Eulogy for a Dungeon Master

By Graham

I came across this poem that I’d written ten years ago to mark the passing of Gary Gygax – creator of Dungeons & Dragons

Eulogy For A Dungeon Master

Countless the basements
You transformed to caverns;
Nameless the kitchen tables
fabled, formed as tabled taverns
Where a pimply 16 year old
Did his best
to bluff the role
Of rugged half-elf grimly
Assigning the next quest or tale
From o’er the rim
Of a dinted pint of frothy ginger ale.

We saw not the chinks
In our own teen-male armour
Nor did we stop to think
If days of playing roles might harm our
Hold on a real world so much more alarming
Than a hoard of charging orcs.

For in that four foot table space
Of paper, dice and figures made of lead
There thrived a truly magic place
where teenies meek were brave instead.

No slick slew of game designers needed we
No 3-D graphics, LANs or fancy Wiis.
With one hardbound spellbook you made the spark
That filled our teenage years with something more
than boredom and a high school pecking order.

Maybe your DM’s rolled a 20 now
Or just grew up and found
a girl, a job, a better place in whatever
World is real to Him.
And in a box in some cosmic attic
Your long forgotten character sheet
Will fade to dust, crumpled up against
An old SAT study guide.

But know this, Gary Gygax:
In every memory that still persists
In every fighter, cleric, thief or mage
Reborn upon a line-ruled page
A piece of you comes back to life.
And so we say adieu and thanks
Until we meet again as NPCs.

 Category: Art

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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:

fitted-death-rates-against-actual-values

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.

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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:

fitted-emissions-to-gdp-against-actual-values

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