Information usage with a goal.

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Information Technology, mathematical statistical foundation.

Decision making, missing foundations.

rethink what has happened TN The distance in a flat mapping looks big,
🎭 another dimension: next door.

🔰 the most logical begin anchor.

This is the part that requires understanding the meaning of information. The decision makers in an organisation being supported by theses philosophy, using information knowledge and awareness on outcomes impacting others.


Reference Topic Squad
Intro Decision making, missing foundations. 01.01
Decide Goal support decison making. 02.01
FF Statistics Statistics - Founding Fathers. 03.01
The Lead Organizing & Leadership. 04.01
The Boss Human Leadership decisions. 05.01
The Computer Automated (machine) decisions. 06.01


analysing the heap

Goal support decison making.

Why Philosophy? There certainly are many technical aspects of modern information and communications technology (ICT) systems and the associated security architectures. Indeed, most of the aspect of how to achieve the goals tend to be of a technical nature. However, questions concerning why need not be technical at all. ..
Instead, the goals tend to be more philosophical. They may be framed in a context of moral and ethics, and sometimes in the framework of legislation and societal rules.
{Geir M. Koien 2020 }
The need for decision making.
Decison making is necessary when there are relationships with others. As soon a conflict arises the choice is solving that by: I used the words that are common in organsizing agile ICT. A question coudl be: why refering to pre-historic human cultures for implemnting new modern organisations?
🤔 Another question is: why when implementing some process on decison making, then why that is avoided to mention?

Multiple aspects ambiguity complexity
Ethicals: Volatility uncertainty complexity ambiguity
Vuca world .
The deeper meaning of each element of VUCA serves to enhance the strategic significance of VUCA foresight and insight as well as the behaviour of groups and individuals in organizations. It discusses systemic failuresand behavioural failures, which are characteristic of organisational failure.
  • V = Volatility: the nature and dynamics of change, and the nature and speed of change forces and change catalysts.
  • U = Uncertainty: the lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.
  • C = Complexity: the multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.
  • A = Ambiguity: the haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.

Ethicals arround decision making.
Within seeking for new silver bullets organising infromation processing, old question are keeping coming back.
It is generally assumed that collaboration is, in and of itself, a "good thing." "Plays well with others" is high praise from kindergarten onward. "All of us are smarter than any of us." "The more participation in design, the better." Now, these attractive propositions are far from self-evident. I will argue that they surely are not universally true. Most great works of the human mind have been made by one mind, or two working closely. This is true of most of the great engineering feats of the 19th and early 20th centuries. But now, team design has become the modern standard, for good reasons. The danger is the loss of conceptual integrity in the product, a very grave loss indeed. So the challenge is how to achieve conceptual integrity while doing team design, and at the same time to achieve the very real benefits of collaboration.
[F. Brooks: The Design of Designs, 2010]

cooperating understanding encoding

Statistics - Founding Fathers.

A huge problem wiht the decison making process is the need for information. What to decide on, which goals, which options to realisations, when to decide, what are the risk, what are the effects, who is involved. For deleviring information the statistical approach for collecting presenting and predicting is invented and got a defined approach in frameworks.
Bayes, 18th century.
Thomas_Bayes Thomas_Bayes (wikipedia) One of the founders for probablity.
Bayesian probability is the name given to several related interpretations of probability as an amount of epistemic confidence " the strength of beliefs, hypotheses etc." rather than a frequency. This allows the application of probability to all sorts of propositions rather than just ones that come with a reference class. "Bayesian" has been used in this sense since about 1950. Since its rebirth in the 1950s, advancements in computing technology have allowed scientists from many disciplines to pair traditional Bayesian statistics with random walk techniques. The use of the Bayes theorem has been extended in science and in other fields.

Laplace, 19th century.
Laplace is more generic. Some of his theory being used at spectral signal processing science. What is called these days "Bayesian" is more likely coming from Laplace.
Pierre-Simon,_marquis_de_Laplace_(1745-1827) laplace (wikipedia)
In 1812, Laplace issued his Theorie analytique des probabilitys in which he laid down many fundamental results in statistics. The first half of this treatise was concerned with probability methods and problems, the second half with statistical methods and applications. Laplace´s proofs are not always rigorous according to the standards of a later day, and his perspective slides back and forth between the Bayesian and non-Bayesian views with an ease that makes some of his investigations difficult to follow, but his conclusions remain basically sound even in those few situations where his analysis goes astray.

Fisher, 20th century.
Youngronaldfisher2.jpg Ronald Fisher (wikipedia) In 1925 he published Statistical Methods for Research Workers, one of the 20th century´s most influential books on statistical methods. Fisher´s method is a technique for data fusion or "meta-analysis" (analysis of analyses). This book also popularized the p-value, and plays a central role in his approach. Fisher proposes the level p=0.05, or a 1 in 20 chance of being exceeded by chance, as a limit for statistical significance, and applies this to a normal distribution (as a two-tailed test), thus yielding the rule of two standard deviations (on a normal distribution) for statistical significance.

The basics on statistics mostly practiced descriptive, the only predcition is extrapolation from a small sample size to a complete population.

leadership confusion .. babylon

Organizing & Leadership.

Go for the low hanging fruit, do not bother you will have uo to 20% failures or things going wrong. Having 80% going well is good enough ... Is it really good enough? &bsp

Real working people feelings.
marionet feeling. Decision making a limited philisophy on those aspects.
Decision-making can be regarded as a problem-solving activity yielding a solution deemed to be optimal, or at least satisfactory. It is therefore a process which can be more or less rational or irrational and can be based on explicit or tacit knowledge and beliefs. Tacit knowledge is often used to fill the gaps in complex decision making processes. Usually both of these types of knowledge, tacit and explicit, are used together in the decision-making process.
The decisions are by control, controllers imperators. That is very selected small group of people. Real working people feelings aare like being marionets.
Devils triangle ICT
Decisions in the ICT devil triangle.
The term elephant test refers to situations in which an idea or thing, "is hard to describe, but instantly recognizable when spotted"
🎭 3 Stages from gathering information into decision.

choosing humans

Human Leadership decisions.

The classic leadership is the top down dictation from a human leader. The line of control in humans hierachry and span of control for a pyramid in administration and management. At the top of the hierarchy the leader everyone should obey.
tsar ceasar
T Tsar instead Technology.
Tsar Tsar a better desciption for the abbrevation "T" in ICT.
The title tsar is derived from the Latin title for the Roman emperors, caesar. In comparison to the corresponding Latin word imperator, the Byzantine Greek term basileus was used differently depending on whether it was in a contemporary political context or in a historical or Biblical context. In the history of the Greek language, basileus had originally meant something like "potentate". It gradually approached the meaning of "king" in the Hellenistic Period, and it came to designate "emperor" after the inception in the Roman Empire. As a consequence, Byzantine sources continued to call the Biblical and ancient kings "basileus" even when that word had come to mean "emperor" when referring to contemporary monarchs, while it was never applied to Western European kings, whose title was transliterated from Latin rex, or to other monarchs, for whom designations ("leader", "chieftain") were used.
As the Greek basileus was consistently rendered as "tsar" in Slavonic translations of Greek texts, the dual meaning was transferred into Church Slavonic. Thus, "tsar" was not only used as an equivalent of Latin imperator (in reference to the rulers of the Byzantine Empire, the Holy Roman Empire and to native rulers) but was also used to refer to Biblical rulers and ancient kings.

process cycle SIAR
Decisions on processes.
A simple single process cycle follows the famous PDCA cycle in some way. Just renaming and reordering that, it is like: 🎭 4 sides of process phases.
There is a continuous change in view on the objects (vertical/horizontal) and the process actions / transformations (diagonals).

elephant-blind-men Having a cycle and two different elements, processes and objects, it resembles the electro magnetically theory using the complex imaginary numbers.
Not knowing the Elephant: "is hard to describe, but instantly recognizable when spotted"
Uncertainties and their effects in the results is an ethical aspect. Showing the real situation or something that looks more prettier.

Pareto principle, statistics & decisions.
Go for the low hanging fruit, do not bother you will have up to 20% failures or things going wrong.
🤔 Having 80% going well is good enough ... Is it really good enough? Pareto-principle
Statistical relevant <-> decisions.
🤔 Is the acceptance of 5% (1 out 20 is a mistake / failure) acceptable or not?
😱 Is searching for cases that fulfil the statistical relevance test a correct approach or not?
In 2016, the American Statistical Association (ASA) published a statement on p-values, saying that "the widespread use of -statistical significance- (generally interpreted as -p le 0.05-) as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process"

Decisons by Ai missing ML

Automated (machine) decisions.

Whether it is supported in advising human decision makers or full getting fully automated. The dictate of a leader, the Tsar, as the only one responsible for decisions is changing. New old issues in this change:
  1. Does the machine replace the human. What about the replaced human?
  2. Will it be possible to do it as was it always done before?
  3. Can we fully understand what and why a machine is working in a way it does when used?
It are the common questions when confronted with a disruptive change. The only thing that is sure that is change is inevitable.  

process cycle value stream
Control on processes.
Any of the four process phases might get split up in sub-processes. Each of them running by an own controller, the leader/boss.
The interesting points for control are at the split of handing over responsibilities for a segment of the cycle. These are the information represented by containers in the diagonals within the control cycle.
Summing up those information containers: 🎭 5 Points of process organisation (x).

End of the dream of a leadership - quichot.
The end of classic leadership.

Lift, accuracy, confusion matrix
Ai missing ML Explainable AI, better understandable ML Machine Learning, automatic decisions is searched for but lacking.
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Understanding with uncertainties & ethical.


Greek fundamentals

The fundamental of the western world are of the old greek. The most famous is:
Pythagoras of Samos Pythagoras ho Samios "Pythagoras the Samian", b. about 570 ? d. about 495 BC was an Ionian Greek philosopher, mathematician, and founder of the religious movement called Pythagoreanism. Most of the information about Pythagoras was written down centuries after he lived, so very little reliable information is known about him.

Aristotle & Plato
These old greek philosophers are stating the problem with the analytics. theory_of_universals Aristotle to Platonic realism

Although modelling data looks to be mathematical proofed there is uncertainty.
The way of doing research on data can even be more art (human intrepretation) than real evidance.

applying usage

Divide & Conquer

There is common approach when not understanding something well to get all kind of unrelated things involved. Even worse when it is clear they are unrelated still keeping them.
Do not ovecomlicate, Keep it stupid simple (KISS)
Occam´s razor, Ockham´s razor, Ocham´s razor (Latin: novacula Occami) or law of parsimony (Latin: lex parsimoniae) is the problem-solving principle that "entities should not be multiplied without necessity." The idea is attributed to English Franciscan friar William of Ockham (c. 1287-1347), a scholastic philosopher and theologian who used a preference for simplicity to defend the idea of divine miracles. It is variously paraphrased by statements like "the simplest explanation is most likely the right one".
KISS principle
The KISS principle states that most systems work best if they are kept simple rather than made complicated; therefore, simplicity should be a key goal in design, and unnecessary complexity should be avoided.
The only question not asked and not answered is what in a situation is simple.
Ai missing ML

Simple games

Choosing and playing random, or not being random.
Three_door_problem (wiki)
Good old Monty Hall! Or, All Probability Is Conditional (wmbriggs)
wheel-of-mythfortune (mythbusters)
Good old Monty Hall! Or, All Probability Is Conditional (wmbriggs)

Ai missing ML

Random numbers

Generating good random numbers is ever lasting question.
Mersenne_twister   Wichman Hill   believe
Benford distribution of numbers
With te conditions of real measures the numbers itself are not random.
Benford´s_law (wiki)   How a Simple Misconception can Trip up a Fraudster and How a Savvy CFE Can Spot It (acfe)  
choosing humans

⚖   coding process   code languages   data info   risks align   tune&perf   learn   ⚖
⚖   Intro   Decide   FF Statistics   The Lead   The Boss   The Computer   ⚖
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© 2012,2020 J.A.Karman
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