The mathematical theory of big game hunting VII
Following the seminal paper by H. Petard in 1938 introducing to the mathematical theory of big game hunting, a good many others were prompted to add to this literature. The following is one of these articles.
We here at The komplex plane include it here, together those other articles of which we are aware. We make no claim that this is a complete compendium, but we do feel they do provide the interested reader a solid introduction into this exciting branch of mathematics.
Further techniques in the theory of big game hunting
Much work has been made on the many advances mathematics can make in the theory of big game hunting. This survey paper briefly describes the further contributions to the theory made by other disciplines.
Mathematicians hunt lions by going to Africa, throwing out everything that is not an lion, and catching one of whatever is left.
Experienced mathematicians will attempt to prove the existence of at least one lion before proceeding to step 1 as a subordinate exercise.
Professors of mathematics will prove the existence of at least one lion and then leave the detection and capture of an actual lion as an exercise for their graduate students.
Computer scientists hunt lions by exercising Algorithm A:
- Go to Africa.
- Start at the Cape of Good Hope.
- Work northward in an orderly manner, traversing the continent alternately east and west.
- During each traverse pass,
- Catch each animal seen.
- Compare each animal caught to a known lion.
- Stop when a match is detected.
Experienced computer programmers modify Algorithm A by placing a known lion in Cairo to ensure that the algorithm will terminate.
Assembly language programmers prefer to execute Algorithm A on their hands and knees.
Engineers hunt lions by going to Africa, catching yellow animals at random, and stopping when any one of them weighs within plus or minus 15 percent of any previously observed lion.
Economists don't hunt lions, but they believe that if lions are paid enough, they will hunt themselves.
Statisticians hunt the first animal they see N times and call it an lion.
Consultants don't hunt lions, and many have never hunted anything at all, but they can be hired by the hour to advise those people who do.
Operations research consultants can also measure the correlation of hat size and bullet color to efficiency of lion-hunting strategies, if someone else will only identify the lions.
Politicians don't hunt lions, but they will share the lions you catch with the people who voted for them.
Lawyers don't hunt lions, but they do follow the prides around arguing about who owns the droppings.
Software lawyers will claim that they own an entire pride based on the look and feel of one dropping.
Vice presidents of engineering, research and development try hard to hunt lions, but their staffs are designed to prevent it. When the vice president does get to hunt lions, the staff will try to ensure that all possible lions are completely nonprehunted lions, afterwhich the staff will (1) compliment the vice president's keen eyesight and (2) enlarge itself to prevent any recurrence.
Senior managers set broad lion-hunting policy based on the assumption that lions are just like field mice, but with deeper voices.
Quality assurance inspectors ignore the lions and look for mistakes the other hunters made when they were packing the jeep.
Sales people don't hunt lions but spend their time selling elephants they haven't caught, for delivery two days before the season opens.
Software sales people ship the first thing they catch and write up an invoice for an lion.
Hardware sales people catch rabbits, paint them yellow, and sell them as desktop lions.