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Scientific Sense Podcast

Tuesday, October 15, 2019

Is rationality real?

In financial markets where standard and divisible instruments are traded, it has been shown that rational outcomes are more likely. Even though individuals act irrationally most of the time, the aggregation of individuals, markets in general, tend toward rational outcomes. It appears that this is unlikely to be true in real markets. In a recent experiment in the US, three entities - one from radio, one from TV and one from a powerful position, have been able to create irrational responses from a very large population - perhaps as much as 50 million. All the "broadcasters," had to do is to repeat incorrect information over and over again. This has broad implications for rationality, policy and the future of humanity.

Rationality is not real in non-financial markets. Humans tend to clump, perhaps an evolutionary trait that kept small clans together. Early in homo-sapien progression, identifying and protecting the clan was dominant. Although early humans used more sophisticated attributes, the modern variety seems to have fallen into using surface heuristics such as the color of the skin, eyes, and hair. The fundamental reason three loudspeakers could lead a large population down an irrational rabbit hole is that they used ideas from hundreds of thousands of years ago. This is not something the "intellectuals," understand. It is not that there aren't rational solutions to the problems we face but rather if such choices align with the human brain created much before modern times. 

Real markets cannot assume rationality. Anybody who assumes rationality exists and design campaigns around that is bound to fail.



Tuesday, October 8, 2019

Next wave of Artificial Intelligence

As Artificial Intelligence matures over half a century, we may be fast approaching the limits of independent developments in software and hardware. Consulting companies seem to have embraced "data science," an ill-conceived and confusing area. Hardware companies, pressured to sell Silicon at any cost, have been creating Pizza sized "smart boxes." and "cognitive networks.". Not to be left behind, companies that specialize in "IoT," things that are on the internet, have been struggling to define how they are different. All of these, aided by massive hype, will likely destroy shareholder value in many ways.

There are two important avenues to make progress in this area. First, the hype created by consulting companies has to be tempered - data scientists do not add value, they typically destroy it. R and Python do not automatically add any value if the users of these somewhat obsolete tools do not understand the problem they are trying to solve. Most of the "new math," has been around for many decades, it is just that fast and cheap computers now have made the incompetent look smart.

It is time to focus on the assimilation of hardware and software to move the field forward. Lack of a theory of consciousness automatically means that humans are better off abandoning the idea of "modeling," the brain. However, we could learn a lot from observing the brain - it is an efficient learning system that gets tired and ages over time. No machine based on conventional computing architecture exhibits these qualities. This means that it is futile to throw more Silicon to a foreign design in an effort to make it act like the brain. In other words, intelligence is never artificial.

Human intelligence, albeit impressive, cannot be the end game. The inability of individual specimens to form a network has substantially restricted their ability to advance. So, replicating the human brain in silicon is not a good idea both because contemporary designs do not allow consciousness and the lack of network capabilities disallow scaling.

It's time software and hardware came together to advance AI.

Sunday, October 6, 2019

Individual's optimization problem

A human has a relatively simple optimization problem. Each specimen is expected to be alive less than 30K days,  divided into 3 horizons. In the first 10K days, they rely on somebody else to survive and live. In the next 10K days, they swim on their own to accumulate resources to take care of themselves for the remaining 10K days. This is a relatively simple optimization problem but humans are not generally impressed by simple ideas and solutions. For most of the 8 billion, irrational thoughts govern, such as optimizing beauty, hair, ego, wealth, tenure and research papers. Most miss the cliff and fly off the handle.

The human appears to be unable to optimize, given harsh constraints. Most run and run but never reach their goals. Some kill and pillage in an effort to climb the hill only to get vertigo as they reach the cliff. Most miss the simple objective function they are given and try to redefine it. As science accepts ignorance to be prevalent, as religion begins to recognize crime does not pay, as governments and societies realize the costs of electing crooked leaders, it is important to keep the 30K horizon in mind. How have humans reached this position?

Advanced human societies from 100K years ago were significantly more advanced. Most were not impressed by the color of their skin, hair and eyes. Most wanted to explore out of their comfortable habitats. Most shared resources across clans and societies. Most wanted to advance on their own and not by making others retreat. Most laughed and stopped laughing when they saw somebody else cry. They were humans and it appears that the modern version is not.

What happened to humans? Where have they gone?


Sunday, September 29, 2019

Diseases of organizations

Over the last hundred thousand years, humans have been successful in the diagnosis, management, treatment and even the cure of physical diseases emanating from external sources and entities. However, they seem to have largely failed to understand diseases of an endemic origin or those affecting the Central Nervous System (CNS). The latter, underdiagnosed and overtreated, is likely responsible for significant loss of life and mind. As an example, humans lose 1 out of 10,000 to suicide every year. The problem is increasingly better understood in the medical profession but with few identifiable solutions. Life sciences companies, in a rush toward economics, have not put enough focus on broad solutions.

More importantly, we have to also recognize that organizations - countries, companies, religions, and institutions - also suffer from both physical and mental diseases. Physical diseases of organizations, largely understood by executives, consultants, and bankers, are well treated. But diseases of the mind and psyche of organizations are not something that is diagnosed or treated. This is likely more detrimental to the success of the firm for strategies and tactics focused on shareholder value, albeit necessary, are not sufficient. The meager attempt at defining such heuristics as culture has not had any measurable effects.

Early diagnosis of mental diseases is critical for the success of organizations (1). Lack of diversity is an early symptom in this regard. This is driven by a simple internal heuristic that maximizes replication. Driven to the extreme, an organization could seek a sterilized structure, devoid of new ideas. Recent developments in the executive branch of the US are symptomatic of a loss of perspective in a closed system. This can only lead to bad decision-making or worse. An organization without a moral and ethical construct is something that may have entered an advanced mental disease state.  Unlike humans, who could be intervened with chemicals, organizations cannot be pulled back, once there.

Leaders of large and complex systems may have to spend more time on the mental health of their organizations. History tells us that mental health is likely more important than the strategies and tactics leaders mostly focus on.


(1) https://www.amazon.com/Flexibility-Flexible-Companies-Uncertain-World-ebook/dp/B008KZ6T6Q/ref=sr_1_1?keywords=gill+eapen&qid=1569810218&sr=8-1

Tuesday, September 24, 2019

Policy and politics

As 8 billion identical human specimens churn across the blue planet, separated by idiotic leaders, religions, science, countries, wealth and ignorance, it is clear that we are heading to predesigned exits. As the speculation of a holographic universe and multiverse mount, it is sad that humans will likely exit before finding the truth. There is no policy questions for the elected, just politics. The system their forefathers handed down in good faith, failed them. Autocrats with no respect for the failing system, democracy, shall rule again. Some of them deriving power from the color of their skin and others by the lack of it, some asserting superiority by belief and others by the lack of it, some by perceived knowledge and others by the lack of it, some in the East and others on the opposite side, some by predicting catastrophe and others by simply drawing bubbles, some by attracting attention and others by mocking it.

It is clear that the human is an inferior life form and she was never expected to survive. It is a miracle that she persisted for hundred thousand years. With crude and simple objective functions borrowed from single cell organisms, this complex life form has been attempting to differentiate without luck. As the scientists ponder the Fermi paradox, they are missing a simpler idea - no extraterrestrial intelligence will ever be interested in making  "contact," with the crudest construct that simply maximizes entropy.

Policy is far fetched - politics is more attaiable. As the cycle continues in predictable 4 and 6 year frequencies, electing those with no concern for humanity, we have to accept what we deserve.



Saturday, September 21, 2019

Infinity and Zero

Humans have had difficulties with two most important concepts in knowledge forever - infinity and zero. But most of their contemporary theories end up in either of these extremes. The best they could do so far is to rename them - singularity and all. Physics, apparently the foundation of it all, dies in the "singularity," not to mention the unknown 94%. Assigning undefined terms to an observation is not knowledge, it is fundamentally the definition of ignorance.

For over two thousand years, humans could not internalize the concept of zero. As they pile up PhD theses and Nobel prizes in ivy covered jails, they could not accept that they are ignorant. Spending billions on heavy steel to smash "particles," to prove the unprovable exist is not engineering, just ignorance. Cobbling strings together as if 10 dimensions are better than less is not knowledge, just pure ignorance. As they claim back holes apparently "radiate away," based on unprovable math, it is not knowledge, just speculation. As dark matter, energy and flow tickle the fancy of theorists and experimentalists alike, they have to understand that ignorance cannot be easily sugar coated.

Just as the contemporary politicians do not understand the emerging generation, those who seek tenures and publications do not understand that simple assertions driven by inexplicable math is not knowledge, it is just silly. If one needs an ever expanding particle zoo to "explain," the universe, or skills in naming the unknown and the unknowable, it is time to look back. There is no understanding Math without a coherent view of infinity and zero.

Humans, appear to progress backward in knowledge, ably aided by their "scientists" and "politicians."


Friday, September 6, 2019

The emerging Principal-Agent-Machine problem in the enterprise

Ever since the owners of organizations put agents in charge of operations, because of growing scale and perceived need for management specialization, shareholder value maximization has not been Pareto optimal for decision-makers. Much has been written and studied in this area with little effect on organizational structure, systems, and strategies (1). From the advent of computers, agents have been effective in claiming superiority over machines because of a lack of transparency for the owners. Although it is difficult to prove that machines possess superior decision-making capabilities in real companies and markets because of the lack of data from long and repeated experiments, it is clearly the case in financial markets.

With clear and consistent data in the financial markets, it has long been clear that financial intermediaries and traders have been destroying alpha, forever. With misguided and a confusing focus on "absolute returns," these agents have been successful in siphoning out wealth from owners in fees and expenses. An illustrious investment bank seems to have recently recognized that "trading," done by humans creates no value for its clients. Machines are infinitely better as they can act based on complete information without bias. Decision-making, thus, is better delegated to machines.

In real markets, this is equally applicable. Because of high diversity in types of decisions and long durations to outcomes, agents have long claimed superior capabilities compared to machines. This is true at all levels of organizations (1) and in every function. Since distributed owners are unable to understand the inner workings of complex organizations, agents simply claimed they are better without any contention. This has significant negative effects on the economy and its potential to grow. A structural change that culminates in the reassignment of human responsibilities in the enterprise may be afoot.

The emerging principal-agent-machine problem is real for modern organizations. Institutionalization of agents since the industrial revolution has run its course. Owners may finally have an opportunity to break this stalemate.

Wednesday, August 21, 2019

Decision-making is different from finding cats and dogs

As AI moves toward the peak of the hype cycle, it is important to recognize that decision-making in an enterprise is distinctly different from training machines to differentiate between cats and dogs. Most of the field is focused on deep neural networks, convoluted and otherwise, to recognize text, pictures, audio clips and patterns. This is certainly interesting but an extrapolation from these techniques to improving decision-making in the enterprise is fraught with danger. As companies find that enterprise productivity is inversely proportional to the number of data scientists they have on staff, reality is beginning to sink in.

As technology and consulting companies try to mop up the last remaining "data scientist," on Earth, it may be interesting to take a measurement of how enterprise productivity is related to them. Data science, an ill-defined field, has been the latest hype that led many companies down rabbit holes with very little to show. Although there are interesting developments in Artificial Intelligence - in robotics and autonomous equipment, much of these are better called expert systems as they do not learn from data but work on coded heuristics. The stars in the field do not prefer the old terms such as "expert systems," and "neural networks," as they believe they have reinvented mathematics. This is symptomatic of a field beginning to go off the rails as the investors have unrealistic expectations of the "second coming," of AI.

Let's not throw out the baby with the bath water. It is just that the baby has a lot of growing up to do. Decision-making takes a lot more than supervised or unsupervised machine learning. Educational institutions do a disservice to the next generation by blindly following the latest trends and spawning "analytics courses," for everybody. The question educators should ask is whether such programs are leading to people who can take advantage of the technology to enhance enterprise value. To do this, they have to first understand how value is created and that is a lot harder than cranking the supercomputer in the cloud.

Artificial Intelligence has a lot of potential, but not in the hands of those who believe it is about games, computers, deep mind and deeper mathematical techniques. The beauty of mathematics is that it is fully democratized. However, to add value to an organization, it has to be combined with many other attributes.