You have probably heard the syllogism (or, as I sometimes call it "silly-gism" depending on the nature of the deductive argument):
All men are mortal; Socrates is a man; therefore, Socrates is mortal.
What does this have to do with life insurance? Plenty. Life insurance is a financial contract where one party (called a policyholder) purchases an insured savings on his own life or the life of someone else (we call it a death benefit) from a life insurance company. The person whose life is insured is called the "insured individual." The savings is paid for over time through premium payments made to the insurance company. The company then pays the savings/death benefit to a third party (we call that third party a "beneficiary") when the insured individual dies.
How do we know when the insured individual will die? While a life insurance company certainly knows that "all men are mortal", it does not know when they are mortal (i.e. when mortality will occur). And, this is precisely what a life insurance company must know in order to operate as a business.
No amount of deductive reasoning will ever give the insurance company the knowledge it needs to be profitable. In other words, the business of life insurance cannot operate purely on deduction or rationalism.
In fact, life insurers cannot operate at all on deductive reasoning when devising mortality tables and life insurance products. They must use inductive reasoning to create and maintain an accurate database of mortality rates of human beings. Where deduction subsumes new instances of the known, induction is the process of generalization from perceptual evidence.
Insurance companies must analyze mounds of data and discover the "one in the many" that unites mortality among human beings. Have they discovered a "one in the many" in regards to when mortality occurs?
You bet they have, and have discovered that it is contextual (imagine that). Insurance companies have been forced, through their very business model, to look at actual death rates among people, when they occur and under what conditions they occur. Life insurance companies have devised methods for focusing their attention on 40 year old smokers or 25 year old's in perfect health or 70 year old individuals with various health problems.
An insurance company observes under what conditions people die. It observes and patiently records data on age, sex, drug use, height, weight, blood and urine content (and make-up). It studies how various factors like blood pressure, drugs, weight and age affect mortality. It studies the cause of death to form causal relationships. The data it collects helps to guide the insurer into making predictions about when people will die.
The insurer not only collects all of this data over a very long period of time, it combines and shares its findings with other life insurance companies. Finally, a statistical model is formed--called a mortality table. While no one particular individual's death is known in advance, the mortality of a percent of the population within a defined context is known with uncanny accuracy.
For example, life insurers devise and rely on mortality tables to tell them what percent of 40 year old smokers will die in any given year. It bases its needs for cash reserves on these mortality tables. If its predictions are wrong, the company will experience serious financial trouble and could fail.
However, most insurance companies do not fail. According to economist Jesus Huerta de Soto:
In the last two hundred years, a negligible number of life insurance companies have disappeared due to financial difficulties.
What do the observations made by de Soto (and presumably other economists) tell us about an insurer's ability to successfully predict mortality? To me, it seems to suggest that life insurers have successfully induced mortality rates. In effect, they have found the "one in the many"--expressed as a probability of mortality--that unites defined segments of the population.