THE GOAL OF KNOWLEDGE

POST-SCIENCE STANDARDS FOR THE

ACCEPTANCE OF KNOWLEDGE

Hugh Ching

Founder of Post-Science

June 2014

THE GOAL OF KNOWLEDGE The first goal of knowledge is to achieve

permanence, and the last is to create Heaven.

Permanent entities are infinitely more valuable than temporary entities.

Permanence is achieved through the solution of complete automation, the process of self-creation, and the expansion of the range of tolerance of the created or the product in order to accommodate all the possibilities in an uncertain future.

The value of a permanent entity has to be analyzed by an infinite spreadsheet, which recalculates a new economic equilibrium, whenever the future expectation changes.

OVERVIEW The Standards of the Acceptance of

Knowledge are:

(NOTE: Solutions in social and life sciences, involving infinity, are not empirically verifiable)

Physical Science: Empirical Verification (of Finite Phenomena with the Assumption of Invariance or Law of Uniformity).

Social Science: Mathematical Verification (to Infinity within the Range of Tolerance).

Life Science: Logical Verification (of the Requirement of Permanence through Complete Automation and through the Expansion of the Range of Tolerance for Covering All the Possibilities in an Uncertain Future).

SCIENCE, SOCIAL SCIENCE, AND LIFE SCIENCE A solution can only be obtained from a deterministic

system, where the number of equations equals the number of unknown.

In science, the remaining final variable for a deterministic system, which occurs within a finite time, is determined from empirical verification.

In social science, the final variable for a deterministic system, including infinity in time, is determined from a mathematically rigorous system, including infinity.

In life science, the Requirement of Permanence for a system of unlimited complexity can only be satisfied with a foundation based on logic represented by integers, namely, 0, 1, 2, and 3 the source code of DNA.

Fuzzy logic introduces the new mathematical concept of the Fuzzy Exact Solution, which is more reliable than the Exact Solution, which in the past is supposedly to captures all the factors affecting a problem.

SOLUTION OF VALUE Mathematical rigor is needed in solving the

problem of value because the deterministic solution involves time infinity and is not subjected to empirical verification.

The problem of value deals with an infinite number of equations of the same form (Cash Return = Sum of Cash Flows + Cash from Resale).

Both the formulation and the inputs in the post-science solution of value are necessarily fuzzy.

An example of fuzzy inputs is %Return = 10%+/- 5

The solution of value introduces the new mathematical concept of Fuzzy Infinite Spreadsheet, which expands the inputs from a point to a range and is more reliable than the Infinite Spreadsheet, which is the Exact Solution.

SOLUTION OF COMPLETELY AUTOMATED SOFTWARE

The completely automated software uses the Universal User Interface, such as

1. Input, 2. Calculate, 3. Files, 4. Branch, 5.Generate, 6. Save and Exit?

Where the human reads human words (linguistic variables) and the computer reads the integers.

Both human words and hypertext are permanently fuzzy sets and must be converted to integers to achieve complete automation, with tolerance.

The tree-structure and the words (Computing With Words) can be auto-updated. The strict logic of integers is expanded into the fuzzy logic of words.

A permanently adjustable mechanism, namely, auto-update, is needed to solve a permanently fuzzy problem, namely, Natural Language Programming.

FUZZY LOGIC IS THE FOURTH REALIZATION OF KNOWLEDGE

Fuzzy logic can be considered mankind’s Fourth Realization of Reliable Human Knowledge.

The 4 Realizations of Knowledge are: (1) Science, (2) Social Science, (3) Life Science, and (4) Fuzzy Logic.

The standards of acceptance of solutions are, respectively, (1) Empirical Verification, (2) Mathematical Verification, (3) Logical Verification, (4) Within the range of tolerance, which increases reliability and relaxes mathematical and logical rigor.

For example, the range of tolerance of the living system has been expanded through design strategies, such as multi-cellular, distinct DNA, mixing of DNA through sexual reproduction, diversification, Epigenome, fuzzy control systems, and Human Associative Memory, which contribute to the fuzzy nature of the living system or reality and sacrifice complete automation.

CONCLUSION What is needed in advancing knowledge

from science to social and life sciences is more rigor, not less rigor.

Problems in science with about 5 variables need empirical verification, in social science with 50 variables need mathematical verification, and in life science with 500 variables need logical verification.

The fuzzy reality is not a reflection of the lack of rigor, but that of the expanded range of tolerance of the (exact) solution to accommodate mainly the uncertain future and to make the solution more reliable.