Agent-Based Causality & the Admissions Paradox – Lecture 12A illustrates how the concept of agent-based causality can be used to clarify the Simpson’s Paradox, in the context of admissions. The paradox is the the overall university data can show overwhelming bias in favor of males, while each of the departments favors females. With an agency-based model, we must look at the goals of the agents – the students and the admissions committee. We show that the identical data may be produced by widely different types of causal structures, which are based on the goals of the agents. Some of these structures support discrimination while others do not. So both causality and discrimination do not lie at the surface – in the data — but must be understood to be a property of the real world structures which generate the data. It is not possible to recover information about goals (and hence about causality) from the data. That is why causality requires “shoe leather” – walking about in the real world to discover the causal mechanisms.

The goal of this lecture is to present, by example, a new theory of causality: goals drive actions which lead to observed consequences, which may or may not match the goal (intended consequences). The actions and observed consequences are observable, but the intended consequences are not. Human experience consists of knowledge gained by the mismatch between the observed and intended consequences, and modifications of actions to create a better match. All of this is lost in the standard approaches to causality, based on the observables only.

Open-And-Shut Case: Discrimination

We consider admissions data for a mythical “Berkeley University”:

1000 Female Applicants 900 Admits

1000 Male     Applicants 100 Admits

With a 90% admit ratio for females, and a 10% admit ratio for males, it seems crystal clear that Berkeley discriminates against males. The numbers can’t lie? Or, can they?

Some Theory and Philosophy

When we say that Berkeley discriminates, we are making a claim is being made about a GOAL of a decision maker; an action taken to favor females. As should become clear after this lecture, GOALS CANNOT be deduced from data. When you try to do so, paradoxical results OFTEN emerge. The data can easily point to directions different from the unobserved goals. The theory of causality that we propose to examine in this lecture sets up the following causal sequence as basic to the study of causality:

                                        Goals => Action => Consequences

The current best account of causality is given by Woodward in “Making Things Happen.” He argues about causality in terms of how different actions have different consequences, and if an action reliably results in an effect, this is causality. But Woodward’s account pushes into the background the Agent and his goals: WHO makes things happen, and WHY? Philosophers deliberately AVOID thinking about these CENTRAL questions, because they reject “Anthropocentric” explanation: causality exists whether or not there are human beings. So, accounts which make human goals central cannot account for more general types of causal effects, which would exist even if no human beings were around. Also, they are wedded to positivism – the idea that only observables matter for science. Human intentions & goals are NOT observable, and hence not suitable to incorporate in scientific theories.

Real Statistics is founded on a new theory of knowledge (Epistemology). We argue that ALL knowledge is anthropocentric – We cannot aspire to Godly status.  Human Experience is basis of all knowledge. Superficially, this also appears to be the claim of “empiricism”, the dominant Western philosophy of knowledge, starting from David Hume. But Hume takes knowledge to be concerned only with the external world, or objective reality. In contrast, we take human knowledge to be built upon as our internal psychological experience. This makes our knowledge  SUBJECTIVE, LOCAL, EPHEMERAL, not UNIVERSAL. According to standard Western theories of knowledge, subjective human experience does not classify as knowledge at all.

A central aspect of our experience is that of AGENCY: Our goals influence our actions, which lead to outcomes, often different from our intended goals. This leads to REFLECTION on why our actions did not create the desired effects. We create THEORIES about the world which link actions to consequences, and use them to decide on how to modify our actions to better achieve desired results. This is called “learning from experience.” But our theories play a crucial role in this learning. IDENTICAL experiences can lead to DIFFERENT lessons for different agents, depending on the theories they use to analyze the experience. For one agent, a sequence of six failures can be interpreted to mean that “I am incapable of achieving success” and lead to abandonment of further effort. For another agent, the same six failures might be interpreted to mean that “I need to try harder” and lead to renewed efforts.   

Back to the Admissions Paradox

We have displayed data which APPEARS to show an open-and-shut case against Berkeley for discrimination against males. BUT, the data cannot show the GOALS of Berkeley, and the REASONS why the admissions ratio for females is high, and that for males is low. To understand goals and reasons, we have to look at the agents – the decision makers. There are two sets of decision makers. Let us first focus on the students. The goals of the students are to get admission, and the action they take to achieve the goal is the application process. But let us look more deeply into these goals. When applicants apply to Berkeley, what are their goals? SUPPOSE that investigation reveals the following:

  • Females seek admission into a Literature program (Lit).
  • Males seek admission into Engineering program (Eng).

Suppose now that

  • Literature has a 90% admit rate: 1000 Female Applicants => 900 Admits
  • Engineering has a 10% admit rate:   1 000 Males    => 100 Admits

It should be clear that there is NO DISCRIMINATION on part of Berkeley. The two types of candidates have different goals – Females want a Lit degree while Males want an Eng degree. Lit has an easy admissions policy, while Eng has a difficult admissions policy. The difference in goals accounts for the difference in application strategy (Lit vs Eng), which accounts for the difference in admit ratio. The two populations are not comparable in terms of goals.  

Simpson’s Paradox

The famous Simpson’s Paradox can be illustrated by a small extension of this example. First, mix up the goals of the two genders a little bit. Suppose that 90% of the females apply to Lit and 10% apply to Eng. Conversely, 10% Males apply for Lit and 90% apply for Eng. Both departments have gender-blind admissions, so 90% of applicants to Lit get in, and 10% of applicants to Eng get in. This yields the following numbers:

1000 Females: 900 => Lit => 810 Admits,  100 => Eng => 10 Admits

1000 Males    : 100 => Lit =>   90 Admits,  900 => Eng => 90 Admits

Male Admits      90+90  :=: 18%

Female Admits      810+10 :=:  82%

According to the admit ratios (18% for Males, 82% for Females), Berkeley University discriminates against males. BUT: Both Departments have gender-blind admissions!  The paradox arises from not looking at GOALS – Actions – Outcomes. If we lump together students who have different goals, and take different actions to achieve their different goals, we mix together two separate populations, and arrive at the wrong conclusion.

We can also change the data so that both Departments FAVOR males over females, but the data shows that the University FAVORS females. Suppose that, in view of the few males in the department, Literature encourages males, and admits ALL males 100% (as against 90% admits for females). On the other hand, Engineering is dominated by male chauvinists, and rejects all females sight unseen. Then the numbers would be:

Females: 900 => Lit => 810 admits  100 => Eng => 0 admits

Males:     100 => Lit => 100 admits  900 => Eng => 90 admits

Both departments discriminate HEAVILY against females, but the data shows that the UNIVERSITY discriminates HEAVILY in favor of females! The point is that the University is NOT a decision-making AGENT, so considering what the university does is problematic. The departments are the agents, and have admissions goals, so they must be considered separately.

Resolving the Paradox using Agents and Goals

From the last data set, we learn that Eng admits 10% Males and 0% Females, and Lit  admits 100% Males and 90% Females. BOTH departments discriminate against females. We learn this from the NARRATIVE, not from the data. The narrative says that Lit would like to encourage the male applicants, because it is overwhelming female, while Eng discriminates against females because of male chauvinism. Without this narrative, we cannot conclude that there is discrimination in the departmental admissions (either for or against males). The data cannot tell us about the goals. We can easily come up with different narratives which will generate exactly the same data, but will show that there is no discrimination by gender at either department. For a very simple such narrative, suppose that all males have SAT Score of 1200 and all females have SAT scores of 1000. Admission are gender-blind at both departments, based solely on SAT scores. If Eng chooses the top candidates, it can end up choosing all males. Similarly, any was the Lit chooses to make the cut, it will end up choosing all the male applicants. This re-inforces the main lesson of real statistics: we must go beyond the data to understand the real world mechanisms which generate the data.

In particular, correct understanding of causal mechanisms requires understanding the goals of the agents, and how they act to achieve these goals. Exactly the SAME data would have different interpretation if actions and goals are different. In understanding causation, we must also take into account the STRUCTURE of the world, or Environmental Variables. Thes also have very important effects on causality. For example, details of HOW the admissions process is carried out at Berkeley, in the two departments, are very important in assessment of discrimination. For example, suppose that admissions process is mechanical, based solely on a weight average of three numbers – SAT Math Score, SAT English Score, and Grade Point Average. Then, regardless of percentages, there is no gender discrimination (interpreted in terms of goals of the admissions committee). We will now give some more examples to show that exactly the same data, with different underlying real world structures, can be based on radically different causal mechanisms. This reinforces the idea that we must understand real world structure to understand causality.

As an example of a different environmental structure, suppose that Berkely Admission Office admits all students (Lit or Eng) via a single, unified process. Students choose majors AFTER being admitted. In the university-wide admissions, we find that 1000 Females applicants lead to 810 female Admits, while 1000 Males Applicants lead to 190 male Admits. Students CHOOSE their majors so that Literature attracts 810 Females and 100 Males, while Engineering attracts 90 Males only. This is exactly the same data as before, but now the interpretation must be radically different. Male chauvinism can no longer be the answer to the question of “Why is Engineering 100% Male?”. To find the correct answer would require deeper research into the reasons why males and females make these choices. ONE simple explanation could be based on the agency of the Male and Female students: Females prefer Literature while Males prefer Engineering. But many other explanations are possible. The Literature and Engineering Departments MAY influence these choices in many ways. ARE the Departments AGENTS? Do they influence the choices of the students? Alternatively, what are the other factors involved in shaping choices made by the students?

The Agency of the Admissions Department

Since data and reality can point in dramatically different directions, it is useful to define Discrimination in terms of Goals, rather than statistics. In particular, we will say that gender-discrimination occurs when the goal of Admissions is to admit more males (or females). Now suppose we observe a highly imbalanced admissions ratio, like the first example: 90% admits for females and only 10% admits for males. When we look into the admissions process, we find that it is gender blind: the admissions committee looks at data which does not provide any clues as to the gender of the student. Then we can eliminate discrimination (goal-based definition) as a cause of this imbalance. Instead, we must look for other causes. One possible explanation might be that admissions is strongly linked to “reading scores”. It turns out that reading scores for females are higher than those for males in all countries. This is just hypothetical explanation to show that details of the admissions process matter in determining causal effects.

Another important issue to note is that the use of SAT scores does not eliminate agency! The Admissions Department can choose other criteria. Average SAT Math scores of males are substantially higher than those of females in the USA. This is a cultural artifact since the reverse holds in some other countries. In any case, use of the SAT math is biased against females, while SAT English is biased against males. To learn about discrimination, we must ask the deeper question of “How/Why” were these criteria chosen by the admissions department?

Suppose that we ask the Admissions Committee about their goals, and they respond that our Goal is to create successful educational outcomes. Then we must ask “What is the meaning of success?”, how they measure success, and how they think that the admissions criteria chosen are helpful in achieving these. A wide variety of answers are possible, and each would create different types of causal mechanism linking gender to admissions.

More Agency Issues

When we expand the scope of causality to include goals of the decision making agents, many more questions arise about this (hypothetical) example. For example, “Why are there 90 people in Eng and 900 in Lit?”. It could be that Berkeley want to specialize in Literature, and keep Engineering small. Alternatively, it is possible that Engineering Dept is mediocre, and few students want to go, preferring to go to better quality departments elsewhere. Or, Engineering is excellent, and deliberately small, for exclusivity? ANSWERS to these questions about causes are NOT in the DATA. They lie in the GOALS and INTENTIONS of the Agents, and the STRUCTURE and ENVIRONMENT which creates causal effects of actions of agents.

An aphorism which expresses a half-truth is: “Causality is in the MODEL, not in the DATA”. Since data do not provide causal information, we MUST use models to assign causal effects to actions. It is also possible for models to incorporate information about goals of agents. But models have varying degrees of reliability, depending on how closely they are matched to the real-world causal mechanisms. Causal Effects have varying degrees of probable connection to action. It is hard to differentiate between a poor causal model, and a weak causal effect. In any case, reality is so complex that our models are never exact replicas. Also, bad Models can successfully predict causal effects within a slowly changing environment. In this kind of a setup, correlations can substitute for causations in the short run. Correlations will lead to good forecasts and policy decisions in the short run. However, when the underlying structure changes, the correlations will break down, and the bad models will fail. Something like this happened after the oil crisis caused by the Yom-e-Kippur War of the early 1970’s. There was a structural change in the underlying economic realities, and most macroeconomic models broke down, producing wildly wrong forecasts. Later research showed that good models can match reality over a broader range of environments.

Causality for ACTION

The difficult work of exploring hidden causal structure is usually done with the intention of improving outcomes. We learn about causal mechanisms in order to be able act more effectively to achieve our goals. When we ask the question “Does Engineering discriminate against women?”, it is because we are interested in taking actions to remove disparity and to end discrimination. To make effective policy decisions, it is essential to know the reason why there are so few females in Engineering. Consider, for example, the following two possible explanations.

  • The male chauvinists want to keep females out of profession.
  • Admissions is gender-blind but uses SAT Math Scores as basis for admission.

The Intervention Strategies are VERY DIFFERENT in the two cases. In the first case, we might try to make laws, or use persuasion, or add female members to the admissions committee. In the second case, we might want to work on coaching female candidates on how to improve math scores, or persuade university to provide remedial math courses to deficient candidates, while trying to balance gender.

Agent-based causal effects simply cannot be learnt from the data, since they are based on intentions of agents. To understand the intentions behind monetary policy decisions, minutes of the meetings are very useful. This approach is aligned with our general framework of “Causality as Child’s Play”. Experiments show that babies are good at reading intentions. ALSO, when babies see objects move, they look for agents, who caused that motion. They are aware that objects are not agents, and cannot move on their own.  

Concluding Remarks

Causal Models are based on attempting to discover if action X causes consequence Y  (X =>Y). Woodward provided an improved account in “Making Things Happen” by asking the question “Can I manipulate X to create changes in Y?”. But this fails to take into account the GOALS of the Agent: How can I ACT to create DESIRED consequences? The Goals and Desires are Unobservable. But failure to achieve Goal is one of the central building blocks of human experience. EXPERIENCE is the name of learning from failure and modifying actions to try to achieve the desired outcomes. Unobservables are central to causality. Also, human agency is central to causality, according to this account. Both are held in low-esteem by philosophers, which is why confusion over causality has lasted for centuries.

Links to Related Materials:

Role of Islam in the Modern World

{} This is, insha Allah, the first in a series of lectures about how to launch an Islamic Revival today. Lecture delivered on Friday, 8:00AM 7th Jan 2022 from Islamabad, Pakistan. Video below is followed by writeup of lecture. The ZOOM session at which the lecture was delivered includes the Q&A session after the lecture, not included in this video:

We start with the observation that Islam is all about KNOWLEDGE:

  • Adam AS was taught the names – THEN the angels prostrated.
  • Allah T’aala INTRODUCES HIMSELF as a teacher! Read! And your Lord is the Most Generous, Who taught by the pen—taught humanity what they knew not.
  • And say: ‘My Lord! Increase me in knowledge’.
  • Say: ‘Are those who know equal to those who know not?’
  • Hadeeth: Whoever follows a path in the pursuit of knowledge, Allah will make a path to paradise easy for him.

 The final message is the Greatest Gift of God to Mankind: Say, “In the bounty of Allah and in His mercy – in that let them rejoice; it is better than what they accumulate.” 

The KNOWLEDGE we have been given is superior to all the (knowledge) that they can gather.

Today I have perfected your religion for you, and I have completed My blessing upon you, and I have approved Islam as your religion.

The KNOWLEDGE given to us (Islam) provides us with complete and perfect guidance until the day of the Judgment (QUESTION: How did it happen that Muslims stopped looking to the Quran for guidance?)

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Causation as Deep Structure

{} One of the central messages of “Real” Statistics is that data cannot be analyzed without knowledge of the real-world structures which generate the data. In particular, correlation is a surface phenomenon, while causation is a real-world phenomenon, so the two are radically different., Furthermore, observational data can NEVER give us certain conclusions about causality, especially the experience-based and goal-driven causality concept that we have been studying. In the previous lecture, we saw that a common cause (Z => X and Z => Y) can create strong correlation between two variables X and Y, without any causal relationship between the two. If Z is unknown or unobserved then correct causal inference is impossible from observations of X and Y. However, if we can MANIPULATE X then we can reject the causal link X => Y suggested by the data, because we will see that setting values of X has no effect on Y. The goal of this lecture is to demonstrate that Causal Effects CANNOT be inferred from OBSERVATIONS. To do this, we will create an example where the underlying real-world causal mechanism which generates the data remains the same throughout. However, some peripheral changes in data gathering mechanism cause massive difference in the “observed” causal connections. That is, vastly different correlation structures can represent the same underlying causal mechanism. From this we deduce that it is necessary to do a deep investigation into the real-world mechanisms, in order to learn about causality – we cannot do this by looking at the numbers alone, without knowledge of their real-world context

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Causal Path Diagrams

{} Introduction: In the previous lecture, we used arrows and nodes to represent different possible causal pathways and causal sequences. Judea Pearl has shown that these diagrams are central to the study of causality. This lecture is devoted the development of basic concepts related to causal path diagrams, and how they help us to understand causal relationships.

1      Directed Acyclic Graphs (DAG)

A graph is a collection of nodes and connections between nodes, which are called paths. The nodes will be the variables of interest, and the paths will be the potential causal links. The graph below has five nodes (A,B,C,D,E) and three paths which connect (A,B) (A,C) and (C,D):

The FULL graph has paths between all pairs of nodes; we can draw the full graph for the five nodes above by connecting all pairs of nodes as follows:

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The Opposition between Markets and Society

Asad Zaman , “Unregulated Markets and the Transformation of Society,” Chapter 18 of Routledge Handbook of Ecological Economics: Nature and Society. Editor Clive Spash. 2016.

Modern Societies inculcate within us two different normative frameworks which are strongly opposed to each other. Consider, for example, offering your mother a $1000 for a festive meal she has prepared for the family with love and effort! What is a reward in a market context is an outrage in a social context. Blood donors routinely volunteer for giving blood for a good cause, but would be repelled by payments offered instead. The social context is one of cooperation and generosity, while the market context creates competition and selfish greed. A history of the origins of this conflict can be found in Polanyi’s brilliant work on the Great Transformation (see The Great Transformation in European Thought). Some aspects of this conflict connected to the environmental crisis and sustainability are discussed below.


In face of the strong conflict between market norms and social norms, peaceful co-existence is impossible. In traditional societies, markets were subordinated to society. Modern society emerged via a number of revolutions which made society subordinate to markets. This led to a reversal of traditional values of social cooperation and harmony with nature. Instead, men, nature, society became objects to be exploited for creating profits. A market society generates profits by exploiting men and nature, and requires increasing profits to sustain itself. This process has run into its limits as planetary resources are being destroyed on a scale large enough to threaten the planet. Saving the planet requires reversing the transition to modernity by subordinating markets to society. This is a difficult task.


As industrialised human society barrels down the fast track to ecological suicide, there is a well-funded campaign to spread stories that create confusion about problems such as climate change, because environmental protection interferes with corporate profits. Species of plants and animals which evolved over billions of years, and cannot be replaced, are becoming extinct at a rapidly increasing rate. Precious environmental treasures like coral reefs and rainforests are being destroyed. The cost of what has already been destroyed cannot be calculated. In addition, industrialised society is using up planetary resources at a rate which is much higher than the ability of the planet to replenish or renew. The wastes being produced by human beings are changing the composition of the atmosphere, oceans, lakes and rivers, and affecting all forms of life. How can some elite groups act as “Merchants of Doubt” (Oreskes and Conway, 2011) prepared to destroy the planet to make a profit?

Experiments show that humans have radically different sets of internalised norms for markets and society. On appeal to social norms, many will gladly volunteer to donate blood, but will refuse to give the same donation for payment. The conflict between the norms of markets and society means that the two cannot coexist peacefully. Throughout history, markets have been subordinate to society. Modern society is unique in having sought to reverse this relationship, subordinating social relations to market norms. This chapter follows the framework of Polanyi (1944), who describes the bloody battles between markets and society as the “Great Transformation”. The operation of a market society required the conversion of human beings and their habitat into marketable commodities, leading to the dissolution of society and environmental destruction. Current efforts to ‘solve’ environmental problems within the market framework fail to either recognise these fundamental conflicts or to go far enough to address the structural causal mechanisms. In line with social ecological economics [Chapter 1], I will argue that radical remedies are required to address the root causes of the problems. In particular, the great transformation needs to be reversed and the subordinate role of markets to society recognised.

One of the key theses of Polanyi (1944) is that unregulated markets are so extremely harmful to society, that society must take steps to protect itself. In order to understand the history of market societies Polanyi introduces the concept of the “double movement”—on the one hand the expansion of markets, and on the other the efforts to protect humans and Nature against harms caused by commodification. The second movement means that society always blocks complete freedom for markets, but this also means that free market ideologues can argue that any failure of capitalism was due to the failure to fully follow policies of laissez faire.

In this chapter the struggle between market societies and traditional societies is explained as occurring simultaneously on two fronts. One is the front of practice—the replacement of traditional institutions and customs by market institutions. The other is the ideological front—the practice of capitalism that requires faith in the accompanying ideology, which is often strongly opposed to natural instincts and traditional social norms.

For full article (5000 words) see: Markets and Society. For a closely related paper, see The rise and fall of the market economy (Zaman – Review of Islamic Economics, 2010)

How Can We Create an Islamic Economic System?

{} Sunday Talk in English on 19th Dec 2021 at the Indonesia Chapter of the Ghazali Project. Video is followed by detailed summary of talk. For an earlier, Urdu Version of this talk, see

Central Questions for this talk are:          

  1. What is an Islamic Economic System?
  2. How can we create such a system?

Institutions Reflect Collective Purpose of Society: The KEY INSIGHT at the heart of our discussion is the following: Institutions of a society REFLECT the COLLECTIVE PURPOSE of the society. To build Islamic Institutions, we must reshape COLLECTIVE purpose of our society. The collective purpose of Western Society has been shaped by the Western Historical Experience. Very briefly, they rejected Christianity, and adopted an approach to life based on maximization of pleasure, power, and profits of this Earthly life. By early 20th Century, Europeans controlled over 90% of the World, colonizing most of the Islamic world. An essential insight here is that  colonization is colonization of minds. Colonization led to absorption of the toxic myths about the superiority of Europe and inferiority of others by the entire humanity (almost); see “Central Myths of Eurocentric History” – shortlink:  

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Causality as Child’s Play


Study of causality confronts us with a huge dilemma. Intense controversy has raged for centuries over this topic among the philosophers. At the same time, studies of child development show that infants learn about causal concepts almost from birth, and toddlers have a sophisticated approach to causality. How can causality be easily understood by babies, but remain confusing and complicated to the best philosophers for centuries? The difficulty is compounded by the fact that philosophical approaches serve as a basis for empirical data analysis in statistics and econometrics. Even though correct estimation of causal effects is essential for policy, widely used econometric textbooks are deeply defective in their approaches to causality. Angrist and Pischke (2017) examine leading popular econometrics textbooks and conclude that these are based on an outmoded paradigm which ignores causality. They call for a pedagogical paradigm shift. Chen and Pearl (2013) also examine six leading econometrics textbooks and come to the same conclusion: these textbooks fail to explain central causal concepts with any degree of clarity. Even though Angrist and Pischke agree with Chen and Pearl on the diagnosis, the two sets of authors offer radically different remedies. Since the 1990’s Pearl and his group have been arguing for an approach based on Directed Acyclic Graphs (DAGs) as central to understanding causality. Angrist and Pischke (2008, 2013) have written two econometrics textbooks which exposit causality using a “Potential Outcomes” approach, and make no mention of DAGs. Thus, while everyone agrees that causality is very poorly handled in econometrics, there is no agreement about the solution to this problem. This has serious implications since philosophical controversies about causality ramify to the policy context involving real data and applications.

“Inversion” is a favorite philosophical device, where all conventional thinking on a subject is replaced by its diametrical opposite. It is natural to think that adults are wiser than children, because they have the advantage of years of experience and learning. In this article, we propose to look at what children can teach the philosophers – and by extension, statisticians, econometricians, and policy makers. We will examine insights about how children learn about causality from the child development literature, and see how they can be used to clarify philosophical controversies about the topic. This examination gives new meaning to the Biblical “You cannot enter the Kingdom of God unless you became as little Children”.


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Theft of History: Western Plagiarists & Revolutionaries

{} The term “Theft of History” has been used by Jack Goody to indicate Western borrowing of inventions of other civilizations, and claiming them for itself. Some specific cases are discussed in the book “Is Science Western in Origin?” by CK Raju. A summary of part 1 of this book was given earlier in “The Myth of Greek Origin of all Knowledge” { }. This first part outlines the grand strategy of the Church for denying credit to the knowledge gathered by the Islamic Civilization. The grand strategy was to pre-date or post-date. All of the addition to knowledge made by non-Western Civilization were either attributed to the Greeks, or to European copyists and plagiarists. This creates a distorted view of history, where some incredibly wise Greeks discussed all possible philosophical questions, followed by a dark period of several centuries where all humanity ceased to have any original ideas. This period ended when the treasure of Greek knowledge was handed over to Europe (without any significant changes or improvements) by the books gathered in the libraries of the Islamic Civilization. This led to the Enlightenment of Europe, and the creation of all useful knowledge by the Europeans. The driving force behind this myth is the racist idea that only White Europeans are capable of rational and scientific thought. To support this, it is necessary to suppress the contributions of all other civilizations to the production of knowledge. This myth provides the philosophical justification of savage and barbaric European destruction of all other world civilizations over the course of centuries. The myth remains strong, and is reinforced by Western education today, which highlights European contributions, and completely ignores all others. The myth is used to justify the greatest crimes in human history, such as the European destruction of Iraq, Libya, Syria, Afghanistan on the grounds that the non-white inhabitants are not fully human, nor completely rational, like the White invaders.

To overcome the deadly effects of this racist myth, which continues to poison minds to this day, it is useful to contemplate seriously the opposite thesis. Instead of taking the Western Civilization as the standard, and regarding all others as savages and barbarians, let us consider the possibility that the primitive and savage barbarians from the West destroyed advanced civilizations all over the globe, and created a reign of terror which has led to increasing ignorance and darkness throughout the globe. One obvious objection to this perspective is the massive advances in science which have taken place over the past few centuries under Western leadership. This phenomenon must be understood like the case of an idiot-savant. An exclusive focus on mechanisms which drive external reality has led to remarkable progress in the power to control, manipulate, and exploit the planet Earth. However, this progress has been accompanied by increasing ignorance about the inner world of human beings, and also the larger philosophical questions we face in leading good lives, and building good societies. As a result, scientific knowledge has caused far greater harm than good, and technological prowess without the wisdom to use it for the benefit of human beings has led to a climate crisis which imperils all life on the planet.   

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The Myth of Greek Origins of All Knowledge

{} This is a detailed summary of part 1 of the book “Is Science Western in Origin?” by Professor C. K. Raju. This part concerns the fabrication of the myth of intellectual superiority of the West over the course of centuries in three stages,, starting with the Crusades. In a nutshell, history provides us with many examples of barbarian incursions on advanced Civilizations in decay. In such cases, the barbarians acquire wealth and knowledge from the conquered, and also get to write a revisionist history, which attributes the conquest to superiority of the barbarians. This process, carried out over centuries, has created a myth that all currently valuable knowledge is Western in origin. In this first part, we consider the fabrication of this myth in general and broad terms. The second part details particular special cases of importance: Euclid, Ptolemy, Copernicus and Newton – see

The East is the East and the West is the West, and never the twain shall meet”. Rudyard Kipling.

Global Colonization of Minds by the West

In early 20th Century European powers controlled about 90% of the planet. It was this conquest and colonization which created the East & the West. Europe’s colonization of the world led to:

  1. decimation or extermination of native peoples,
  2. the extinction of lifestyles, cultural life forms, and the biological, cultural, and social inheritance of colonized societies.
  3. everywhere the colonizers sought to impose upon the colonized their worldview.
  4. The fundamental fact of colonialism and the post-colonial era: every conquest is, in the first instance, a conquest of knowledge.
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The Ghazali Project: Indonesia Chapter

{} How to Launch an Islamic Revival Today?  Keynote Speech at: Launch of the Ghazali Project in Indonesia on Wednesday, 27th October 2021

When our Prophet Mohammad SAW was sitting in the cave of Hira, searching for Guidance,  the first few verses of the revelation sent to him by God via the agngel Jibraeel were the followin

•            إِقْرَأْ بِاسْمِ رَبِّكَ الَّذِي خَلَقَ {1} خَلَقَ الْإِنْسَانَ مِنْ عَلَقٍ {2} إِقْرَأْ وَرَبُّكَ الْأَكْرَمُ {3} أَلَّذِي عَلَّمَ بِالْقَلَمِ {4} عَلَّمَ الْإِنْسَانَ مَا لَمْ يَعْلَمْ {5

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