Life , Immortality and Probability
Probability in a mathematical sense is defined as the ratios of the number of occurrences of equally likely possible events . It is a circular argument , as “equally likely” introduces the probability concept in the definition of probability .
Example: flip a coin .
The ideal coin has only two sides , with equal likelihood between heads or tails .
The real-world coin is totally deterministic on one throw , but can become chaotic on multiple throws . Also , it can be heads , tails or side . So , not only the initial states of the throw has to be considered , but also the end-states (ie if the coin lands on a grid of vertical slats where only sides are allowed , or horreur ! ,slanted.).
The risk concept here was based purely on statistics :
An example is the best explanation :
Choose a group of humans and an age , say ,45:
Count (all humans with Age 45) = L45 and count the survivors of the group a year later at age 46 =L46 .
The frequency of deaths in the 45 year old echelon per start for your chosen group is :
q45 = (L45 – L46 ) / L45
This factor is (q45) is then assumed to be the probability of death in the next year for somebody in the 45 year-old echelon and belonging to the chosen group .
This qx factor (where x is the age ) , is fundamental concept of the whole trillion-dollar life insurance industry . Of course , it can get very complicated , but this is the core .
For example :
The statistical probability of surviving from age 45 to age 46 = ( 1- q45 ) by definition .
The statistical probability of surviving from age 45 to age 77 is
(1-q45) * (1-q46) * (1-q47) * … * (1-q76) , where * denotes multiplication .
So , to get an age from where you would have a 70% chance of surviving to age 77 ,
Begin with (1-q76) and multiply with successive (1-q75) , (1-q74)) etc until the product equals 0.7 .
Obtaining the qx’s :
The mortality experience is different for different groups . Britain , Japan ,SA are obviously different for all ages and socio-economic groups . Because of the multiplication in calculating the end-probability , even small successive differences in the qx’s can make a huge difference .
For insurance companies , this makes the difference between profit in loss when in a competitive situation . They also cheat , and price-fixing is rife . The amounts involved are huge . A major problem is that an insurance company cannot tell whether is solvent or not , as the inherent uncertainty in the underlying figures , as well as the lack of record-keeping of all previous fudging of the qx’s obscures the company’s liabilities . The actuaries will keep on hedging on the high side , until collapse is unavoidable . Hence , Insurance companies usually collapse rapidly and with little warning (eg Sage , etc)
Actuarial tables of qx’s or Lx’s are available from most libraries , but only for the very broadest of groups (ages per country) .
As Insurance companies became more competitive , the groups of qx’s were sliced finer ( per sex , race , Socio-Economic group , etc ) . The mortality (qx) of a female ,white wife of a company director is obviously much lower than that of shanty dweller .
The problem of the finer slices is that the number people in the groups falls sharply . The statistical side of things get a bit dicey . They are also usually propriatal . (Think thumbsuck)
Notice that the cross-subsidization ( the whole point of insurance) is now only inside the group (unless the company cheats and subsidises the rich from the poor to get bigger premiums : a common occurrence)
Enter genetical testing :
This is a “real” probability based on the individual or on a group of genes , not on a statistical probability . At the present moment , this is still an imperfect science . As it becomes better , the moment of non-accidental death becomes more-and-more fixed and the incentive to insure becomes the same as the incentive to save .
Cross-subsidization vanishes except for accidents and cheating .
Immortality (except for accidents):
An immortal needs only take out accident insurance . It is interesting that the same is true of someone whose genetic risks are perfectly known , but who ages .
How long will an immortal live?
A meaningless question . More insightful is how many out of 1000 immortals will survive after 24 000 years . According to present accident mortalities , about 5 . But the accidental mortality rate for immortals would be much , much smaller than for mortals . It makes much more sense to talk of the half-life of immortals . A power-law is more likely . More immortals in the short term , but about the same in the long term. Still about 5 after 24 000 years.
How many immortals can a society support?
Max about 5 % . The same percentage as the leadership in any human group , regardless of size . Think of it as a recessive virtual gene . Longevity is enhanced as recessives match in the lower strata of society , displacing the top ones . Societies are inherently unstable , because the longevity genes are not dominant . (Dominant longevity genes in any life-system suicided in Malthusian extravagances very early-on)
The 5% leadership class acts like a virtual immortal class , but with internal and external pressures forcing change . Quite elegant , actually .
For human societies , it is immaterial whether the ruling group is immortal or not . An end state which must be extremely frustrating to actual immortals , since any action they take will wiggle back to the start point after a few centuries .
You can do the rest yourselves