Life Expectancy and Weighted Voting

Today, as with all election days, I waited in line and internally judged all the decrepit husks of barely-living people around me and wondered why they should have a hand in forming government policy when they probably wouldn’t live through the term. Many of them couldn’t walk, hell-several of them couldn’t even breathe on their own without compressed oxygen. Yet they get a say in how future generations will live.

Why? I don’t presume to know, so I’ll defer to a historical political precedent for a reference point: age requirements for political offices. Specifically, the POTUS, which maintains a minimum age of 35. Apparently when this rule was enacted, it was done so on the grounds that an unquantifiable degree of experience that could only be obtained through living long enough should be in the candidate’s background.

Conversely, while minimum age requirements remain in effect, maximum age restrictions for political office remain primarily absent. Apart from the fact that people eventually die.

So I’m going to call out a number of inferred points:

  • Life experience is needed to make good political decisions
  • 18 is the minimum age requirement to officially make any political decisions
  • 18 is therefore the publicly-accepted minimum life experience requirement for politics
  • 35 is the minimum age requirement to hold the office of the US Presidency
  • 35 is therefore the minimum age requirement to officially make political decisions of the greatest import
  • 43 is the age at which a president will enter the last year of a second-term presidency (assuming they’re sequential, which they usually are)
  • 43 is therefore the maximum age at which we expect the president to be fully competent to make the most important political decisions
  • Death is the ultimate limiter for making any political decisions
  • 79 is the current American life expectancy

Therefore 18 to 79 is the age range in which we can make political decisions, with 35 being the age at which we are qualified to make the most important political decisions.

Next point to consider: does this mean that 35 to 79 is the period in which we are fully suited to making the most important political decisions? Cognitively-speaking, the jury is out on that. Without citing specific sources, I’ll say that from the studies I’ve seen reported, peak intelligence occurs earlier in life, with some mental decline thereafter, but long term memory stays intact and contributes to total intelligence until dementia sets in. So rather than argue for a specific age limit on voting or holding office, which no one has agreed on yet, I’ll make a simpler point:

  • Who is most impacted by our voting decisions?

Or rather: younger people have to live longer with a political decision unless a future vote changes the policy.

More pragmatically: if we all vote in our own self-interest, we have less time to benefit from doing so as we get older, and any such policies enacted in this space of time will be of greater impact to those who are younger. Once we hit the age of average life expectancy, it’s a crapshoot how long we’ll live to see the results of how we vote.

Now to the point. I will offer a final formula that weighs an individual’s vote based on age, with the following criteria (that’s right-it’s a Quantitative Philosophy post!):

  • 18 and under: static weight of 0% since you can’t legally vote yet.
  • 18-35: increasing weight to account for increasing experience, culminating in a maximum weight of 100% at age 35, the age we decided as a country that you have sufficient life experience to hold the highest political office and make the most impactful decisions.
  • 35-43: the tenure period for a sequential two-term presidency, which assumes this is the age range during which someone is most qualified to make the most impactful political decisions-therefore a static weight of 100%.
  • 43-79: decreasing weight to account for the decrease in time that we have left alive, corresponding to how many years we potentially have left to live under any new political policy changes.
  • 80+: static weight of 50%. At this point you’re still entitled to vote, but the uncertainty of living to see the impact of your voting should greatly limit how much your vote counts.

Formula (in Excel format, because I work in finance and that’s the format I know):

For: age = X


I’m 39 and my vote should count as 100% of one vote (for now). The kid in highschool gets counted as 76% of a vote. A new retiree is counted as 69% of a vote. And that old geezer on oxygen and living on Medicare and Social Security gets counted as a half vote.

Live in the present and shape the future, but then abdicate it to those who follow.

(Oh, and no one’s using abortion as birth control…whatever the fuck that means.)


XP Padding

Did you know that Liz and I have a total of 23 years of finance experience?  That’s pretty amazing to think about.  A family unit has over half an entire career lifetime’s worth of knowledge in an industry?  Wow!

That means, collectively, we know as much about the credit/deposit industry as someone who’s worked in it since the 1990s.  And to think that in 1998, we were in middle school.

Yes, I’m being obnoxiously sarcastic here, because this crap needs to stop.

It’s encountered more among younger managers with lower payband teams.  Some smoothskin fresh out of business school wants to make a large group of grunts feel important, so they come up with ways to make menial work sound valued with big numbers.  Now, pulling from my own career experience, a 1000 people with 1-2 years tenure in a call center have, according to this asinine logic, 1-2 thousand years experience with the company!  Big numbers are exciting and I feel like I’m actually contributing significantly to the bottom line!

No, I don’t.  I felt patronized.

I will explain why this is stupid.

Given that entry level employees share the same basic knowledge pool from their training, this knowledge overlaps.  It doesn’t compound.

Given that knowledge is dependent on the individual’s memory to be of use.

Given that memories fade after their creation.

Then a large pool of shared knowledge only increases the chance that a selection of said knowledge is retained somewhere in the group, but still fails on the individual level at the same rate.

Therefore increasing the labor pool only increases the chance that someone retains an element of training, not that the collective unit as a whole can all access this information simply because one person has it.

Therefore experience is not cumulative across a group.  It can only complement the total group’s value.  It’s part of the equation, certainly, but a different formula is needed beyond Excel 101 sum(A:A).  Something more complicated is required.


I will begin with Hermann Ebbinghaus’s oft-referenced simplified formula on memory loss.  Where t is time and S is the relative strength of a memory, then R equals the probability of that memory being recalled:

R = exp(-t/S)

For the sake of this exercise, I will assign t to the number of days since the memory was created, and S to a static value of 25–which I’m arbitrarily defining as a 25% value to the individual, because work training material is really riveting.

In this example, a person trying to recall a fact after 7 days would have a 76% chance of doing so.

Now if we scale this to a group, cumulative probability would calculate the chance at which all people with a group, P, would recall that memory (Rc):

Rc = (exp(-t/S))^P

Let’s say 3 people are in this group.  Scaling the above example would yield a 43% chance of every person remembering the fact.  The more people we add to the group, the less the chance that all members would remember the same fact.

I’m going to get crazy here and use this as a basis for my own theorem: Simon’s Theorem on Group Memory Loss Dynamic Experience Offset over Time.

And theorem’s are great, because they’re hypothetical formula extrapolated as mathematical representations of empirical observations.  As long as the math itself is correct, no one can deny what I’ve witnessed personally.  Ergo, while I can never prove my theorem to be right, no one can prove it’s wrong.  Suck it!

Ahem.  Anyway…

I’ll assign a value to the group now (Ev).  As in usefulness, not numerical.  A 1:1 would be the ideal ratio, but that’s not going to happen because of the initial premise.

Ev = P((exp(-t/S))^P)

So after 7 days, the data retention of those 3 people on a 25%-level of interest piece of information turns these people’s usefulness, as units of the whole, into the equivalent of 1.3 people.  Note how increasing the personnel further reduces the usefulness.  That’s because, again, information isn’t pooled across the group.

But also remember that increasing the group size increases the probability that any one individual will remember the information (Rg).  So we take the individual retention rate and raise it to the inverse of the group size.  Retention will never be perfect.  A data point may be lost to time no matter how many people are hired.  But it does continually raise the probability:

Rg = exp(-t/S)^(1/P)

Of those 3 people, individually there’s only a 76% chance that a specific individual will remember a piece of information, and of the group there’s only a 43% chance that they will all retain that information, but across the group there’s a 91% chance that any of them will remember that information.

This is where the group size makes an impact–on the chance that across the group as a whole, one of them will prove their use having retained the necessary information.  By increasing the group size, we increase that possibility.

But let’s go even further.  Because if you’re still reading, I feel we’re now on a journey together and I don’t want to disappoint.  I’ve grown fond of you, dear internet reader.

And because, if you’re very attentive, you’ll note that time will still gnaw away at the group recollection chance.  More people will increase the chance, but that’s not scalable.  What we need is a third way to increase value, since we can’t ever reduce time, and staff size always has a limit.  We need another variable.

That’s right!  We increase the number of informational items, which we have to do over time, else memory loss will still degrade the total usefulness at the same rate.  So we increase the total number of informational points learned per day.

I offer one final formula: the ultimate value of the group (Uv), which incorporates the logic of the prior formulas, quantifies the equivalent value of the group based on the equivalent value of people as units, but taking into account the chance of any one person remembering a select piece of information, and increases the value based on the number of information points presented per day (I) for the duration of t:

Uv = exp(-t/S)P((exp(-t/S))^P)tI

As mentioned, this value degrades with time, but can be increased with additional information points.  Also known as experience.  Ah, we’ve come full circle finally.


The value of a group is more complicated than its collective time.  If we base the value on total information, we can’t assume that all members of a group retain that information, and a linear function doesn’t apply.  We can increase the value of the group by increasing its number, which in turn will increase the chance that information will be retained by an individual, but to ultimately avoid group value loss, additional information–or novel experience–must find its way into each individual of a group on a continual basis.

And this is why we can’t just add up everyone’s tenure.  Experience isn’t cumulative.  It’s one variable in a probability function that someone in a sample size will increase group value through novel experience recollection.

Maybe lower management should cut back on the 3 martini lunch team building.


  • t = # days
  • S = strength of memory (25%)
  • P = total # of people trying to remember
  • I = items of value learned per t
  • R = probability of memory retention
  • Rc = Chance of all people remembering
  • Ev = Equivalent value of total people as units
  • Rg = Chance of any one person remembering from total # of people
  • Uv = Ultimate value of group


The fear of cutting wood at heights

Also: Phobia Quotient!

The neighbors rented a boom.

(A tangent here–I don’t think I’ve ever created a name for these neighbors, probably because they’re nice and reasonably normal.  I’ve just called them by their first names: Brian and Kelly.  Let’s change that now.  I shall call them the Busybees.  Because they’re always rather busy.)

Anyway, they hate trees.  Well, to be fair, all Ohioans hate trees.  Almost as much as they hate dressing appropriately for the weather.  Liz is a prime example.  She also hates trees.  Here’s a typical conversation:

Statement: “This tree looks a little brown.”

Response: “Cut it down!”

Statement: “This branch looks dead.”

Response: “Cut it down!”

Statement: “This tree isn’t perfectly erect.”

Response: “‘Erect’…*teehee….Cut it down!”

But this year the trees in question really did look dead, and so I agreed after much insistence to cut them down.  Liz, the Ohioan, had already been convinced.

Cut it down!

So after this roundabout lengthy preamble, I arrive at the point of my post: I don’t like heights.  Never did.  Figured those who do are idiots or showoffs.  Of course, in my youthful egocentric stubbornness, I forced myself to endure them.  Indoor rock climbing, rappelling, mountain hiking, amusement parks–been there; done that.  And while being young grants a greater allowance for risk in the face of death, probably due to the amount of testosterone that was oozing out of my every orifice, approaching middle age has forced a more practical approach to death–like fearing things that cause it.

Consequently, my parasympathetic nervous system now strongly advises me that death should be avoided and doing certain things increases its risk potential.

But damned if I didn’t try.  I went up there twice and cut branches, though in the end, Liz did the bulk of the work.

So this got me thinking.  Is my phobia truly debilitating, or just a common healthy fear of death, albeit somewhat too strong?  Internet time!

I didn’t vet this information at all, but it seems sound.  Let’s see how I stack up:

  1. Snakes?  Some Indiana Jones shit right there.  But they do have a creepy shape and are among the few large terrestrial animals that are venomous, so I get it.  I do not have this fear.  Pass.
  2. Heights.  Already discussed.  Good to know this is #2.  Fail.
  3. Public Speaking.  I don’t really think this is a phobia.  It’s anxiety over social acceptance, not a life or death scenario, unless you consider the tribal fear of being banished which might lead to death.  Exempted.
  4. Spiders.  See #2, though they’re smaller.  I like spiders.  Pass.
  5. Claustrophobia.  I don’t like being restrained, probably from childhood memories.  My parents thought it was funny to sit on me for extended lengths of time.  Sick Boomer humor.  But small places don’t bother me.  Pass.
  6. Airplanes.  Nah.  I hate them more than fear them.  Smell farts for hours, get felt up by security, then packed in like an Amazon warehouse.  But not fear.  Pass.
  7. Mice?  No.  Pass.
  8. Needles.  I hate getting poked.  Triggers a primal fear, though I don’t have a panic attack from it.  Pass.
  9. Crowds.  Nah.  Just an inconvenience.  Pass.
  10. Darkness?  Only after watching Alien or Jurassic ParkPass.
  11. Blood?  Only my own.  Pass.
  12. Dogs.  I love dogs.  Pass.
  13. Clowns?  I hate them, but it’s not fear.  Sort of like cats.  Shoot them for entertainment, but that’s it.  Pass.

My total score: 1/12.  But, these are weighted based on commonality, so I will use sketchy math to quantify this.

I’ll take the inverse of each item (only counting the “very afraid” numbers, because really, most of us are probably “a little afraid” of many of these, which does not a phobia make), multiplying by 100, and excluding #3, the total equals 169.9.  This is the total max sissy quotient, which I’ll set as the baseline of 100% total sissy.

I posses #2, inverse of which is 4.2.  Then to scale it with the baseline, that’ll be 4.2*100/169.9, which equals 2.5%.  I am a 2.5% sissy.

But where is the median sissy?  I really don’t know, because I don’t see these as cumulative probability, so let’s take a nice midpoint in the range: 5+((32-5)/2)=18.5.  1/18.5*100=5.4.  5.4*100/169.9=3.2% sissy.  So I’m lower than baseline, according to my questionable math from unvetted sources.

I guess I’m pretty normal after all.

But you’re a total sissy if you fear blood.


Blue Collar Cost

It’s been a while since I added an entry to the Quantitative Philosophy section.  And in light of the recent glass door replacement debacle, as well as my growing experience with home-ownership in general, I have enough information now to present a new calculator: The Blue Collar Cost Estimator!

What is this calculator?  Well, ever notice how what would seem like an affordable project immediately becomes cost-prohibitive when requiring hired help?  So here’s how it works: for any home renovation/repair, input what you think would be the conservative estimate for the raw materials.  The calculator will then add the contractor’s up-charge and account for the cost of labor (which is substantial).  Here’s the formula:

Estimated Materials Cost * 1.45 * 4 = Final Cost

Here’s the logic.  The 1.45x multiplier seems, at least anecdotally, to be the materials’ up-charge.  The 4x multiplier seems to be the labor charge, which inexplicably scales directly with the initial cost of the materials.  I guess they figure the risk of damage warrants greater skill/care?  Dunno.

But that’s it.  Nice and simple.  For calibration, I tested two expenses.  The latest was the door replacement, which I estimated would have a materials cost of $1000.   1000*1.45*4=$5800, the exact amount of the final cost.  We also had a garage door spring replaced, which I estimated at $120.  120*1.45*4=$696, which is pretty close to the $700-ish final cost we paid.

There you have it: the scaling cost of blue collar labor.  Glad I figured out how to install laminate flooring.  The last room I did would have cost us almost $2500.  So try to be handy–your wallet depends on it.


Football Conversations

As a non-football watcher, I’ve spent many a conversation pretending to have watched something I didn’t, or to care about something I don’t, and to use grammatically unsound complex sentences of negation.

At first, I would maintain the charade as football fans, when discussing football, are complete conversational narcissists, and would never notice that I wasn’t adding anything meaningful to the conversation.  These one-sided discussions would invariably crescendo to an emotionally-charged climax, upon which I would just agree with whatever was said last and laugh, which in turn led to some mutual conclusion that escaped me because I don’t watch football.

Now, I just don’t care enough about garnering favor with random people at the coffee station, so I don’t humor the smalltalk anymore, or so was my intent.  Unfortunately, a surprising majority of people take the dismissive comment to be a joke (for what kind of American doesn’t watch football?), and interpret it as encouragement–thus putting me into the conversation anyway.

So I decided that, as it’s been said: If you can’t beat ’em–kill everyone.  Or rather, inwardly sigh sadly and pretend to follow along.  But I need assistance.  I need information…obtained through any other means than reading, watching TV, or conversing with my fell Man.

I needed an aggregator and summarizer.  I needed the absolute bare minimum content required to form a cohesive thought.  I needed the equivalent of a Twitter feed of sports commentary, but without the racism/sexism/homophobia (the entire social aspect, basically).  I needed a means by which to trawl football articles and identify the most-used words, negating general sentence structure such as definite articles and conjunctions.

Fortunately I found this site:  Probably not its intended use–I began pasting the top football news articles into its form and analyzing their content.  I checked 5 such posts, and compiled their keywords:

The first two articles didn’t have enough meaningful content for a full 10 words

Okay, I could work with this.  This Bryant fellow seems to be a highlight.  I’m sure I could muddle through the rest.

I decided to test my theory on Liz, and texted her the following message:

“I heard that in Bryant’s week one, he scored enough points that it’ll be his big season.  He’ll make a good five-star Fantasy Football pick.  Despite the initial loss, Arkansas will recover with enough victories to stay in the running.”

Liz responded:

“What are you reading?”

She was intrigued!  Had I pulled it off?!  I replied, ambiguously:

“Just the highlights.”

She validated my success by sending me an unrelated photo of a dog that was up for adoption.

…Okay, maybe my method needs a little refinement.  Maybe I can pull a larger sampling of articles and write a formula to analyze the character strings.

Or maybe, just maybe…when I tell you I don’t watch football you could stop talking to me about football and I wouldn’t have to design a logic-based analysis of textual media to formulate responses to your banal and pointless rambling.  Now quit hogging the coffee machine.