The economics of altruism – David Friedman – is an analysis of the”rational” behavior by an individual who may value the welfare of another. As it turns out, we may use economics to analyze the behavior – rational or otherwise – of an altruist and of the object of his/her altruism.

Before launching out on our main topic, altruism and economics, it will be worth while to assume that the effort required to learn to give (economist or not) is justified by the pleasure it offers. The conviction is born of experience; and indeed shared by many of the world’s wisest if not richest beings. From a theological point of view, we might begin by asking whether the universal desire for altruism serves a useful purpose. Hunger and thirst for example ensure our bodily survival. Fear also has survival value! But – to put the question insufficiently – what is “a” reason for altruism? What personal or evolutionary end is met by giving?
But, I digress.

I’ll let scholars of theology and philosophy ponder on those questions.

Needless to say, the effort to confine its practice to either objective or subjective standards may prove fruitless. It seems to be more accurate to regard it as an interaction between the mind and the welfare of others – not unlike the effect between the mind and an object or idea – which necessarily arouses emotion.

My own testimony is that altruism may provide comfort, charm, edification, and prowess.
John Richard Jefferies, an English poet/writer, wrote: “times when we’re absorbed by giving are times when we really live.”

Suffice it to say, I’m not complete if not I am also something of an altruist!

The main point I wish to emphasize here is not the satisfaction imparted to the altruist by his/her altruism, but rather the vision of those who may empathetically share (or call forth) their research to the importance of altruism. Is this not, effectively, what economists achieve on behalf of humankind – a widening and clarifying of interaction between object and mind?

Notwithstanding, there are those who assert that economists, in particular, care for oneself and oneself only – not unlike the “traditional” economic approach such that economists, like any rational being, make choices to maximize own welfare leaving much of the rest of the world if you will to ponder giving or care to others.

But, as we shall see in a moment, not all decisions maximize self-welfare, even including those made by traditional economists alike.

Bounded Rationality

A student who aspires to gain an insight into the philosophy of altruism should fairly fortify himself with a working hypothesis concerning altruism in a wider context – say with he help of modern views on the nature of behavioral economics as framed by Richard Thaler, Robert P. Gwinn distinguished professor of behavioral science, economics, and finance in the Graduate School of Business, the University of Chicago.
Fundamental to such a hypothesis, says Thaler, is the recognition that the altruist experience is also an emotional act, and not simply a rational “mental” act.
This basic notion substantiates the relevance of what has been called by some “the greatest discovery of the nineteenth century” – the so-called subconscious mind or personal unconscious – forgotten memories which, while they cannot be recalled at will, are still made manifest in dreams – Carl Jung.

The dipstick of rational – traditionalist – analysis cannot plumb hidden values.

Accordingly, traditional economics teaches that humans are rational actors who make decisions in ways that maximize “their” well-being; behavioral economics, on the other hand, relies on cognitive-psychology principles to relax those assumptions, teaching instead that humans have “bounded rationality”—a term coined in 1957 by economics Nobelist Herbert A. Simon —and so make biased  (or prejudiced) decisions that sometimes run “counter” to their best interests.
For example, voting against self-interest as a matter of ideological loyalty .

Most economists – traditionalist or otherwise – recognize that not all people are fully rational all of the time; and not all of the time indeed matters, says Thaler.
Yet, traditionalists aren’t willing to grant bounded rationality much weight in a market setting despite the number of rational acts not canceling the number of irrational acts – as traditionalists would otherwise have you believe. Behavioral economists, including Thaler, argue that “bounded rationality” does intersect the market’s so-called invisible hand. Economic conviction indeed may dictate that people act on behalf of their own objectives – own interests – but, as David Friedman also asserts, there is no reason why those objectives or convictions must be selfish (conscious or otherwise).
“Some of the things some of us value is the welfare of others.”

Arithmetic

In no uncertain terms, the so-called altruist, in deciding how much of his/her income to distribute, will have to divide the combined income between his self-interests and charity – be this “rational” or otherwise – on the condition that he/she may only give, not receive; in other words, the beneficiary may not end up with less than he/she starts.

For example, a $1000 decrease in either the altruist’s or the beneficiary’s income should add up such that there is now $1000 less to be divided between them.

As long as there exists situations in which the altruist chooses a transfer, changes in the combined income (altruist + beneficiary) will have equal effect with respect to consumption for both; regardless of changes in either the altruist’s or the beneficiary’s income.

Thus, the beneficiary, if he understands this analysis, will find it in his interest to pay equal attention to sustaining both their income.
In this respect, says Friedman, the beneficiary shall conclude acting rather as though he too were an altruist despite the indifference with regards to the altruist’s welfare.

Hence, it will suffice for the beneficiary to take action efficacious to net gains ( himself + the altruist); in exactly the same sense in which gains may occur in the context of “Marshall” efficiency (economic model in improvement which results in change in the aggregate making some people worse off and others better off).

Example:
Suppose we add all the gains and losses.

If their sum was a net gain, we would say that the change was a Marshall improvement. In particular, a change that benefits the altruist say by $100 but impacts the beneficiary by $75 will result in the altruist increasing her transfer to the beneficiary by no less than $75 but not more than $100.

Even an altruist has limits!

In other words,  a change that may impact the altruist by “x” dollars but benefits the receiver by “y”dollars will result in a reduction of the transfer in the range between “y” and “x” dollars. This result is easily proven graphically in the simple two-dimensional case where all changes are nominal; the proof in the more general case, where the loss might be any number of things – such as a wallet, broken handle, or even a broken heart – is similar but more complicated — see Friedman.

Conclusion

If the beneficiary is willing to impact (negatively) the altruist – as would he according to behavioral economics – then the altruist will punish him by reducing the transfer; so the beneficiary should find it in his interest not to offend his benefactor. But, as “bounded rationality” shows, the beneficiary is likely to make decisions “countering” his own interests. On the other hand, the altruist may not know who (or what) is responsible for the change. Exactly the same thing will happen in the case of a change produced by some third party, or environment.

If the change is a Marshall improvement both beneficiary and altruist end up better off after the change. If it is a worsening, both may in fact end with less.

In either case, the “traditionalists” fall prey to “bounded rationality.”

Good reading,

razmik b. ekmekdjian
author/managing editor

1. INTRODUCTION

“Probability, “as Bishop Butler remarked in the 18^th century, “is the [best] guide to life.” None of us can accurately infer what the future holds. We make most choices, decisions, and plans as best we can in the face of incomplete knowledge – about what the world will be like and what others will do. Probabilities play a crucial role in most of these decisions – given we often find ourselves in a position to judge the probabilities of different outcomes before making decisions. For example, we may pursue legal practice because we believe that what we learn and offer in jurisprudence will probably enhance the quality of not only our lives but also of society at large.
However, the probability of some outcome may occur isn’t the only consideration on which we shall base our decision. We shall also be concerned with “costs” and “benefits.” Now as we focus our attention to exercising our favorite Alternative Dispute Resolution – arbitration – our competitive edge discovers a rational outlet: critiquing claimants and lawyers who are involved in the game we love.

2. ARBITRATION AND DECISION THEORY

In Arbitration – said to provide a relatively cost-efficient and timely alternative to the demands and downsides of trial – a neutral person (or persons) called an “arbitrator” hears arguments including, of course, evidence from each side in an attempt to decide the outcome of the dispute. Arbitration is generally perceived less formal than a trial, and, therefore the rules of evidence are likely to be relaxed. In the state of California arbitration may be either “binding” or “nonbinding” but not both; i.e., the results shall be considered mutually exclusive. Binding arbitration: contexts in which the parties agree to waive their right to a trial in exchange for the arbitrator’s decision without challenge. There is no right to appeal an arbitrator’s decision. Nonbinding arbitration: contexts in which the parties may request a trial if either refuses the arbitrator’s decision.

How May we Select a Decision?

“Decision theory” -  the area of study that focuses on how decisions are made and/or vary in the degree of available knowledge is considered sufficient should a case go to trial or arbitration. (Here, a “decision” is a decision to take the course of action in arbitration.) Our goal is to develop criteria for rational (reasonable) decision-making should we choose arbitration. We will assume (without loss of generality) that the general context in which such decisions occur are considered “decisions under risk:” Contexts in which we can assign various probabilities to the outcomes of our actions. Our knowledge in these contexts is said to be partial or incomplete.

To evaluate decisions under risk correctly, a ‘rule’ or method that accounts for both utility and probability is necessary. The term utility is here used to refer to the desirability (positive utility) or undesirability (negative utility) of our situation. Thus, we could say that a fruitless exercise in trial may have negative utility and that this utility should be considered along with the probability of loosing the case – not to mention the enormous cost – when contemplating a choice to arbitrate or not. (Note: arbitration does NOT minimize the probability of “risk” rather it limits the risk to either party involved.)
It is thus reasonable to choose the action that maximizes expected utility as such a rule. To understand this rule, we need to know how to calculate expected utilities. Before this can be done, however, we must be able to quantify utilities; then and only then can a measure be performed.
But, how do we measure/quantify utilities? How, for example, can we place a numerical value on extracting a win, or on undergoing a successful exercise in arbitration, or, inversely, loosing an arbitration case? Objectivity is not the case here! The measurement of utilities need not be objective: All that is required is that parties confronted with the decision to arbitrate assign a measure with respect to their respective utilities.
Suppose we may correlate units of money with units of utility. (Here, “utility” is the end objective or payoff.)  Then it isn’t very difficult to assign a measure of the utility of various amounts lost or won.
We will consider a problem that involves only money to illustrate how expected utilities work; then we may consider a problem in arbitration, in which units of utility may not be identifiable with dollar amounts.

3. COUNTING EXPECTED UTILITY

In general, the rule for calculating expected utility of some decision is to multiply the probability of each possible outcome by the number of units (the total amount) of utility associated with that outcome (the payoff). The sum of these probability products minus the cost is said to be the expected utility of that decision. We are entitled to consider various probabilities associated with the following constraints of resources i) time frame, ii) selection (be it neutral or not), and iii) location limits if any of a case. Resolution of Discovery Disputes is then necessary to address motions, hearings, and decision writing.

Example. Suppose prevailing business decisions – decisions under risk – may comport with the dictates of economic efficiency – decisions between say “arbitration or trial vs settlement.”
We want an “expected utility” model – a measure /analysis approximating the cost-benefit of arbitration. 

An example illustrating this process in a civil case:

Your decision must consider the “constraints of resources” as well as the probability of winning (or loosing) should the case go to trial. In order to simplify our problem, let’s consider the constraint of the probability of winning in its own sake. We can always add if needed the fractions of probabilities with respect to all other constraints we may consider.

Let each party involved in case of the trial agree that the judgment if the defendant is found liable, will be $200,000, and that the probability of the plaintiff prevailing is 0.40. This probability measure is not by accident. It is obtained by analyzing precedent.
Suppose the plaintiff’s trial costs are estimated at $40,000 and the defendant’s trial costs estimated $20,000. The plaintiff’s expected gain (utility) is therefore $40,000 (= $200,000 x 0.40 – $40,000) = [(judgment value) x (prevailing decimal) – (cost)], and the defendant’s expected loss is $100,000 (= $200,000 x 0.40 + $20,000).
Given that the plaintiff’s expected gain – assuming he prevails – is $40,000, he will be willing to settle out-of-court for any amount greater than this. Similarly, the defendant, with expected costs of $100,000, will be willing to settle for any amount less than $100,000. There is thus a $60,000 difference or range within which a settlement can occur. If, on the other hand, arbitration does not lead to trial, then given its lower transactional costs, with prevailing probability of 0.40, the plaintiff’s expected gain is even greater. The probability of loosing – namely, 0.60 – is similarly calculated and then added to the probability of winning.

Here are a few more “constraints” to consider:

1. What is the utility (= payoff) with respect to each possible outcome?
2. What are the probabilities of winning or loosing (expressed as a decimal) of each possible outcome?
3. What is the cost (if any)? Note: there is ‘time’ and ‘$.’
4. Account for non-action; in other words, doing nothing (= no payoff)
5. How many possible outcomes are there?

In the above civil case, for example, there were three possible outcomes: i) win, ii) lose, or iii) neither party wins or loses. In each case, we estimated that there will be either a winner or not, loser or not, or neither. By enumeration our utility is 8.
Three ways in which at least one of lose, win, or neither will occur. Three ways in which the outcome of both win and lose, or win and neither, or lose and neither will occur. And, finally, one-way in which all may occur simultaneously – although highly unlikely.

The 3^rd probability Diagram. We could have reached the same conclusion if we had taken each probability w, l, & n, and define a set – in three probabilities. In this case, we could ask what is the number of subsets of our probability? It turns out, there are 2^3 for a three-probability set. This answer is easily obtained in steps. First, we think of either winning or not. There are 2 choices. Then we either include lose or not. There are again 2 choices. Finally, we neither lose nor win. There are again 2 choices. The total number of ways of calculating for the probabilities is given by 2 x 2 x 2 = 8.

4. CONCLUSION

After the calculations, the plaintiff’s party bursts out that there are exactly 8 possible outcomes. And by estimating each constraint with a correlated decimal we can account for the probability in whole For example, 0.37 that the case will be held at ‘X’ location. During their exclamation of how amazing it is for one attorney to be able to choose exactly the correct method, the last stance of the case in point begins to unfold. The plaintiff’s attorney has one last ploy, scans the opposing defense, recognizes their posture, and decides the best course of action. He sets his face down and heaves the decision towards settlement. The defense wonders if their decision was the right one for their team.

Good reading,

razmik b. ekmekdjian
author/managing director

Now as we focus our attention to enumerating costs and benefit we pay particular attention to players and positions involved in the game of decision making.

Decision Theory

Decision theory is the area of study that focuses on how decisions are made. (Here, a “decision” is a decision to take the course of action as per say a trial or arbitration.) Our goal is to develop criteria for rational (reasonable) decision-making with respect to some course of action. We will assume (without loss of generality) that the general context in which most decisions occur are  “decisions under risk:” Contexts in which we can assign various probabilities to the outcomes of our actions.

Our knowledge in these contexts is said to be partial or incomplete.

To evaluate decisions under risk correctly a ‘rule’ or ‘method’ that accounts for both utility and probability is necessary. The term utility is here used to refer to the desirability (positive utility) or undesirability (negative utility) of our situation. For example, we would say that a fruitless exercise of expenditure – say a trial -  may have negative utility subject to constraints of trial plus cost of loosing. The better and less costly alternative may be an Alternative Dispute Resolution say an arbitration. (Note: arbitration does NOT minimize the probability of “risk” rather it limits the risk to either party involved.)
It will suffice that we choose the action that maximizes expected utility as such a rule. To understand this rule, we need to know how to calculate expected utilities. Before this can be done, however, we must be able to quantify utilities; then and only then can a measure be performed.

Quantifying Utilities

How may we place a numerical value on an outcome? Objectivity is not the answer here. The measurement of utilities need not be objective: All that is required is that parties confronted with the decision – say a trial vs settlement – assign a measure with respect to their respective utility.
Suppose we correlate units of money with units of utility. (Here, “utility” is the end objective or payoff.)  Then it isn’t very difficult to assign a measure of the utility of various amounts lost or won.
We will consider a problem that involves only money to illustrate how expected utilities work.

We may calculate expected utility of some decision by multiplying the probability of each possible outcome by the number of units (the total amount) of utility associated with that outcome (the payoff). The sum of these products (probabilities) minus the cost is said to be the expected utility of that decision. We are entitled to consider various probabilities associated with various constraints of resources – such as cost.

Example

Suppose prevailing business decisions – decisions under risk – comport with the dictates of economic efficiency – decisions between say “arbitration or trial” vs “settlement.”
We want an “expected utility” model – a measure /analysis approximating the cost-benefit of the differences between settlement and trial.

An example illustrating this process in a civil case:

Your decision must consider the “constraints of resources” as well as the probability of winning (or loosing) should the case go to trial. In order to simplify our problem, let’s consider the probability of winning on its own sake.

Let each party involved in case of the trial agree that the judgment if the defendant is found liable, will be $200,000, and that the probability of the plaintiff prevailing is 0.40. This probability measure is not by accident. It is obtained by analyzing precedent.
Suppose the plaintiff’s trial costs are estimated at $40,000 and the defendant’s trial costs estimated $20,000. The plaintiff’s expected gain (utility) is therefore $40,000 (= $200,000 x 0.40 – $40,000) = [(judgment value) x (prevailing decimal) – (cost)], and the defendant’s expected loss is $100,000 (= $200,000 x 0.40 + $20,000).
Given that the plaintiff’s expected gain – assuming he prevails – is $40,000, he will be willing to settle out-of-court for any amount greater than this. Similarly, the defendant, with expected costs of $100,000, will be willing to settle for any amount less than $100,000. There is thus a $60,000 difference or range within which a settlement can occur.
The probability of loosing – namely, 0.60 – is similarly calculated, and, if necessary, added to the probability of winning…

Enumeration

In the above civil case there were three possible outcomes: i) win, ii) lose, or iii) neither party wins or looses. In each case, we estimated that there will be either a winner or not, looser or not, or neither.
By enumeration our utility is 8:
Three ways in which at least one of loose, win, or neither will occur. Three ways in which the outcome of both win and loose, or win and neither, or loose and neither will occur. And, finally, one-way in which all may occur simultaneously – although highly unlikely.

We could have reached the same conclusion if we had taken each outcome, w, l, & n, and define a set. In this case, we could ask what is the number of subsets in our outcome set? It turns out, there are 2 ^3 for a three-outcome set. This answer is easily obtained in steps. First, we think of either winning or not. There are 2 choices. Then we either include loose or not. There are again 2 choices. Finally, we neither loose nor win. There are again 2 choices. The total number of ways of calculating for the probabilities is given by 2 x 2 x 2 = 8.

Thus, by estimating each constraint as a correlated decimal and then multiplying respective utilities minus cost, if any, will help guide the right decision.

Good reading,

razmik b. ekmekdjian
author/managing editor

What had you been aware?
Why, for instance, may you NOT save in the short-term?

Suffice it to say, mathematical consciousness in economic variables and how it may influence your decision-making is a condition of life.

Let’s dive in, shall we?

It shouldn’t surprise the reader that due to government activity, not all of your income is available to spend, save, and/or invest.
Case in point: direct taxation.
This is taken out of your income before it even reaches your household. Under a proportional income tax model, T = tY, where T is the net flow from your household to the government, t for tax rate, and Y is income, the income that is available for you to spend – or save as you wish -  is defined as disposable income.
Mathematically, we define disposable income by

Y(d) = Y – T = Y – tY = Y(1 – t).

Now, given your disposable income, what is not consumed is considered saved.
In other words, Y(d) = C + S.

But, what exactly is savings?

In layman terms, to save involves consuming less than your current income, which makes it possible to consume more at a later date. Contrast this with “borrowing,” which makes it possible to consume more than your current income; of course, on condition that consumption is less in the future – below future income – in order to repay borrowed money.
A decision to save (or borrow) is therefore a rearrangement of consumption between various time periods. By careful examination of your choice to save or to borrow, you can determine factors that influence your decisions – namely, to save, invest, and/or borrow.

Let’s address a choice in savings/borrowing in the following hypothetical problem.
In order to simplify our argument let’s assume there are just two years, years 1 and 2. If your income in the current year (year 1) is $20,000, but less in year 2 – say only $2,500, and, if the rate of borrowing is at 10%, r = 10%, with inflation = 0% so that a dollar will purchase the same quantity of goods in both years, then you’re in a position to plot a budget line between your savings and borrowing.
There is no less assuming this has been done.

See figure below

(Source: Krugman et al)

Suppose you are able to save all your income from year 1 of $20,000. In this case, your consumption in year 1 is $0, but in year 2 you could consume $22,500 = $20,000 + $2,500.
But wait!
You also save 10% interest in the first year – namely, $2,000, so that your total (implied) savings is $24,500 in year 2.

How much can you borrow?

The maximum amount of money that you can otherwise borrow depends on how much you can repay in year 2. In other words, if you could borrow $2,000, year 1 consumption can be $22,000 ($20,000 + $2,000). However, you could actually borrow more than $2,000 – namely, you may borrow up to $2,270, because at 10% interest ($2270 x .10), or $2,497, is less than your income in year 2; recall, the amount borrowed must be repaid in year 2, and, therefore, must approximate to year 2 earnings with nothing left over for consumption.

Of course, there is a price to be paid.

Irving Fisher (1867-1947) who developed theory of interest based on Austrian theories famously declared that there is a price for not waiting, namely, 1 + r, or

where r is the real interest rate, i is the nominal interest rate, and inflation is a measure of the increase in the price level. When inflation is sufficiently low, the real interest rate can be approximated as the nominal interest rate minus the expected rate in inflation.
In other words,  r = i – P.

So, there you have it!

There is a cost to borrowing, namely, less towards, say saving for retirement or other long-term goals: you want time on your side! Hence, the sooner you begin saving or investing, the greater wealth you may accumulate.

Good reading,

razmik b. ekmekdjian
author/managing editor

Ceteris Paribus

Not unlike in mathematics, theoretical models used in economics observe and study the relationships between things and NOT the things themselves. A model of the market for gold, for example, might seek to explain gold prices and not gold itself. Further, it might explain gold prices with a number of quantifiable variables, such as, wages of gold miners, technology, and, indeed, consumer incomes.
This stinginess in theoretical specification permits the study of gold pricing or any other market in a simplified setting in which it is possible to understand how certain economic forces interact. Although any economist will no doubt recognize that many “other” factors (shifts in consumer attitudes in style or fashion, presence of a geopolitical instability) affect the price of gold, these other factors are mostly held constant in discussions of theory.
It is important to note, however, that economists aren’t assuming that other factors do not affect gold prices. Rather, such other variables are assumed not to change during the period of study.
In this context the affect of only the desired variables can be studied in a simplified setting. Such ceteris paribus (other things being equal) assumptions are thus axiomatic in most economic discussions.
Consider, for example, the housing market. All other things being equal, prices may reach equilibrium if and only when the supply and demand schedules intersect. At this point, the corresponding price will be the equilibrium price, and the corresponding quantity will be the equilibrium quantity exchanged in the market.
Use of the ceteris paribus axiom is of course but one feature used in most economic models.

Positive-Normative Distinction

Another and perhaps more important is the differentiation (colloquial use of the term and not the mathematical) between the “positive” and “normative.” So far in my treatise I’ve been concerned primarily with positive economics. Such “scientific” theories take the real world as an object to be studied, attempting to explain observed economic interactions.
Positive economics is said to describe how resources are in fact allocated in an economy. A somewhat different use of economic theory is normative, taking a certain and definite stance about what should be done. Under this heading, economists and economic actors have a great deal to say about how resources should be allocated.
For example, says Walter E. Nicholson of Amherst College, an economist engaged in positive analysis might explore why and how the American health care industry employs the quantities of Total Factor Productivity (capital, labor, and land) that are currently devoted to providing medical services. He might even choose, insists Nicholson, to measure the costs and benefits of devoting yet more resources to health care. But when an economist – such as Paul Krugman – takes the position such that more resources should be allocated to health; or, inversely, a conservative such as Paul Ryan advocating an end to Medicare as we know it – due to resource constraints – they have implicitly moved into normative analysis.
Not all economists believe, thus, that the only proper economic analysis is a positive analysis.
To take moral positions and to argue in favor of special interests are NOT assumed outside the intellectual framework of an economist or those acting as economists. Yet other economists (including the author of this text) believe strict application of the positive-normative distinction to economic matters is NOT appropriate let alone possible. We assert that the study of economics – not unlike any other real world event – necessarily involves our own views about ethics, morality, sciences, and fairness. Searching for “objectivity” in economic circumstances let alone the world is hopeless.

Despite this ambiguity, this treatise uses a mainly positivist tone, leaving most normative concerns to the reader to decide for themselves.

Good reading,

razmik b. ekmekdjian
author/managing editor

The question we face is: Do capital markets – prices of assets – follow (or reflect) a system of patterns and forms? To this question diverse answers have been made, but most of them may be reduced to one main criterion.
“Economic theory,” certain economists have replied, “has for its object description of a group of related data experimentally observed.”
For the most part, the practice of economics follows the above maxim.
Indeed, it is antecedent of most research papers. Consider, the so-called “Elliot Wave Theory,” in which the Fibonacci sequence and King’s Law – I will explain these sequences in a moment – enjoy corollary status in relation to forms and patterns otherwise observed.
In its simplest form, the Fibonacci sequence is observed on condition of capital market behavior between price and quantity of commodities, and is said to apply to not just prices in capital markets but also market volatility.

Like many other observations in economics, the description of the Fibonacci sequence as an inherent aspect of capital markets is but an abstraction of a mathematical finding in certain theoretical curiosities.

Suffice it to say, the history of science (social or otherwise) provides numerous examples of such “mathematical” curiosities. Kepler, for example, related the ratio and proportions of the vibratory rate of the notes of the musical scales to the rate of orbit and eccentricity of all the known planets. It is now well-known the musical model’s accurate prediction of planetary orbit and existence of a formerly missing object – Ceres, the largest asteroid found nearly two hundred years after Kepler’s prediction.
Similarly, the logical framework in Einsteins equations about space, time, and light – well within orderly correspondence to the innovations introduced by artists, such as, Manet, Monet, and Cezanne.
Such curiosities which make up propositions in space, time, and waves, are to the highest degree, universally excepted interests of the physicist, not to mention the mathematician. Since classical Greece, philosophers and physicists alike have made repeated attempts to sort out patterns and/or relationships in nature, and, by extension, human activity. For example, the practice of economics.

Economists (not less curious) too have dedicated themselves to understanding the interplay among patterns and forms of human activity – economic activity.

The obvious logical problem (for the economist) has been to show how a particular mathematical observation localized in the “objective” world, for example, the Fibonacci sequence, can also be the expanded and sustained pattern, capital market behavior. The so-called school of Tech theory – economic institute – not only says unequivocally that prices in capital markets extend both ways, it also claims to provide the mathematical formalism governing what is otherwise observed to coordinate our experience with capital market prices. In “technical” school jargon, there is a one-to-one correspondence between price and quantity (or other economic factors) and its supposed reflection of a system of patterns and forms.
Needless to say, for every economic maxim there exists an equal but opposite criterion.

The so-called “random walk” school of economists for one insist that patterns otherwise observed reflect an ephemeral correlation to price – a subset of disconnected or disordered surface economic activity. For random walk economists, patterns and forms in price and market, respectively, are simply transient and without predictive relevance; namely, prices follow no forms beyond chance and “Brownian Motion.” (Brownian Motion is the mathematical model used to describe random movements of particles, and, indeed, have been applied to stock market fluctuations.)

What has consistently troubled tech economists about random walk is, however, that it implies we can no longer observe preexisting truths of physical reality through the lenses of physical theory in the classical sense.

Tech economists insist the interpretation of data in capital markets on the basis of classical “Greek” world view; random walk, on the other hand, interprets data from the “Modern” perspective – abrogating classical causality. Now, for some, these apparent  differences have remained mostly irreconcilable. But, for others, such as Paul Montgomery, an economist, writer, and philosopher in his own right, there appears but one fundamental feature that distinguishes the two approaches: visual.

Fibonacci, Tech Theory, and King’s Law

The Fibonacci sequence observed among market prices, which may be crudely and inappropriately evinced as a preexisting truth, is responsible for the formation of the principle assumption of tech hypothesis. Leonardo Fibonacci originally presented the Fibonacci sequence (1202) to the Western world, when he is said to have asked: “how many pairs of rabbits enclosed [within] may be produced in one year starting with one pair if each pair gives birth to a new pair each month, starting with the second month?”
144 pairs, of course!
The number of pairs each of the twelve consecutive months being, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, and 144, and each number in the sequence being the sum of the previous two numbers. Once the series has been expanded, the ratio of any number to the previous actually approximates 1.618 = phi. The so-called Golden Mean in mathematics circles. In geometry, this mean governs the Golden Rectangle and Spiral. In fractal economics, this is responsible for the formation of preexisting patterns in “Elliot Wave Theory,” which is said to describe virtually all significant movements in the capital markets. (Fractals are mathematical structures which on an ever smaller-scale infinitely repeat themselves.)

Indulging in a bit of whimsy, we might ask if history be viewed in terms of fractals. Is there any qualm if history “repeats” itself?
But, this is a discussion onto itself.

For the moment, we want to consider the following case: Do prices, indeed, appear to replicate a random walk or, are they predictable (preexisting) as per Fibonacci or King’s Law?
((King’s Law is a mathematical correlation between price and supply quantity of a commodity.  Gregory King, an advisor to the Crown from mid to late 1600s, studied the behavior of English and Continental grain prices beginning in 1300s to late 1600s. From these he developed a formula that predicted the market price of a commodity (grain) based on supply above or below trend. (I will give an example in a moment.))

Let’s first revisit the main proposition of technical theory, namely, price of a commodity is evident in capital markets.

Our purpose here, of course, isn’t to prove these rather complicated notions using mathematical formalism, but to generally point out what is otherwise essential in the so-called Elliot Wave Theory which rests upon them.

If, indeed, price and quantity in capital markets are observed along the path of patterns and forms, then there is a one-to-one correspondence between observation and the object of description – namely, economic activity. In that case, classical causality and the relativistic principle, namely, as one of several points of view, no one of which has exclusive authority, are in one-to-one correspondence.
In other words, we may observe these so-called mathematical patterns and forms with respect to price and quantity in economics with relativistic certainty.

Relativity, art, and, indeed, economics have long shared probabilistic distortions – namely, in every day time, space, and data; regular occurrences in each respective discipline.

For example, Gertrude Stein (1874-1946) – an American Jewish writer, poet, and art collector, aptly once described her hometown, in relativistic terms, “There is no there, there.” This applies to condition of space at the speed of light: There is no there, there because it is all here. This so-called mental exercise demands that we imagine all of space and, by extension, economic data, along the path of observation from multiple locations at the same time.
That is, without distinction between past, present, and future, and, by extension, between the Greek world view and the Modern.

Naturally, this oversimplifies matters.

From the intuitive but mathematical point of view, however, distortions in data, including the intervals between quantity of commodity and change in price, yield a single but disconnected piece – a part which may be represented as the union of two disjoint (irreconcilable) sets-data. A paradoxical entity. (For a detailed discussion on sets and spaces see “Topology and Modern Analysis,” Simmons.)
A conflict between what “is” and what your feeling (observation) of what “ought” to be?

In matter of fact, relativity theory distorts what “is” otherwise the Modern position such that fundamental particles have casual – transient – properties, and in so doing, suggests data be seen simultaneously from all view points, including the Greek claim that the unity and forms and patterns of nature, and, by extension, human activities – such as economics – are themselves transient and casual factors.

Suppose, then, we consider – as has suggested Paul Montgomery – capital market behavior as sub-category of human (animal) behavior. In this case, shouldn’t form and patterns be just as evident in market price data – capital markets?

Indeed, this is the primary assumption of technical analytic theory.

Example

Below is an arithmetic example of how price and quantity repeat in proportion over time.

King’s Law – correlation of price and supply quantity of commodity.

Quantity   Price
——————-

1.0          1.0
.9         1.3
.8         1.8
.7         2.6
.6         3.8
.5         5.5

As per table above:
quantity decrease (shortages) lead to price increases. A 10% shortage, for example, should lead to prices 130% of normal, a 30% shortage should lead to prices 260% of normal. A 10%  surplus, on the other hand, should lead to prices 77% of normal (quantity, price) (1.1, .77).

Mathematically, the formula for supply shortages is given by: relative prices = [.98807915 x quantity] ^-2.691

Good reading,

razmik b. ekmekdjian
author/managing editor

Economics, like other human endeavors, has both a “what” and a “how.” The “what” is the subject matter of economics, ranging from price to unemployment to a firm’s structure and beyond. The “how” depends mostly upon who is practicing economics. For example, it is often convenient, says David Friedman, son of Milton Friedman and an economist in his own right, to describe consumption in terms of quantities – such as numbers of bars, gallons of milk, and so forth. To an economist – for that matter to us all – 10 bars consumed  at once may not have the same value as 10 bars consumed over time. Friedman suggests that we address such discrepancies by expressing consumption in terms of rates and not of quantities–6 bars per week, 7 eggs per month, and so on. For example, “Income,” says Friedman, “is not a number of dollars but rather a number of dollars per week.” Thus, we express 3 bars per week not as $7, but $7 per week.
Why?

Because, value is considered as a flow. For example, GDP = C + I + G + (X – M)…

It follows that we think of all quantities as flows; and, assuming we limit our thoughts to analyzing situations in which income, prices, as well as preferences do not change over long periods, then, says Friedman, we avoid most if not all difficulties that time weighs on economics – complications in what is otherwise an ever-changing process. Hence, economists prefer to exercise economics in a perfectly static and predictable world, in which, for instance, tomorrow is not distinct from today.
The flow of time ignored.

Accordingly, in drawing utility curves – see diagram below – Friedman insists that the consumer would not spend only part of his/her income so to save the rest for a so-called rainy day; either it is raining today, reminds us Friedman on consumers, or there are no rainy days.

Diagram –

Total Value and its Slope. V/Q is the average slope of total value between A and B. As V and Q become very small, A and B move together, and V/Q approaches the slope of total value at a point-which is marginal value – Price Theory (1960).

In thinking of consumption say in gallons of milk per year instead of just a lump sum of gallons of milk is that it lets us vary consumption continuously – smoothly.

Otherwise, consumption or any other metric flow would occur at once in the here and now, right?

As you may have guessed there are challenges, warns us Friedman, with changing the number of gallons of milk you may or not consume at a time; for example, how or when do you consume the remainder? It turns out it is enough to change the rate at which you consume gallons of milk per week–drink, on average, less (or more) gallons of milk per year. I’ll spare you the arithmetic details. Suffice it to say, we may define marginal utility or value - the last good to be consumed -  in terms of small amounts of goods without first converting the goods into – say creamy texture or a pile of crumbs – infinitely smaller parts.

Another issue associated with time that we should note, says Friedman, is in describing the process of choice, “I talk about ‘doing this, then doing that, then . . .,”   says Friedman, “increasing consumption from 4 [bars] to 5, then from 5 to 6, then from . . . and so on.”

“All if it happening inside your head – the process of solving the problem of how much of each good to consume – [be it conscious or not].”

First, for example, you imagine the choice to consume some good say a bar and then consider the resulting if any bundle of goods. Thus, you imagine that you consume some good instead of none and compare that bundle with the previous one. Then double instead of one… an so on. . . . Finally, after you have made your choice on what level of consumption maximizes your utility – most desirable outcome – and having ignored such unusual possibilities, intentionality clicks, consumption starts, and you begin your practice.

On the other hand, if you find it difficult or inscrutable to address time in the sense of an imaginary calculus by which you decide what to do if and only if you actually do it, you may instead want to think about utilities. For example, imagine instead a situation – say income – that will lead to choices with the highest utility despite the rate at which you may consume a good.
If no single outcome has the highest utility, then you may choose any of the outcomes that would otherwise yield the highest return – utility.

Good reading,

razmik b. ekmekdjian
author/managing editor

In his works, Stabilizing an Unstable Economy, Minsky (1919 – 1996) points to the observation by John Maynard Keynes, in that financial intermediaries – such as banks – which play a critical not to mention an ever-growing role in modern economics, bind creditors and debtors in elaborate and complex financial webs. “The interposition of this veil of money,” wrote Keynes, “is an especially marked characteristic of the modern world.”
For Minsky, Keynes seems to have offered a “well-ordered” analysis of how financial forces interact with variables of production and consumption, on the one hand, and output, employment, and prices on the other.

All of this stood in contrast to the economics profession in the post war era – namely, the neoclassical synthesis, the likes of Milton Friedman, David Hume, and Anna Jacobson Schwartz, who argued that it was, in fact, monetary instability (unexpected supply or shortage in the supply of money) and not demand that caused changes in real variables – such as output and unemployment.
In other words, argued Friedman and Schwartz, known changes in the supply of money – while it caused changes in the price levels – did not affect aggregate demand or output.

Recall, by definition, demand (or aggregate demand) is the total measure of goods and services demanded within an economy. Geometrically, it is represented as the relationship between price levels and total output or income.
In general, this relationship is inversely defined.

See figure below



Mathematically, aggregate demand, AD = Consumption + Investment + Government Expenditures +Exports – Imports.

Keynes (1883 – 1946), on the other hand, insisted that changes in the price levels indeed caused changes in output.

Specifically, he argued, change in unemployment was “elastic” to changes in price levels. In other words, quantity demanded in labor was very sensitive to changes in price levels.
Accordingly, unemployment is inefficient because labor is traded at the wrong wage, and, Keynes argued, more people would be willing to work at the current wage.

For example, as prices fall, wages (w/p) go up, and, in turn, labor demand falls – as employers demand fewer and fewer workers,  unemployment rises which then leads to lower output or GDP.

Therefore, argued Keynes, a change in the price level affects real variables – such as, GDP and employment.
This was Keynes’ simple measure explaining the relationship between aggregate demand and price levels and – by extension – the Great Depression.

The main idea in the classical model, however, emphasizes “money as neutral.” For example, a change in the stock of money affects only nominal variables, such as, prices and wages, but all real (inflation-adjusted) magnitudes, such as, employment and real output, remain unchanged.
Whereas, for classical economists, the labor market is always and everywhere in equilibrium – that is, supply and demand of labor remain equal – Keynes argued that the labor market isn’t in equilibrium, and, more importantly, unemployment is typically “involuntary.”

So, by classical standards, banks play no role in causing changes in output, since changes in the stock of money or reserves and/or prices shall not cause changes in real variables such as unemployment or output.

Neoclassical theorists thus paid little emphasis of late, if any, to banks and other financial institutions – despite the fact that changes in prices or money reserves may affect real changes in unemployment or output as described by Keynes.

Minsky set out to change this state of affairs! Accordingly, banks or financial institutions could, as they became increasingly complex and interdependent, may bring the entire system down.
The centerpiece of his analysis?
Debt!

As per Keynes, he recognized that banks and other financial institutions injected uncertainty into the economic calculus. In good times, sustained growth and profits allayed uncertainty. But, in bad times, uncertainty would force financial players to curtail lending, reduce risk/exposure, and hoard capital and/or cash!
Sound familiar?

In the following weeks, I will describe the so-called Financial Instability Hypothesis by Minsky which classifies debtors in a given economy into three groups – in line with the financial instrument in use: namely, hedge borrowers, speculative borrowers, and, last but not least, Ponzi borrowers.

Which class of borrower are you?

Good reading,

razmik b. ekmekdjian
author/managing editor

The fact is – be it a theorist or empiricist – no one can determine with strong certainty what future inflation, if any, may hold.

We make most choices, decisions, in the face of less than complete knowledge about the real value of the dollar.

Many of these decisions, because we’re often in a position to spend, consume, invest, and/or save for tomorrow – if not consume today – dictate our expectations of inflation.
How?

The answer of course depends on one’s view – the empiricist or theorist.

For those of us that care about savings and investment in the long-run, we try to arrive at an estimate of expected inflation by considering the “real” value of our investments.
We may pursue higher savings because we are satisfied with the rate of return in interest, or pursue an investment because money is cheap.

However, the probability that most of us are able to maximize utility, that is, save, subject to inflation is low.

Notwithstanding, suppose we want to know what, indeed, exist between”inflation” and economic determinants?
In other words, how shall we explore the causes of inflation?

Empiricists analyze data of past inflation as well as unemployment emphasizing the correlation between unemployment and inflation.
Inflation rises when unemployment falls…
an inverse relationship.

The theorists emphasize the “quantity theory of money” – a theory of money demand. To understand better the theorist model, we discuss first the measure known as aggregates – namely, monetary aggregates.

By definition monetary aggregates are wide measures of the total value of the money supply within an economy. In the United States, for example, the standardized monetary aggregates and their measured subsets (contents) are known as

M0 = amount in physical cash and coin,
M1 = M0 + demand deposits, traveler’s checks, and,
M2 = M1 + savings deposits, money market shares,
so on and on…

The Federal Reserve counts monetary aggregates to measure the effects of open-market operations, such as changing the discount rate or trading in Treasury securities.
Monetary aggregates are also an important tool for economists and investors, as we gain a visual perspective of the size of the working money supply.

The quantity theory shows, all other things being equal, that there is a relationship between monetary aggregates and inflation.

Accordingly, there is a one-to-one relation between the metrics of inflation and growth rates in the supply of money. In other words, for each change in the rate of the money supply there is, all other things held constant, a similar change in the rate of inflation – Friedman.

Thus, quantity theory equates inflation to money growth by subtracting real growth from the stock of money.

Mathematically, we say that the price level (p) is determined by Mv/Y, or p = Mv/Y, where M is the money stock, v is velocity of money, constant, and Y is GDP, such that, it is determined by preferences, endowments, and technology.

The proposition is that if a change in quantity of (nominal) money were engineered – say by monetary authorities (FED), then the long-run effect would be a change in the price level (and other nominal variables) of the same proportion as the money stock. In not so simple terms, theorists claim that there exists a long-run proportionate reaction to price levels given an exogenous – say government expenditure – increase in the nominal money stock.
By exogenous we mean things that we can control (e.g. the interest rate, government spending, tax rates).

There is, on the other hand, the relationship between other metrics and inflation – as empiricists emphasize.

For example, the cost of enjoying lower unemployment rates is higher rates in inflation – trade-off between inflation and unemployment – Phillips Curve.

Se figure -

A strong correlation also exists between wages and labor – supply and demand.
But, that is a subject for another column.

Expectations for rising inflation may also be reflected in Fed Funds Futures as is shown below – incidentally, rates were suggested to rise in 2009. So, two years hence …

See the chart below of Fed Tightening expectations – any moment presently!

Given that there is rising expectations of future inflation and the Federal Reserve’s endless purchase of Treasuries, may we suggest that there will come increased nominal interest rates and therefore an elevated spread between rates of return on money as well as securities.

Good reading,

razmik b. ekmekdjian
author/managing editor

Savings and investment are determined by way of movements in the real interest rate, r – not-unlike wages, w/p, for which it determines the supply and demand of labor.
In other words, the real interest rate, r, is found in between investment and savings.

See figure –
Investment (I) and Savings (S) with r.

In the following column, we will discuss Investment versus Savings in the private sector.

We know that the private sector – the part of the economy that is not state controlled and where most jobs are held – contains both Firms and Households.
The circular flow between household and the firm (or producer) shows the relationship between each of the components as per flow of money, factors of production, and goods and services…

See figure -

The household sector is the eating, breathing, consuming population of the economy. In other words, it contains “everyone,” all consumers, all persons but not all things.
It (household) is the sector of all people’s needs and wants, now, and/or in the future. Irving Fisher (1867-1947)  – who developed theory of interest based on Austrian theories  - famously declared that the price of not waiting is 1 + r, where r = i – P, nominal rate of interest minus expected inflation.
Interest rate r is the “real” interest rate for a standard bundle of commodities, whereas “nominal” interest rate is the rate on money.
Thus, by waiting and building capital more can be done in the future.

Note that P = Dp/p, p for price.

For a firm as well what is not invested is, in general, consumed. Bottom line -
profit maximizing – firms determine Investments. If revenues are defined as consumption or Investments, Y = C + I, where Y (output) = P  + (1 + r) x I, implies that output for your firm is a function in either consumption or investing. In other words, if your firm sells (Y) output, earns (p) profit, pays back (1 + r) interest – tomorrow, and, borrows (I) and then invests – the remainder may be consumed.
Thus, weighing the benefit of investment against the cost of borrowing helps determine your bottom line.

However, for a household what is not consumed is saved.
In other words, Y = C + S, where Y is income.

See figure -

I - 1

The greater the rate of interest the more your household will – or shall we say “should” save.

Now, savings is determined by utility maximizing households – namely, by wages, w/p, or income.
Households then divide income between consumption and savings in order to maximize utility – not unlike the firm, which should divide its revenues between investment and consumption in order for it to maximize profits.

Note: if your household should survive in the long-run – consumption today is likely less than consumption tomorrow.

I’ll let the reader justify the above conditional.

Good reading,

Razmik b. ekmekdjian
author/managing editor