Decision Options: The Art and Science of Making Decisions (Chapman & Hall/CRC Finance)

Decision Options: The Art and Science of Making Decisions

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Toggle navigation Additional Book Information. Description Table of Contents Author s Bio. Summary Although uncertainty and flexibility are important attributes that drive the value of an investment, they are seldom systematically considered in traditional financial analysis.

Request an e-inspection copy. A Practical Guide, Second Edition. Chapter 3 illustrates how decisions can be viewed as a combination of three fundamental entities: Chapter 4 describes in detail what risks can be characterized as private risks, how they relate to decisions, and how they can be captured and represented in decision problems.

Chapter 5 explores market risks and the use of stochastic processes to represent them in decision problems. Chapter 6 contains an introduction to self-standing options such as financial options and options pricing theory. This chapter touches on risk-neutral pricing of options and the assumptions underlying financial options pricing. It also provides a brief introduction to the Black-Scholes equation, a closedform solution for single-standing options when the underlying asset follows a specific price process called random walk. Chapter 7 describes how decision options are different from single-standing financial options and why techniques of financial options pricing do not directly transfer to the decision options world.

An introduction to a software tool, Decision Options Technology DoT , is provided in illustrating how to formulate, model, and solve simple decision options problems. Chapter 8 deals with a special case of decision options in pricing employee stock options, something that has characteristics of both financial and decision options. Chapters 9—12 deal with case studies of practical applications of decision options. We look at applications in different industries such as pharmaceuticals, energy, manufacturing, and financial services.

Chapter 9 deals with life sciences, including pharmaceuticals, biotechnology, and devices. This is followed by technology and manufacturing examples in Chapter Chapter 11 provides examples from the different world of commodities, including publicly traded metals and energy. Finally, Chapter 12 provides an assortment of cases in financial services including venture capital, incentives, and hedging using financial instruments. In Chapter 13, some of the common misperceptions that exist around the term real options and the use of it in decision making are discussed.

Chapter 14 deals with the impediments that currently exist in large companies for the systematic practice of decision options. These include academic challenges as well as many practical issues that one has to overcome in the quest to move organizations to consider uncertainty and flexibility in decisions. Finally in Chapter 15, I provide a qualitative assessment of how organizations have changed and what they have to focus on to become successful in the future.

Sometimes we make them without much thought, and sometimes we spend a lot of time analyzing. For example, the decisions made by a batter in a baseball game are taken rapidly, possibly based on information on the pitch as well as other information such as the style of the pitcher, weather conditions, and the state of the game. On the other hand, the decision made by Intel to site a new manufacturing facility in South Korea was taken after considerable analysis.

Such an analysis may have considered market conditions, price and margin expectations, cost characteristics, currency expectations, country risk, technology trends, as well as the possible actions of the competitors and the governments. Some of this information is not precise; some is no more than a guess.

But, imprecise information is always better than no information, and decisions based on imprecise information are better than random ones. This led us to analyses that need deterministic and precise data—a single number to represent an input. However, outside the corporate environment, many are more comfortable using imprecise information in decision making.

For example, we almost always make decisions based on imprecise information in our personal lives. The decision to carry an umbrella on a trip is a decision based on available but imprecise information. There are big decisions, such as the decision to move from one job to another, the decision to buy a home, the decision to return to school, the decision to get married, the decision to have children, and the like. There are also less significant decisions such as what to have for dinner, when to exercise, and whether to travel by air or by car. In all cases, we are given some imprecise information, some based on past experience and some based on forecasts.

The information gives us an assessment of various types of risks, and it may present us with choices or options. We may intuitively weigh the risks in various choices or perform a valuation of the choices considering costs, benefits, and risks to pick the best possible one. We may pick a choice that appears to be of the highest value and in some cases to be of the lowest risk.

If there is not enough time to employ a systematic decision process, as in the case of imminent danger, we may fall back on our 3 4 Decision Options: The Art and Science of Making Decisions instincts, a sort of autopilot that takes over decision making, mostly driven by past experiences. We all have differing decision processes to take imprecise information and reach conclusions.

There are many different types of intuitive decision makers also; for some, intuition is based on experience accumulated from the better or worse outcomes of decisions made in the past in similar situations. For others, intuition may be merely a feeling uncluttered by experience or historical data. There are many different types of analytical decision makers as well; some may reduce imprecise data to more deterministic figures and use them to reach conclusions.

For others, imprecise information allows a probabilistic understanding, and decisions are made by minimizing risk or maximizing value. Some of these analyses are not necessarily mathematical but may depend on other senses, such as decisions made from visualization of the imprecise information. Such decisions may depend on pattern finding and comparing such patterns to past experiences.

The brain is a complex and fascinating machine, able to digest and visualize imprecise information, guess optimally, and conclude fast. The competency in decision making may have deep roots in human evolution. The ability to make good decisions would have been a very important survival skill: Those who had a slightly higher probability of getting this right than wrong survived. Over the ages, the selection process may have resulted in certain types of decision processes dominating others. We cannot, however, assume that the decision processes most employed today are the best for two important reasons.

First, selection acts on a bundle of characteristics exhibited by an individual; thus, any single competency cannot be selected optimally. Second, the environment and the availability and the type of information that existed tens of thousands of years ago were substantially different from today. So, all we can assume is that how our brains process information and reach conclusions may have an evolutionary base, but what we did may not have been optimal then and is likely not optimal now.

One of the more difficult decisions for us is making judgments about another human being, whether it is in hiring, marriage, or in court. Although many may not admit it, there are certainly emotions generated by visual images such as the shape of the skull or face that get tangled up in other imprecise information as we make decisions. Pattern matching may be at Decisions, Options, and Decision Options 5 work here, perhaps based on recent experiences or based on evolutionary empiricism.

The genealogy of decision processes is an important area for further research. Recent research shows that our genes predict who is likely to go to the voting booth and who may not. One cannot predict the candidate they may vote for from the genes, but the likelihood of the act of voting can be predicted. This indicates a possible hardwiring in our brains regarding decisions and the act of decision making. Similarly, people from different locations and cultures may reach differing decisions when presented with the same data. Although the effect of training and experience human software cannot be removed in such experiments, the notion of the hardware design of the brain as an important aspect of decision process cannot be rejected.

Now, let us think about decisions systematically and create a nomenclature that I consistently follow through the book. A decision is a conclusion reached or action taken regarding a present or future event based on past, present, or forecasted information. This definition has the following implications: There are no decisions in the past.

Decisions already taken are not decisions we consider in this context. As defined in finance, decisions in the past are like sunk costs. They may have an impact on the decision at hand only through the information they generated in the past. Decisions may use past and present information that is observed. Decisions may also use forecasted information as necessary. Decisions may or may not result in an action. In all cases, decisions relate to a conclusion reached from the information used.

There are no qualifications on the type of information used in reaching decisions. Information can be imprecise, historical or forecasted, based on theory or empiricism, and based on data or opinions. Let us look at some examples. Consider the decision by an individual investor to buy a stock in the market. The decision here is to buy or not to buy; this is a binary decision.

It has two defined and opposite actions. This decision is not directly affected by a past decision to buy or not to buy the same stock or other stocks. This decision is taken today, and past decisions are not relevant. However, the information created from a past decision can influence the present decision.

One example of this will be the risk aversion of the investor. If the investor lost money buying 6 Decision Options: The Art and Science of Making Decisions the same stock in the past, it may bias the current decision toward not to buy. The more relevant information for the current decision will be the financials of the company as well as future forecasts of the prospects of the company, its industry, and the overall economy. If you are a strict believer of the efficient market hypothesis which proposes that the current price already reflects all known information , one could argue that the financials of the company do not matter as the current price already reflects such information.

In this context, forecasted information requires further consideration. To forecast information, one has to follow a process. In most of the discussions here, I follow a normative process—one that is based on market expectations. Now consider a similar binary decision, a decision to get married or not to get married.

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The information considered here is complex, and most of the information may be related to the other person in the marriage scenario. In certain cultures, it can get more complex as the scope of information may need to include families, clans, and in some cases entire societies. In any case, the basic elements remain the same. This decision has characteristics of an option; it gives the holder the right but not the obligation to act, assuming that the person considered for marriage is willing since the counterparty also has an option to reject the proposal. Since this action cannot be immediately remedied such as selling the stock later after realizing it was issued by a bad company , the option to delay the decision is more important in this case.

If the information considered in reaching the decision is highly imprecise or shows a lot of variability , it may be intuitive that the right to delay the action is more valuable. The higher the variability in the information used to make the decision, the higher the chance that the decision will be delayed. In this case, the future profits may include the value of utility gained from companionship, availability of future options such as the ability to have children, as well as the incremental economic value gained by living together, which may include scale advantages in the purchase, production, and consumption of food, housing, and transportation as well as tax advantages such as filing a joint tax return.

Both parties may continue the relationship longer in an attempt to gather more information and thus reduce uncertainty. If the marriage option is perpetual does not have an expiry , it may never be exercised. However, the longer one waits, the lower the benefits. As both parties age, some of the expected benefits of the marriage may no longer exist; thus, there is a trade-off between the delay in exercise and the loss in benefits, somewhat like a call option on a stock that pays large dividends and thus loses value.

To take this one more step, briefly consider the decision to commit suicide.

Alternately, if the present value of the future expectations of the individual is zero or negative, exercise of the put option may be optimal. As expected, most people delay the exercise of this put option as long as possible. In , Fischer Black and Myron Scholes, then at the University of Chicago, discovered a closed-form solution to price financial stock options.

A financial call option called a derivative on a stock gives its owner the right but not an obligation to buy one share of the stock the underlying at a prescribed future time expiry for a preset price strike price. The holder of the option will exercise the option at expiry only if the stock price at that time is higher than the strike price.

The stochastic processes, including the GBM, are investigated in Chapter 5, and how the fundamental arguments in options theory are derived is shown. It is, however, important to note that the closed-form solutions, such as the Black-Scholes equation, are valid only under certain conditions, such as a single option on a stock, the price of which follows the GBM stochastic process. In most decisions, we have many options that interact with each other, and it is impossible to derive an elegant closed-form solution.

Often, the stochastic processes that drive the prices of the underlying assets may not follow the highly mathematically tractable GBM. A good example of this is the price of commodities such as oil. The price of oil is affected by both demand and supply, which are both dependent on the price itself. As the price of oil goes up, demand declines aided by conservation and higher efficiency , and supply increases as more production capacity comes on line enticed by the higher price. These tend to drive the prices down to a longrun equilibrium level.

If the price of oil goes down, the opposite happens, driving prices back up. This type of a price process is called the mean reverting process MRV , and an option based on this type of asset cannot be solved by the original Black-Scholes equation. There are two types of simple options: The American type can be exercised any time before expiry, and the European type can only be exercised on the day of expiry. Options present in decisions can be both types, and it is generally not optimal to exercise an option before expiry.

One category of options typically exercised earlier than expiry is employee stock options ESOs given by companies to key employees as an incentive for performance. The idea is that ESOs are typically issued with a strike price equal to the current price of the stock and with an expiry date and vesting date in the future.

The employee is free to exercise the option after it vests and before it expires. If employees have higher productivity and make 8 Decision Options: The Art and Science of Making Decisions better decisions, the stock price of the company goes up, and the employees profit from the exercise of the ESOs given to them by the company.

Since ESOs behave just like exchange-traded options, it is not optimal to exercise them before expiry if one can sell them. Since employees, typically, cannot sell ESOs, they may be forced to suboptimally exercise them prior to expiry. The Financial Accounting Standards Board FASB recently made an accounting change that requires companies to expense these options in their financial statements. ESO valuation is described in detail in Chapter 8, taking into account past employee behavior, such as early exercise, as a function of stock price or time after award. For the past decade, a debate has been raging among finance practitioners involved in investment decision making.

A few have been advocating something called real options as an alternative to the more conventional and well-known discounted cash flow DCF techniques, both for valuing investment opportunities and for choosing among such opportunities when faced with budget constraints.

The term real options was coined in the late s to describe decisions on real assets that show option-like characteristics. Since these are options on real assets as opposed to financial assets , the term seemed appropriate. As shown in the many cases discussed in this book, real options represent a way of thinking about corporate strategy, and if appropriate, it is a technique for quantifying the value of corporate assets and strategies. Stylized and academic applications of real options, however, are not sufficient to make faster and better decisions in companies.

Real options have to be removed from the shackles of academics and brought to the enterprise in an understandable format that is able to solve real problems. The managers responsible for the decision did not call the method real options, but they clearly understood the value of postponing certain aspects of the manufacturing process, such as product customization, and thus keep the options alive.

They decided to delay the decision to customize. The product in this case was an inkjet printer in a box, including manuals, power cords, and all the other accessories. Once a printer had been put in a Japanese box with a Japanese manual and a V power supply, it could be sold only to customers in Japan; at that point HP had lost the value of any postponement options as the company has already exercised those options. The alternative approach, which HP began to put in place in the early s, was to standardize parts and assemble them in stages, reaching higher and higher levels of customization only as they approached the eventual sale.

Such progressive customization had the benefit of keeping the postponement Decisions, Options, and Decision Options 9 option alive until it was optimal to exercise while increasing standardization upstream, which provided scale advantages in manufacturing. Another example of real options is a software company that was developing software to help customize products in the computer hardware industry. This platform product could then be customized as appropriate to the needs of the ultimate customer. This decision, although somewhat delaying the market entry of the new software, significantly broadened its market potential.

A third example of real options at work can be seen at a large pharmaceutical company that was considering acquiring a prototype molecule from a biotechnology company. Since these transactions are quite common in the biotechnology industry, one would assume that similar questions had been asked and answered numerous times in the past, and that formal valuation techniques had been developed as a result.

This was not the case. They are something like this: Pay as little as possible. Try to avoid royalties and give cash up front, if possible. The problem with such rules, however, is their failure to provide any clear guidance regarding what a reasonable price might be, especially when no precedents can be established.

It is also possible that a particular rule of thumb such as avoiding royalties resulted from a single bad experience in the past and is not a reliable guide for future decisions. But, this result contradicted the intuition of the decision makers. The Art and Science of Making Decisions How could a product that showed such promise and had no legal issues have a negative value? Baffled by this result, they looked for answers but could not establish a normative process fast enough to help the transaction.

So, they fell back on what is typically done in the industry: This means that the deal terms are based on past transactions either within the company or with competitors. This is hard to do as new inventions in this industry are unique and may not have allowed usable proxies in the past. Decisions with embedded options as described can get quite complex. It is a long and daunting process, taking over a decade for an idea eventually to make it to a marketed drug after hundreds of millions of dollars in expenses. Typically, such programs are conducted in stages, often called phases.

In each phase or stage , a variety of decisions has to be made related to the manufacturing of the new chemical entity the prototype chemical tried as a new drug , testing on animals and humans, and eventually filing for an approval from the Food and Drug Administration FDA. In each phase, such decisions will result in an investment outlay. Actions taken in accordance with the decisions also result in new information. As new information arrives, we may want to change or rethink our strategy, and future decisions will be clearly be affected by the information revealed in past actions.

Similarly, the experiments in animal models and occasionally in human models may have shown unacceptable toxic effects that may preclude the approval and marketing of the drug, and this may force abandonment of the program. There are also other types of less drastic changes that may need to be incorporated into the program in light of new information, such as redoing or redesigning experiments, delaying certain future experiments, and if we are lucky, accelerating the program to market. It is important to note that almost always the information we have is imprecise. So, often it is not precise data gained but rather a probability distribution of possible future events.

In some cases, experiments are designed to narrow the uncertainty—tighten the distribution of probabilities. At other times, experiments may be conducted to tease out information that allows the drug to be designed to increase its scope and thus reduce uncertainty. It may not be intuitively clear why someone will design a prototype to increase uncertainty in future outcomes.

Uncertainty is valuable for options. The more uncertain the outcome, the more valuable options are. This may be counterintuitive to those who have gotten accustomed to choosing investments to always reduce uncertainty. This may be a good way to manage if there are Decisions, Options, and Decision Options 11 no future investment decisions with options characteristics related to the present one.

But, in the presence of future decision flexibility, uncertainty can be good. The following common themes are investigated in detail in other chapters: Most information gained is not precise. Information may be in the shape of a probability distribution of future events. Not all experiments are designed to decrease uncertainty, and sometimes reducing uncertainty is not necessarily optimal.

We want to get information that reduces intrinsic uncertainty, but we may also buy information to allow us to design a prototype to increase uncertainty in future outcomes. I now expand the nomenclature with the introduction of some new items. I started with the definition of decisions in this framework and then introduced options as part of that framework.

Uncertainty risk is unbundled into private risks and market risks. The process also has private risks, such as the risk of losing a specific chip design patent the company holds in a lawsuit. The private risks unique risks or unsystematic risks can be diversified away by holding a large number of companies in a portfolio.

Intuitively, we can understand that the private events to which any specific company is exposed are not correlated to other companies; thus if there are many different companies in a portfolio, the shock of a private event can be diluted and, in the extreme, completely diversified as there may be positive and negative shocks.

As such, the private risks will not be priced by the market, that is no one will pay a premium to assume private risks as they can be costlessly diversified in a well-functioning and broad stock market. The market risk systematic risk , however, is correlated with the overall economy and thus cannot be diversified by having a large number of differing companies in the portfolio.

For example, recession in the U. In the decision options framework, private risks and market risks are treated differently just as in traditional frameworks such as the single-factor 12 Decision Options: Even though most companies practice a version of CAPM in discounting cash flows to determine the NPV of investments, it is generally applied in an ad hoc fashion without a clear delineation of private and market risks.

In CAPM, systematic risks are captured in the discount rates and private risks in probability adjustments to the cash flows in the numerator. Often, in the practice of DCF analysis both private and markets risks are bundled into one with arbitrary adjustment to the discount rate to account for both together. In many cases, an internal rate of return IRR is calculated from the cash flows and then compared against a threshold level. An IRR generally incorporates private and market risks into a single number and is thus inconsistent with traditional theories such as the CAPM. This can be made more personal by considering a real-life situation of a decision options problem with private risks embedded.

Suppose you would like to buy a piece of land and construct an office building to rent to others. The decision to be made is how much to pay for the land today. Perhaps two options can be seen: In the absence of any additional data, this decision options problem can be solved as two sequential and interacting options driven by market processes such as real estate prices and rental rates in the area.

But, it is made more interesting by introducing a private risk. An example of such a risk would be the probability of a zoning change. The land is currently zoned commercial, and there is a chance, say in 2 years, that the zoning will be changed to residential. Also assume that the land acquisition decision must be made in 1 year the first option and the construction decision in 3 years second option. Between these two options, there is now a private risk—the risk of a zoning change—that is not associated with market processes such as real estate prices and rental rates.

The chance of a zoning change is really at the whim of the county regulators, who are notoriously fickle and make decisions uncorrelated with the broad market. This decision option problem has two options connected by a private risk. For those who seek pain, additional private risks can be imagined, such as the risk of unearthing hazardous materials while excavating the land for the foundation of the office building. Such a risk is also private and is unrelated to the market. Some components of decisions can be represented as swaps or options to swap.

For example, consider an electricity generation plant. First, consider a base load plant, a plant that operates continuously to provide a base level of power to the grid. The demand for power will always be greater than what is provided by the base load plants, and they have to operate without stoppage. The plant is fired with oil and produces electricity that is supplied to the grid. If the price of electricity is greater than the cost of fuel and maintenance, the plant operator makes a profit. If not, the plant will run at a loss.

Such a plant can be represented as a swap between electricity and fuel. If profits and losses every hour are examined, the transaction will be an hourly swap during the time of operation until the next expected outage. If the Decisions, Options, and Decision Options 13 price of electricity is higher than the cost of operation including fuel and personnel costs , the plant will make money. Otherwise, it will lose money. We can also think of a peaking plant: Also assume that this plant was designed to be flexible; this plant can be turned on or off at will.

Now there is a very interesting decision options problem. The plant operator can chose from gas or oil to fire the plant the operator may use the cheaper fuel and decide to operate or shut down, depending on the price of electricity. Electricity is a unique commodity that does not yet allow a very efficient storage mechanism. This means that electricity needs to be used once produced or is only produced when there is a demand. This makes the electric grid a dynamic place where the price can wildly fluctuate. One does not want a power failure in the middle of a heart transplant operation, so if the demand is there and the supply is limited, price will climb.

This is a custom-tailored situation for the operator of the dual-fuel flexible plant. Every hour the operator considers prevailing prices and decides to start the plant or shut down. If plant will be operated, a decision on which fuel to use also needs to be made. In many cases, there may be contracts and fuel hedges in place that maximize the overall profits of the company that owns the plant. This may make the plant operating decision a very complex one. In any case, the plant can be visualized as a series of options to swap between electricity prices and the lowest-cost fuel prices.

The decision options framework allows a variety of such derivative instruments to interact in a complex decision process. Now you are ready to explore the decision options framework—a framework that allows better decisions to be reached systematically, to represent all components of a decision e.

Always remember the two important ingredients of a decision options problem: When uncertainty and flexibility are present, decisions will have options like characteristics, and traditional frameworks that ignore one or both fail to provide reasonable answers. The utility of a generalized framework that always considers uncertainty and flexibility will become obvious as we take this journey together. These three components form a decision options framework.

We are only concerned about the future, and any decisions or occurrences in the past have no impact on future decisions. Each of these components has distinctly different characteristics. A predetermined outcome is something that is expected with certainty in the future. As many know, death and taxes fit this criterion well. Although there are no future events with a probability of 1. In the decision options framework, we further generalize predetermined outcomes and the time of occurrences as not constants but probabilistic.

In this case, the cash inflow in the future and the time of inflow are both probabilistic. Chapter 4 treats probability distributions in detail; it will suffice to remember now that a probabilistic outcome can also be a predetermined outcome as long as the characteristics of the distribution are predetermined. In this complex contract, we predetermined that the cash inflow will occur at a time that is a sample from a lognormal function with predetermined characteristics average and standard deviation , and the amount of inflow is also predetermined as a sample from a normal distribution with predetermined characteristics average and standard deviation.

We assume that all contracts will be honored, and we live in a society with well established contract laws and property rights. An option is a right but not an obligation to take certain action in the future. The action can be taken at a predetermined time European option or anytime before a predetermined time American option. This is a simple and self-standing option with no interactions and can be solved with available closed-form solutions.

The third component of the decision options framework is risk. Risk can come in different flavors. The most basic type of risk is a probability that an event will happen. This is a binary event with a constant probability of occurring. In the traditional finance based on the capital asset pricing model CAPM , we identify two distinct types of risks: The other type of risk is called a private risk.

If the event is uncorrelated with the market such as the probability of rain tomorrow , it can be fully diversified away by combining a large number of uncorrelated events. For example, consider three uncorrelated events: If you set out to catch the train without an umbrella, the net effect of your getting drenched by the rain depends on the probability of all of the uncorrelated events. If it did not rain, you reach the train without any problem. If it rained but you secured a parking spot close to the station, you avoid an encounter with rain.

If it rained and there were no parking spots close to the station but you found a friend with an umbrella, you accomplish the mission. With the availability of these three uncorrelated events, the risk of being drenched by the rain is substantially reduced. This portfolio effect of the ability to diversify uncorrelated risk is at the heart of the traditional financial theory. In the decision options framework, private risks are incorporated in different ways.

Private risks can lead to a probabilistic outcome as discussed. They can also be incorporated into a time series as a sudden change a jump. In Decisions as Predetermined Outcomes, Options, and Risks 17 Chapter 5, stochastic functions time series such as random walk are detailed. A time series shows the value of a variable such as the Dow Jones Industrial Average over time. Readers may be familiar with time series charts shown in various business newspapers and on television.

They show how a price has changed over time. It is easy to draw prices from the past as we have already observed the prices , but forecasting them into the future is a different story. Although many pundits claim that they have perfect foresight predicting prices of stocks and commodities, the evidence for this is scant. The more important question is, so what? Did anybody really think that the rock and water in the most uninteresting corner of the Milky Way is unique?.

Exo-planets have tickled the fa. Biological entanglement 2 has been showing up in many places 1. Although conceptual without hard proof, it is symptomatic of the fact that there is something wrong with our understanding of the universe. There is ample evidence that the human brain represents a quantum field 3. And if so, using it as a conventional computer with logical processing is not optimum. It is as if humans strive to be rational in a universe without rationality.

That behavior is unambiguously irrational. A world, largely governed by people from the last generation, is suffering from discontinuities in policy heuristics.

1st Edition

A recent conversation 1 attempts to portray that media multitasking is a bad. What the contemporary researchers don't seem to understand is that the world has indeed changed in a step function fashion and anybody on the lower tier is at a distinct disadvantage to see beyond the horizon.

Education systems that relied on rote memorization in Asia have already suffered a major set. From inception, humans have been prone to dreaming, with and without full control of their faculties. Nightmares may have awakened them from short slumbers with distinct survival benefits in the African Savannah. Later, they dreamed of places beyond the horizon and kept walking to reach them. Over many generations, the journey took them to North Africa and into the Middle East. Later they will embark on separate one way trips to South Asia and China. Their dreams kept them going as if the world.

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Black boxes, Artificial Intelligence and FinTech - likely a deadly combination that is going to destroy a lot of wealth. We have seen this before - fresh graduates from business schools coupled with mathematicians and physicists, descending on Wall Street to make the world go around in the opposite direction. As they appear on TV after hours - mad and fast - to confuse and pillage small money, ascertaining where every stock is going and even the market, there are 20 million fat fingers across th.

Recent transit of cigar shaped Oumuamua that raised hopes of galactic panspermia 1 is a double edged sword. Hitching rides on stable objects over millennia appears plausible for robust life but the implications of such transference could be catastrophic for the blue planet. It is not the green men and women we have to worry about but deadly single cell organisms from another galaxy. If the space agency ever stops taking shots at everything near in an effort to prove life exists elsewhere, the.

The value of breadth. As the present regime shifts to one controlled by uncertainty and accelerating technology, the premium on breadth of knowledge compared to depth, continues to increase. Innovation appears to happen at the intersection of fields and not in secluded domains and that is an important issue that educational institutions need to consider as they design futuristic curriculums.

It is also highly problematic. As an example, business graduates tend to be broad and shallow and if the stated hy. Right brained Artificial Intelligence. Conventional AI appears to be largely driven by the left brain as engineers, data scientists, and technologists flock to the dream, ably assisted by capital, seeking returns somewhere. Generally speaking, that is a prescription for disaster as technology, data and mathematics do no. The fragmentation of knowledge. Philosophers of yesteryear have argued that knowledge emanates from integration and not fragmentation.

This is not surprising. For over half a million year. The value of time. Some run without destinations, others stay put without ambitions, some rely on unprovable heuristics, others create theories without the need for proof, some ignore inconvenient data, others create convenient data, some travel, others remain absolutely still, some cry, others laugh but none of these maximize utility within such ha. The game is rigged.

Recent leaders have advocated cultural purity and ethnic cleansing and close to a quarter of the world population now believe in these ideas. Time to wake up and face technology. A recent article 1 that notes that advancing technology initiated productive scientific regimes, speculates that Artificial Intelligence could be the next engineering innovation that speeds up biological and chemical advancements.

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