DRAFT RESEARCH
DOCUMENT

US Finance System

Hyperinflation Caused "Transaction Processing Failure" Systemic Risk Assessment

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.

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

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.

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.

Hyperinflation FAQ

Before we go further, let’s be clear about what a hyperinflation really means in practical, every day terms

- Imagine taking every long term contract in your file cabinet – your mortgage, auto loan, student loan, insurance policy, health care insurance, and so on – and ripping them up. There is no insurance or credit in a hyperinflation. All transactions are cash or barter.
- Imagine an ongoing public panic into non-paper assets. In the early stages of a hyperinflation, the most efficient inflation hedges, due to their compact size and liquidity –- precious metals -- disappear in a few weeks. At extremes of hyperinflation, over 1000% per year, hoarding becomes absurd, with items of seemingly little value used as stores of value, such as copper plumbing fittings and brass doorknobs.
- Anything made of copper, brass, lead, aluminium that is not either nailed down or guarded is stolen and used as a substitute for cash.
- Imagine society breaking down. Money based relationships disintegrate. As the purchasing power of everyone’s savings is gone, everyone becomes completely dependent for cash for day-to-day survival from income earned from whatever they can do or sell.
- Sensible employers will index salaries to inflation. Yet this will not stop the economy from coming to a standstill with output collapsing. The unemployment rate will probably exceed 50%.
- Without tax revenue, state and local governments are crippled, and the police are as desperate as everyone else. Crime flourishes. (A friend recently back from Argentina told me that he was repeatedly stopped by police on the street and asked for money.)

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)

- lack of software updatability

- lack of hardware updatability

- lack of transaction processing product design object
orientation, albeit this is subject to interpretation

- use of coins
- producer or maintainer bankruptcy, software or hardware
- supply chain unreliability, that is to say transaction
processing devices in remote areas will not be able to adapt
as quickly or even not at all

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

- a
- b
- c
- d

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.

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.

- Handwritten cheques, they are essentially custom "single use" currency issues: scientific notation can save the day.
- 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

- n

- n
- n

- Most online shopping cart systems that have no centralized
code management or dynamically up-datable API parameters or
codebase

- 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
- Cash registers at US fast food chains
- Cash registers at US supermarket chains, universally said to be unfit
- 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
- Parking meters and parking systems, electronic EFT types
- 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

- Hyperinflation (general topic)
- Large_numbers (important related reading)
- Floating_point (supports up to 64 bit floating point maths, not universally implemented)
- Cell_microprocessor (the only CPU to implement 128 bit hardware real numbers, unused for global finance computations)
- Cell_architecture
(for those that might be interested)

- Arbitrary-precision_arithmetic
(aka Bignum libraries)

- Googolplex (effectively it takes 333 bits to represent, only 1 CPU ever designed uses 128 bit floating point)

Created by Max Power |
Created 22 August 2008 |
Last revised 20 September 2012 (minor fixes) |
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