Category Archives: Books

Book reviews of the Unreal kind. Here, I discuss the books I have read, and share my impressions with my readers. I read mostly non-fiction or classics. And when I say read books, I mean listen to them in audiobook (always unabridged) form. Audiobooks have the ability to make your commute or gym workout something you look forward to, rather than dread. When reviewed, they present a disadvantage though, that they cannot be referred to. Thus quotes from them become paraphrasing, names get misspelled and so on. Please excuse such shortcomings…

Note that these are not real reviews. Most of these books are so well-known that they are really beyond reviews. So my Unreal reviews are more like my impressions and thoughts, often containing spoilers.

Trading Desks

At the heart of Front Office are the trading desks. In terms of prestige and power, they really are the reason for the whole infrastructure of Front Office, including economists, sales, structuring, quants, quantitative developers etc. After all, they make the profits. And consequently, the vocal and volatile traders hold enormous sway. At their beck and call, quantitative developers provide instant service on trading platform issues; quants develop pricing models based on their requirements.

Trading Desks

Trading desks interact with the external world of brokers and counterparties. They take their input on market moves from highly responsive market data providers and base their positional views on staff economists. They have an army of trading assistants (junior traders themselves) who help them book and monitor their trades with the help of risk management professionals associated with the desks.

Their interaction with the rest of the bank is mainly mediated by the trading platform. When the book a trade, for instance, it goes into the trading platform and ends up with some middle office professional who will decide whether to accept it or bounce it back for further modifications. Various risk management staff from the middle office also will take a hard look at the trade, as we shall see later in the trade lifecycle.

The desk risk management team get their cues also from the Middle Office risk management, in terms of approved limits and daily marked-to-market and sensitivity reports. All these channels of communication need to be facilitated in the trading platform.

Sales and Structuring

Those in sales tend to be personable, extroverted and persuasive people. A good salesman can sell a refrigerator to an eskimo, I’m told. IN the context of the front office in the global treasury or global markets, sales people are tasked with sniffing out trading needs from their customers and offer compelling products from the trading and structuring desks to fulfill them.


The life of a sales professional is a tough one. They need to meet progressively more aggressive targets in sales volumes to stay in the game. The moment they meet the target one year, it jumps the next year to an even more unattainable level. The sales staff, therefor, find themselves under constant (or even increasing) pressure. Knowledge of mathematical finance, while useful, is not a pre-requisite for sales jobs. In fact, mathematically inclined folks tend to be reserved and introverted, and tend not to have the qualities that make for a good salesman. In other words, if you are a quant, your most ideal role is not in sales, although the structuring side may offer some interesting opportunities.


Economists have too many hands. On the one hand, they may feel that oil prices are going to go up because of increased demand from emerging giants like India and China. On the other hand, there is a global slowdown, and the overall demand is likely to fall, putting downward pressure on the prices. Then again, the rampant corruption and inherent deficiencies in markets like India might push the prices high. On another hand, price-driven improvements on the supply-side (like better production techniques) may eventually push the prices down. My ex-boss, an economist himself, once told me that he wished he could chop off some of these hands!


But seriously, economists are the mouthpieces of the bank. When you see someone from an investment bank on TV making an intelligent observation about the market outlook, it is likely to be a staff economist. If you manage to navigate through the jumble of hands, you will hear what the bank wants to say to the world.

That is precisely why you have to pay close attention and try to read between the lines. You are listening to what the bank wants you to hear. When you listen to a Morgan-Stanley economist assert that Facebook is the best thing since sliced bread, should you trust him, given that they were the investment bank behind Facebook IPO? Of course, the lingo you will hear from the economist would be a lot nicer, subtler and more convincing than I can muster. But you should still ask yourself, “Is there a hidden agenda?”

Economists may seek to influence the market; they also distill and condense their take on the market and share it with the traders and executives so that they can formulate and fine-tune their tactics and strategies. Thus, they play an important role in the directional views that banks take when navigating the tricky waters of trading and speculating.

Front Office

The Front Office looks quite complex in the slide below. All we need to remember is that the “Front” in Front Office is for world-facing. So in the slide below, you can see functional teams, some of which interact with the external world. These teams are:

  • Economists who talk to the media and receive market inputs that they condense into useable intelligence for the rest of the bank.
  • Sales and Structuring teams who talk to customers to sniff out potential opportunities.
  • Trading Desks, interacting with their peers in other financial institutions and other counter parties.

Front Office

One level removed from this layer of customer or world-facing teams are the quants and quantitative developers of Front Office, along with the risk managing professionals associated with the desks. They play supporting roles to the first layer of teams. Quants develop pricing models, quantitative developers roll them out into trading platforms, and the desk risk management team helps traders monitor and hedge their exposures. Note that almost all the interactions among these teams are mediated by the trading platform. The platform also acts as a conduit between the functions of Front Office and Middle Office. The nature of these functions and interactions of each team will be the topic of the next few posts, starting with the economists. Stay tuned.

Structure of a Bank

The trading arm of a bank consists of three so-called “offices” — the Front Office, the Middle Office and the Back Office. The Front Office (which may go by the names Global Treasury, Global Markets etc.) is the customer facing part. It houses the loud and strong-willed traders, extremely articulate economists, personable sales staff along with some mathematicians with thick glasses and bulging foreheads. The Front Office is considered the profit-making part of the trading activity — it is a profit center. All other teams in the other two offices are cost centers, which is a fact that is well reflected in the compensation structure.

Trading Platform

The Middle Office faces the Front Office, not the external world. They busy themselves with trade validation, lifecycle management, risk calculation, monitoring, limits enforcements etc. The Back Office is far removed from the Front (and from the sphere of influence of a quant or a quantitative developer). They take care of vitally important aspects of trading — namely settlements, taking and paying money. They also control the numbers that appear in the very visible annual reports.

By the way, the naming of the offices has nothing to do with their geographic location — a fact I learned early in my banking career about seven years ago when my boss wanted to take me to meet someone in the Back Office. I couldn’t figure out why he wasn’t actually leading me to the back of the building, I am embarrassed to admit.

Trading Platform

All the Offices are supported by multiple departments in the bank, most notably the Information Technology (which may go by the names Group Technology or any other transmogrificaiton of it). Also supporting everything happening in a bank (or in any corporate body, for that matter) would be Human Resources, Finance etc.

Before we conclude this post, we have to highlight a couple of caveats. The structure described above is by no means the whole bank. It is only the trading arm of the investment banking side of a modern bank. This part happens to be the one most relevant to quantitative developers. Even in this limited remit, the details of the structure (which we will get to in the subsequent posts) are not cast in stone. Each bank may have its own partitions, naming conventions and organizational and hierarchical structures around the various offices. Despite such differences, the static topology of the various offices haas enough commonality that we can talk about it general terms. As we will explore how the omnipresent trading platform mediates almost all interactions among these offices and their teams in the subsequent posts, we will get into more details of the structure.

Trading Platform

A trading platform is a program that enables the front office traders to price and book trades, the middle office professionals to manage the trade lifecycle and risk, and the back office staff to settle them. This definition contains a lot of jargon: front/middle/back offices, booking a trade, trade lifecycle, risk management, settlement etc. Don’t worry, we will go through the lingo in great detail in the subsequent posts. Some of it will become clear in this post.

Trading Platform

First, let’s be clear about what we mean by a trading platform. It is a piece of software that answers to a set of requirements coming from the business side as well as from the software architecture perspective. From the business side, the trading platform acts like the repository of the pricing models coming from the in-house quants. Since most of these models would not be ready when the system goes live, we should be able to add models on the fly. In other words, the trading platform should be incrementally deployable. It should also have built-in sockets to receive and archive market data feeds from multiple providers. In addition to persisting the market data, the trading platform should have a database backend with a robust schema to persist the trade data. It should be able to support regular processes like daily marking-to-market of the trades, flagging fixings and cash-flow requests etc. As with all financial programs, the trading platform should be able to provide indelible audit-trails, coupled with a highly granular access control mechanism. These security and authentication features have become even more relevant in light of the high-profile rogue trader instances of last decade.

All these high-level business requirements translate to architectural choices in the program. The design of the trading platform calls for a higher level of code maintainability than is obvious in normal software engineering, because the banking field suffers from a rather large personnel attrition rate. In order to minimize the key-person risk, we should insist on detailed documentation in addition to sound development practices. The scalability requirement of a trading platform is also more stringent than is common in normal programs. The volume of trades can jump from a handful to hundreds of thousands in a matter of weeks when the system goes live. Similar to that kind of scalability is another requirement — the ability to incrementally add modules to roll out the pricing models originating from the mathematicians of the bank, which calls for a very careful design. The robustness of the system will also have to the very high even at single transaction level. We have to ensure transactional integrity (no half-booked trades, for instance), and zero downtime because, after all, time is money in the bank. The authentication and security mechanisms are to be top-notch. To top it all, the performance has to be top-notch as well. So the design of trading platform is a daunting task from a software architecture perspective.

Why a Trading Platform

The question is not whether a modern bank should have a trading platform. All banks do. In fact, they have multiple trading platforms. The question is not even whether they should attempt to build a trading platform in-house. Again, most modern investment banks do build their own in-house platforms. The question I want to explore here is regarding the advantages and disadvantages of doing so. And to study some of the options when it comes to deciding how deep we want to go in the endeavor of building a trading platform in-house.

The real impetus behind any endeavor in a bank, of course, is money. An in-house trading platform is essential to harness the efforts of the highly paid model quants. In its absence, their mathematical models and implementations will be a confusing mess of prototypes and spreadsheets. A well-designed quant library and a trading platform riding on it can turn them into revenue generators. If the trading platform is built in-house, it offers additional advantages of speediness to respond to transient market conditions. For these reasons, most modern banks decide to invest in at least one in-house trading platform.

How to Get a Trading Platform

Once we decide to build it in-house, we have a slew of choices. First, we can think of extending the existing commercial trading platform. We can ask our vendor to incorporate our models and thus customize the platform. But this option usually doesn’t work out well because it tends to be slow and expensive. Besides, once the modules are developed for us, the vendor might want to sell the system to our competitors as well, unless we are prepared to accept even more expensive terms and conditions. This aspect will pretty much nullify any profit motivations that the bank had to begin with.

Another option is a middle ground of using the vendor’s interfaces (API) to implement our models on the commercial system. Although it might initially look attractive, it’s allure diminishes at closer inspection; once we realize that vendors have no incentive in making it easy for the users to modify the system. If anything, it only increases their support headaches with uninitiated IT managers mucking up the core functionalities. Perhaps for such reasons, vendor APIs tend to be both expensive and incomprehensible. Besides, this route of designing a customized trading platform ends up creating highly-skilled and mobile key persons, with the associated risks.

For ultimate control and flexibility (and for most fun), nothing beats a fully in-house designed trading platform. It can be highly nimble and responsive. But it is also an adventurous and error-prone undertaking. Nonetheless, it is this route we will explore in great detail in my book, and to a lesser degree, in this series of posts.


In the dog-eat-dog corporate jungle, there always is a hidden agenda. Always. In writing this series of posts, I have a hidden agenda as well. It is to promote my book – Principles of Quantitative Development. Everything I say here is described in much more detail in the book. And, the book goes into topics that I do not plan to touch upon here – like a review of computing principles for quants, quant developers and people involved in trading and trade lifecycle management. Finally, the book comes with a mini trading platform illustrating many of the principles described.

Hidden Agendas

If these compelling reasons have failed to convince you to fork out fifty or so dollars to order the book from Amazon, consider your own hidden agenda. Why are you reading these posts? You are probably considering a lucrative career as a quantitative professional in a bank. Or, as a junior quant professional, you would like to know more about how the whole thing works. And Principles of Quantitative Development may help you in that quest.

To get back to my point, there always is a hidden agenda, and the associated petty politics. If you cannot play the political game, a bank is not the right place for you. That may sound like bad news to you. Let me give you the good news. Almost everybody is better at politics that they think they are, And almost everybody in the bank, regardless of how high they are, goes about feeling that they are not doing as well as they should, because they don’t play the political game . So don’t worry too much about it even if you fee that you are not good at it — you are probably better than you think you are.

My real point is just that you should be aware of hidden agendas — in day-to-day interactions, corporate memos and announcements etc. For instance, let’s suppose you get a congratulatory email from your big boss about a project you are working on, saying you did an excellent job, it’s going to save or make so many millions of dollars, and everybody is mighty pleased about it. You may also feel mighty pleased about the message, and start thinking of that big break, promotion, bonus, corner office, expense account etc. But it may turn out to be a precursor to letting you go — after all, you did such a wonderful job, and your work here is done!


Regarding the agenda of these posts, this series of posts will cover the items listed in the picture above. In the next post, we will go through what we call a Trading Platform because that is the arena of Quantitative Development. The next few posts will be on the structure of an investment bank, from the perspective of a quant and a quantitative developer. The structure, in some sense, is the static topology. How trades flow through it will be the subsequent few posts, which will be the dynamic evolution of a trade. As ia trade moves from one department to another in a bank, the players involved use their own work paradigms and perspectives to view and deal with it. It is important to understand these perspectives so that a quant developer can understand and appreciate the myriads of requirements thrown at him. After all, his product — the trading platform — mediates everything.

In order to give you more of a flavor for the workings of a bank, the whole series of posts will be peppered with some little tidbits of information that may read like newspaper columns — after all, I started my writing career as a columnist. In my book, these tidbits are called the BIG PICTURE.

Off the Beaten Track

Recently, I gave an invited lecture to the Master of Financial Engineering students at the Nanyang Technological University in Singapore. I thought I would make a series of blog posts out of my talk with the belief that there is a wider audience out there who would like to know how an investment bank (or, more precisely, the structured and exotic products trading side of a bank) works.

Principles of Quantitative Development

First things first. I work for Standard Chartered Bank, Singapore. But the views expressed here in the talk and in this series of posts are my own. They are not influenced by my employer’s policies or client relationships. They are not meant to be any kind of investment or career advice. This disclaimer is a legal necessity before I can say anything related to banking and finance.

Off the Beaten Track

Since the talk was originally given to MFE students, who are expected to be pretty well-versed in the mathematics of quantitative finance, and possibly of computing as well, I tried to tell you something different. In any case, all the mathematics and computing stuff is something you can pick up from any number of standard text books. The stuff I’m about to share with you is something you will learn from only a few books, or by working in a bank for a while. That brings me to my hidden agenda. (Well, not so-hidden after this introduction.) And to the first moral of this lecture — there always is a hidden agenda in the corporate jungle. I will have more to say about it in the next post.

Since this series of posts is not quite on quantitative finance, nor on computing, it is a bit off the beaten track. Hope you enjoy it. In any case, you will develop and appreciation for the “Big Picture”. A few years ago, I published a well received article in the Wilmott Magazine on the same theme, and the positive feedback I received from it was the inspiration to write my book.

In this talk, and in my book, I lay out the foundations of Quantitative Development. Quantitative Developers (who tend to be computer science professionals) are different from Quants (who tend to be mathematicians). Quants tend to develop pricing models or other mathematical tools for the rest of the banks to use, and make them available in the form of prototype programs, or the so-called quant libraries. Quantitative developers make them available in existing, familiar systems (“productionize” them, to use the horrible jargon) so that the rainmakers of the bank can bring in profits. In that sense, their role in the bank is sandwiched between the model quants and the traders, from a functional perspective. If you don’t like that perspective, and would like to have a more abstract, mathematical sort of view, you can think of quantitative development as being in between pricing models and systems (which we will call Trading Platforms very soon). Or from a corporate hierarchy perspective, the job of quantitative developers falls in between the front office and the information technology division, so much so that they can actually be integrated with either one of them.

Quiet Me

I’m an introvert. In today’s world where articulation is often mistaken for accomplishment, introversion is a bit of a baggage. But I have no complaints about my baggage, for I have been more successful than I expected or wanted to be. That’s one good thing about being an introvert — his ambition is aways superseded by the need for reflection and introspection. To an introvert, the definition of success doesn’t necessarily include popular adulation or financial rewards, but lies in the pleasure of finding things out and of dreaming up and carrying out whatever it is that he wants to do. Well, there may be a disingenuous hint of the proverbial sour grapes in that assertion, and I will get back to it later in this post.

The reason for writing up this post is that I’m about to read this book that a friend of mine recommended — “Quiet: The Power of Introverts in a World That Can’t Stop Talking” by Susan Cain. I wanted to pen down an idea I had in mind because I’m pretty sure that idea will change after I read the book. The idea calls for a slightly windy introduction, which is the only kind of introduction I like (when I make it, that is).

Like most things in life, extroversion, if we could quantify it, is likely to make a bell-curve distribution. So would IQ or other measures of academic intelligence. Or kinesthetic intelligence, for that matter. Those lucky enough to be near the top end of any of these distributions are likely to be successful, unless they mistake their favoured curve to be something else. I mean, just because you are pretty smart academically doesn’t mean that you can play a good game of tennis. Similarly, your position on the introvert bell curve has no bearing on your other abilities. Whether you are an introvert or an extrovert, you will be badly and equally beaten if you try to play Federer — a fact perhaps more obvious to introverts than extroverts. Therein lies the rub. Extroverts enjoy a level of social acceptance that makes them feel as though they can succeed in anything, just like a typical MBA feels that they can manage anything despite a total lack of domain knowledge. That misplaced confidence, when combined with a loud assertiveness hallmark of extroversion, may translate into a success and make for a self fulfilling prophesy.

That is the state of affairs. I don’t want to rant against it although I don’t like it. And I wouldn’t, because I estimate that I would fall about one sigma below the mean on the extroversion curve. I think of it this way: say you go and join a local tennis club. The players are all better than you; they all have better kinesthetic intelligence than you can muster. Do you sit around complaining that the game or the club is unfair? No. What you would have to do is to find another club or a bunch of friends more at your level, or find another game. The situation is similar in the case of extroversion. Extroverts are, by definition, social and gregarious people. They like society. Society is their club. And society likes them back because it is a collection of extroverts. So there is social acceptance for extroversion. This is a self-fueling positive feedback cycle.

So, if you are introvert, and you are seeking societal approval or other associated glories, you are playing a wrong game. I guess Susan Cain will make the rest of it pretty clear. And I will get back to this topic after I finish the book. I just wanted to pen down my thoughts on the obvious feature of the society that it is social in nature (duh!), and therefore extrovert-friendly. I think this obviousness is lost on some of us introverts who cry foul at the status quo.

To get back to the suspicion of sour-grapishness, I know that I also would like to have some level of social approbation. Otherwise I wouldn’t want to write up these thoughts and publish it, hoping that my friends would hit the “Like” button, would I? This is perhaps understandable — I’m not at the rock bottom of the extroversion distribution, and I do have some extrovert urges. I’m only about a sigma or so below the mean, (and, as a compensation, perhaps a couple of sigmas above the mean in the academic scale.)

Bernard ShawMy wife, on the other hand, is a couple of sigmas above the mean on the extroversion department, and, not surprisingly, a very successful business woman. I always felt that it would be swell if our kids inherited my position on the academic curve, and her position in the people-skills curve. But it could have backfired, as the exchange between George Bernard Shaw and a beautiful actress illustrates. As the story goes, Mrs Campbell (for whom Shaw wrote the part of Eliza Dolittle in Pygmalion) suggested to him that they should have a child so that it would inherit his brains and her beauty to which Shaw replied: “My dear lady, have you considered that it might inherit my beauty and your brains?”

The Unreal Universe

We know that our universe is a bit unreal. The stars we see in the night sky, for instance, are not really there. They may have moved or even died by the time we get to see them. It takes light time to travel from the distant stars and galaxies to reach us. We know of this delay. The sun that we see now is already eight minutes old by the time we see it, which is not a big deal. If we want to know what is going on at the sun right now, all we have to do is to wait for eight minutes. Nonetheless, we do have to “correct” for the delay in our perception due to the finite speed of light before we can trust what we see.

Now, this effect raises an interesting question — what is the “real” thing that we see? If seeing is believing, the stuff that we see should be the real thing. Then again, we know of the light travel time effect. So we should correct what we see before believing it. What then does “seeing” mean? When we say we see something, what do we really mean?

Seeing involves light, obviously. It is the finite (albeit very high) speed of light influences and distorts the way we see things, like the delay in seeing objects like stars. What is surprising (and seldom highlighted) is that when it comes to seeing moving objects, we cannot back-calculate the same way we take out the delay in seeing the sun. If we see a celestial body moving at an improbably high speed, we cannot figure out how fast and in what direction it is “really” moving without making further assumptions. One way of handling this difficulty is to ascribe the distortions in our perception to the fundamental properties of the arena of physics — space and time. Another course of action is to accept the disconnection between our perception and the underlying “reality” and deal with it in some way.

This disconnect between what we see and what is out there is not unknown to many philosophical schools of thought. Phenomenalism, for instance, holds the view that space and time are not objective realities. They are merely the medium of our perception. All the phenomena that happen in space and time are merely bundles of our perception. In other words, space and time are cognitive constructs arising from perception. Thus, all the physical properties that we ascribe to space and time can only apply to the phenomenal reality (the reality as we sense it). The noumenal reality (which holds the physical causes of our perception), by contrast, remains beyond our cognitive reach.

One, almost accidental, difficulty in redefining the effects of the finite speed of light as the properties of space and time is that any effect that we do understand gets instantly relegated to the realm of optical illusions. For instance, the eight-minute delay in seeing the sun, because we can readily understand it and disassociate it from our perception using simple arithmetic, is considered a mere optical illusion. However, the distortions in our perception of fast moving objects, although originating from the same source are considered a property of space and time because they are more complex. At some point, we have to come to terms with the fact that when it comes to seeing the universe, there is no such thing as an optical illusion, which is probably what Goethe pointed out when he said, “Optical illusion is optical truth.”

More about The Unreal UniverseThe distinction (or lack thereof) between optical illusion and truth is one of the oldest debates in philosophy. After all, it is about the distinction between knowledge and reality. Knowledge is considered our view about something that, in reality, is “actually the case.” In other words, knowledge is a reflection, or a mental image of something external. In this picture, the external reality goes through a process of becoming our knowledge, which includes perception, cognitive activities, and the exercise of pure reason. This is the picture that physics has come to accept. While acknowledging that our perception may be imperfect, physics assumes that we can get closer and closer to the external reality through increasingly finer experimentation, and, more importantly, through better theorization. The Special and General Theories of Relativity are examples of brilliant applications of this view of reality where simple physical principles are relentlessly pursued using the formidable machine of pure reason to their logically inevitable conclusions.

But there is another, competing view of knowledge and reality that has been around for a long time. This is the view that regards perceived reality as an internal cognitive representation of our sensory inputs. In this view, knowledge and perceived reality are both internal cognitive constructs, although we have come to think of them as separate. What is external is not the reality as we perceive it, but an unknowable entity giving rise to the physical causes behind sensory inputs. In this school of thought, we build our reality in two, often overlapping, steps. The first step consists of the process of sensing, and the second one is that of cognitive and logical reasoning. We can apply this view of reality and knowledge to science, but in order do so, we have to guess the nature of the absolute reality, unknowable as it is.

The ramifications of these two different philosophical stances described above are tremendous. Since modern physics has embraced a non-phenomenalistic view of space and time, it finds itself at odds with that branch of philosophy. This chasm between philosophy and physics has grown to such a degree that the Nobel prize winning physicist, Steven Weinberg, wondered (in his book “Dreams of a Final Theory”) why the contribution from philosophy to physics have been so surprisingly small. It also prompts philosophers to make statements like, “Whether ‘noumenal reality causes phenomenal reality’ or whether ‘noumenal reality is independent of our sensing it’ or whether ‘we sense noumenal reality,’ the problem remains that the concept of noumenal reality is a totally redundant concept for the analysis of science.”

From the perspective of cognitive neuroscience, everything we see, sense, feel and think is the result of the neuronal interconnections in our brain and the tiny electrical signals in them. This view must be right. What else is there? All our thoughts and worries, knowledge and beliefs, ego and reality, life and death — everything is merely neuronal firings in the one and half kilograms of gooey, grey material that we call our brain. There is nothing else. Nothing!

In fact, this view of reality in neuroscience is an exact echo of phenomenalism, which considers everything a bundle of perception or mental constructs. Space and time are also cognitive constructs in our brain, like everything else. They are mental pictures our brains concoct out of the sensory inputs that our senses receive. Generated from our sensory perception and fabricated by our cognitive process, the space-time continuum is the arena of physics. Of all our senses, sight is by far the dominant one. The sensory input to sight is light. In a space created by the brain out of the light falling on our retinas (or on the photo sensors of the Hubble telescope), is it a surprise that nothing can travel faster than light?

This philosophical stance is the basis of my book, The Unreal Universe, which explores the common threads binding physics and philosophy. Such philosophical musings usually get a bad rap from us physicists. To physicists, philosophy is an entirely different field, another silo of knowledge, which holds no relevance to their endeavors. We need to change this belief and appreciate the overlap among different knowledge silos. It is in this overlap that we can expect to find great breakthroughs in human thought.

The twist to this story of light and reality is that we seem to have known all this for a long time. Classical philosophical schools seem to have thought along lines very similar to Einstein’s reasonings. The role of light in creating our reality or universe is at the heart of Western religious thinking. A universe devoid of light is not simply a world where you have switched off the lights. It is indeed a universe devoid of itself, a universe that doesn’t exist. It is in this context that we have to understand the wisdom behind the statement that “the earth was without form, and void” until God caused light to be, by saying “Let there be light.”

The Quran also says, “Allah is the light of the heavens and the earth,” which is mirrored in one of the ancient Hindu writings: “Lead me from darkness to light, lead me from the unreal to the real.” The role of light in taking us from the unreal void (the nothingness) to a reality was indeed understood for a long, long time. Is it possible that the ancient saints and prophets knew things that we are only now beginning to uncover with all our supposed advances in knowledge?

I know I may be rushing in where angels fear to tread, for reinterpreting the scriptures is a dangerous game. Such alien interpretations are seldom welcome in the theological circles. But I seek refuge in the fact that I am looking for concurrence in the metaphysical views of spiritual philosophies, without diminishing their mystical and theological value.

The parallels between the noumenal-phenomenal distinction in phenomenalism and the Brahman-Maya distinction in Advaita are hard to ignore. This time-tested wisdom on the nature of reality from the repertoire of spirituality is now being reinvented in modern neuroscience, which treats reality as a cognitive representation created by the brain. The brain uses the sensory inputs, memory, consciousness, and even language as ingredients in concocting our sense of reality. This view of reality, however, is something physics is yet to come to terms with. But to the extent that its arena (space and time) is a part of reality, physics is not immune to philosophy.

As we push the boundaries of our knowledge further and further, we are beginning to discover hitherto unsuspected and often surprising interconnections between different branches of human efforts. In the final analysis, how can the diverse domains of our knowledge be independent of each other when all our knowledge resides in our brain? Knowledge is a cognitive representation of our experiences. But then, so is reality; it is a cognitive representation of our sensory inputs. It is a fallacy to think that knowledge is our internal representation of an external reality, and therefore distinct from it. Knowledge and reality are both internal cognitive constructs, although we have come to think of them as separate.

Recognizing and making use of the interconnections among the different domains of human endeavor may be the catalyst for the next breakthrough in our collective wisdom that we have been waiting for.