12 December 2025

♟️Strategic Management: Relationships (Just the Quotes)

"The insights gained and garnered by the mind in its wanderings among basic concepts are benefits that theory can provide. Theory cannot equip the mind with formulas for solving problems, nor can it mark the narrow path on which the sole solution is supposed to lie by planting a hedge of principles on either side. But it can give the mind insight into the great mass of phenomena and of their relationships, then leave it free to rise into the higher realms of action." (Carl von Clausewitz, "On War", 1832)

"An Organization Chart is a cross section picture covering every relationship in the bank. It is a schematic survey showing department functions and interrelations, lines of authority, responsibility, communication and counsel. Its purpose is 'to bring the various human parts of the organization into effective correlation and co-operation'." (John W Schulze, "Office Administration", 1919)

"The pattern of personal characteristics of the leader must bear some relevant relationship to the characteristics, activities, and goals of the followers. [...] It becomes clear that an adequate analysis of leadership involves not only a study of leadership but also of situations." (R M Stodgill, "Journal of Psychology", 1948)

"The most elementary aspect of administration is organization the structure of social institutions and their constituent parts, the composition of economic enterprises and their various branches, the organization of governmental agencies and their numerous departments. As it is mainly a matter of structure, organization bears the same rudimentary relationship to administration as does the science of anatomy or skeletology to the field of medicine. An administrative organization can be sketched and charted just as the human body can be physically depicted. Apart from its graphic convenience and its 'teachable' quality, however, what intrinsic relationship does organization bear to administration?" (Albert Lepawsky, "Administration: the art and science of organization and management", 1949)

"Organization planning is the process of defining and grouping the activities of the enterprise so that they may be most logically assigned and effectively executed. It is concerned with the establishment of relationships among the units so as to further the objectives of the enterprise." (Ernest Dale, "Planning and developing the company organization structure", 1952)

"[...] authority - the right by which superiors are able to require conformity of subordinates to decisions - is the basis for responsibility and the force that binds organization together. The process of organizing encompasses grouping of activities for purposes of management and specification of authority relationships between superiors and subordinates and horizontally between managers. Consequently, authority and responsibility relationships come into being in all associative undertakings where the superior-subordinate link exists. It is these relationships that create the basic character of the managerial job." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"If charts do not reflect actual organization and if the organization is intended to be as charted, it is the job of effective management to see that actual organization conforms with that desired. Organization charts cannot supplant good organizing, nor can a chart take the place of spelling out authority relationships clearly and completely, of outlining duties of managers and their subordinates, and of defining responsibilities." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"It is probable that one day we shall begin to draw organization charts as a series of linked groups rather than as a hierarchical structure of individual 'reporting' relationships." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"Linking the basic parts are communication, balance or system parts maintained in harmonious relationship with each other and decision making. The system theory include both man-machine and interpersonal relationships. Goals, man, machine, method, and process are woven together into a dynamic unity which reacts." (George R Terry, "Principles of Management", 1960)

"The leadership and other processes of the organization must be such as to ensure a maximum probability that in all interactions and all interactions and all relationships with the organization each member will, in the light of his background, values, and expectations, view the experience as supportive and one which builds and maintains his sense of personal worth and importance." (Rensis Likert, "New patterns of management", 1961)

"In some firms role relationships prescribed by the chart seemed to be of secondary importance to personal relationships between individuals." (Joan Woodward, Industrial Organization: Theory and practice", 1965)

"In complex systems cause and effect are often not closely related in either time or space. The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by nonlinear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops. In the complex system the cause of a difficulty may lie far back in time from the symptoms, or in a completely different and remote part of the system. In fact, causes are usually found, not in prior events, but in the structure and policies of the system." (Jay W Forrester, "Urban dynamics", 1969)

"The systems approach to problems focuses on systems taken as a whole, not on their parts taken separately. Such an approach is concerned with total - system performance even when a change in only one or a few of its parts is contemplated because there are some properties of systems that can only be treated adequately from a holistic point of view. These properties derive from the relationship between parts of systems: how the parts interact and fit together." (Russell L Ackoff, "Towards a System of Systems Concepts", 1971) 

"Managing upward relies on informal relationships, timing, exploiting ambiguity, and implicit communication. And the irony of it all is that these most subtle skills must be learned and mastered by younger managers who not only lack education and directed experience in benign guerilla warfare but are further misguided by management myths which contribute to false expectations and a misleading perception of reality." (Richard T Pascale & Anthony G Athos, "The Art of Japanese Management", 1981)

"Every company has two organizational structures: the formal one is written on the charts; the other is the everyday living relationship of the men and women in the organization." (Harold Geneen & Alvin Moscow, "Managing", 1984)

"Most managers are reluctant to comment on ineffective or inappropriate interpersonal behavior. But these areas are often crucial for professional task success. This hesitancy is doubly felt when there is a poor relationship between the two. [...] Too few managers have any experience in how to confront others effectively; generally they can more easily give feedback on inadequate task performance than on issues dealing with another's personal style." (David L Bradford & Allan R Cohen, "Managing for Excellence", 1984)

"It seems to me that we too often focus on the inside aspects of the job of management, failing to give proper attention to the requirement for a good manager to maintain those relationships between his organization and the environment in which it must operate which permits it to move ahead and get the job done." (Breene Kerr, Giants in Management, 1985) 

"Operating managers should in no way ignore short-term performance imperatives [when implementing productivity improvement programs.] The pressures arise from many sources and must be dealt with. Moreover, unless managers know that the day-to-day job is under control and improvements are being made, they will not have the time, the perspective, the self-confidence, or the good working relationships that are essential for creative, realistic strategic thinking and decision making." (Robert H Schaefer, Harvard Business Review, 1986)

"Architecture is defined as a clear representation of a conceptual framework of components and their relationships at a point in time […] a discussion of architecture must take into account different levels of architecture. These levels can be illustrated by a pyramid, with the business unit at the top and the delivery system at the base. An enterprise is composed of one or more Business Units that are responsible for a specific business area. The five levels of architecture are Business Unit, Information, Information System, Data and Delivery System. The levels are separate yet interrelated. [...] The idea if an enterprise architecture reflects an awareness that the levels are logically connected and that a depiction at one level assumes or dictates that architectures at the higher level." (W Bradford Rigdon, "Architectures and Standards", 1989)

"Leadership is always dependent upon the context, but the context is established by the relationships." (Margaret J Wheatley, "Leadership and the New Science: Discovering Order in a Chaotic World", 1992)

"Although the concept of an enterprise architecture (EA) has not been well defined and agreed upon, EAs are being developed to support information system development and enterprise reengineering. Most EAs differ in content and nature, and most are incomplete because they represent only data and process aspects of the enterprise. […] An EA is a conceptual framework that describes how an enterprise is constructed by defining its primary components and the relationships among these components." (M A Roos, "Enterprise architecture: definition, content, and utility", Enabling Technologies: Infrastructure for Collaborative Enterprises, 1994)

"Trust is the glue of life. It's the most essential ingredient in effective communication. It's the foundational principle that holds all relationships - marriages, families, and organizations of every kind - together." (Stephen Covey, "First Things First", 1994)

"A strategy is a set of hypotheses about cause and effect. The measurement system should make the relationships (hypotheses) among objectives" (and measures) in the various perspectives explicit so that they can be managed and validated. The chain of cause and effect should pervade all four perspectives of a Balanced Scorecard." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"[Schemata are] knowledge structures that represent objects or events and provide default assumptions about their characteristics, relationships, and entailments under conditions of incomplete information." (Paul J DiMaggio, "Culture and Cognition", Annual Review of Sociology No. 23, 1997)

"The Enterprise Architecture is the explicit description of the current and desired relationships among business and management process and information technology. It describes the 'target' situation which the agency wishes to create and maintain by managing its IT portfolio." (Franklin D Raines, 1997)

"Leadership has long been associated with authority - we tend to concentrate on the leader, to think of them as innately superior in some way, and take the followers for granted. But formal authority is only one possible part of leadership. Many leaders do not have it. In some cases, perhaps ‘companionship’ better describes the relationship between leader and followers." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"An information system architecture typically encompasses an overview of the entire information system - including the software, hardware, and information architectures" (the structure of the data that systems will use). In this sense, the information system architecture is a meta-architecture. An enterprise architecture is also a meta-architecture in that it comprises many information systems and their relationships (technical infrastructure). However, because it can also contain other views of an enterprise - including work, function, and information - it is at the highest level in the architecture pyramid. It is important to begin any architecture development effort with a clear definition of what you mean by 'architecture'." (Frank J Armour et al, "A big-picture look at enterprise architectures", IT professional Vol 1" (1), 1999)

"Enterprise architecture is a family of related architecture components. This include information architecture, organization and business process architecture, and information technology architecture. Each consists of architectural representations, definitions of architecture entities, their relationships, and specification of function and purpose. Enterprise architecture guides the construction and development of business organizations and business processes, and the construction and development of supporting information systems." (Gordon B Davis, "The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"The key element of an organization is not a building or a set of policies and procedures; organizations are made up of people and their relationships with one another. An organization exists when people interact with one another to perform essential functions that help attain goals." (Richard Daft, "The Leadership Experience", 2002)

"Organization charts are subject to important limitations. A chart shows only formal authority relationships and omits the many significant informal and informational relationships." (Harold Koontz and Heinz Weihrich, "Essentials Of Management", 2006)

"Enterprise architecture is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise's future state and enable its evolution. The scope of the enterprise architecture includes the people, processes, information and technology of the enterprise, and their relationships to one another and to the external environment. Enterprise architects compose holistic solutions that address the business challenges of the enterprise and support the governance needed to implement them." (Anne Lapkin et al, "Gartner Clarifies the Definition of the Term 'Enterprise Architecture", 2008)

"Systems thinking is a mental discipline and framework for seeing patterns and interrelationships. It is important to see organizational systems as a whole because of their complexity. Complexity can overwhelm managers, undermining confidence. When leaders can see the structures that underlie complex situations, they can facilitate improvement. But doing that requires a focus on the big picture." (Richard L Daft, "The Leadership Experience", 2008)

"One advantage that decision tree modeling has over other pattern recognition techniques lies in the interpretability of the decision model. Due to this interpretability, information relating to the identification of important features and interclass relationships can be used to support the design of future experiments and data analysis." (S D Brown, A J Myles, in Comprehensive Chemometrics, 2009)


06 December 2025

💎💫SQL Reloaded: Schema Differences between Database Versions - Part I: INFORMATION_SCHEMA version

During data migrations and other similar activities it's important to check what changed in the database at the various levels. Usually, it's useful to check when schemas, object names or table definitions changed, even if the changes are thoroughly documented. One can write a script to point out all the differences in one output, though it's recommended to check the differences at each level of detail

For this purpose one can use the INFORMATION_SCHEMA available for many of the RDBMS implementing it. This allows to easily port the scripts between platforms. The below queries were run on SQL Server 2025 in combination with Dynamics 365 schemas, though they should run on the earlier versions, incl. (Azure) SQL Databases. 

Such comparisons must be done from the both sides, this implying a FULL OUTER JOIN when writing a single SELECT statement, however the results can become easily hard to read and even interpret when the number of columns in output increases. Therefore, it's recommended to keep the number of columns at a minimum while addressing the scope, respectively break the FULL OUTER JOIN in two LEFT JOINs.

The simplest check is at schema level, and this can be easily done from both sides (note that database names needed to be replaced accordingly):

-- difference schemas (objects not available in the new schema)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER NEW_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME NEW_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [old database_name].INFORMATION_SCHEMA.SCHEMATA DB1
	     LEFT JOIN [new database name].INFORMATION_SCHEMA.SCHEMATA DB2
	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME


-- difference schemas (new objects)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER OLD_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME OLD_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [new database name].INFORMATION_SCHEMA.SCHEMATA DB1
	     LEFT JOIN [old database name].INFORMATION_SCHEMA.SCHEMATA DB2
	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME

Comments:
1) The two queries can be easily combined via a UNION ALL, though it might be a good idea then to add a column to indicate the direction of the comparison. 

The next step would be to check which objects has been changed:

-- table-based objects only in the old schema (tables & views)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.TABLE_TYPE
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, DB2.TABLE_TYPE NEW_TABLE_TYPE
	, CASE 
		WHEN DB2.TABLE_NAME IS NULL THEN 'objects only in old db'
		WHEN DB1.TABLE_TYPE <> IsNull(DB2.TABLE_TYPE, '') THEN 'different table type'
		--WHEN DB1.TABLE_CATALOG <> IsNull(DB2.TABLE_CATALOG, '') THEN 'different table catalog'
	  END Comment
	FROM [old database name].INFORMATION_SCHEMA.TABLES DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.TABLES DB2
	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME

Comments:
1) If the database was imported under another name, then the TABLE_CATALOG will have different values as well.

At column level, the query increases in complexity, given the many aspects that must be considered:

-- difference columns (columns not available in the new scheam, respectively changes in definitions)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.COLUMN_NAME 
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, CASE WHEN DB2.TABLE_NAME IS NULL THEN 'column only in old db' END Comment
	, DB1.DATA_TYPE
	, DB2.DATA_TYPE NEW_DATA_TYPE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.DATA_TYPE, '') <> IsNull(DB2.DATA_TYPE, '') THEN 'Yes' END Different_data_type
	, DB1.CHARACTER_MAXIMUM_LENGTH
	, DB2.CHARACTER_MAXIMUM_LENGTH NEW_CHARACTER_MAXIMUM_LENGTH
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_MAXIMUM_LENGTH, '') <> IsNull(DB2.CHARACTER_MAXIMUM_LENGTH, '') THEN 'Yes' END Different_maximum_length
	, DB1.NUMERIC_PRECISION
	, DB2.NUMERIC_PRECISION NEW_NUMERIC_PRECISION
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_PRECISION, '') <> IsNull(DB2.NUMERIC_PRECISION, '') THEN 'Yes' END Different_numeric_precision
	, DB1.NUMERIC_SCALE
	, DB2.NUMERIC_SCALE NEW_NUMERIC_SCALE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_SCALE, '') <> IsNull(DB2.NUMERIC_SCALE,'') THEN 'Yes' END Different_numeric_scale
	, DB1.CHARACTER_SET_NAME
	, DB2.CHARACTER_SET_NAME NEW_CHARACTER_SET_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_SET_NAME, '') <> IsNull(DB2.CHARACTER_SET_NAME, '') THEN 'Yes' END Different_character_set_name 
	, DB1.COLLATION_NAME
	, DB2.COLLATION_NAME NEW_COLLATION_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.COLLATION_NAME, '') <> IsNull(DB2.COLLATION_NAME, '') THEN 'Yes' END Different_collation_name
	, DB1.ORDINAL_POSITION
	, DB2.ORDINAL_POSITION NEW_ORDINAL_POSITION
	, DB1.COLUMN_DEFAULT
	, DB2.COLUMN_DEFAULT NEW_COLUMN_DEFAULT
	, DB1.IS_NULLABLE
	, DB2.IS_NULLABLE NEW_IS_NULLABLE
	FROM [old database name].INFORMATION_SCHEMA.COLUMNS DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.COLUMNS DB2
	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
	     AND DB1.COLUMN_NAME = DB2.COLUMN_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
  OR IsNull(DAT.Different_data_type,'') = 'Yes'
  OR IsNull(DAT.Different_maximum_length,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_precision,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_scale,'') = 'Yes'
  OR IsNull(DAT.Different_character_set_name,'') = 'Yes'
  OR IsNull(DAT.Different_collation_name,'') = 'Yes'
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME
, DAT.COLLATION_NAME

Comments:
1) The query targets only the most common scenarios, therefore must be changed to handle further cases (e.g. different column defaults, different attributes like nullable, etc.)!
2) The other perspective can be obtained by inverting the table names (without aliases) and changing the name of the columns from "NEW_' to "OLD_" (see the queries for schemas).
3) One can move the column-based conditions for the differences in the main query, though then is needed to duplicate the logic, which will make the code more challenging to change and debug. 

Happy coding!

🕸Systems Engineering: Fractals (Just the Quotes)

"[…] chaos and fractals are part of an even grander subject known as dynamics. This is the subject that deals with change, with systems that evolve in time. Whether the system in question settles down to equilibrium, keeps repeating in cycles, or does something more complicated, it is dynamics that we use to analyze the behavior." (Steven H Strogatz, "Non-Linear Dynamics and Chaos, 1994)

"It is time to employ fractal geometry and its associated subjects of chaos and nonlinear dynamics to study systems engineering methodology (SEM). [...] Fractal geometry and chaos theory can convey a new level of understanding to systems engineering and make it more effective." (Arthur D Hall, "The fractal architecture of the systems engineering method", "Systems, Man and Cybernetics", Vol. 28 (4), 1998)

"What is renormalization? First of all, if scaling is present we can go to smaller scales and get exactly the same result. In a sense we are looking at the system with a microscope of increasing power. If you take the limit of such a process you get a stability that is not otherwise present. In short, in the renormalized system, the self-similarity is exact, not approximate as it usually is. So renormalization gives stability and exactness." (Barry R Parker, "Chaos in the Cosmos: The stunning complexity of the universe", 1996)

"The self-similarity of fractal structures implies that there is some redundancy because of the repetition of details at all scales. Even though some of these structures may appear to teeter on the edge of randomness, they actually represent complex systems at the interface of order and disorder." (Edward Beltrami, "What is Random?: Chaos and Order in Mathematics and Life", 1999)

"If financial markets aren't efficient, then what are they? According to the 'fractal market hypothesis', they are highly unstable dynamic systems that generate stock prices which appear random, but behind which lie deterministic patterns." (Steve Keen, "Debunking Economics: The Naked Emperor Of The Social Sciences", 2001)

"Wherever we look in our world the complex systems of nature and time seem to preserve the look of details at finer and finer scales. Fractals show a holistic hidden order behind things, a harmony in which everything affects everything else, and, above all, an endless variety of interwoven patterns. Fractal geometry allows bounded curves of infinite length, as well as closed surfaces with infinite area. It even allows curves with positive volume and arbitrarily large groups of shapes with exactly the same boundary." (Philip Tetlow, "The Web’s Awake: An Introduction to the Field of Web Science and the Concept of Web Life", 2007)

"The economy is a nonlinear fractal system, where the smallest scales are linked to the largest, and the decisions of the central bank are affected by the gut instincts of the people on the street." (David Orrell, "The Other Side Of The Coin", 2008)

"A mathematical fractal is generated by an infinitely recursive process, in which the final level of detail is never reached, and never can be reached by increasing the scale at which observations are made. In reality, fractals are generated by finite processes, and exhibit no visible change in detail after a certain resolution limit. This behavior of natural fractal objects is similar to the exponential cutoff, which can be observed in many degree distributions of real networks." (Péter Csermely, "Weak Links: The Universal Key to the Stabilityof Networks and Complex Systems", 2009)

"Fractals are self-similar objects. However, not every self-similar object is a fractal, with a scale-free form distribution. If we put identical cubes on top of each other, we get a self-similar object. However, this object will not have scale-free statistics: since it has only one measure of rectangular forms, it is single-scaled. We need a growing number of smaller and smaller self-similar objects to satisfy the scale-free distribution." (Péter Csermely, "Weak Links: The Universal Key to the Stabilityof Networks and Complex Systems", 2009)

"In the telephone system a century ago, messages dispersed across the network in a pattern that mathematicians associate with randomness. But in the last decade, the flow of bits has become statistically more similar to the patterns found in self-organized systems. For one thing, the global network exhibits self-similarity, also known as a fractal pattern. We see this kind of fractal pattern in the way the jagged outline of tree branches look similar no matter whether we look at them up close or far away. Today messages disperse through the global telecommunications system in the fractal pattern of self-organization." (Kevin Kelly, "What Technology Wants", 2010)

"Fractals are different from chaos. Fractals are self-similar geometric objects, while chaos is a type of deterministic yet unpredictable dynamical behavior. Nevertheless, the two ideas or areas of study have several interesting and important links. Fractal objects at first blush seem intricate and complex. However, they are often the product of very simple dynamical systems. So the two areas of study - chaos and fractals - are naturally paired, even though they are distinct concepts." (David P Feldman,"Chaos and Fractals: An Elementary Introduction", 2012)

"Only at the edge of chaos can complex systems flourish. This threshold line, that edge between anarchy and frozen rigidity, is not a like a fence line, it is a fractal line; it possesses nonlinearity." (Stephen H Buhner, "Plant Intelligence and the Imaginal Realm: Beyond the Doors of Perception into the Dreaming of Earth", 2014)

05 December 2025

♟️Strategic Management: Relations (Just the Quotes)

"The art of management has been defined, 'As knowing exactly what you want men to do, and then seeing that they do it in the best and cheapest way.' No concise definition can fully describe an art, but the relations between employers and men form without question the most important part of this art. In considering the subject, therefore, until this part of the problem has been fully discussed, the remainder of the art may be left in the background." (Frederick W Taylor, "Shop Management", 1903)

"The writer has found, in analyzing and diagnosing organization and accounting work, that charts can express more on one page than is sometimes expressed in several chapters of writing, and has been the author and originator of many methods of charting industrial expressions. It is necessary, as a first step, for analytical and other purposes, to make a chart expressing all of the relations governing the organization of a business so as to show the very foundation upon which all authorities, accounting, and business transactions are based and conducted. There have been more failures scored both personally and financially for lack of these very elements in a business than by reason of any other one thing. As well try to build a house without a foundation as to try to conduct a business, especially a manufacturing business, without proper organization." (Clinton E Woods, "Organizing a factory", 1905)

"The relations outlined on an organization chart provide a framework within which fuller and more spontaneous human behavior takes place. The formal system may draw upon that behavior for added strength; it will in its turn be subordinated to personal and group egotism." (Philip Selznick, "Leadership in Administration: A Sociological Interpretation", 1957) 

"The productivity of a work group seems to depend on how the group members see their own goals in relation to the goals of the organization." (Paul Hersey & Kenneth H Blanchard, "Management of Organizational Behavior", 1972)

"The degree of confirmation assigned to any given hypothesis is sensitive to properties of the entire belief system [...] simplicity, plausibility, and conservatism are properties that theories have in virtue of their relation to the whole structure of scientific beliefs taken collectively. A measure of conservatism or simplicity would be a metric over global properties of belief systems." (Jerry Fodor, "Modularity of Mind", 1983)

"The formal structure of a decision problem in any area can be put into four parts: (1) the choice of an objective function denning the relative desirability of different outcomes; (2) specification of the policy alternatives which are available to the agent, or decisionmaker, (3) specification of the model, that is, empirical relations that link the objective function, or the variables that enter into it, with the policy alternatives and possibly other variables; and (4) computational methods for choosing among the policy alternatives that one which performs best as measured by the objective function." (Kenneth Arrow, "The Economics of Information", 1984)

"Culture [is] a pattern of basic assumptions invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems." (Edgar H Schein, "Organizational Culture and Leadership", 1985)

"Action often creates the orderly relations that originally were mere presumptions summarized in a cause map. Thus language trappings of organizations such as strategic plans are important components in the process of creating order. They hold events together long enough and tightly enough in people's heads so that they act in the belief that their actions will be influential and make sense." (Karl E Weick, "Organizational culture as a source of high reliability", 1987)

"Formulation of a mathematical model is the first step in the process of analyzing the behaviour of any real system. However, to produce a useful model, one must first adopt a set of simplifying assumptions which have to be relevant in relation to the physical features of the system to be modelled and to the specific information one is interested in. Thus, the aim of modelling is to produce an idealized description of reality, which is both expressible in a tractable mathematical form and sufficiently close to reality as far as the physical mechanisms of interest are concerned." (Francois Axisa, "Discrete Systems" Vol. I, 2001)

26 November 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 119: Market Volatility and Herding Behavior)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blog post of 600 words on how AI could influence the financial markets negatively"

Introduction

AI-driven trading systems excel at identifying patterns and executing rapid transactions. Yet, when many firms deploy similar models, markets risk herding effects. If algorithms simultaneously react to signals - such as a sudden drop in sentiment - this can trigger flash crashes or exaggerated swings. The 2010 “Flash Crash” was a precursor, but with AI’s speed and scale, future disruptions could be more severe.

  • Risk monoculture: As highlighted by CEPR, reliance on similar AI models creates fragility. A single miscalibration could cascade across institutions.
  • Amplified feedback loops: AI systems may reinforce trends rather than balance them, worsening bubbles or panics.

Operational and Cyber Risks

The European Central Bank warns that widespread AI adoption increases operational risk, especially if concentrated among a few providers. Financial institutions depending on the same AI infrastructure face systemic vulnerabilities:

  • Cybersecurity threats: AI systems are attractive targets for hackers. Manipulating algorithms could distort markets or enable fraud.
  • Too-big-to-fail dynamics: If dominant AI providers suffer outages or breaches, the ripple effects could destabilize global markets.

Misuse and Misalignment

AI’s ability to process vast data sets is powerful, but it can also be misused:

  • Malicious exploitation: Bad actors could weaponize AI to manipulate trading signals or spread misinformation.
  • Model misalignment: AI systems trained on biased or incomplete data may make flawed decisions, mispricing risk or misjudging creditworthiness.
  • Evasion of control: Autonomous systems may act in ways regulators cannot easily monitor, undermining oversight.

Regulatory Challenges

The Financial Stability Board stresses that regulators face information gaps in monitoring AI’s role in finance. Traditional frameworks may not capture:

  • Accountability when AI executes trades independently.
  • Transparency in decision-making, as complex models often operate as “black boxes.”
  • Cross-border risks, since AI systems are deployed globally but regulation remains fragmented.
  • Without updated oversight, AI could outpace regulators, leaving markets exposed to unchecked systemic risks.

Concentration and Inequality

AI adoption may concentrate power among large institutions with resources to develop advanced systems. Smaller firms risk being marginalized, reducing competition and deepening inequality in access to financial opportunities. This concentration also magnifies systemic risk: if a few players dominate AI-driven finance, their failures could destabilize entire markets.

Long-Term Stability Concerns

The IMF warns that generative AI could reshape financial markets in unpredictable ways:

  • Unintended consequences: AI models may behave unexpectedly under stress, creating shocks regulators cannot anticipate.
  • Loss of human judgment: Overreliance on AI risks sidelining human oversight, weakening resilience when algorithms fail.
  • Ethical dilemmas: Bias in AI decision-making could distort credit allocation, reinforcing social inequalities.

Conclusion

AI’s negative influence on financial markets lies not in its capabilities but in its unchecked deployment. By amplifying volatility, concentrating risks, and challenging regulatory frameworks, AI could undermine stability rather than enhance it. The path forward requires balanced adoption: leveraging AI’s strengths while building safeguards against its vulnerabilities.

In short: AI may accelerate efficiency but also magnify fragility. Financial markets must prepare for both outcomes, ensuring innovation does not come at the cost of resilience.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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25 November 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 118: AI in Trading and Market Efficiency)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blog post of 600 words on how AI could influence the financial markets
"

Introduction

One of the most immediate impacts of Artificial Intelligence (AI) is in algorithmic trading. Machine learning models can process vast datasets - economic indicators, corporate earnings, even social media sentiment - at speeds far beyond human capability. This enables:

  • Faster price discovery: AI can identify mispriced assets and arbitrage opportunities in real time.
  • Predictive analytics: Models trained on historical data can forecast short-term market movements, giving firms a competitive edge.
  • Reduced transaction costs: Automation streamlines execution, lowering costs for institutional investors and potentially improving liquidity.

However, this efficiency comes with risks. If many firms rely on similar AI-driven strategies, markets could experience herding behavior, amplifying volatility during stress events.

Risk Management and Credit Analysis

AI is revolutionizing risk assessment. Financial institutions are deploying machine learning to:

  • Evaluate creditworthiness using non-traditional data (e.g., digital footprints, transaction histories).
  • Detect fraud by spotting anomalies in transaction patterns.
  • Model systemic risks by simulating complex interdependencies across markets.

For example, firms like Surfin Meta Digital Technology have developed proprietary AI-based social credit scoring models, enabling financial inclusion in emerging markets. This demonstrates how AI can expand access to capital while improving risk pricing.

Legal and Regulatory Implications

The Financial Markets Law Committee (FMLC) has highlighted that AI introduces new private law issues in wholesale markets. Questions arise around liability when AI systems execute trades or make decisions autonomously. Regulators must adapt frameworks to ensure accountability without stifling innovation.

Moreover, concentration of AI providers could create systemic risks. If a handful of firms dominate AI infrastructure, failures or cyberattacks could ripple across the global financial system.

Macroeconomic and Investment Trends

AI is not just a tool - it is becoming an investment theme itself. Companies like Nvidia have seen record revenues driven by demand for AI chips, influencing broader market sentiment. Investors increasingly view AI as both a driver of productivity and a sector-specific growth opportunity.

Private investment in AI reached $252.3 billion in 2024, with mergers and acquisitions rising by over 12%. This surge reflects confidence in AI’s ability to optimize tasks and create value across industries, including finance.

Risks to Financial Stability

While AI promises efficiency, it also raises concerns:

  • Operational risk: Complex models may fail in unexpected ways, especially under extreme market conditions.
  • Cybersecurity threats: AI systems are vulnerable to manipulation, posing risks to market integrity.
  • Too-big-to-fail dynamics: Heavy reliance on a few AI providers could magnify systemic vulnerabilities.

The IMF warns that generative AI could significantly impact financial stability if not properly managed. Balancing innovation with safeguards will be critical.

The Road Ahead

AI’s influence on financial markets will be transformative but uneven. Benefits include:

  • Greater efficiency and liquidity.
  • Improved risk management and fraud detection.
  • Expanded financial inclusion in underserved regions.

Challenges involve:

  • Regulatory adaptation.
  • Systemic risks from concentration and herding.
  • Ethical concerns around data use and bias.
Ultimately, AI will not replace human judgment but will augment it. The most successful financial institutions will be those that integrate AI responsibly - leveraging its strengths while preparing for its risks.

Conclusion

AI will accelerate trading, reshape risk management, and create new regulatory challenges. Its dual nature - offering efficiency while introducing systemic risks - means that financial markets must evolve carefully to harness its potential without compromising stability.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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19 November 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 117: Beyond Logical Consistency - Can AI-Based Machines Achieve More?)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a post of 600 words on whether AI-based machines can achieve more than logically consistency"

Introduction

Logical consistency is essential for usability. Without it, Artificial Intelligence (AI) risks producing contradictory outputs that erode trust. For example, a medical AI that diagnoses a patient with two mutually exclusive conditions would be unusable. Similarly, an engineering AI that recommends contradictory design parameters would undermine safety.

Thus, logical consistency forms the baseline requirement for AI systems. It ensures predictability, reliability, and coherence in reasoning. But consistency alone does not capture the full potential of AI.

Beyond Consistency: Higher Cognitive Capacities

AI-based machines can aspire to capacities that go beyond mere logical coherence:

  • Creativity: Logical consistency ensures correctness, but creativity allows AI to generate novel solutions. For example, generative models can design new molecules or propose innovative engineering structures. Creativity often involves breaking or bending strict logical rules to explore new possibilities.
  • Adaptability: Real-world environments are dynamic. AI must adapt to changing contexts, incomplete information, and evolving goals. Adaptability sometimes requires prioritizing flexibility over rigid consistency.
  • Judgment under uncertainty: Humans excel at making decisions with incomplete data. AI can emulate this by balancing probabilistic reasoning with logical frameworks. This capacity goes beyond consistency, enabling AI to act effectively in ambiguous situations.
  • Ethical reasoning: Logical consistency does not guarantee ethical outcomes. AI must integrate values, fairness, and human-centered principles. Ethical reasoning requires balancing competing priorities, which may involve tolerating controlled inconsistencies for the sake of justice or compassion.

The Role of Human-Like Inconsistency

Interestingly, humans are not perfectly consistent, yet our reasoning is effective. We rely on heuristics, intuition, and context. AI that mirrors human cognition may need to embrace a degree of inconsistency to remain useful. For example:

  • In creative writing, strict logical consistency would stifle imagination.
  • In social interaction, empathy often overrides logical rules.
  • In strategic decision-making, flexibility can be more valuable than rigid coherence.
  • Thus, achieving more than consistency may mean integrating controlled inconsistency - a balance between logic and adaptability.

Practical Milestones Beyond Consistency

AI can surpass logical consistency by achieving:

  • Transparency: Systems that explain their reasoning steps, even when inconsistent, foster trust.
  • Self-correction: AI that detects and resolves its own contradictions demonstrates meta-reasoning beyond consistency.
  • Cross-domain integration: Consistency within one domain is insufficient. AI must integrate knowledge across disciplines - science, ethics, psychology - without collapsing under contradictions.
  • Human alignment: Ultimately, AI must align with human goals and values, which requires more than logical rigor.

Philosophical Implications

If AI achieves more than logical consistency, it begins to approximate wisdom rather than mere reasoning. Wisdom involves judgment, empathy, and foresight - qualities that transcend strict logic. This raises profound questions:

  • Should AI aim to be perfectly consistent, or should it embrace human-like flexibility?
  • Can AI balance logic with creativity, ethics, and adaptability without undermining trust?
  • Is the future of AI about building 'machines of reason' or 'machines of understanding'?

Conclusion

AI-based machines can achieve more than logical consistency. While consistency is a necessary foundation, true usability and trustworthiness require higher capacities: creativity, adaptability, ethical reasoning, and transparency. These qualities allow AI to function effectively in complex, uncertain, and human-centered environments.

In short, logical consistency is not the ceiling but the floor. The real promise of AI lies in transcending consistency to become systems that are not only coherent but also creative, adaptive, and aligned with human values.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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Koeln, NRW, Germany
IT Professional with more than 25 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.