The Wonder Car for India is Here

Oil imports have a big impact on the economy of India. Any development that reduces this dependence on imports is bound to be big news. The exciting news is that we in India can now run cars on fuel that is a mixture of petrol and a whopping 85–100% of ethanol (ethyl alcohol). 

Brazil already runs cars with 100% ethanol. And now Flex-Fuel Vehicles (FFVs) (cars and motorcycles) will soon be running on Indian roads. E85 is the name given to the fuel with 85% ethanol and 15% petrol. That’s a tremendous saving on petrol, and a big boost for the Indian farmer who can grow sugarcane etc., and produce ethanol by a simple fermentation process, followed by distillation. 

It was interesting for me to learn that the internal combustion engine that runs the car did not have to be redesigned from scratch. It is an adaptation of conventional petrol engines. Manufacturers modify the existing design to handle ethanol’s different combustion characteristics (lower energy density, higher oxygen content, and different vaporization behaviour). So, while the core architecture remains similar, the fuel system and calibration are newly engineered for flexibility and durability. Here are some more details:

 Modified fuel system: FFVs have fuel lines, injectors, seals, and gaskets made from materials resistant to ethanol’s corrosive properties. 

 Adaptive engine control unit (ECU): The ECU automatically adjusts ignition timing, air–fuel ratios, fuel injection, compression, and sensors, depending on the ethanol content in the fuel. 

 Dual-fuel capability: These engines can run on pure petrol, E20, E85, or any blend in between — no manual switching required. 

In India, automakers like Maruti Suzuki, Toyota, and Bajaj have begun developing FFVs for the Indian market, aligning with the government’s ethanol-blending goals. 

Ethanol has less energy density than petrol. Vehicles on E85 give lower mileage. Actual savings depend on your car’s efficiency. 

Impact: Reduces crude oil imports, boosts sugarcane/ethanol production, higher farmer income, lower emissions. A boon for tackling traffic pollution, particularly in big cities. 

Important: Only FFVs can use this fuel. Your current petrol car is not compatible. 

As of June 2026, here’s what’s actually on sale in Delhi that can run on E85: 

Cars 

1. Maruti Suzuki WagonR Bioflex - India’s first flex-fuel car 

2. Price: ₹7.24 lakh ex-showroom 

3. Engine: 1.2L K12N petrol, modified with new injectors, fuel pump, fuel lines, ethanol sensor, ECU 

4. Fuel: Runs on E20 to E100, homologated for E85 

5. Catch: ₹86,000 costlier than regular WagonR ZXi+ MT 

6. Availability: Currently for commercial/taxi/fleet use only. Private sales haven’t started yet. 

Toyota: Minister Hardeep Puri said “Toyota is one of the best-selling vehicles to be operated on E85”. Toyota showed an Innova Hycross FFV prototype earlier, but no confirmed retail launch with price yet. 

Others coming soon: Minister Nitin Gadkari said Suzuki, MG, and Hyundai will launch 100% ethanol-compatible vehicles within the next month and a half. 

Bikes 

Hero MotoCorp Splendour+ Flex Fuel: ₹82,710 ex-showroom Delhi 

 

Hero HF Deluxe Flex Fuel: ₹72,792 ex-showroom Delhi 

Both run on E20 to E85. 

Unlike methyl alcohol which is poisonous, ethyl alcohol is the stuff that makes your drink intoxicating but not poisonous. Desperate drunkards do not spare even cough syrup for its alcohol content. What if they try to find ways to extract the ethyl alcohol from E85?! Perhaps adding a little methyl alcohol will deter them. [On a more serious note, it is nearly impossible to unmix petrol and ethanol completely. Petrol is a mixture of some 100+ hydrocarbons, with boiling points ranging from 30 C to 200 C. And the boiling point of ethanol is ~78 C. Any fractional distillation attempt will be very messy, even dangerous. A further problem is the formation of 'azeotropes' (e.g. between ethanol and some hydrocarbons): They boil together at a fixed ratio, and cannot be split further by distillation alone.]

Review of Vinod Wadhawan’s book “Revisiting the Scientific Method”

(This blog post is written by Mr. P. Menon)

In the book Revisiting the Scientific Method, renowned physicist and thinker Dr. Vinod Kumar Wadhawan presents a deeply compelling and timely re-evaluation of the principles that have long governed scientific inquiry. As a former scientist from the Department of Atomic Energy, Govt. of India, and a respected author on complexity and material science, Dr. Wadhawan brings both intellectual depth and philosophical clarity to his latest work.

The book’s central thesis — that the traditional scientific method, while immensely successful, must evolve to accommodate the study of complex systems and nonphysical phenomena like consciousness — is both bold and convincingly argued. Dr. Wadhawan lays out the classic 8-fold path of scientific investigation with clarity, from asking the right question to the falsifiability requirement. But where this book shines is in its critical examination of the limits imposed by overly rigid adherence to these traditional norms, especially when applied to emerging fields of study.

 

Through lucid prose and well-researched examples, the author illustrates how modern science needs to expand its toolkit. The role of Big Data, the inadequacies of reductionist thinking, and the need to incorporate subjective and experiential knowledge — especially in the context of studying consciousness — are explored in depth. The book’s openness to philosophical thought, including insights from Eastern traditions and thinkers like Subhash Kak, is particularly refreshing. It shows a willingness to blur the lines between science, philosophy, and spirituality without compromising intellectual rigor.

 

The sections on artificial intelligence and consciousness are particularly thought-provoking. Dr. Wadhawan deftly navigates the frontier where machine intelligence meets human awareness, cautioning against simplistic assumptions and advocating for a more nuanced scientific dialogue. His consideration of "superintuition" and the creative process, as seen in historical figures like Ramanujan and Tesla, adds a humanistic and almost poetic touch to an otherwise methodical narrative.

 

What makes this book especially valuable is its humility and openness. Dr. Wadhawan does not claim to have all the answers. Instead, he provides a framework for asking better, broader questions and invites the reader — scientist, philosopher, or curious layperson — to engage in the process of rethinking science itself.

 

In sum, Revisiting the Scientific Method is a masterful synthesis of science, philosophy, and forward-thinking inquiry. It is a valuable contribution to contemporary scientific literature and a call for a more inclusive, expansive, and empathetic approach to understanding reality. Dr. Wadhawan’s work deserves appreciation and engagement from all who care about the future of science and its role in human progress.

Written by 

 P. Menon,

ashirwadmenon@yahoo.co.in

Big Data and the Future of the Scientific Method

[This blog post is based on a chapter in my new book ‘REVISITING THE SCIENTIFIC METHOD: The Need to Make Science More Inclusive in Scope’ (Wadhawan 2025).

A problem, as well as a boon, of modern times is that we have an overwhelming amount of data at our disposal. The term Big Data has been coined for this. Big Data means a complex and voluminous set of information comprising structured, unstructured, and semi-structured datasets, that is challenging to manage using traditional data-processing tools, and that requires additional infrastructure to govern, analyse, and convert into insights or knowledge. It is characterised by ‘volume’, ‘velocity’, ‘variety’, ‘veracity’, and ‘value’ (BasuMallik 2022; Tiao 2024). 

Credit: (22) The Role of Big Data in Scientific Research | LinkedIn

The volume of data at our disposal is now measured in zettabytes (10­­­²¹ bytes), and yottabytes (10²⁴ bytes), and it is growing exponentially rapidly. ‘A Big Data system has two closely related parts: storage and analysis. Analysis is based on storage, which facilitates access. Storage is based on analysis, which reduces volume. Analytical solutions that really respond to this problem have two features: induction and speed’ (Malle 2013).

A feature of Big Data is that just about any correlation can be found in it. This means that the dictum ‘correlation does not necessarily imply causation’ can be under threat, and we have to find sophisticated ways of deciding whether to take a correlation seriously or not.

‘The end of theory: The data deluge makes the scientific method obsolete’ was the attention-grabbing title of a provocative and much discussed article by the then Editor in Chief of the Wired magazine, Chris Anderson (2008). The article begins by quoting George Box (a statistician), who had written in 1976 that ‘all models are wrong, but some are useful’. The justification for such a statement comes from the nature, or rather the limitation, of deductive logic. Any model or hypothesis or theory is a generalisation based on the assumption that its underlying axioms are true. From the model we draw conclusions by deductive logic, and then go about checking their validity/falsifiability against the available information. As more and more information pours in with the passage of time, the model may fall by the wayside, to be replaced by a better model. This has happened several times in the history of science. Some examples are: Newton’s models of gravity and of space (replaced by Einstein’s theory of spacetime); the classical model about the simultaneity of events (replaced by Einstein’s theory of relativity); theory of classical mechanics (replaced by quantum mechanics); theories of human behaviour (modified or replaced again and again). The new models may, in turn, prove to be inadequate or ‘wrong’, to be replaced by still better or new models in the light of additional information that keeps pouring in. And so on. So, any model is liable to be ‘wrong’.

As information has gone on piling up, a stage has come when ‘information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later’ (Anderson 2008). Anderson gave the example of the success achieved by Google in the advertising world: ‘Google conquered the advertising world with nothing more than applied mathematics. It didn't pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right. Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required. That's why Google can translate languages without actually "knowing" them (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German). And why it can match ads to content without any knowledge or assumptions about the ads or the content’. He quoted Google's research director Peter Norvig as saying: ‘All models are wrong, and increasingly you can succeed without them’.

This is a far cry from the traditional way of doing science, which is based on testable hypotheses. But we must admit that there is indeed a stalemate of sorts in physics at present. No big (conceptual) breakthroughs have been coming for quite some time now.  We have the string theory, which we are unable to verify. It has not been possible to unify quantum mechanics with the theory of gravity. And then there is dark matter. And so on. Do we need to change tracks altogether? Many people think that we do. Here is Anderson’s (2008) take on this: ‘The reason physics has drifted into theoretical speculation about n-dimensional grand unified models over the past few decades (the "beautiful story" phase of a discipline starved of data) is that we don't know how to run the experiments that would falsify the hypotheses — the energies are too high, the accelerators too expensive, and so on. Now biology is heading in the same direction. The models we were taught in school about "dominant" and "recessive" genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton's laws. The discovery of gene-protein interactions and other aspects of epigenetics has challenged the view of DNA as destiny and even introduced evidence that environment can influence inheritable traits, something once considered a genetic impossibility’. He is overstating a bit, but still has a point there.

Anderson’s way out was to assert that correlations are enough, and that knowledge about the causes behind the observed correlations can take a back seat to start with. He advocated a new kind of science: data-driven science. In it, we can stop looking for models or hypotheses. We can analyse the data without any preconceived hypotheses or biases about what it might show. We throw the numbers into big computing clusters and let sophisticated statistical algorithms find patterns where the existing science cannot. Such an approach is particularly relevant for bioinformatics, systems biology, epidemiology, ecology, etc. But can it replace the usual hypothesis-driven way of doing science? Not really, though it can be an important adjunct to it. Below I discuss some reactions to the points made by Anderson.

Mazzocchi (2015) examined the issue from the epistemological point of view. He asked: ‘Is data-driven research a genuine mode of knowledge production, or is it above all a tool to identify potentially useful information? Given the amount of scientific data available, is it now possible to dismiss the role of theoretical assumptions and hypotheses?’ He began by pointing out that, long before Anderson (2008), it was Francis Bacon who, as far back as in 1620, had argued in his Novum Organum that ‘scientific knowledge should not be based on preconceived notions but on experimental data. Deductive reasoning, he argued, is eventually limited because setting a premise in advance of an experiment would constrain the reasoning so as to match that premise. Instead, he advocated a bottom-up approach: In contrast to deductive reasoning, which has dominated science since Aristotle, inductive reasoning should be based on facts to generalize their meaning, drawing inferences from observations and data’.

Thus Big-Data based science has renewed the primacy of inductive reasoning in the form of technology-based empiricism. It is believed by some that this hypothesis-neutral way of creating knowledge will replace the hypothesis-driven way of doing research. Data mining can throw up unexpected correlations and patterns, which can then be used for generating new hypotheses for discovering the causes behind the correlations (see Hassanien et al. 2015). So, the new computational route ends up doing hypothesis-generating research rather than hypothesis-testing research.

Inductive algorithms occupy centre-stage here. Mazzocchi quotes Malle (2013): ‘Inductive reasoning generally produces no finished status. The results of inferences are likely to alter the inferences already made. It is possible to continue the reasoning indefinitely. The best inductive algorithms can evolve: they “learn”, they refine their way of processing data according to the most appropriate use which can be made. Permanent learning, never completed, produces imperfect but useful knowledge. Any resemblance with the human brain is certainly not a coincidence’. [As Malle explains, ‘induction, unlike deduction, is a mechanism used by the human brain at almost every moment. Indeed, despite the fact that deduction is considered as cleaner, more scientific, it occupies only a small portion of the processing time of our brain. It is particularly relevant when analysing a situation out of its context’.]

Mazzocchi and many others have expressed concerns that unscrupulous agents can influence what kind of Big Data is generated and made public, to the exclusion of information inimical to their interests or designs. Think of the Dark web. Perhaps we never get the ‘full’ picture at any point of time in our history. And, even when the intentions are not questionable and there is no manipulation, it is wrong to presume data-neutrality, even in good science. Data are not collected randomly. Experiments are designed and carried out within theoretical, methodological and instrumental limitations. There always are hypotheses and assumptions at play. Data collection is seldom unbiased. ‘Scientific research is carried out by human beings whose cognitive stance has been formed by many years of incorporating and developing cultural, social, rational, disciplinary ideas, preconceptions and values, together with practical knowledge. Scientists form their ideas and hypotheses based on specific theoretical and disciplinary backgrounds, which again are the result of decades or even centuries of history of scientific and philosophical thought’ (Mazzocchi 2015).

Calude and Longo (2017) delivered a severe blow to the predictions of Big Data enthusiasts about the ‘end of the scientific method’, who had been asserting that the hypothesis-free computer-discovered correlations found in an apparently ‘unbiased’ way from the Big Data are enough for the advancement of knowledge, and that we can ignore all pre-conceived causation aspects of any observation or data. They (Calude and Longo) used classical results from ergodic theory, Ramsey theory, and algorithmic information theory to prove that very large databases must contain arbitrary correlations, and that these correlations or ‘regularities’ appear only due to the large size, and not the nature, of data. ‘Such correlations can be found in ‘randomly generated, large-enough databases’. They proved that most correlations are spurious. The scientific method can be enriched by data mining in very large databases, but not replaced by it.

I discuss next the work of Succi and Coveney (2019), which I believe to be particularly important because it addresses the impact of Big Data on the science of complex systems. A very large chunk of activity in modern science is about complex systems. This century is the century of complexity, as was predicted by Stephen Hawking. Succi and Coveney point out that ‘once the most extravagant claims of Big Data are properly discarded, a synergistic merging of Big Data with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, nonlocality and hyperdimensions which one encounters frequently in multiscale modelling’. They make the following four points:

1. Complex systems do not (generally) obey Gaussian statistics. This is almost a defining feature of complex systems. Their strongly correlated nature makes them generally obey power-law statistics (see Wadhawan 2018). This means that the law of large numbers (so characteristic of Gaussian statistics) generally does not hold for complex systems: For them, an increase in sample size (a major feature of Big data) is no guarantee that the error or uncertainty in the estimated or mean value decreases monotonically. In a system obeying Gaussian statistics, the chances of occurrence of an event far from the mean value are small; the bell-shaped Gaussian curve has a small tail. Not so for power-law statistics; the tail may be far from being small. ‘This explains why the Big Data trumpets should be toned down: when rare events are not so rare, convergence rates can be frustratingly slow even in the face of petabytes of data’.

2. No data is big enough for systems with strong sensitivity to data inaccuracies. The evolution of a complex or chaotic system is well-known to be a very sensitive function of ‘initial conditions’ or ‘data inaccuracies’ (see Wadhawan 2018). Think of the ‘butterfly effect’. This flies in the face of Big Data radicalism: The main claim of Big Data enthusiasts is that we can extract patterns from data, or discover correlations between phenomena we never thought of as connected, simply because of the large sample sizes.

3. Correlation does not imply causation, the link between the two becoming exponentially fainter at increasing data sizes. Correlations between two sets of data or signals can be either true correlations (TC) or false correlations (FC). TCs indicate a causative relationship or connection. And an FC is that which just happens to be observed for apparently no likely reason; it is a ‘spurious’ correlation. But distinguishing between a true and a false correlation can be tough at times. And the problem is compounded by the fact that as data sizes grow, false correlations become more and more common (as proved by Calude and Longo 2017). It is also true that, as shown by Meng (2014), to be able to make statistically reliable inferences one needs to have access to more than 50% of the data on which to perform one’s machine learning for detecting patterns or correlations. According to Succi and Coveney (2019), what we need are ‘many more theorems that reliably specify the domain of validity of the methods and the amounts of data to produce statistically reliable conclusions’. They cite the example of a paper by Karbalayghareh et al. (2018) as a step in the right direction.

4. In a finite-capacity world, too much data is just as bad as no data. We extract information from data, knowledge from information, and wisdom from knowledge. The step from knowledge to wisdom may well involve hypothesising a model for the underlying cause(s). And the wisdom gained can be utilised for optimising the model by a repetitive, circular, process of reasoning. Offhand we might think that an expanded database should lead to a corresponding increase in information, knowledge, and wisdom, in a linear sort of way. But the linearity is not guaranteed, particularly for complex systems. Succi and Coveney (2019) make the point that for finite complex systems a state of nonlinear saturation is reached sooner or later as more and more data pour in: ‘This is the very general competition-driven phenomenon by which increasing data supply leads to saturation and sometimes even loss of information; adding further data actually destroys information. … Beyond a certain threshold, further data does not add any information, simply because additional data contain less and less new information, and ultimately no new information at all.  .  .  . We speculate, without proof, that this is a general rule in the natural world’. [Destruction of information occurs if new and old data are mutually contradictory.]

Here are some final conclusions in the essay by Succi and Coveney (2019): ‘There is no doubt that the “Big Data/machine learning/artificial intelligence” approach has plenty of scope to play a creative and important role in addressing major scientific problems. Among the applications, pattern recognition is particularly powerful in detecting patterns which might otherwise remain hidden indefinitely (modulo the problem of false positives). Possibly the most important role is likely to be in establishing patterns which then demand further explanation, where scientific theories are required to make sense of what is discovered.  … Instead of rendering theory, modelling and simulation obsolete, Big Data should and will ultimately be used to complement and enhance it. Examples are flourishing in the current literature, with machine learning techniques being embedded to assist large-scale simulations of complex systems in materials science, turbulence, and also to provide major strides towards personalised medicine, a prototypical problem for which statistical knowledge will never be a replacement for patient-specific modelling. It is not hard to predict that major progress may result from an inventive blend of the two, perhaps emerging as a new scientific methodology’.

In the beginning we had theoretical science and experimental science. The advent of computers gave rise to a third kind of science, namely computational science: It happens often that the mathematical formulation of a model involves the writing of differential equations that are too difficult, if not impossible, to solve analytically. But the availability of powerful computers enables us to formulate the model in terms of difference equations, rather than differential equations. The difference equations can be solved to a desired degree of accuracy using powerful computers. The cellular-automata approach is an example of computational science (see Wadhawan 2018). And now the data deluge has given rise to a fourth type of science: data-driven science: Find the correlations first, and then go about looking for the reasons or causes behind the observed correlations. Big Data analytics plays a big role in this (see What is Big Data Analytics? | IBM).

Big Data theory is already a field of research in its own right. It is a set of generalized principles that explain the foundations, knowledge, and methods used in the practice of data-driven science (Big Data Theory | SpringerLink).

Not only ‘Big Data theory’, but also theories from Big Data! Yes, physical theories. ‘The Next Einstein: New AI Can Develop New Theories of Physics’ is the title of an article by Juelich (2024), in which the work of Merger et al. (2023) is described. Many scientists produce a large amount of data through their research. We may call them data producers. Then, once in a while, comes along a smart scientist who is able to see in the published data a common trend or pattern of great fundamental importance, leading to a leap of progress in science. A great example is that of J. C. Maxwell, whose Maxwell equations are textbook stuff. He not only unified the work of many earlier scientists, but also discovered ‘displacement current’ in the process. Some other examples are those of Newton, Einstein, and (P. W.) Anderson. They were data users rather than data producers. Efforts are afoot at present to develop AI that can play the game-changing role of the data-user type of scientist. The work of Merger et al. (2023) is a step in that direction. Their paper has the title ‘Learning Interacting Theories from Data’. I quote from its Abstract: ‘One challenge of physics is to explain how collective properties arise from microscopic interactions. Indeed, interactions form the building blocks of almost all physical theories and are described by polynomial terms in the action. The traditional approach is to derive these terms from elementary processes and then use the resulting model to make predictions for the entire system. But what if the underlying processes are unknown? Can we reverse the approach and learn the microscopic action by observing the entire system? We use invertible neural networks (INNs) to first learn the observed data distribution. By the choice of a suitable nonlinearity for the neuronal activation function, we are then able to compute the action from the weights of the trained model; a diagrammatic language expresses the change of the action from layer to layer. This process uncovers how the network hierarchically constructs interactions via nonlinear transformations of pairwise relations.’

In other words, it is a top-down approach, rather than a bottom-up approach for doing science. The data are the end results of the microscopic interactions. They lie at the top of the hierarchy, at the bottom of which are the microscopic interactions which have given rise to the data we observe. Machine learning has been used for generating a theory, without any prior knowledge about the nature of the microscopic interactions involved. I quote from their paper again: ‘Key to this approach is the use of a generative neural network, which maps a complicated data distribution to a simpler one. By decomposing this mapping into interactions between simpler features, we can better understand how and why models make predictions. We hence unravel the complex, hierarchical structure that has been learned by a neural network and explain it in a form that is central to physics: interactions between degrees of freedom’.

INNs can be applicable in a variety of other fields also: genomics, epidemiology, condensed-matter physics, astrophysics, climate modelling, ecology, economics, sociology, neuroscience.

‘Big Data marked a break in the evolution of information systems from three points of view: the explosion of available data, the increasing variety of these data, and their constant renewal. Processing these data demands more than just computing power. It requires a complete break from Cartesian logic. It calls for the non-scientific part of human thought: inductive reasoning’(Malle 2013).

‘Big Data, distributed computing and sophisticated data analysis all played a crucial role in the discovery of the Higgs boson—and perhaps in finding new ‘patterns’ they might also generate new hypotheses in this field. But the discovery of the Higgs boson was not data-driven. The collider experiments were mostly driven by theoretical predictions: It is because scientists were attempting to confirm the Standard Model of elementary particles that the discovery of the Higgs boson—the only missing piece—could occur’ (Fulvio Mazzocchi 2015).

==

References cited

Anderson, C. (2008). ‘The end of theory: The data deluge makes the scientific method obsolete’. Wired magazine, 16(7), 16. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete (statlit.org)

BasuMallik, C. (2022). ‘What Is Big Data? Definition, Types, Importance, and Best Practices’. What Is Big Data? Definition and Best Practices (spiceworks.com).

Calude, C.S. and G. Longo (2017). ‘The Deluge of Spurious Correlations in Big Data’. Foundations of Science, 22, 595–612. https://doi.org/10.1007/s10699-016-9489-4. The Deluge of Spurious Correlations in Big Data | Foundations of Science (springer.com)

Malle, J.-P. (2013). ‘Big Data: Farewell to Cartesian Thinking?’. Paris-Tech Review (http://www.paristechreview.com/2013/03/15/big-data-cartesian-thinking/

Mazzocchi, F. (2015). ‘Could big data be the end of theory in science?: A few remarks on the epistemology of data-driven science’. EMBO reports, 16(10), 1250–1255. Could Big Data be the end of theory in science? (embopress.org)

Hassanien, A. E. (Eds.) (2015). Big Data in Complex Systems: Challenges and Opportunities. Big Data in Complex Systems: Challenges and Opportunities | SpringerLink.

Juelich, F. (March 12, 2024). ‘The Next Einstein: New AI Can Develop New Theories of Physics’. SciTechDaily. The Next Einstein: New AI Can Develop New Theories of Physics (scitechdaily.com)

Karbalayghareh, A., X. Qian, and E. R. Dougherty (2018). ‘Optimal Bayesian transfer learning’. IEEE Transactions on Signal Processing, 66 (14).

Meng, X. L. (2014). ‘A trio of inference problems that could win you a Nobel prize in statistics (if you help fund it)’. In X. Lin, C. Genest, D. L. Banks, G. Molenberghs, D. W. Scott, and J. -L. Wang (Eds.):  Past, Present, and Future of Statistical Science, pp. 537–562. CRC Press, Boca Raton, FL.

Merger, C. et al. (20 November 2023). ‘Learning Interacting Theories from Data’. Physical Review X. DOI: 10.1103/PhysRevX.13.041033.

Succi, S. and P. V. Coveney (2019). ‘Big data: the end of the scientific method?’. Philosophical Transactions of the Royal Society A, 377(2142), 20180145. 1807.09515 (arxiv.org)

Tiao, S. (2024). ‘What is Big data?’. What Is Big Data? | Oracle

Wadhawan, V. K. (2018). Understanding Natural Phenomena: Self-Organization and Emergence in Complex Systems. CreateSpace Independent Publishing Platform, SC, USA.

Vinod Wadhawan’s new book ‘Revisiting the Scientific Method’

‘REVISITING THE SCIENTIFIC METHOD: The Need to Make Science More Inclusive in Scope’ 

by Vinod Kumar Wadhawan 

 

Published by the author.  

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 Revisiting the Scientific Method: The Need to Make Science More Inclusive in Scope: Wadhawan, Vinod: 9798307226155: Amazon.com: Books

This book is a follow-up to the previous book by the author (‘The 8-Fold Way of the Scientific Method’). The previous book introduced the reader to the way science is done: Strict adherence to objectivity, rationality, and transparency in handling information about a natural phenomenon we want to understand. The present book goes a step further and takes a critical look at the Scientific Method to see what can be done to make science more inclusive in scope; for example by giving due importance to subjective or experiential information also, and not only empirical information. Together the two books provide a fairly comprehensive account of the nature of scientific research, and can serve as course material for the training of an aspiring scientist. The theme of consciousness runs throughout the present book, because it is the most important example of a nonphysical phenomenon or entity that current science, by and large, tends to stay away from. Ways are suggested for dealing with this problem by relaxing in a carefully guarded manner some of the eight tenets of the present Scientific Method. Big Data is a rather recent development in the history of science. Its availability is going to have far-reaching consequences for the way science is going to be done now. The book discusses its promises and pitfalls. Present-day science, which is mostly reductionistic in approach, is not adequate enough for dealing with complex systems. Here again, Big Data may be of big help because pattern formation is an important characteristic of many complex systems, and Big Data is very good (rather too good!) at discerning patterns or correlations. Discussion of Karl Popper’s falsifiability criterion occupies substantial space in this short book. This is because this criterion is the main reason why the present Scientific Method labels many questions about Nature as unscientific or nonscientific, thus limiting the scope of scientific enquiry. In a more inclusive approach one would also give due importance to the philosophical rival of falsificationism, namely verificationism. Another way of making science more inclusive is to use a diluted version of falsificationism, formulated by Imre Lakatos. There is also a discussion of the work of some other philosophers of science, notably Nicholas Maxwell and Thomas Kuhn. Popper’s philosophy for doing science has proved to be very successful, but it is desirable that the student of science be also aware of other models of how science can progress. 

== 

CONTENTS 

 Preface vii 

 

1. The Scientific Method 1 

1.1 Asking the right question 2 

1.2 Objective observation of the world 4 

1.3 Coming up with hypotheses for understanding the data 6 

1.4 Reproducible verification of predictions of hypotheses 12 

1.5 A theory for explaining the hypotheses 15 

1.6 Use of unambiguous language and logic 18 

1.7 Choice of the smallest necessary set of axioms 21 

1.8 The falsification requirement 25 

 2. Complex Systems, Complexity Transitions 27 

2.1 Complex systems 27 

2.2 Towards a formal definition of a complex system 30 

2.3 Complexity transitions 34 

2.4 Emergence 36 

2.5 Reductionistic science is inadequate for dealing with complex systems 38 

 3. Big Data and the Future of the Scientific Method 41 

 4. Life, Intelligence, Consciousness 53 

4.1 Life 53 

4.2 Intelligence 60 

4.3 Consciousness 62 

4.4 One consciousness or many? 73 

 5. Artificial Life, Intelligence, Consciousness 77 

5.1 Artificial life 77 

5.2 Artificial intelligence 80 

5.3 ChatGPT 81 

5.4 Artificial consciousness, or machine consciousness 87 

5.5 Can a robot acquire consciousness? 90 

5.6 Have some of our machines already become sentient? 91 

5.7 Apocalyptic AI and transhumanism 94 

 6. Towards a More Inclusive Scientific Method 99 

6.1 Asking the right question 99 

6.2 Observation of the world 100 

6.3 Coming up with hypotheses for understanding the data 102 

6.4 Testing of predictions of hypotheses 104 

6.5 A theory for explaining the hypotheses 105 

6.6 The language and logic of science 106 

6.7 Choice of axioms 107 

6.8 How justified is the falsifiability requirement? 108 

 7. Concluding Remarks 115 

 Bibliography 123

 Index 135 

 == 

Preface 

 Nature is all there is. All phenomena are natural phenomena, and the Scientific Method is the method of choice for investigating them. The study of all natural phenomena should come within the purview of science. But the strictness of the Scientific Method presently used makes it inapplicable for investigating certain nonphysical phenomena in their entirety, a good example being that of consciousness. In a general sense, consciousness is a state of awareness of one's thoughts, feelings, sensations, and surroundings. It is the subjective experience of perception, cognition, and emotions that is sometimes described as the ‘sense of self’. Consciousness is a fundamental aspect of human experience and an essential characteristic of the human mindbody, and of many other life forms. An important requirement of the Scientific Method is that in any scientific discourse, every word used must convey the same meaning to all concerned. Words like ‘consciousness’ create problems on that score. Although the neural correlates of consciousness help us in taking an empirical view of the situation, the problem remains that we do not know how physical processes in the brain give rise to subjective experiences. How to remedy the situation so that we can have meaningful scientific dialogue on such natural phenomena also? In my previous book (The 8-Fold Way of the Scientific Method) I gave a fairly comprehensive discussion of what the Scientific Method is all about, and how science is done by applying it. The achievements of the method have been spectacular, and we humans can be truly proud that we invented it. But, as pointed out near the end of that book, there is a need to relax its dictums (in a carefully guarded way) so that the scope of scientific investigations can become more inclusive. I suggested some ways there, and discuss the matter more comprehensively in the present book. There is a related issue, namely that of understanding complex systems. The human mindbody is perhaps the most complex system of them all. The present Scientific Method is suitable mainly for doing reductionistic science. A characteristic feature of a complex system is that it must be investigated as a whole, and not by reducing it into parts and assuming that if we understand the parts, we understand the whole. For complex systems, the whole is more than the sum of the parts, in a mutually interactive manner. An extended Scientific Method that is effective for studying complex systems will automatically become more inclusive in scope. Big Data is an inalienable feature of life in modern times. It is already influencing science and many other activities like finance, business, advertising, entertainment, government, warfare, etc. There is a viewpoint that Big Data can make it possible to do science without having to postulate hypotheses/models/theories beforehand. This may well be too optimistic, but there is no denying the fact that the availability of Big Data offers new opportunities for making science progress rapidly, particularly for investigating complex systems. Big Data Analytics can throw up unexpected correlations in the data, which may provide new leads for understanding a complex system. There is already a report that an artificial-intelligence system has been developed that is capable of formulating physical theories by recognizing patterns in complex data sets. So, we need an extended Scientific Method that makes science more inclusive in scope, that enables science to investigate complex systems by going beyond reductionism, and that incorporates the use of Big Data as an additional tool for doing science (data-driven science). In this book I explore the possibilities and make some suggestions. If insistence on empirical evidence limits the scope of science, then go for non-empirical and experiential evidence also. If the falsifiability requirement of the Scientific Method is too restrictive, then see what can be done to relax it in a carefully guarded and tentative manner. Big Data is offering wonderful new and highly unconventional ways of doing science. The training of scientists must now include awareness of this option, as also an exposure to the basics of how such research should be done. 

 Vinod Wadhawan New Delhi (January 2025)

Author’s response to Dr. Abhas Mitra’s review of the book THE 8-FOLD WAY OF THE SCIENTIFIC METHOD

Dr. Abhas Mitra, author of the bestseller book The Rise and Fall of the Blackhole Paradigm (2021), has published an Amazon-India review of my book, which I reproduce below. This is followed by my clarification of some of the points made by him. The points I respond to are marked with superscripts ⁽ⁱ⁾, ⁽ⁱⁱ⁾, etc.

Dr. Mitra’s review reads as follows:
==========
* * * * * One of the most valued books I have ever read, though the cheapest one
It may sound disappointing that many scientists practising science do so as a routine profession without worrying too much about “Scientific Methods”. Of course good scientists and good science students do possess some ideas about the scientific methods to be employed while doing science. However even the good scientists may not feel the requirement to delve into various latent dimensions of correct scientific methods and might occasionally be tempted to arrive at wrong scientific conclusions. For instance, many physicists now think that String Theory, one of the most hyped topics of theoretical physics ever, might have been degenerated into some sort of pseudo science even when most brilliant theoretical physicists have struggled to develop it over past four decades. And of course general public is liable to be enamored by even pseudo-sciences, for instance by astrology.

To the best of my knowledge, the present book might be the only one on the science and philosophy of scientific methods after the publication of The Logic of Scientific Discovery (1959), a book about the philosophy of science by the philosopher Karl Popper, in which the author argued that science should adopt a methodology based on falsifiability. This is so because no number of experiments can ever prove a theory, but a reproducible experiment or observation can refute one. In his book, the author first dwells on the eight tenets of the Scientific Method, namely, (1) Right questions, (2) Right (objective or empirical) observations, (3) Right hypothesis to explain the observations, (4) Right testing of predictions of the hypothesis, (5) Right theory, (6) Right language and logic, (7) Right (minimum number of) axioms and (8) Rightly worded (falsifiable) statements. And the author asserts that these eight tenets are someway analogous to the eight steps to Nirvana emphasized by Buddha: (1) Right beliefs, (2) Right intentions, (3) Right speech, (4) Right conduct, (5) Right livelihood, (6) Right effort, (7) Right mindfulness and finally (8) Right concentration.

As the author succinctly puts it: A crucial aspect of science is systematizing the knowledge acquired and, even more importantly, of making the knowledge available to everybody for scrutiny. Thus at least in principle, science is always self-correcting. For instance, in 2020, all top scientists believed that Covid-19 virus does not float in air for too long and hence the disease does not propagate through air. But by May 2021, it appears that Covid-19 viruses are likely to freely propagate through air. Such a logical virtue of science might however be dishonored by the intellectual arrogance of the proponents of hypotheses which are not verifiable by observations and experimentations, as is the case with several aspects of Theoretical Physics and in particular the String Theory.

But this book goes much beyond such mundane aspects as the author points out that all phenomena are natural and there is really nothing “supernatural”. If so, in some way religion and mysticism too come under the purview of science in a broader sense. Simultaneously, he delves deeper into some complex issues of scientific interest. Accordingly, the 2nd part of this book explores newer dimensions. For instance, there are small chapters entitled “How to Live Well Forever” and “Reversal of Chronic Diseases”. However, I have not yet gone through these chapters and feel that such topics do not gel well with the general character of this great book and were avoidable (they could be published separately).⁽ⁱ⁾

The author is bold and his book even contains a subsection “The end of theoretical physics as we know it?”. Unfortunately this subsection is based entirely on a recent popular physics book by the German theoretical physicist Sabine Hossenfelder. And though I personally like this content, I feel it has (not) been organically connected with his overall book.⁽ⁱⁱ⁾ The 2nd part also contains insightful long discussions on works of Stephan Wolfram on complexity. I am afraid, such sections though highly valuable in their own rights, look like add-ons and have been some sort of distraction from the central theme of the book.⁽ⁱⁱⁱ⁾

On the other hand, I feel that the section entitled “Which is the most scientific natural language?” is important for a comprehensive appreciation of scientific methods because computer science has started aiding science in a major way. The author points out that for artificial intelligence, and computational linguistics, there is a subfield called natural language processing (NLP), or computer linguistics, which is about using computational techniques to learn, understand and produce human language content. Here the author highlights that Sanskrit language whose literal meaning is ‘sculpted to perfection’ is the most scientific natural language. In this context, he explains why long ago, Charles Babbage (1791 – 1871), who is sometimes called as “father of computers’’ mentioned that “The structure of Pāṇinian Grammar is nothing but a computer program.”

The author is an atheist and rationalist in the true sense and not as an intellectual fashion statement so prevalent in modern India. Accordingly, the author has no inhibition in highlighting the ‘thought and philosophy’ behind Vedas the same way Ernest Schrodinger had no hesitation in comparing the weirdness of Quantum Mechanics to some of the oriental mysticism. In particular, the author points out that the ancient Indian school of philosophy, Nyāya, considers the five elements essential to correct reasoning, beginning with the statement that “The reason (evidence) must be present in the case under consideration.’

Overall, it’s a rich and exotic concoction of conventional studies on methodologies of science, history, philosophy both western and Indian. Though it is no easy read, this is one of most valuable books I have ever read. However, I have a complaint against the author. Such a unique and precocious book ought to be published through some reputable international publisher in order that it would garner real international traction that it deserves. Unfortunately, it has been self-published.^(iv) Yet I strongly recommend that all science lovers should enrich their collection by this gem whose e-version is available almost free (Rs. 149 or US $2.0).
==========

I am thankful to Dr. Mitra for his kind words about the book. Here is my response to some of the points raised by him (marked above with superscripts).

(i) Why are there as many as three chapters on human health and longevity in a book on the Scientific Method? There are at least two reasons for this:

(a) One underlying thought that influenced my entire planning and writing of this book was that science has to now find ways to go beyond its conventional reductionistic approach, so as to be able to investigate even complex systems effectively. The present century will be the century of complexity science. To bring home to the lay reader the fact that complex systems are all around us, I picked up the example of the human mindbody, which has consciousness as one of its ‘emergent’ properties. Our health and longevity issues are something that interest everybody. The chapter ‘How to Live Well Forever’ provides a powerful example of how modern science and technology (an outcome of the Scientific Method) enables us to realistically cherish the hope of living well forever, if we wish to. Similarly, the chapter ‘Reversal of Chronic Diseases’ serves to hold the hope that so many chronic diseases can indeed the reversed, thanks to the fruits of the application of the Scientific Method to the health sciences.

(b) By now it is well established that ancient Indian science and technology, as also philosophy and mathematics, got very bad treatment at the hands of Western historians of science, as also their misguided and/or politically motivated Indian cohorts (see, e.g., Kak (2021): ‘A Brief History of Indian Science’, (99+) (PDF) A Brief History of Indian Science | Subhash Kak - Academia.edu). In this book I have done my bit to restore the balance to some extent by including a chapter on the history of science. But the achievements of pre-modern India were so prolific that I had trouble keeping that chapter reasonably brief. One thing I have done is to move some part of the material to a chapter on the ‘The 8-Fold Yogic Way of Living’. So this is the third chapter on the health sciences. The three chapter together also served to enable me to compare the three approaches to human health and longevity. It goes without saying that the yogic way of living is the best: for the individual, for society, for world peace, and for living in complete harmony with Nature.

(ii) ‘The end of theoretical physics as we know it?’ is a section in the chapter ‘Going Beyond Reductionism in Science’. Dr. Mitra would agree with me readily that we have all been pining for long for the next golden age in theoretical physics. There has been a stalemate of sorts for the last several decades. No fundamental breakthroughs have come. People have been wondering why. Sabine Hossenfelder (2018) in her book has opined that perhaps it is because of our excessive obsession with the beauty and symmetry of the equations that embody our theories. That is one opinion. In fact I myself highlighted the crucial role played by conservation theorems in the discoveries of hidden (broken) symmetries (Wadhawan (2018): Latent, Manifest, and Broken Symmetry). So this approach has paid rich dividends. But perhaps we have already milked this cow too much. Other approaches are needed. The same chapter in the book gives a lot of space to the work of Stephen Wolfram, who has been advocating the use of cellular automata and local interactions for getting the hang of all sorts of research problems, including those in theoretical physics. I think theoretical physicists should take Wolfram’s claims more seriously. Who knows, they may end up getting the breakthrough ideas so sorely needed at the present juncture in the history of theoretical science.

(iii) Giving so much space to Wolfram’s work is also because of my agenda to highlight every possible approach that can help make progress in complexity science. So much so that I end up making some suggestions for relaxing the very strict Scientific Method a bit so as to bring the study of even highly complex systems (like the consciousness aspect of the human mindbody) into mainstream science.

(iv) This is perhaps the first and the only comprehensive book on the scientific method. And availability of such a book in high-school and college libraries can go a long way in promoting scientific temper in society. But there may well be scope for improvement of the book. For this I keep inviting suggestions and critical comments. Since I am also the publisher of the book, it is very easy for me to make corrections and improvements continuously. What is more, it would take me just a day or two to bring out a corrected / improved version, or even a new edition: All I have to do for this is to upload a revised pdf file. So please keep giving me your feedback.

Vinod Wadhawan’s new book ‘The 8-Fold Way of the Scientific Method’

(This blog post was updated on 8 December 2021, and again on 24 June 2022)

The 8-Fold Way of the Scientific Method:

What science has been all about so far,

and how it should be done now

 

Vinod Kumar Wadhawan

Published by the author

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Advance Praise for the Book

‘Most people think that science is a body of knowledge which uses a lot of jargon and math. Dr. Vinod Wadhawan makes it clear that science is, first and foremost, a method for producing reliable knowledge. The book also serves as a ready reference for a vast collection of scientific ideas. Not just ‘hard science’ but also ideas like logical fallacies, the nature of evidence, and philosophical concepts like falsifiability. All of these are invaluable for aspiring rationalists, especially if they are (like myself) themselves not scientists. The author also suggests ways in which science can go beyond reductionism to further the study of complex systems’.

— Ambar Nag

Data scientist and rationalist

Quora: https://www.quora.com/profile/Ambar-Nag 

Medium: https://atheistblogger101.medium.com/

 

‘The noted particle-physicist Murray Gell-Mann was inspired by the grand Eightfold Path of Buddhism when he invoked it for describing the deep interlinkings among the fundamental particles. Vinod Wadhawan’s book is comparable more to the Buddhist 8-fold way than to Gell-Mann’s material and prosaic 8-fold way. This book is a profound inner view of not only science, but also of history of science, philosophy, ontology, epistemology, partly sociology, and overall about life itself. 

‘The book contains not just eight chapters about the methodology of science, but its reach is much wider, as unfolded in six more chapters. Though the author is a noted physicist, and has no hesitation in saying that he is an atheist, he is not dogmatic like many other atheists. His atheism is no fashionable declaration to announce “I am an intellectual”. His atheism is an outcome of honest pursuit of scientific methodology which does not restrain him from delving into the golden pots of the Vedas and the Upanishads, or quoting that “Pāṇini’s grammar, Aṣṭādhyāyī, is the only complete, explicit and rule-bound grammar of any human language and as such has been appreciated as “the greatest monument of human intelligence”. His mind is free from all prejudices and so is the book. 

‘Several chapters are dedicated to important aspects of life, including how to live well and longer. I am not exaggerating, this book has touched an epic character, and that too not just any epic. The only epic I can compare it with is The Mahabharata for the grandeur of the Scientific Method and for the “good life inspired by love and guided by knowledge”’.

— Abhas Mitra

Astrophysicist

Author of The Rise and Fall of the Black Hole Paradigm (2021)

 

‘Whereas science has this geeky reputation and gives one a vision of a mad scientist with wild hair in a dusty lab among books and beakers, one doesn’t realize that the Scientific Method, which is a prerequisite for good science, is ubiquitous. For example, not many people realize that our legal and legislative systems are based on the Scientific Method or that the stock market is designed using the Scientific Method.

‘Dr. Wadhawan has used the medium of this book to share his insights on this less discussed but all-important subject. Coming from a real-life man of science, the book explores the practice of science, transcending realms ranging from philosophy to science with a uniquely Indian perspective. It answers real questions ranging from those asked by a curious lay person to an academic philosopher. In the knowledge economy that we find ourselves in, a book like this should be made mandatory reading for all college courses, setting the stage for real-world miracles, and ushering in a sustainable better quality of life for our next generation’.

— Anjan Madhwesh

IT professional, science enthusiast, science vlogger

https://youtube.com/channel/UCCTJvufWrgHs6G41HoakhRw

 

‘A compelling read covering a wide range of everyday topics around science. Emphasizes independent thinking and forming unbiased opinions based on scientific facts, rather than going with blind faith. Dr. Wadhawan also delves into how we can live well forever by adopting a yogic way of living and following guidelines of good nutrition and regular exercise. Very fitting for a 21^st century growth mindset’.

— Namrata Joshi

IT professional

‘Dr. Vinod Wadhawan — an accomplished scientist — has distilled and synthesised available published knowledge with rare insight, explored the traditional wisdom of ancient Indian Rishis, and added new scholarly interpretations in describing the 8-fold way of the Scientific Method for attaining “intellectual nirvana”. And he has succeeded in doing this in a user-friendly manner in this book. Homo sapiens — a geologically insignificant and young entity — have evolved from an animal species of little significance to ride the cognitive revolution since the last 70,000 years, and are now leading the Anthropocene changes on planet Earth. The Scientific Method has been a leading enabling factor in all this during the last few centuries.

‘Scientists with their Scientific Method should also serve society and constructively guide its aspirational growth. It is my firm belief that this book will ignite interest amongst student scholars and the general public about scientific matters, and will motivate them to unlearn certain limiting deficiencies like superstitions, blind faith, and other irrational belief systems’.

— Sudesh K. Wadhawan

Former Director General, Geological Survey of India

 

‘In this remarkable book the author has uniquely and thoroughly analysed the ‘conventional’ way of doing science, namely the one based on experimental observations, that has led scientists to make several fundamental discoveries and great inventions. But in his opinion, we must now add another important dimension to scientific investigations for understanding natural phenomena more inclusively. This requires the use of direct experiential evidence as well, so far excluded from the ambit of science. The author gives many examples to make this point. His arguments are convincing and impressive. He also proposes that the term ‘complexity science’ should now be used as a technical term for that part of science which goes beyond reductionism, and which relaxes some of the eight tenets of the very strict Scientific Method, so that even experiential evidence can be used directly. The informed reader would almost certainly accept his line of reasoning.

‘Exposure to the scientific approach to things will help eradicate, or at least minimize, pseudoscientific practices that are fooling innocent people all over the world. Wider and deeper prevalence of the much-desired scientific temperament and science literacy in the general public is a must, and this book will help promote that. The book is truly thought-provoking. The author’s presentation of the facts and arguments is very powerful. It is a must-read book by both scientific as well as enlightened non-scientific members of the society’.

— Rakesh K. Bhandari

Former Director, Variable Energy Cyclotron Centre, Kolkata

Distinguished Scientist, Department of Atomic Energy, Govt. of India

 

‘I liked the way the book brings out the importance of asking the right questions, presents the role of subjective information in a field dominated by objective information, highlights errors of reasoning such as confirmation bias, and tells us about the importance of simplicity and parsimony of assumptions in the form of Ockham’s razor. I also appreciate Dr. Wadhawan pointing out how, through complexity-science research, science has been moving beyond reductionism; that is, understanding the world through the interactions of its parts and sub-parts, rather than just actions and reactions. I hope the book inspires the young and the old in adopting scientific temperament in daily life’.

— Vinay Dabholkar

Consultant, teacher, co-author of 8 Steps to Innovation (2013),

and author of Mindfulness: Connecting with the Real You (2019)

‘Vinod Wadhawan in his book embarks on a noteworthy voyage to discover for us the intricacies of the ‘Scientific Method’ — a catchphrase most of us take for granted, but understand so little, and practise even less. The extent of our ignorance about it, or at best our patchy understanding of it, dawns upon us page after page, thanks to the lucid narrative achieved by the author by way of expression as well as explanation.  No sacred cows here; only the guiding principles of cold logic and reason — spurred by a passionate search for The Truth and a zest for enquiry into the unknown. That is why the book is a voyage of discovery — perhaps as much for the author as for the reader. The book addresses not only people in the scientific community, but also those who have any interest in knowing about the unknown and the barely known’.

— Upendra Rawat

Banker, poet, science enthusiast

‘The book concerns the science behind the Scientific Method itself — a strict formalized prescription for how science has to be done.  Under the overarching umbrella of the Scientific Method a diverse range of topics has been covered with great erudition. Nothing seems to have escaped the attention of Vinod Wadhawan, be it astronomy, science, philosophy, religion, mathematics, linguistics, grammar, health, medicine, neuroscience, quantum physics, logic, computers, technology, education, and most importantly the knowledge from pre-modern India, to name some.  The latter finds a place of pride in the book. Also, there is elaborate discussion on esoteric complex phenomena like the emergence of consciousness that do not seem to fit into the domain of the classical 8-fold way of the Scientific Method. Vinod proposes a guarded relaxation of some of the tenets of this method for making scientific studies more inclusive, so that experiential evidence and other subjective information also finds direct use in science.

‘The achievements of ancient India were outstanding, yet not widely known even today. There is good exposition in the book of this profound wisdom and knowledge that came from pre-modern ascetics (who were detached from the mundane!). Historically it has all but gone into oblivion, or has been lost due to destruction by invaders. It is great that it is now finding attention, exploration and scientific scrutiny by several investigators.  The very scientific presentation of all this in the book is extremely valuable and should help ignite interest afresh, and lead to a renaissance in re-discovering and understanding our past epochal achievements. 

‘The science of complex systems has been the forte of Vinod. In this context he has aptly exploited the second law of thermodynamics in his description of the emergence of complexity. As he said somewhere, the second law for open thermodynamic systems is the mother of all organising principles. And this is also an important component in the narrative of his present book. The book undoubtedly is a culmination of long years of an intense thought process, along with research, and collection, compilation and meticulous organization of a large body of precious distilled knowledge from the past and present. At the fundamental level, it was interesting to read about the significance of simple rules, and how they can produce the complexity of a system. The computational approach of Stephen Wolfram is very interesting in the same context, as it shows graphically how a simple rule to begin with can give rise to fascinatingly complex patterns. 

‘There can be queries that go beyond the ambit of the Scientific Method. The God question is one. Creator or no creator, super-being or none, etc.? This has occupied many minds. The arguments in support are circular and do not as such stand the scrutiny of the Scientific Method. The book gives a lot of coverage to this. The question remains unanswered, though. The mind-body question and consciousness are all relevant questions that the author addresses in an eloquent manner. He has coined the term ‘mindbody’ as an improvement over ‘mind-body’ to connote a single entity, and I believe he hence also subscribes to the notion of consciousness as contained in the mindbody, and not distinct from it. There is a wealth of information in the book to relish and to learn from’.

— Veer Chand Rakhecha

Former senior physicist, Bhabha Atomic Research Centre, Mumbai

 

Contents

 

Foreword 1                                                

 

Foreword 2                                                

 

Preface                                   

 

Part I. THE SCIENTIFIC METHOD SO FAR

 

1. Introduction and Overview                            

1.1    The Scientific Method 

1.2    The nature of reality 

1.3    Scientific pantheism 

1.4    The first and the second laws of                                 thermodynamics 

1.5    Probability, statistics 

1.6    Information theory 

1.7    Complex systems 

1.8    Evolution 

1.9    Cognitive science 

1.10  Does the Scientific Method have limitations? 

1.11  Childhood conditioning and scientific temper           in society 

1.12  Organisation of the book 

 

2. Asking the Right Question                           

2.1    Some famous right questions 

2.2    Asking scientific questions 

2.3    Cutting-edge science, protoscience 

2.4    Non-scientific questions 

2.5    Science and pseudoscience 

 

3.   Objective Observation of the World           

3.1    Empirical vs. rational evidence 

3.2    Quantitative vs. qualitative data 

3.3    Empirical, experiential, and anecdotal                         information 

3.4    Double-blind, randomized, placebo-controlled

clinical trials 

3.5    Correlation is not necessarily causation also 

3.6    Objective vs. subjective information 

3.7    The nature of evidence 

3.8    Thought experiments 

 

4.   Coming up with Hypotheses for Understanding the Data 

4.1    Hypothesis in science 

4.2    Hypothesis in formal logic 

4.3    Working hypothesis 

4.4    The creative aspect of coming up with a hypothesis 

4.5    The God hypothesis 

4.6    Vedas and the hypotheses of quantum mechanics 

 

5.   Reproducible Verification of Predictions of Hypotheses 

5.1    The rigours of verification of scientific hypotheses 

5.2    Hempel’s and Popper’s approaches to verification of hypotheses 

 

6.   A Theory for Explaining the Hypothesis      

6.1    What is a theory? 

6.2    Good theories and bad theories 

6.3    Distinction between hypothesis, theory, and law

6.4    Has physics gone off the rails in the 21^st century? 

 

7.   The Language and Logic of Science          

7.1    Language and humans

7.2    The role of language in science 

7.3    Which is the most scientific natural language? 

7.4    Logical systems 

7.5    Logic in Vedic thought and philosophy 

7.6    Roots of modern logic 

7.7    Logical fallacies 

7.8    What is a good argument? 

 

8.   Choice of the Smallest Necessary Set of Axioms      

8.1    The principle of maximum parsimony 

8.2    Ockham’s razor and the Copenhagen interpretation 

 

9. The Falsifiability Requirement                   

9.1    Falsifiability and inductive reasoning 

9.2    Hempel’s raven paradox 

 

Part II. MY VIEWS ON SOME COMPLEX ISSUES

 OF SCIENTIFIC INTEREST

 

10. Going Beyond Reductionism in Science     

10.1  Complex adaptive systems 

10.2  ‘A New Kind of Science’ (NKS) 

10.3  Universal cellular automata 

10.4  Wolfram’s principle of computational equivalence 

10.5  Fifteen years after the NKS book 

10.6  The end of theoretical physics as we know it? 

10.7  Our mindbody, the quintessential

complex adaptive system 

 

11. How to Live Well Forever                        

11.1  The problem with modern health sciences 

11.2  A matter of hardware-software incompatibility 

11.3  Nine steps to living well forever 

 

12. Reversal of Chronic Diseases                   

12.1  The Ornish way of reversing chronic diseases 

12.2  Eat well 

12.3  Move more 

12.4  Stress less 

12.5  Love more 

 

13. The 8-Fold Yogic Way of Living                

13.1 Ashtanga yoga

13.2 Ayurveda, the science of longevity

14. Historical                                               

14.1  The philosophy of science after the 10^th century 

14.2 Epistemology: empiricism, rationalism, skepticism, and constructivism after the 10^th century 

14.3 The Vedas and the Upanishads 

14.4 The Sarasvati civilisation

14.5 Philosophy, science, mathematics, and technology before the 10^th century 

14.6 Astrology 

14.7 The educational system in ancient India 

 

15. Science is What Scientists Do

15.1  Why so much difference of opinion

even among the experts? 

15.2  Health sciences 

15.3  The scientist as role model, influencer, hero

16.  The Scientific Method Needs to be Revisited 

16.1  Consciousness 

16.2 Proposals for making the Scientific Method more inclusive

 Bibliography                                                 

Index                                                          

Acknowledgements                                       

About the Author                                     

 

Foreword 1

Have you ever wondered what the scientific method is all about? Ask a scientist and you will probably get many different answers, none of them utterly satisfactory. Ask a member of the public, and you will probably gain no more insight into the question. The standard answer is something to do with the notion of objectivity, namely that a scientific discovery or result should be testable, reproducible and arrived at without the personal influence of the scientist who made the discovery. In practice, in my experience, this high ideal is rarely met, if ever.

Vinod Wadhawan, in this new book, sets out to dispel many of the myths about the scientific method and about the notion of science itself. It will come as no surprise to the reader that, like me, he does not believe in a God that rules the world we live in. Instead, rational arguments have to be made, with no recourse to the mystic universe, paranormal and pseudoscience.

Vinod begins with the concept of the scientific method and exposes some of its triumphs and weaknesses as practiced by real scientists. All scientists are human beings, of course, and so are liable to be led astray, no matter how strictly they try to follow a rigorously objective route. I know from my own work as a scientist working in the field of crystallography, how tempting it is to come to a conclusion based on slender, and possibly even questionable, evidence, and then go on to defend it, even when evidence is later produced to refute the original conclusion. We do not like to be proved wrong: a human frailty with which I think we all suffer. Also, we scientists tend to see ourselves as being apart from the general masses, with some justification because we have knowledge that others do not. Despite this, one of the problems today is the scientific illiteracy that pervades the general public and the political classes who rule us.

You can see an example of this with the antivaxxer movements around the world. Despite the horrors of the current pandemic, irrational notions are strong enough to prevent people from realising that vaccination is a good thing to have. This is despite its long history of success (for example the complete elimination of small pox). Such pseudoscientific notions are harmful to society.  To some extent this comes from skepticism and distrust of experts, rather in the way we tend to distrust the political class, but also to a general ignorance of science and what it means. As Vinod explains, the answer to this is to go out and explain science to the public, but do it in a way that makes them feel that they are part of the discussion. The other day, I heard a medical expert explaining to a group of people how the SARS-Cov2 virus attacks the human cells. He made no attempt at talking down to them, and was using fairly technical language about spike proteins, messenger RNA, ACE1 on the surface of cells, and so on. When it came to questions, it became clear that the audience, despite never having had any scientific training, had taken it all on board and enthusiastically began to ask rather advanced questions such as one might expect from scientifically literate persons. It shows that if you do not dumb down your scientific presentation, there is a good chance that you will be listened to and understood. This notion is one of the objectives of the book. I recall a similar experience many years ago when I explained some ideas about quantum theory and relativity to a few people gathered together in a pub. They became really interested, despite not having much educational background, and from their comments it was clear that they had followed the main arguments, and best of all, they were fascinated by the concepts. 

Scientific illiteracy in my view is a curse. It gets in the way of proper understanding of the world and can even impede progress. My own bugbear is with the crystal healing community. Just type the word crystal into Google and immediately you find thousands of websites talking about crystals, not in order for you to study, but for you to use in some mystic ceremony that will influence your wellbeing. Thus you find something called a “Positive mind” set of crystals. Or what to do with a quartz crystal in order to find peace of mind. There are many books published on the healing powers of crystals. Some of them begin rationally enough, explaining what is meant by a crystal and even using the correct jargon that we crystallographers use. But the cat is out of the bag pretty soon when they start talking about “energy levels”, as if the author has the faintest notion of quantum mechanics. But it gets worse when you read instructions on how to treat your crystal, talk to it in the right way, pray to it and how to use the energy emanating from the crystal. This reminds me that a few years ago I saw a beautiful crystal of calcite in a shop window, and so I went in and bought it. The shop assistant instructed me to place it beside my bed and that this would ensure a harmonious night’s sleep. I tried it. It didn’t.  I usually upset such people when I explain to them that crystals are the “deadest” objects in the universe. No energy is emitted, no auras, nothing — absolutely cold stone; lifeless. I once even had my life threatened when I explained this to a believer. People do not like having their beliefs attacked. So, from my perspective, I am pleased to read a book in which scientific rationality is championed. It is vital that we scientists explain to the public what we are doing, what excites us and, most importantly of all, how to discriminate between scientific and unscientific “fake news”.

The belief in the supernatural is a feature of humankind and has a long history. For example, it has been discovered that at the sites of ancient hominids, homo erectus, living over one million years ago, there are collections of quartz crystals. Interestingly, they are not modified in any way, and have not been used as tools. It seems that the ancient hominids found some value in the quartz crystals, and possibly this was the beginning of magical thinking. It has been pointed out that in their world, they hardly ever saw any straight lines (rivers, trees, mountains – all curves). The discovery of crystals must have seemed especially striking to them, and like the monolith in the film 2001 A Space Odyssey, this may have awakened in them the first ideas to investigate the world around them.  Since then, of course, magical thinking has pervaded human history, but gradually as rational thinking began to dominate, the scientific revolutions became possible. Nonetheless, it is surprising that in the 21^st century there is still so much belief in magic. One can excuse the uneducated, I suppose, but for me it is difficult to understand educated scientists who still hold on to these ancient ideas. There are many physicists who still believe in the notion of God, and attend religious ceremonies. Yet, when they publish their scientific work, they never include in their conclusions the notion of divine intervention as one of the possible explanations of their observations.

The book describes what Vinod calls the 8-fold way of the scientific method, which is roughly what we have been doing up till now. Now, while there is still much to be discovered through the standard scientific methods, it is becoming clearer, as Vinod points out, that we need to adjust our way of thinking to deal with ever more complex situations. In science, we have a tendency to reduce complicated problems to simpler situations which we can solve, and then expect that the results apply more broadly. There is that famous joke about a group of veterinary surgeons, statisticians and theoretical physicists being commissioned by a race-horse owners association to determine how to breed faster race-horses. After a year they went back to learn about the results. The veterinary surgeons said that they had dissected one hundred horses and discovered that to breed better horses, one should concentrate on those with strong back legs. The statisticians said that they had studied the statistics of all the races since 1900 and found a small, but significant, correlation between the winners and the colour of the horses. It seems that chestnut horses have a slight edge over all others. The theoretical physicists reported that they had managed to solve the spherical horse model, but now needed further funding to go on to the ellipsoidal horse case! The fact is that we humans look for simplicity even when encountering difficult subjects. But there is increasing evidence that simple, apparently beautiful interpretations actually can mislead us from understanding the true nature of the world.

So, in conclusion, Vinod’s latest book is a fascinating collection of ideas and philosophy of science, which certainly made me reassess my own thoughts on what science actually means. I hope you will enjoy it as much as I did.

A. Mike Glazer

Oxford

Emeritus Professor of Physics and Emeritus Fellow of Jesus College, Oxford

Visiting Professor at the University of Warwick

Crystallographer

Editor of the Newsletter of the International Union of Crystallography

https://www.iucr.org/news/newsletter

February 2021

  

Foreword 2

Vinod Wadhawan and I have been friends for more than half a century. We started our career in the Bhabha Atomic Research Centre at the same time, and I have seen him grow in stature as a researcher in his professional career, and later as an author of some outstanding books. I am an admirer of his capabilities in crystallography, and in expressing his thoughts, often on complex subjects, lucidly and accurately. A few years back I was attending a session in an international conference on materials science, where a renowned Professor from the Carnegie Mellon University, sitting next to me, asked whether I knew Vinod Wadhawan. On my affirmative answer he commented on Wadhawan’s book entitled ‘Introduction to Ferroic Materials’. He made a categorical statement that this book is undoubtedly the best book on the subject. That was indeed a proud moment for me to let him know that I have a long and close association with Vinod.

When Vinod asked me to write my impressions on this book, I was thrilled. As I received the final version about two weeks back, I started reading it and I realised that the task was not so easy. This is not a book which one can read fast, as there is a lot to digest. Let me admit that I am writing my comments without fully digesting all the messages which he has communicated in this book after spending considerable time in deep thinking on subjects which are of fundamental importance, not just in science but also in philosophy and in our existence. When I received an ultimatum from him for giving my views within 48 hours, and knowing how tough a task master he is, I started penning down these ‘first impressions’.

Vinod’s research career has been in experimental solid-state physics, with a clear emphasis on crystallography and crystal physics. What I enjoyed most in this book is that he has treated the complex subject of the nature of science itself from the perspective of an experimental physicist. He is totally sincere in expressing his thoughts and has not tempered with anything to suit commonly prevailing views. There is no dispute on the eight tenets of the Scientific Method, namely, Right questions — Right (objective or empirical) observations — Right hypothesis to explain the observations — Right testing of predictions of the hypothesis — Right theory — Right language and logic — Right (minimum number of) axioms – and Rightly worded (falsifiable) statements. These are generally accepted in the scientific literature. What I find novel is his assertion that they have an analogy with the eight steps to Nirvana as taught by Buddha: Right beliefs — Right intentions — Right speech —Right conduct — Right livelihood — Right effort — Right mindfulness — Right concentration.

There is no doubt that both in science and philosophy our understanding gets enriched by the ‘courage of our questions and the depth of our answers’. Extending such discussions, Vinod takes us to the question of the existence of God. He has picked up many quotable quotes from great thinkers, primarily from the realm of science. The fundamental questions which have been arising in our minds from times immemorial are: Who is the creator of all that we see around us in nature happening with clockwork precision? ‘If there is watch there must be a watchmaker’; who is it? When all the things and phenomena came into being? What is our destiny? Is there any deeper meaning to all that is happening in the universe? Or is it that all that we see is meaningless? Just maya? Science has attempted answering a few of these questions. There has been a fairly universal agreement on the Big Bang theory which says that the universe we live in started about 14 billion years back, and provides a time line of significant events for the evolution of the universe. The evolution of living beings on our planet has also been charted by detailed scientific work. Still, many of the questions mentioned above remain unanswered. Wherever we find no answer, we resort to God. But there is no unique concept of God, as the human mind has conceptualised it in multiple variants. As of today, our knowledge of the human mind itself is abysmally poor and so is the case with our feelings and sentiments. There is no escape but to rely on poets, philosophers and thinkers for dealing with these issues.

As far as my understanding goes, there is one point of convergence in the views of scientists and philosophers: Astrophysicists confirm today that not all the elements present in our planet and in our body were generated in our solar system. Nucleosynthesis of several heavy elements required an event like a merger of neutron stars or a supernova explosion. Therefore all of us, living and nonliving, carry a part, howsoever small it is, of the products that resulted from such big events. Therefore we all are directly connected with this big universe. In the same manner the billions of micro-organisms residing in our bodies are responsible for important functions such as digestion of food and production of vitamins. Scientists are in full agreement that the harmonious presence of the entire biota, not only of a local region but of the whole planet, is essential for our very existence. A cruel illustration of this fact is getting revealed in recent times in the occurrence of the present pandemic. This important fact of our links with the whole universe from both macroscopic and microscopic dimensions had been sensed by poets and philosophers, even of ancient times. They also raised the question ‘Where from come the creative traits of the human mind?’. In Einstein’s words ‘the intellect has little to do on the road to discovery.

Often there comes a leap in your consciousness, call it intuition ……. The solution comes to you and you don’t know why’. The great mathematician Ramanujan believed that much of his original work was due to divine grace. This totality of nature encompasses everything, material and mental, in the universe, and we as individuals are connected in a cosmic web. Tagore narrates this concept of unison of individuals and nature as a whole in a beautiful way in many of his poems and songs. Indian philosophy goes one step further. The monist view proclaims that an individual and the whole of the nature are not just linked, but they are indeed one and indivisible. I don’t think we can design an experiment to prove or disprove this hypothesis. But this concept has no contradictions with the scientific knowledge of today. Vinod in his book describes this viewpoint as scientific pantheism, or the naturalistic worldview. The science of complexity has been discussed in this book and in his previous book, with a number of illustrations and plenty of quotations. Vinod has been working in this area for a while, and the depth of his understanding is reflected in the lucidity of his presentation on this ‘complex’ subject.

The thermodynamic basis of the process of evolution has been quite enjoyable for me to read. Conceptually the process of crystallisation from a liquid or an amorphous structure can very well be considered as building up a three-dimensional edifice with a periodic arrangement of units or motifs using a given algorithm. If a translational periodicity is invoked one generates an infinitely large perfect crystal. With two or more units and a set of rules, defining translational periodicity, a three-dimensional space, expanding to infinity, can as well be created. The result is a quasi-crystalline structure, which was initially conceived as a result of a mathematical game and later proved to occur in real systems. The concept of infinity which is difficult to visualise, but encountered in irrational numbers and infinite series, can thus be illustrated in real terms. The two basic principles, one of lowering of the free energy of the system, and the other of increasing of the entropy of the assembly of the system and its surroundings, govern the processes of evolution in nature. Vinod has explained this with the help of first and second laws of thermodynamics. This part is not just enjoyable reading for those who are familiar with these concepts, but are extremely valuable for those who are getting introduced to this subject. In these discussions I can easily identify the author who has extensive experience in the field of phase transitions. The issues connected with stability, metastability and un-stability, elaborated in the book with the help of a figure of a Mexican-hat shaped potential, are definitely of importance from pedagogic considerations.

The book, as I have scanned through so far, is not something that you read up in a continuous stretch. You need to take a pause, spend time to think, often go back to earlier pages, jump some pages and read a new chapter, and so on. It is a companion book which provides you food for thought. It is a storehouse of valuable quotes. It makes you think, it helps you inculcate the habit of objective and rational decision-making in complex situations. It gives some useful health tips and even gives solace in difficult circumstances. The book goes much beyond what its title may convey. This is a book written by an experimental scientist with an Indian mind, who is trying to understand things in nature and events happening around him with his personal scientific insights. I welcome this valuable addition to literature in the domain bordering science and philosophy, and I congratulate the author.

Srikumar Banerjee

Chancellor, Homi Bhabha National Institute (DAE), Mumbai

Former Chairman, Atomic Energy Commission of India

February 21, 2021

 

Preface to the First Edition

This book celebrates science and the Scientific Method. It takes you into the world of science and scientists, and explains how natural phenomena are investigated. The term ‘Nature’ means all that there is, so there is nothing outside or ‘above’ Nature. Therefore, all phenomena are natural phenomena; nothing is ‘supernatural’. In science we investigate all natural phenomena; with varying degrees of success, of course.

As Brian Cox said, ‘science is too important not to be a part of popular culture’. Our collective intelligence has enabled us to evolve the all-important Scientific Method for investigating natural phenomena. But science is not just about investigations. It also has the crucial feature of systematizing the knowledge acquired and, even more importantly, of making the knowledge available to everybody for scrutiny. Scientists do further research based on what is known and published in journals of science. This leads to continual progress in our quest for knowledge and understanding. Newton put it very aptly: ‘If I have seen further it is by standing on the shoulders of Giants’. The cumulative (and exponentially rapid) growth in our knowledge would not have been possible in the absence of knowledge-sharing.

A great feature of the Scientific Method is that it is self-correcting. This makes science a dependable and exciting venture. In fact, the (empirical-evidence based) Scientific Method is the only method we have for arriving at objective knowledge that we can trust with a high degree of confidence. ‘The good thing about science is that it’s true whether or not you believe in it’ (Neil deGrasse Tyson).

Just imagine what our lives would be like without all the science and technology at our disposal today. And there is promise of exponentially increasing rates of progress in the days to come. All this has become possible because we humans have invented and perfected what I call the 8-fold way of the Scientific Method for understanding natural phenomena. Here is a glimpse of what it is all about:

Suppose we are curious about some natural phenomenon and we have made some observations about it. We want to understand it, and to generalize the conclusions if possible. Here is how we go about doing this in science: We begin by asking a well-formulated question we want to answer. Then we make observations and measurements that we believe will help answer the question. The next thing to do is to make a guess, i.e., formulate a hypothesis, that can explain what we have observed and measured. The fourth step is to argue that if our guess is correct, we should also observe in Nature certain consequences of our guess, apart from understanding our initial set of observations. That is, we make predictions and verify them. If the verification is not satisfactory, we go back to modifying the guess, or even coming up with a different one. We may also have to go back to making more extensive and accurate observations of what we want to understand. Finally, if the hypothesis and the predictions made by it stand verified, we gain confidence in it. It may turn out that another related set of observations or phenomena can be explained by another good hypothesis. One or more good and related hypotheses in a scientific discipline of enquiry deserve a logical explanation as to why they work. This is done by formulating a theory. A classic example is Newton’s theory of gravity for explaining Kepler’s analysis and generalisation of observations about planetary motion.

But is that all there is to the scientific process? No. The above five steps of the Scientific Method for arriving at objective truths are actually embedded in an ecosystem defined by the following three additional stipulations or tenets:

1. For describing anything we need a language. The language used in science must always have a strict and unambiguous logical structure. In science we impose the severe restriction that each word used must be defined so unambiguously that it means the same thing to everybody. Moreover the rules of logic must be self-consistent, and universally acceptable.

2. For formulating a hypothesis or a theory, and for other discourse on the subject, we should work with only a minimum necessary set of axioms. The axioms may be truths already established, or they may be something just presumed to be tentatively valid. And the litmus test for the presumed axioms is that the conclusions drawn from them must agree with objectively obtained experimental data (or empirical evidence).

3. Finally we have the so-called falsifiability (or refutability) requirement. It says that only those claims or conclusions are permitted in science that are so worded that it should be possible to prove them false by a suitable experiment or argument.

This 8-fold way (the set of eight tenets) of the Scientific Method has been assisting humanity in attaining progressively a kind of intellectual Nirvana, by conquering ignorance bit by bit. We are mortals, but the Scientific Method enables us to achieve a kind of immortality in the ever-expanding and self-correcting edifice of scientific knowledge we create in common; that is, by acquiring, interpreting, sharing and recording (for ourselves and for posterity) empirical information and tentative conclusions about natural phenomena.

A common perception, particularly among those exposed entirely to what is called ‘Western science’, is that the Scientific Method for investigating natural phenomena is about 400 years old only, if not less. According to the Oxford English Dictionary the Scientific Method is ‘a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses’. Science has indeed made very rapid strides in the last 400 years. But it would be wrong to think that there was no scientific activity before this period. This issue is also examined in this book. I am an Indian brought up in India. Therefore I am in a somewhat better position to empathise with the Indian claims about the philosophic, scientific, technological, and mathematical achievements in our hoary past.

Here is an English translation (by A. L. Basham) of the Nasadiya Sukta, also known as the Hymn of Creation; it is from the Rigveda (10:129) and is about cosmology and the question of the origin of the universe:

Then even nothingness was not, nor existence,

There was no air then, nor the heavens beyond it.

What covered it? Where was it? In whose keeping?

Was there then cosmic water, in depths unfathomed?

 

Then there was neither death nor immortality

nor was there then the torch of night and day.

The One breathed windlessly and self-sustaining.

There was that One then, and there was no other.

 

At first there was only darkness wrapped in darkness.

All this was only unillumined cosmic water.

That One which came to be, enclosed in nothing,

arose at last, born of the power of heat.

 

In the beginning desire descended on it -

that was the primal seed, born of the mind.

The sages who have searched their hearts with wisdom

know that which is kin to that which is not.

 

And they have stretched their cord across the void,

and know what was above, and what below.

Seminal powers made fertile mighty forces.

Below was strength, and over it was impulse.

 

But, after all, who knows, and who can say

Whence it all came, and how creation happened?

The Devas (minor gods) themselves are later than creation,

so who knows truly whence it has arisen?

 

Whence all creation had its origin,

he, whether he fashioned it or whether he did not,

he, who surveys it all from highest heaven,

he knows - or maybe even he does not know.

I love that last line: ‘he knows - or maybe even he does not know’ (emphasis added). That is the essence of what the Scientific Method is all about. An open mind. A rational approach. Skepticism. Willingness to change one’s views in the light of new information, making it a self-correcting venture.

This book has four main objectives. The first is to describe the methodology used in science for investigating natural phenomena objectively. The fairly detailed account of the process is intended to give the reader a feel for the pains scientists take for ensuring that the empirical knowledge so gained has the highest attainable degree of credibility. The self-correcting feature of the Scientific Method is highlighted, as also the high degree of skepticism adopted by scientists in their quest for knowledge. The level of presentation is designed to take the message of science to as many people as possible, but without dumbing down the narrative unnecessarily.

The second objective is to emphasize why the scientific approach should not be confined to the laboratory alone, but should be applied to all human dealings and discourses. Scientific temper in society is a must, and this book is one more effort for promoting that.

My third objective is to highlight the empowering effect of science literacy on all of us. After all, science is done by humans, and the human mindbody is a highly complex system. This fact of life colours just about every aspect of the world of science and scientists. Science literacy in the public at large is a must for distinguishing between the scientific and the unscientific (or pseudoscientific). Science literacy is also important for your physical and mental wellbeing: Your very choice of proper doctors and other health and nutrition experts depends on how well you are already informed about the basics of the health sciences, as also of the machinations of some big pharma companies for increasing the sales of their merchandise.

Fourthly I make a case that there is now a need to make scientific investigations more inclusive by guardedly relaxing some of the tenets of the very strict 8-fold way of the Scientific Method that we have been following so far (with astounding successes, no doubt). This has become necessary for making headway in the science of what are technically called ‘complex systems’. The human mindbody is among the most complex of them all, and many people, including several eminent mainstream scientists, take the concept of human consciousness very seriously. At present, experiential knowledge, so characteristic of a sentient being, is largely kept outside the purview of science because of our insistence that any statement or claim in science must be reproducible by everybody, everywhere, any time. Some proposals are made in this book as to how we could possibly enlarge the scope of the Scientific Method by including for investigation in mainstream science certain esoteric phenomena occurring in complex systems, rather than letting studies on them continue to lurk in the shadows. Science should aim at investigating all natural phenomena, including those occurring in the experiential domain. New nomenclature is proposed in this context, assigning distinct technical meanings to the terms ‘science’ and ‘complexity science’. The former will continue to have the meaning it has at present, along with strict adherence to the Scientific Method applied till date. And the technical term ‘complexity science’ should henceforth be used for that science in which some of the eight tenets of the present Scientific Method are relaxed a bit to give ‘complexity science’ a more inclusive scope for bringing concepts like consciousness into mainstream science. All this calls for a debate, though.

Vinod Wadhawan

New Delhi

February 2021

Preface to the Second Edition

An effort has been made to factor-in the feedback obtained on the first edition of the book.  Some less relevant material has been removed to make space for new information. A section ‘Ayurveda, the science of longevity’ has been added in the chapter ‘The 8-Fold Yogic Way of Living’. I decided to do this after reading a recently published book by Gangadharan (2021): Ayurveda: The True Way to Restore Your Health and Happiness. Dr. Gangadharan talks like a real scientist; in fact, he is one. It was through his book that I learnt about an extensive study by Govindaraj et al., the results of which were published in a paper in Nature in 2015; title: ‘Genome-wide analysis correlates Ayurveda Prakriti’. The study identified 52 genes that determine the prakriti of a person, meaning that the ancient Ayurvedic concept of prakriti has a scientific rationalisation, and that it can now be identified by studying these genes also. Another development in modern science that has had a counterpart in Ayurveda is that of the field of epigenetics. Our genes cannot be changed readily, but their expression or otherwise is in our hands to a substantial extent. This resonates well with the Ayurvedic premise that nurture can overrule Nature to a very significant extent. This means that even the seemingly ‘incurable’ diseases can generally be overcome through adequate lifestyle-changes, aided by holistic Ayurvedic medication. Another addition to the book is a brief discussion of some implications of the rather recent discovery that the mighty river Sarasvati was a reality, and not a myth. Evidence indicates that the land around this now extinct river was probably the primary cradle of the entire human civilisation — and of ancient Indian science.

Vinod Wadhawan

New Delhi

December 2021