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Rules for scientific research in economics the alpha beta method

The Alpha-Beta

Adolfo Figueroa

Rules for Scientific Research in Economics

Adolfo Figueroa

Rules for Scientific
Research in
The Alpha-Beta Method

Adolfo Figueroa
Pontifical Catholic University of Peru
Lima, Peru

ISBN 978-3-319-30541-7
ISBN 978-3-319-30542-4
DOI 10.1007/978-3-319-30542-4


Library of Congress Control Number: 2016944657
© The Editor(s) (if applicable) and The Author(s) 2016
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To Nicholas Georgescu-Roegen, my teacher, in memoriam


Why has the growth of scientific knowledge in the social sciences proceeded at a rate that is slower than that of the natural sciences? The
basic reason seems to rest upon the differences in the complexity of the
reality they study. Compared to the natural sciences, the social sciences
seek to explain the functioning of the social world, which is a much

more complex world than the physical world. As biologist Edward
Wilson pointed out:
Everyone knows that the social sciences are hypercomplex. They are inherently far more difficult than physics and chemistry, and as a result they, not
physics and chemistry, should be called the hard sciences (1998, p. 183)

Methodology deals with the problem of how to construct scientific
knowledge. Is the understanding of the social world more demanding
on methodology than understanding the physical world? Economist Paul
Samuelson argued in his classic book Foundations of Economic Analysis
that indeed this is the case:
[This] book may hold some interest for the reader who is curious about
the methodology of the social sciences…[I]n a hard, exact science [as physics] a practitioner does not really have to know much about methodology.
Indeed, even if he is a definitely misguided methodologist, the subject
itself has a self-cleansing property which renders harmless his aberrations.
By contrast, a scholar in economics who is fundamentally confused concerning [methodology] may spend a lifetime shadow-boxing with reality.




In a sense, therefore, in order to earn his daily bread as a fruitful contributor to knowledge, the practitioner of an intermediately hard science like
economics must come to terms with methodological problems. (1947, pp.

Paraphrasing both Wilson and Samuelson, the researcher’s good command of methodology is more critical for producing scientific knowledge
on the highly complex sciences (social sciences) than in the less complex
sciences (natural sciences). Therefore, the answer to the question posed
above seems to be that the difference lies in methodology. Social sciences
development needs to use better methodology and more intensively. This
book intends to contribute to that development.
Methodology is also called epistemology (from the Greek episteme,
knowledge). Epistemology or methodology is usually presented as part
of philosophy of science. In this view, epistemology is a branch of philosophy that seeks to scrutinize the philosophical problems that arise in
the practice of science, such as epistemological, metaphysical, and ethical problems. Philosophy of economics is the particular field that deals
with philosophical problems in economics, as economists practice it. To
be sure, this book is not about philosophy of economics. There are good
recent books that show the state of this discipline (e.g. Reiss 2013).
The approach followed in this book will be different. It will correspond
to the view of epistemology as the theory of knowledge—the logic of
scientific knowledge. Then epistemology will be seen as part of the formal
science of logic, not of philosophy. Indeed, some textbooks of logic now
deal with the logic of scientific knowledge (e.g. Hurley 2008).
The book will show practical rules for the construction and growth of
scientific knowledge in economics, which will be derived logically from a
particular theory of knowledge or epistemology. No such rules exist currently in economics; that is, economists follow a diversity of rules, derived
from a diversity of epistemologies or having no epistemological justification. The intended contribution of the book is then normative: what rules
of scientific research ought economists to follow. This view of epistemology is more natural for working scientists, who are epistemology users
rather than makers.
The epistemology proposed by Karl Popper (1968) will be adopted in
this book. This is one of the most popular epistemologies in the literature.
It essentially says that theory is required for scientific knowledge, but this
theory must be empirically falsifiable or refutable; thus, good theories will



prevail and bad theories will be eliminated, as in a Darwinian competition.
Scientific progress will result from this competition.
However, Popperian falsification epistemology is also the most debated.
Many authors have argued that Popperian epistemology is not applicable in economics. The arguments are clearly summarized in the Stanford
Encyclopedia of Philosophy by Daniel Hausman (2013), a leading philosopher of economics. They are
1. Economic theories are rarely falsifiable.
2. When they are, they are rarely submitted to testing.
3. When they fail the test, they are rarely repudiated.
Consequently, we can understand why in economics we observe that
no theory is ever eliminated and that progress in scientific knowledge is
relatively limited, in spite of large amounts of research work.
Problems (2) and (3) refer to what economists do and why. These are
not within the scope of this book. Problem (1) is the subject of this book.
The challenge is how to make Popperian epistemology applicable and
operational in economics. Can we logically derive from Popperian epistemology a set of practical rules for scientific research in economics? As the
book will show, this derivation is subject to the transformation of a complex social world into a simple abstract world. Popperian epistemology
might be suitable for physics, but whether it is so for economics, a science
dealing with a complex world, is another question. In fact, problem (1)
has to do with the complexity of the social world.
How to make knowable a complex reality, such as the social world? The
late Vanderbilt University professor of economics, Nicholas GeorgescuRoegen (1971) proposed a solution to this problem, and developed the
process epistemology. Georgescu-Roegen is mostly known as the founder of
bio-economics, an economic school different from standard economics,
but his contribution to epistemology is less known.
Consider now combining the epistemologies of Popper and GeorgescuRoegen into a single one, as they do not contradict each other. Call this
combination the composite epistemology. Then, as will be shown in this
book, a set of rules for scientific research in economics can be derived from
the composite epistemology. This set of rules will thus constitute a scientific research method, as it will have epistemological justification or logical foundations. This will be called the alpha-beta method. This method
intends to solve the falsification problem in economics, the problem that



“economic theories are rarely falsifiable”—the problem (1) of Popperian
epistemology, cited above. The alpha-beta method is a scientific research
method that ensures economic theories be always falsifiable. Thus, the
alpha-beta method is not another name for a known method, but a truly
new scientific research method, the application of which should contribute to scientific progress in economics. The book is thus intended to be
Economics is a social science. However, this definition of economics
is not always accepted and the term social science is usually reserved for
sociology, anthropology, and political science. Although scientific rules are
derived for economics only, the book will show that extensions to the other
social sciences are nearly straightforward. This procedure means that economics is presented as an example of the social sciences, not as the exemplar.
Differences in the complexity of the social world compared to the physical world must be reflected in the different epistemologies social sciences
and natural sciences use. The book presents a comparison between these
epistemologies, just to better understand the epistemology of economics
and the other social sciences.
Therefore, this book is concerned with the problem of how sciences
ought to seek scientific knowledge, not with what scientists actually do.
The common proposition “Science is what scientists do” ignores this distinction. Therefore, this book deals with the question of how scientific
research in economics ought to operate. The question of what economists
actually do and why is outside the scope of this book, for the answer
would require a scientific theory to explain that behavior. The book takes
the epistemologies of Popper and Georgescu-Roegen as given, and deals
with the problem of deriving logically from them a set of practical rules for
scientific research in economics.
The book includes 10 chapters. Chapters 1, 2, 3 and 4 deal with the
construction of the alpha-beta method and its application to economics.
Chapters 5 and 6 show the logic of statistical testing of economic theories
under the particular alpha-beta method. Chapter 7 compares the alphabeta method with other empirical research methods. Chapter 8 discusses
the most common fallacies found in economics that are uncovered by the
alpha-beta method. Chapter 9 compares the epistemologies of natural sciences and economics in the light of the alpha-beta method. Chapter 10
presents the conclusions of the book.
In sum, the objective of this book is to present a set of rules for scientific research in economics, which are contained in the alpha-beta method.



These rules are scarcely used today, which is reflected in the fact that no
economic theory has been eliminated so far, and thus we observe the coexistence of the same economic theories (classical, neoclassical, Keynesian,
and others) over time, with the consequent lack of Darwinian competition
of theories. Scientific progress is the result of such evolutionary competition. Therefore, the book seeks to contribute to the scientific progress
of economics by proposing the use of the alpha-beta method, a method
designed for the evolutionary progress of economics.
The book is primarily addressed to students of economics at advanced
undergraduate and graduate levels. Students in the other social sciences
may also find it useful in the task of increasing the growth of interdisciplinary research within the social sciences. Even students of the natural
sciences may benefit from the book by learning the differences in the rules
of scientific research of their own sciences with that of the social sciences.
This understanding will prepare economists, physicists, and biologists to
work in interdisciplinary research projects, such as the relations between
economic growth and degradation of the biophysical environment, which
is, certainly, one of the fundamental problems of our time.


Parts of this book have been taught in economics courses at the Social
Science School and in the epistemology course in the Doctorate in
Business Administration at CENTRUM Graduate Business School, both
at Pontifical Catholic University of Peru, and at the Universities of Notre
Dame, Texas at Austin, and Wisconsin at Madison, where I have been
Visiting Professor. I would like to thank the students in these courses for
their valuable comments and questions about my proposal of the Alphabeta Method.
I am also grateful to the three anonymous reviewers appointed by
Palgrave Macmillan. Their comments and suggestions to my manuscript
were very useful to make revisions and produce the book. Sarah Lawrence,
the Economics & Finance Editor of Palgrave Macmillan, has been most
helpful to go through the review process of the book project.
My gratitude is immense with my current institution, CENTRUM
Graduate Business School, Pontifical Catholic University of Peru, and
with its Director Fernando D’Alessio, for providing me with great support for the preparation of this book.





Science Is Epistemology


Alpha-Beta: A Scientific Research Method



The Economic Process



The Alpha-Beta Method in Economics



Falsifying Economic Theories (I)



Falsifying Economic Theories (II)



The Alpha-Beta Method and Other Methods



Fallacies in Scientific Argumentation



Comparing Economics and Natural Sciences





10 Conclusions







Fig. 1.1
Fig. 3.1
Fig. 3.2
Fig. 6.1
Fig. 6.2



Diagrammatic representation of an abstract process
Types of economic processes: static, dynamic, and
Deterministic and stochastic static processes
Assumptions of regression analysis
Breakdown of the variation of Yj into two components




Table 1.1
Table 1.2
Table 2.1
Table 2.2
Table 3.1
Table 4.1
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 6.1
Table 7.1



Meta-assumptions of the theory of knowledge
Scientific research rules derived from Popperian
The alpha-beta method
Matrix of beta propositions or matrix of causality
Economic process according to E-theory
The alpha-beta method in economics
Frequency distribution of income in the population B
Distribution of sample means for n = 2 drawn from
population B
Frequency distribution of income in the population C
Distribution of sample means for n = 2 drawn from
population C
Kinds of reality based on Searle’s classification
Research methods: scientific and empirical



Chapter 1

Science Is Epistemology

Abstract  What is the criterion to accept or reject propositions about
the social reality as scientific? We need rules for that, which must have
some rationality, some logic. This logic is called epistemology. Science is
epistemology. What is the epistemology of economics? The answer is still
debated. The use of the falsification epistemology of Karl Popper in economics has been questioned. This chapter presents this epistemology and
analyzes the reasons for its shortcomings. Then the chapter introduces
the process epistemology of Nicholas Georgescu-Roegen, which deals with
complex realities, and shows that the two epistemologies are complementary and thus can be combined into a single composite epistemology. The
composite epistemology is now applicable to sciences dealing with complex realities, such as those studied by economics.
Scientific knowledge seeks to establish relations between objects. The
objects can be mental or physical. Formal sciences study the relations
between mental objects, whereas factual sciences study the relations
between material objects. Mathematics and logic are examples of formal
science; physics and economics are instances of factual sciences.
Scientific knowledge takes the form of propositions that intend to be
error-free. Scientific knowledge is therefore a particular type of human
knowledge. What would be the criterion to accept or reject a proposition as scientific? It depends upon the type of science. In the formal
sciences, the criterion seems to be rather straightforward: The relations
© The Editor(s) (if applicable) and The Author(s) 2016
A. Figueroa, Rules for Scientific Research in Economics,
DOI 10.1007/978-3-319-30542-4_1




established must be free of internal logical contradictions, as in a mathematical theorem.
In the factual sciences, by contrast, the criteria are more involved. As
will be shown in this book, factual science propositions are based on formal science propositions; that is, the propositions of a factual science must
also be free of internal logical contradictions. However, this rule constitutes just a necessary condition, for the propositions must also be confronted against real-world data.
Scientific knowledge in the factual sciences can be defined as the set
of propositions about the existence of relations between material objects
together with the explanations about the reasons for the existence of such
relationships. Therefore, it seeks to determine causality relations: what
causes what and why. It also seeks to be error-free knowledge, as said above.
We can think of several criteria to accept or reject a proposition in the
factual science. Common sense is the most frequent criterion utilized in
everyday life. Common sense refers to human intuition, which is a strong
force in human knowledge. Intuition is the natural method of human
The assumption taken in this book is that intuitive knowledge is subject
to substantial errors. Intuitive knowledge is based on human perceptions,
which can be deceiving. Galileo’s proposition that the Earth spins on its
axis and orbits around the sun was not generally accepted for a long time
(even up to now) because it contradicted intuitive knowledge: People cannot feel the Earth spinning and what they can see is rather that the sun
is going around the Earth. The same can be said about today’s climate
change because the greenhouse gases are invisible to human eyes. Intuitive
knowledge is thus the primitive form of human knowledge.
As said earlier, science seeks to produce error-free human knowledge.
Therefore, human knowledge in the form of scientific knowledge requires
the use of a scientific method, which needs to be learned and educated. Thus,
science has to do with method. Thus, the criteria for accepting or rejecting propositions as scientific in the factual sciences—the scientific method—
needs to be constructed. This construction is the task of epistemology.

The Role of Epistemology

in Scientific


In this book, epistemology is viewed as the field that studies the logic of
scientific knowledge in the factual sciences. Epistemology sees scientific
knowledge as fundamentally problematic and in need of justification, of

Science Is Epistemology 


proof, of validation, of foundation, of legitimation. Therefore, the objective of epistemology is to investigate the validity of scientific knowledge.
For this we need a criterion to determine whether and when scientific
knowledge is valid. This criterion cannot be based on facts, for they are the
objective of having a criterion; thus, the criterion can only be established
logically. Scientific knowledge must have a logic, a rationality, established
by a set of assumptions. Therefore, the criterion is given by a theory of
knowledge, which as any theory is a set of assumptions that constitute a
logical system.
Epistemology will thus be seen as theory of knowledge, as a logical system. In this book, the concept of theory will be applied to the
logic of scientific knowledge as well as to the scientific knowledge itself.
Consequently, two very useful definitions in parallel are needed at the very
Theory of knowledge is the set of assumptions that gives us a logical criterion
to determine the validity of scientific knowledge, from which a set of rules
for scientific research can be derived. The set of assumptions constitutes a
logical system, free of internal contradictions.
Scientific theory is the set of assumptions about the essential underlying factors operating in the observed functioning of the real world, from which
empirically testable propositions can be logically derived. The set of assumptions constitutes a logical system, free of internal contradictions.

Any factual science needs to solve the criterion of knowledge before
doing its work because this question cannot be solved within the factual
science. The logical impossibility of obtaining the criterion from within
the factual science is relatively easy to proof. Let S represent any factual
science. Then
Factual science (S) is a set of relations (R) between material objects X and
material objects Y, which are established according to criterion (L).

This proposition can be represented as follows:

S = {R ( X, Y ) / L}




How would L be determined? If L were part of S, then L would be established through the relations between physical objects, that is, relations
between atoms (physical world) or between people (social world); however, this leads us to the logical problem of circular reasoning because
we need L precisely to explain the relations between atoms or between
The criterion L will thus have to be determined outside the factual science. How? The alternative is to go to the formal science, in particular to
the science of logic. The criterion L is now justified by a logical system.
This logical system is precisely the theory of knowledge (T), which as any
theory is a set of assumptions (A). Then we can write
S = {R ( X, Y ) / L}
L = {T ( A ) / B}


B = {T ′ ( A ′ ) / B′}


The first line of system Eq. (1.2) just repeats the definition of factual science. The second says that criterion L is logically justified by deriving it from
the theory of knowledge T, which includes a set of assumptions A, given the
set of assumptions B that is able to justify A. The set B constitutes the metaassumptions, the assumptions underlying the set of assumptions A. The set
B is logically unavoidable, for the set A needs justification. (e.g., why do
I assume that there is heaven? Because I assume there is God? Why do I
assume that there is God? Because…, etc.). Therefore, the set B needs a logical justification by using another theory T′, which now contains assumptions
A′, which in turn are based on meta-assumptions B′, and so on. Hence, we
would need to determine the assumptions of the assumptions of the assumptions. This algorithm leads us to the logical problem of infinite regress.
The logical problem of infinite regress is a torment in science. A classical
anecdote is worth telling at this point (adapted from Hawking 1996, p. 2):
An old person challenged the explanation of the universe given by an astronomer in a public lecture by saying:

–– “What you have told us is rubbish. The world is really a flat plate
supported on the back of a giant tortoise.”

Science Is Epistemology 


The scientist gave a superior smile before replying:

–– “What is the tortoise standing on?”
–– “You’re very clever young man, very clever,” said the old person.
“But it is turtles all the way down.”
How could science escape from the infinite regress problem? This is a
classical problem, the solution of which goes back to Aristotle’s “unmoved
mover.” Everything that is in motion is moved by something else, but
there cannot be an infinite series of moved movers. Thus, we must assume
that there exists an unmoved mover.
In order to construct scientific knowledge, we need an unmoved mover,
an initial point, established as axiom, without justification, just to be able
to start playing the scientific game, which includes eventually revising the
initial point, and changing it if necessary. The scientific game includes the
use of an algorithm, that is, a procedure for solving a problem by trial and
error, in a finite number of steps, which frequently involves repetition of
an operation. Thus, the initial point is not established forever; it is only
a logical artifice. If the route to his desired destination is unknown, the
walker could better start walking in any direction and will be able to find
the route by trial and error, instead of staying paralyzed.
In the system Eq. (1.2) above, the only way to avoid the infinite
regress problem in the theory of knowledge is by starting with the meta-­
assumption B as given, and thus ignoring the third line and the rest. Then
the set of assumptions B will constitute the foundation or pillar of the
theory of knowledge T, which in turn will be the foundation or pillar of
the criterion L, which we can use to construct the theory of knowledge.
The infinite regress problem is thus circumvented and we are able to walk.
The role of the theory of knowledge in the growth of scientific knowledge is to derive scientific rules that minimize logical errors in the task
of accepting or rejecting propositions that are intended to be scientific
knowledge. The theory of knowledge needs foundations, that is, meta-­
assumptions. Consider that the meta-assumptions B of the current theories of knowledge include those listed in Table 1.1.
As shown earlier, these meta-assumptions need no justification. (Please
do not try to justify them! We need to move on.) Thus, this initial set of
assumptions constitutes just the beginning of an algorithm to find the best
set of assumptions. Given these initial or fundamental assumptions, we
have a rule to follow: Any particular theory of knowledge will have to be
logically consistent with these four general principles.

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