Review of Philip K Dick’s “The Penultimate Truth”

By Graham

The Penultimate TruthThe Penultimate Truth by Philip K. Dick
My rating: 2 of 5 stars

Philip K. Dick is above all a writer of ideas. To him we are indebted for some of the most innovative concepts to come out of 20th Century sci-fi. For me the debt is also personal. With the release of the movie Bladerunner, his ideas began to go mainstream at roughly the same time as I was gaining literary and creative consciousness, and so I will forever associate his work with that delicious awakening.

But ideas do not a great novel make. For that, you need other elements, such as literary craftsmanship, compelling characters and good plotting. Taking these three things in order, I have to say this book does not rise above a good C+. Most of the prose is pedestrian, relying on an overuse of adverbs and needless jargon (why robots have to be called ‘leadies’ is beyond me). Where Dick’s prose does accelerate, it becomes torturously overwrought. (Example: “Anything which might mitigate the quality striven for, that of free and easy authenticity; this simulacrum, out of all which they, the Yance-men, were involved in, required the greatest semblance of the actuality which it mimicked.”)

The characters never evolve beyond mere props; wooden actors through which the events are channelled. They are a means of telling the story. So much so that in one instance, Dick himself seems to forget whose point-of-view he is narrating, and attributes the wrong train of thought to the wrong character; an easy mistake to make when they are all essentially the same soulless person.

This leaves the plot. [Spoiler alert] In theory, plot should be the element of writing which Dick, as an ideas man, would master most easily. And indeed, in some of his best known works this is the case. But sadly, he does only half a job in The Penultimate Truth. Although the pacing is excellent and the premise is brilliant (most of humanity forced to toil away in subterranean ‘tanks’ under a false pretext), for some reason he sees fit, about halfway through the story, to introduce devices (both figuratively, as plot devices; and literally, as devices of war) which overcomplicate the story, un-suspend the reader’s disbelief and disobey the internal logic of the world he has created. The most obscene of these is time travel – a spice so pungent it can spoil the best sci-fi soup if not added with extreme caution. To top it all off, the story finishes too quickly, and without a clear resolution of the key conflict point. We (or at any rate I) don’t even understand what the meaning of the title was supposed to be, and are left with the suspicion that it was merely chosen because it would look snappy on a bookshelf.

Whatever the penultimate truth was supposed to have been, for me the ultimate truth is that this book is a disappointment to the good idea from which it was born.

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As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:


While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.


We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values: