DRAFT RESEARCH DOCUMENT

US Finance System
Hyperinflation Caused "Transaction Processing Failure" Systemic Risk Assessment





Abstract
Where the vast bulk of today’s money is not physical, but electronic, however, chances of adapting to Hyperinflation here are virtually nil.
 
In terms of hyperinflation, there have been a variety of definitions used over time. The circumstance envisioned ahead is not one of double- or triple-digit annual inflation, but more along the lines of seven- to 10-digit inflation seen in other circumstances during the last century. Under such circumstances, the currency in question becomes worthless, as seen in Germany (Wiemar Republic) in the early 1920s, in Hungary after World War II and in the dismembered Yugoslavia of the early 1990s.
 
Where the US Federal Reserve may hold roughly $210 billion in currency outside of $50 billion in commercial bank vault cash, the bulk of roughly $780 billion in currency outside the banks is not in the United States. Back in 2000, the Fed estimated that 50% to 70% of U.S. dollar cash existent was outside the US. That number is higher today, with perhaps as little as $200 billion in physical cash in circulation in the United States. No matter how you run the numbers, there is not enough cash available for a US economy in a hyperinflation mode.
 
Think of the time, work and effort that went into preparing computer systems for Y2K, or even problems with the recent early shift to daylight savings time. Systems would have to be adjusted for variable, rather than fixed pricing, credit card lines would need to be expanded daily, the number of digits used in tallying dollar-denominated transactions would need to be expanded sharply.
 
It is understood that a number of businesses have accounting software that can handle any number of digits, but probably most of the US (and Canadian or NAFTA "banking infrastructure" in general) is not ready.

Hyperinflation FAQ

Before we go further, let’s be clear about what a hyperinflation really means in practical, every day terms
On the international scene, for any country that uses the US dollar as a nominal anchor to stabilize its own currency: US dollar hyperinflation will mean almost certain hyperinflation. Countries will quickly delink their currency's exchange rate from the dollar before that happens. Countries that use the dollar for international trade will drop the dollar.





Important hardware and software issues
Floating-point numbers are typically packed into a computer datum as the sign bit, the exponent field, and the significand (mantissa), from left to right.

For the IEEE 754 binary formats they are apportioned as follows
Type Sign Exponent Exponent bias significand total buts used used by
Half (IEEE 754r) 1 5 15 10 16 embedded systems
Single 1 8 127 23 32 embedded systems, accounting software
Single40
1
8
127
23+?
40
Climate Prediction Coprocessor theoretical model
Double
1 11 1023 52 64 science oriented programs, uncommon
Quad
1 15 16383 112 128 has never really been used in hardware or software
Bignum libraries
1
varies
varies
varies
varies
used in science and global finance, unpredictable and unstandardized

While the exponent can be positive or negative, in binary formats it is stored as an unsigned number that has a fixed "bias" added to it. Values of all 0s and all 1s in this field are reserved for special treatment. Therefore the legal exponent range for normalized numbers is [−126, 127] for single precision, [−1022, 1023] for double, or [−16382, 16383] for quad.

As described earlier, when a binary number is normalized the leftmost bit of the significand is known to be 1. In the IEEE binary interchange formats that bit is not actually stored in the computer datum. It is called the "hidden" or "implicit" bit. Because of this, single precision format actually has a significand with 24 bits of precision, double precision format has 53, and quad has 113.

Arbitrary-precision arithmetic, also called bignum arithmetic, is a technique whereby computer programs perform calculations on integers or rational numbers (including floating-point numbers) with an arbitrary number of digits of precision, typically limited only by the available memory of the host system. Using many digits of precision, as opposed to the approximately 6–16 decimal digits available in most hardware arithmetic, is important for a number of applications as described below; the most widespread usage is probably for cryptography used in every modern web browser.

It is often implemented by storing a number as a variable-length array of digits in some base such as 10 or 10000 or 256 or 65536, etc., in contrast to most computer arithmetic which uses a fixed number of bits in binary related to the size of the processor registers. Numbers can be stored in a fixed-point format, or in a floating-point format as a significand multiplied by an arbitrary exponent. However, since division almost immediately introduces infinitely repeating sequences of digits (such as 4/7 in decimal), should this possibility arise then either the representation would be truncated at some satisfactory size or else rational numbers would be used: a large integer for the numerator and for the denominator, with the greatest common divisor divided out. Unfortunately, arithmetic with rational numbers can become unwieldy very swiftly: 1/99 - 1/100 = 1/9900, and if 1/101 is then added the result is 10001/999900.

An early widespread implementation was available via the IBM 1620 of 1959-1970 which was a decimal-digit machine that despite using discrete transistors had hardware that performed integer or floating-point arithmetic (via lookup tables) on digit strings of a length that could be from two to whatever memory was available, though the mantissa of floating-point numbers was restricted to 100 digits or less and the exponent of floating-point numbers was restricted to two digits only: the largest memory supplied offered sixty thousand digits. Compilers for the IBM 1620 (Fortran), however, settled on some fixed size (which could be specified on a control card if the default was not satisfactory), such as ten digits. IBM's first business computer, the IBM 702, which was a vacuum tube machine, implemented integer arithmetic entirely in hardware on digit strings of any length from one to 511 digits. The earliest widespread software implementation of arbitrary precision arithmetic was probably that in Maclisp. Later, around 1980, the VAX/VMS and VM/CMS operating systems offered bignum facilities as a collection of string functions in the one case and in the EXEC 2 and REXX languages in the other. Today, arbitrary-precision libraries are available for most modern programming languages (see below). Almost all computer algebra systems implement arbitrary-precision arithmetic.

Arbitrary-precision arithmetic is sometimes called infinite-precision arithmetic, which is something of a misnomer: the number of digits of precision always remains finite (and is bounded in practice), although it can grow very large. Aside from the question of the total storage available, the variables used by the software to index the digit strings are themselves limited in size. Arbitrary-precision arithmetic should not be confused with symbolic computation, as provided by computer algebra systems. The latter represent numbers by symbolic expressions such as πsin(3), or even by computer programs, and in this way can symbolically represent any computable number (limited by available memory). Numeric results can still only be provided to arbitrary (finite) precision in general, however, by evaluating the symbolic expression using arbitrary-precision arithmetic.
Reach of computer numbers.
1 = 1!
2 = 2!
6 = 3!
24 = 4!
120 = 5! 8-bit unsigned
720 = 6!
5040 = 7!
40320 = 8! 16-bit unsigned (devices using this math will be the first to fail)
362880 = 9!
3628800 = 10!
39916800 = 11!
479001600 = 12! 32-bit unsigned (failures will take longer, but it will be universal)
6227020800 = 13!
87178291200 = 14!
1307674368000 = 15!
20922789888000 = 16!
355687428096000 = 17!
6402373705728000 = 18!
121645100408832000 = 19!
2432902008176640000 = 20! 64-bit unsigned (last hardware and software to fail)
51090942171709440000 = 21!
1124000727777607680000 = 22!
25852016738884976640000 = 23!
620448401733239439360000 = 24!
15511210043330985984000000 = 25!
403291461126605635584000000 = 26!
10888869450418352160768000000 = 27!
304888344611713860501504000000 = 28!
8841761993739701954543616000000 = 29!
265252859812191058636308480000000 = 30!
8222838654177922817725562880000000 = 31!
263130836933693530167218012160000000 = 32!
8683317618811886495518194401280000000 = 33!
295232799039604140847618609643520000000 = 34! 128-bit unsigned (unimplemented)
10333147966386144929666651337523200000000 = 35!


Known probable risk factors (unweighted)


Social modelling assumptions for failure, that would contribute to the failure


The List

List of classes computer money transfer systems in the USA that will fail, with general rankings in terms of probabilistic fitness for sustained US hyperinflation.

Highest on the list, predicted most immunity; lowest on the list subject to near immediate failure.
  1. Handwritten cheques, they are essentially custom "single use" currency issues: scientific notation can save the day.
  2. FOREX trading systems (both consumer and finance sector, cause: Yen and use of FOREX software in the developing world) -- if an only if there is a continuous audit process in effect and debugging and codebase redesign possible out of country
  3. n
  4. n
  5. n
  6. Most online shopping cart systems that have no centralized code management or dynamically up-datable API parameters or codebase
  7. Most accounting and tax software that is specific to the US economy, where no Yen / etc ... versions exist or where no FOREX orientation exists in the program
  8. Cash registers at US fast food chains
  9. Cash registers at US supermarket chains, universally said to be unfit
  10. ABMs or ATMs as they are called in the US -- unless the computer can get its software / firmware updated nightly, and even fit ABMs will fail if the overall software architecture and codebase is iffy or sub-par for general global use
  11. Parking meters and parking systems, electronic EFT types
  12. Coin based vending machines of any kind from Washer / Dryers to Cigarettes to who knows what. Coins become moot within the 1st year of hyperinflation as a general rule.
 

 
It has been posted to COMP.RISKS many times about a number of gas stations having older gasoline pumps that cannot register more than two digits’ worth of dollars in their totals -or- more than $4.99 per gallon of gas.
 
From a practical standpoint, the electronic quasi-cashless society of today also would shut down early in a hyperinflation. Unfortunately, this circumstance rapidly would exacerbate any ongoing economic collapse. With standard currency and electronic payment systems either non-functional or randomly function or malfunctioning ... it would not take too long for commerce to quickly devolve into black markets for goods and services and a barter system.
 
Unlike Zimbabwe, the United States does not have "widely available (for circulation) backup reserve currency" [short of full blown use the Canadian Dollar, or Mexican Peso] for general use in place of an inflating domestic currency.

Further reading









Created by
Max Power

Created
22 August 2008

Last revised
20 September 2012 (minor fixes)