeper problem with this and any other similar scheme is that it conceals the true nature of politics, and supports the false idea that government can be a purely technical exercise in doing whatever works. Politics is actually about conflicts of interests of different groups and classes. Portraying political issues as technical ones works to hide these truly political aspects. Governments and opposition parties are very happy to do this because they are both largely supporting the interests of the same classes/groups – typically the wealthier ones. This shouldn’t be surprising because the decision making part of the government and state largely consists of, is staffed by and supported by people in these classes.
I don’t want to suggest that there are not technical considerations in policy making, nor that expertise is irrelevant. In the case of drugs policy, for example, the evidence is overwhelming that tobacco and alcohol are more dangerous than cannabis and many other illegal drugs, making a mockery of government policy. However, I do doubt that an institutional arrangement can be devised which allows for a useful and non-political injection of expert advice into decision making. I would suggest instead that experts should be entirely independent of government. A well informed and scientifically literate press – something that is very far from what we have today – would be hugely preferable to any number of advisory councils selected by and working for the government. This would allow an injection of expertise into an explicitly political process, rather than supporting a fictional non-political one.
Filed under: Uncategorized
I’ve made a minor change to my comments policy for this blog in the light of the growing number of spam comments making it past the spam filters which I have to delete by hand.
Shame about the title though. I think the words “Yes we can” should probably be banned. Anyway, here’s Robin Hahnel on “Change how the world works? Yes we can“:
Until capitalism is replaced, we want the tail to stop wagging the dog. Finance should serve the real economy instead of the other way around. If the financial sector improves the efficiency of the real economy, it is helpful. But if it misdirects investment resources to where they are less productive, it reduces production in the real economy by obstructing the flow of credit altogether. Then it is failing to accomplish its only social purpose. Jobs producing useful goods and services, and investments which help us to produce what we need with less human toil and less strain on the environment, are what count. Increases in the profit rates and stock prices of financial corporations count for nothing when they fail to correspond to real increases in productivity, as has too often been the case.
We have offered several positive alternatives to capital liberalization and to the governing structures and policies of the International Monetary Fund (IMF) and the World Bank, such as capital controls and a Tobin tax to protect smaller economies from volatile speculative flows. We have made suggestions on how national governments can restore competent regulation of their traditional financial sectors, and stressed the urgency of extending regulation to cover new financial institutions which were allowed to grow outside existing regulatory structures.
Filed under: Mathematics, Programming, python | Tags: fast mandelbrot, mandelbrot, mandelbrot set, numpy, vectorisation, vectorization
This will be of little interest to people who regularly read my blog, but might be of some interest to people who find their way here by the power of Google.
The standard way to compute fractals like the Mandelbrot set using Python and numpy is to use vectorisation and do the operations on a whole set of points. The problem is that this is slower than it needs to be because you keep doing computations on points that have already escaped. This can be avoided though, and the version below is about 3x faster than the standard way of doing it with numpy.
The trick is to create a new array at each iteration that stores only the points which haven’t yet escaped. The slight complication is that if you do this you need to keep track of the x, y coordinates of each of the points as well as the values of the iterate z. The same trick can be applied to many types of fractals and makes Python and numpy almost as good as C++ for mathematical exploration of fractals.
I’ve included the code below, both with and without explanatory comments. This 400×400 image below using 100 iterations took 1.1s to compute on my 1.8GHz laptop:
Uncommented version:
def mandel(n, m, itermax, xmin, xmax, ymin, ymax):
ix, iy = mgrid[0:n, 0:m]
x = linspace(xmin, xmax, n)[ix]
y = linspace(ymin, ymax, m)[iy]
c = x+complex(0,1)*y
del x, y
img = zeros(c.shape, dtype=int)
ix.shape = n*m
iy.shape = n*m
c.shape = n*m
z = copy(c)
for i in xrange(itermax):
if not len(z): break
multiply(z, z, z)
add(z, c, z)
rem = abs(z)>2.0
img[ix[rem], iy[rem]] = i+1
rem = -rem
z = z[rem]
ix, iy = ix[rem], iy[rem]
c = c[rem]
return img
Commented version:
from numpy import *
def mandel(n, m, itermax, xmin, xmax, ymin, ymax):
'''
Fast mandelbrot computation using numpy.
(n, m) are the output image dimensions
itermax is the maximum number of iterations to do
xmin, xmax, ymin, ymax specify the region of the
set to compute.
'''
# The point of ix and iy is that they are 2D arrays
# giving the x-coord and y-coord at each point in
# the array. The reason for doing this will become
# clear below...
ix, iy = mgrid[0:n, 0:m]
# Now x and y are the x-values and y-values at each
# point in the array, linspace(start, end, n)
# is an array of n linearly spaced points between
# start and end, and we then index this array using
# numpy fancy indexing. If A is an array and I is
# an array of indices, then A[I] has the same shape
# as I and at each place i in I has the value A[i].
x = linspace(xmin, xmax, n)[ix]
y = linspace(ymin, ymax, m)[iy]
# c is the complex number with the given x, y coords
c = x+complex(0,1)*y
del x, y # save a bit of memory, we only need z
# the output image coloured according to the number
# of iterations it takes to get to the boundary
# abs(z)>2
img = zeros(c.shape, dtype=int)
# Here is where the improvement over the standard
# algorithm for drawing fractals in numpy comes in.
# We flatten all the arrays ix, iy and c. This
# flattening doesn't use any more memory because
# we are just changing the shape of the array, the
# data in memory stays the same. It also affects
# each array in the same way, so that index i in
# array c has x, y coords ix[i], iy[i]. The way the
# algorithm works is that whenever abs(z)>2 we
# remove the corresponding index from each of the
# arrays ix, iy and c. Since we do the same thing
# to each array, the correspondence between c and
# the x, y coords stored in ix and iy is kept.
ix.shape = n*m
iy.shape = n*m
c.shape = n*m
# we iterate z->z^2+c with z starting at 0, but the
# first iteration makes z=c so we just start there.
# We need to copy c because otherwise the operation
# z->z^2 will send c->c^2.
z = copy(c)
for i in xrange(itermax):
if not len(z): break # all points have escaped
# equivalent to z = z*z+c but quicker and uses
# less memory
multiply(z, z, z)
add(z, c, z)
# these are the points that have escaped
rem = abs(z)>2.0
# colour them with the iteration number, we
# add one so that points which haven't
# escaped have 0 as their iteration number,
# this is why we keep the arrays ix and iy
# because we need to know which point in img
# to colour
img[ix[rem], iy[rem]] = i+1
# -rem is the array of points which haven't
# escaped, in numpy -A for a boolean array A
# is the NOT operation.
rem = -rem
# So we select out the points in
# z, ix, iy and c which are still to be
# iterated on in the next step
z = z[rem]
ix, iy = ix[rem], iy[rem]
c = c[rem]
return img
if __name__=='__main__':
from pylab import *
import time
start = time.time()
I = mandel(400, 400, 100, -2, .5, -1.25, 1.25)
print 'Time taken:', time.time()-start
I[I==0] = 101
img = imshow(I.T, origin='lower left')
img.write_png('mandel.png', noscale=True)
show()
George Monbiot has an interesting article linking capitalism and privatisation with growing prison populations:
This revolting trade in human lives creates a permanent incentive to lock people up; not because prison works; not because it makes us safer, but because it makes money. Privatisation appears to have locked this country into mass imprisonment.
It’s not clear to me that this is enough to explain the whole problem, but it’s worth considering.
Alderson has an interesting piece on religion over at Directionless Bones:
[Alderson's view] also implies a certain set of priorities, that changing people’s lives is more important than changing their minds (though obviously not unrelated), and that often religion will persist regardless of rational arguments if the conditions that produce it persist.
The Mathematics Genealogy Project has a huge database of mathematicians, showing who was supervised by whom, and what students everyone had. If you’re a mathematician, you can use this to trace back who your mathematical ancestors were and it can be quite fun. Below is a chart I made of my own mathematical genealogy. It’s nice to see exciting names from the history of mathematics and science there, such as Poisson, Laplace, Lagrange, d’Alembert, Euler, the Bernoullis, Leibniz, and Huygens (I stopped at that point). The dates are when they finished their doctorate, or if they didn’t do one, when they lived.
Reg article reporting on Nigel Inkster, former Assistant Chief of MI6:
There are limits to what we can sensibly aspire to…
Efforts to establish a global repository of counterterrorist information are unlikely ever to succeed. We need to be wary of rebuilding our world to deal with just one problem, one which might not be by any means the most serious we face.
…
We need to keep terrorism in some kind of context, for example, every year in the UK, more people die in road accidents than have been killed by terrorists in all of recorded history.
…
We should keep our nerve and our faith in our own values. Our own behaviour – especially with respect to the rule of law – is very important.
