University of Economics in Prague

Faculty of Economics

Study program: Economics

THE ANALYIS OF AUSTRIAN BUSINESS

CYCLE THEORY FROM COMPLEXITY

ECONOMICS APPROACH

Bachelors thesis

Author: Alessandra Lanzafame

Supervisor: Ing. Luk Bernat

Year: 2014

I hereby declare on my honor that I have written my bachelors thesis

individually and unaided using the literature referenced in the bibliography.

Alessandra Lanzafame

In Prague, on 18th August, 2014

I would like to thank my thesis supervisor Luk Bernat for his guidance,

support, willingness to help and for giving me the valuable pieces of advice

about the topic. I would like to also thank Brandi Hokama who corrected the

grammar in the thesis.

Abstract

This thesis analyzes the Austrian business cycle theory (ABCT) from the Complexity

Economics approach. Complexity Economics is an approach which comes from the

complexity science. It has started to develop since 1980s in Santa Fe Institute, the leading

institution engaging in the research of complexity. In the thesis I mention the terms which

are bound to Complexity Economics, for example emergence, the bottom-up, self-

organization, bounded rationality etc. The evolution of the markets, networks and

nonlinear dynamics are included in Complexity Economics as well. Complexity

Economics views an economy as a dynamical system which never reaches the equilibrium

unlike the Traditional Economics approach which main focus is on the general equilibrium

of the economy. Another difference would be the way of modelling economies in which

Complexity Economics is leaving the unrealistic assumptions because it utilizes the agent-

based simulations which enable to do that. In the thesis I also focus on the similarities

between Austrian and Complexity Economics, for example the heterogeneity of the agents

or the emergence of the price system. These and others similarities are then applied in the

analysis of ABCT which serves as an example of demonstration that Austrian Economics

could provide the theoretical framework for Complexity Economics which aspires to

become a new paradigm in economics.

Key words: complexity economics, Austrian business cycle theory, traditional economics,

equilibrium

JEL Classification: B41, B53, E32

Abstrakt

Tato bakalsk prce analyzuje rakouskou teorii hospodskho cyklu (ABCT) pohledem

komplexn ekonomie. Komplexn ekonomie je pstup vychzejc z komplexn vdy, je

se zaala rozvjet v 80. letech minulho stolet v Santa Fe Institute, pednm stavu

zabvajcm se tmto pstupem. V prci zmiuji pojmy, kter jsou spjaty s komplexn

ekonomi, jako napklad emergence, pstup zdola-nahoru, samoorganizace, omezen

racionalita apod. Komplexn ekonomie zahrnuje do sv analzy tak evoluci trh, st,

nelinern dynamiku a dv se na ekonomiku jako na dynamick systm, kter nikdy

nedoshne rovnovhy, co ji zcela odliuje od pstupu mainstreamovho, kter se

zamuje na veobecnou rovnovhu ekonomiky. Za dal rozdl meme povaovat pstup

k modelovn ekonomik, v nm komplexn ekonomie opout nereln pedpoklady,

jeliko ke sv analze vyuv multiagentnch simulac, kter toto umouj. Prce se tak

zabv podobnostmi mezi rakouskou a komplexn ekonomi, napklad je kladen draz na

heterogenitu agent, na spontnn emergenci cenovho systmu atd. Tyto a dal

podobnosti jsou pak na zvr aplikovny v analze ABCT, kter slou jako demonstrace

toho, e rakousk kola me poskytnout teoretick rmec pro komplexn ekonomii, kter

aspiruje na to stt se novm paradigmatem v ekonomii.

Klov slova: komplexn ekonomie, rakousk teorie hospodskho cyklu, tradin

ekonomie, rovnovha

JEL klasifikace: B41, B53, E32

Table of contents

Introduction ........................................................................................................................... 1

I. Introduction to Complexity Economics ......................................................................... 3

1. What is complexity? ...................................................................................................... 3

1.1. (Nonlinear) dynamics ............................................................................................. 4

1.1.1. Path dependence .............................................................................................. 5

1.2. Four classes of behavior ......................................................................................... 5

1.3. Emergence .............................................................................................................. 7

1.3.1. From the bottom up ......................................................................................... 7

1.3.2. Landscapes....................................................................................................... 8

1.4. Agents ..................................................................................................................... 8

1.4.1. Bounded rationality ......................................................................................... 9

1.4.2. Inductive reasoning........................................................................................ 10

1.5. Interesting in-between ........................................................................................... 12

2. Review of Complexity Economics .............................................................................. 12

2.1. Self-organization in the economy ......................................................................... 13

2.2. Emergence of the markets ..................................................................................... 14

2.2.1. Market competition ....................................................................................... 15

2.2.2. Evolution in the markets ................................................................................ 15

2.3. Positive and negative feedbacks ........................................................................... 16

2.4. Non-equilibrium .................................................................................................... 17

2.5. Networks ............................................................................................................... 18

3. Summary ...................................................................................................................... 20

II. Comparative analysis of Complexity, Austrian and Traditional Economics .............. 21

1. Principles of Traditional Economics ........................................................................... 21

1.1. Assumptions in economic models ........................................................................ 22

1.1.1. Positivism in economics ................................................................................ 23

1.1.2. Equilibrium approach .................................................................................... 23

1.1.3. Perfect rationality and full information ......................................................... 24

1.1.4. Exogenous shocks ......................................................................................... 25

1.2. Comparative statics ............................................................................................... 26

1.3. Dynamics in Traditional Economics ..................................................................... 27

1.3.1. Neoclassical growth model Solow ............................................................. 27

1.3.2. Real business cycle theory ............................................................................. 27

1.3.3. DSGE dynamic stochastic general equilibrium .......................................... 28

1.4. Summary ............................................................................................................... 28

2. Austrian school of economics ..................................................................................... 28

2.1. Principles of Austrian school of economics ......................................................... 30

2.1.1. Methodological individualism ....................................................................... 30

2.1.2. Methodological subjectivism ......................................................................... 30

2.1.3. A priori deductive reasoning ......................................................................... 31

2.1.4. Time ............................................................................................................... 31

2.1.5. Evenly rotating economy ............................................................................... 32

3. Agreements and possible disagreements of Austrian and Complexity economics ..... 32

3.1. Agreements between Complexity and Austrian Economics ................................. 33

3.1.1. BRICE ........................................................................................................... 33

3.2. (In)solvable differences ........................................................................................ 34

3.2.1. Inductive reasoning versus axiomatic-deductive method .............................. 35

4. Differences between Traditional and Complexity Economics .................................... 36

4.1. Five Big ideas that distinguish Traditional and Complexity economics ........... 38

5. Summary ...................................................................................................................... 38

III. Austrian business cycle theory and Complexity Economics .................................... 39

1. Robinson Crusoe Economy ......................................................................................... 39

1.1. Production and consumption ................................................................................ 40

1.1.1. Savings and investments ................................................................................ 41

1.1.2. Path dependence of Robinson ....................................................................... 41

1.1.3. More savings equals more consumption ....................................................... 42

1.2. Dynamics of the single-agent economy ................................................................ 42

2. Robinson and Fridays economy ................................................................................. 43

2.1. Wealth is an emergent phenomenon ..................................................................... 44

2.1.1. Heterogeneity allows the trade ...................................................................... 44

2.2. Time preference .................................................................................................... 45

2.3. Loans, investments and interest rate ..................................................................... 45

3. Multiple-agent economy .............................................................................................. 46

3.1. Indirect exchange and money ............................................................................... 46

3.1.1. Indirect exchange ........................................................................................... 47

3.1.2. Money ............................................................................................................ 48

3.1.3. The price system and economic calculation .................................................. 48

3.1.4. The market of loanable funds ........................................................................ 50

3.1.5. Natural interest rate ....................................................................................... 50

3.1.6. Banks and fiduciary media ............................................................................ 51

3.2. Complexity as a built-in property ...................................................................... 52

4. Austrian business cycle theory .................................................................................... 53

4.1. Capital structure and roundabout methods ........................................................... 53

4.1.1. Hayekian triangle ........................................................................................... 54

4.1.2. Path dependence of production...................................................................... 56

4.1.3. Production as a set of networks ..................................................................... 56

4.2. Economic growth .................................................................................................. 57

4.2.1. Technological innovations and the growth .................................................... 58

4.3. Business cycles ..................................................................................................... 59

4.3.1. Boom ............................................................................................................. 60

4.3.2. Robustness of the economy ........................................................................... 61

4.3.3. Bust the positive or negative feedbacks? .................................................... 61

4.3.4. Course of the cycle with an intervention of the central bank ........................ 63

Conclusion ........................................................................................................................... 64

List of Graphs and Figures .................................................................................................. 66

Bibliography ........................................................................................................................ 67

1

Introduction

The field of economics is going through the greatest change in over a hundred

years. Complexity Economics offers a new approach to the economics but many ideas

have deep historical roots, for example in Austrian school of economics. However,

Complexity Economics has started to develop since late 1970s when a small number of

economists, physicists and social scientists began to wonder if there might be a

fundamentally new way to look at the economy. (Beinhocker 2006) In my opinion, the

concepts of Complexity Economics have a great chance to provide the foundations of

economic theory in the next decades. The aim of this thesis is to introduce Complexity

Economics and the concepts related to this field and apply them in the analysis of

Austrian business cycle theory. The aim of the first chapter is to introduce these

concepts path dependence, classes of behavior, emergence, nonlinear dynamics and so

on.

Why exactly the analysis of Austrian business cycle theory? While doing the

research about the complexity science, I have noticed that there were a lot of similar

things in both approaches. The heterogeneity, dynamics, self-organization or

emergence, these all made me think about if it was possible to join the ideas of

Complexity and Austrian Economics and create their synthesis. I had in my mind the

idea that if these two approaches had unified, they both could have contributed

something new to each other, especially Austrian Economics could adopt the agent-

based modelling as, at least a complementary, method of research. Contrarily,

Complexity Economics could have adopted some theoretical ideas from Austrian

Economics because the theoretical framework of Complexity Economics is still in

progress.

To even think about this joint of Complexity and Austrian Economics, it is

necessary to compare these heterodox approaches with the mainstream economics

which is generally accepted as a consensus within the field of economics. This is the

aim of the second chapter, in which I am also analyzing the similarities and

discrepancies between Complexity and Austrian Economics. The main difference lies in

the notion of equilibrium. Complexity Economics views an economy as a dynamical

system which never reaches equilibrium. The possibility of making agent-based

simulation also allows to model economies without unrealistic assumptions, for

2

example the assumption of perfect rationality, exogenously created novelty or the

indirect connection of economic agents through the price system. The possible

differences between Austrian and Complexity Economics are also interesting. For

example Austrians use axiomatic-deductive method and it means that all economic

theories are logically deduced from the principal axiom that humans act. Instead,

Complexity Economics uses the inductive method and above mentioned agent-based

models as an experimental and empirical evidence of tested theories.

The last chapter is focused on the analysis of ABCT and application of our

gained knowledge from Complexity Economics. The structure of capital and production

can be viewed as a network of manufacturers, producers and suppliers. How

interconnected is this network determines how frightful the recession is going to be. The

path dependence also determines the structure of an economy and actions of agents

might cause positive or negative feedbacks according to how they are going to react to

the signals of their environment. We will also find out that the natural interest rate is an

emergent phenomenon and the artificial interventions of central bank can be either

absorbed by the system, or can cause self-organized criticality.

3

I. Introduction to Complexity Economics

The real voyage of discovery consists not

of seeking new landscapes,

but in having new eyes.

Marcel Proust

Have you ever thought about how language, organizations, markets and

economies were created? We know they are created by people. But how do these

patterns emerge? How does simple interaction between you and your butcher allow

such a complicated and complex system as an economy to be created? This is a question

which I am going to answer in this whole unit.

1. What is complexity?

Complexity science brings a new approach to science and comes from the study

of complex systems. It is an interdisciplinary field explaining how the interactions

between simple entities at the micro level create something different at the macro level,

how these entities coordinate, self-organize and create interesting patterns without any

central control. In many cases, the complex systems also have an ability to evolve and

those entities are able to learn and process the information gained from the

environment. (Mitchell 2009, p. 13) Some examples of complex systems are ant

colonies, fish schools but also consciousness, intelligence or an economy. Social

sciences utilize more and more knowledge from complexity science; for example, the

study of culture - how does it emerge and coordinate, why some traditions and

institutions survive, how they change and so on. Also, economics has started to head

toward complexity, resulting in the field of complexity economics to emerge relatively

recently.

Complexity science is a cross-disciplinary field because it is composed of many

core disciplines. It utilizes the knowledge from computer science, physics, biology,

cognitive sciences and so forth. That is why the definition of complexity has not been

unified yet and I suppose it never will be. But there are some specific characteristics

common for all complex systems nonlinearity, self-organization, non-equilibrium,

4

simple agents, emergence, dynamics and attractors, evolution and many others. I will

discuss them one by one because the understanding of these characteristics is crucial for

subsequent analysis. I would like to start with the dynamics and attractors which are

important for the comprehension of the difference between simple and complex

systems..

1.1. (Nonlinear) dynamics

Lets now focus more deeply on dynamics and the difference between linear and

nonlinear dynamics. System dynamics are represented by the movement of a system

from one point to another or rather from the one state in time t to another in time t+1.

The system takes place in a space and it is called phase space. This space represents all

possible states of the system and these possible states correspond to one unique point in

the phase space. The states that system reaches create some trajectories called phase

portrait (Goldstein 2011)

Certain phase portraits then display attractors as the long term stable sets of points in

the dynamical system. They are the locations in the phase portrait towards which the

systems dynamics tend to move regardless on the initial conditions (Goldstein 2011,

p. 5)

There are three types of attractors: fixed point, periodic and chaotic. (Mitchell

2009, p. 32) Each system has some pattern and Stephen Wolfram had studied these

patterns on cellular automata and discovered that these patterns are specific and in some

way always very similar. He discovered that these patterns belonged to four classes of

behavior. According to what the attractor is, the system might be linear or nonlinear.

The linear systems are the ones you can understand by understanding their individual

parts and then putting them together. In this case, the whole is equal to the sum of the

parts. Whereas in nonlinear systems, the whole is different from the sum of its parts.

What occurs at the macro level is not easily comprehensible from the observation of the

micro level interactions. That is why the behavior of the nonlinear systems is rather

unpredictable. An example model of the nonlinear system would be a logistic map.1

1 For further details see Melanie Mitchell, Complexity: A guided tour, p. 27

5

1.1.1. Path dependence

The initial conditions of the system matter because they determine the path

which the system is going to evolve. This stands also for agents. The agents past

decisions put them into some situation and this has influence on their future decisions.

Their past decision influence the emergent phenomena of the system. One single

decision can change everything. The systems structure acts as the memory of system.

(Page 2014) Page (2009, p. 32) also talks about path dependence. He says that path-

dependent processes are not predictable. The unpredictability is not given by

randomness but it depends on actions along the path. Page refers to the example of

QWERTY keyboard as the example of path dependence.

1.2. Four classes of behavior

Every pattern a system produces can be assigned to one of the class of behavior.

Wolfram (2001, p. 231) has numbered these classes regarding to their increase of

complexity.

Class 1 behavior is uniform behavior, represented by a fixed point attractor. All patterns

evolve over time and are gradually attracted into stable state equilibrium. A System

with this behavior is sensitive to initial conditions and any randomness disappears in

time.

Figure 1 rolling marble (source: Complex Systems Tutorial)

Rolling marble is often used in Traditional Economics to demonstrate how an economy

behaves. Economy is hit by an external shock and deviated from the equilibrium but it

tends to get back.

6

Class 2 behavior is represented by a periodic/limit-cycle attractor. The initial conditions

influence the structure of the pattern but quickly evolve into repeating cycles oscillating

around the same values.

Figure 2 limit cycle attractor (source: Complex Systems Tutorial)

Class 3 is more complicated behavior. It belongs within the chaotic/strange attractors

and is very sensitive to initial conditions. Even though the system tends to evolve in a

chaotic and random manner, the chaotic systems also have some universal properties.

The stable structures never survive because of the surrounding noise.

Strange attractor

Figure 3 Halvorsens attractor (source: SPROT 2008)

Class 4 represents complex behavior. These patterns evolve into structures that interact

in complicated ways. The structures are formed locally and are able to survive for long

periods. The complexity arises at the edge of the chaos. The eventual outcome of these

systems might be stable or oscillating structures as in the class 2 but would require a lot

of time and many time steps to reach this state. Initial patterns are usually simple but

emerge to complex patterns.

7

The last class the complex behavior is the one we will be interested in, in the

context of economics. For example, how economies work, which complex phenomena

we can observe in the economy, how markets or business cycles emerge and so forth.

Emergence is very important property of the complex system and determines the

essence of this approach.

1.3. Emergence

Complexity itself is a property of the system and we can look at it as an

emergent phenomena. Emergence creates novelty, something that could not be deduced

from the properties of the individual parts. But among their interactions something new

and different is created. And this is exactly what complexity is. Complexity is created

by interactions of simple individual parts at the micro level. They have some properties

but we cannot deduce from them what is going to be created at the macro level. We

cannot explain the property of the macro level on the basis of understanding the micro

level. That is why emergence does not have logical properties because it cannot be

predicted. (Corning 2002, p. 7) One example of emergent phenomena is wetness.

Wetness is created by weak hydrogen bonds holding together water molecules. A single

water molecule does not feel wet, but water does. The wetness is something that was not

built-in. We could not deduce this from observing one single water molecule. (Page

2009, p. 21)

Emergence is a product of a dynamical process where individuals (agents) form

collective behavior. For example, ant colonies: ants as individuals are very simple

creatures that seek to satisfy their needs food, respond to chemical signals of other

ants in the colony, fight intruders and so forth. The individuals perform actions

following simple rules but when they work together, they create complex structures that

are important for survival of the colony as a whole. (Mitchell 2009, p. 177)

1.3.1. From the bottom up

Emergent behavior is produced from the bottom up. It is the spontaneous

creation of order which is created by the interactions of agents without any central

control. The bottom-up approach let us study the system by looking at the individual

parts agents - and their interactions. (Page 2009, p. 21) We will talk about the agents

and their properties in the next sub-unit.

8

It is also important not to confuse the terms complicated and complex.

Complicated does not necessarily mean complex. If a system is complicated, it may

have many diverse parts, but if these parts are not adaptive they cannot be complex.

Moreover, complex systems are robust, creating large events that are dynamic. These

dynamics change the patterns of the system and the agents are forced to adapt to these

changes. They must process the information and act in the way that best fits the current

conditions, resulting in the system improving itself by this process of learning. So the

system evolves and adapts to the environmental changes. (Page 2009, p. 4)

1.3.2. Landscapes

Page (2009, p. 6) also introduced the concept of simple, rugged and dancing

landscape models to demonstrate what complexity is and how it is created. He points

out that in the simple landscape there is a global peak which is also the local peak. We

can understand this as an equilibrium state where once a system reaches it, it is not

going to change because the interactions do not take place. The rugged landscapes

usually have more local peaks and one global peak, which is difficult to find. We can

imagine the ruggedness of the landscapes as the diversity of the system which is created

by the interactions of the agents. However, rugged landscapes are not complex. Sooner

or later, agents reach their respective global and local peaks and the system will become

static. Complexity is created when landscapes dance.

The dancing landscapes contain interacting agents who are interdependent and

must adapt. This means that my choice is somehow dependent on the current and

previous actions of other agents in the system. Agents interact locally and create local

peaks. These local peaks are the best nearby options and the global peak is the best

possible action. As landscapes dance, the local peaks change position, which makes it

hard to find the optimal solution because the agents must adapt to the new conditions of

the system.

1.4. Agents

Since we try to explain the complex patterns, structures, macro behavior and

outcomes, we should also focus on the micro level the agents. Agents form the bottom

of the system and they have some properties and abilities. These determine the

complexity of the system and I will mention a few of them.

9

For agents to interact with each other, they must communicate and perceive

information from their environment. (alamon 2009, p. 68) The communication is an

important prerequisite because it allows agents to cooperate, coordinate and compete

with each other. They are also able to evaluate the outputs from the environment and

react, therefore they are adaptive. Agents change their actions based on ongoing events.

Agents are also goal oriented but they do not accomplish their goals by

themselves, instead they accomplish them with the cooperation with other agents. All

their actions and decisions are evaluated with respect to the agents objectives and

agents perform only those actions that are in accordance with their goals. (alamon

2009, p. 25) It is also called the if-then or condition-action rule when an agent evaluates

the current state of the world with respect to his goals. (Beinhocker 2006, p. 110)

Nonetheless, agents are autonomous because even though they cooperate, every action

is performed by the individual. Agents also have various preferences, they are diverse in

their abilities and skills, and they have different goals they are heterogeneous.

The heterogeneity of the agents is one of the key differences between the

complexity approach and the traditional economics approach which I will discuss later

when comparing these two approaches. Another important property of agents is

bounded rationality. Bounded rationality and heterogeneity is what makes a world

complex and unpredictable. I will focus on bounded rationality in an independent sub-

unit because this assumption often lead to different implications than the perfect

rationality in traditional economics models. We will discuss why the assumption of

perfect rationality can be a limit in explaining real world phenomena.

1.4.1. Bounded rationality

One assumption in Traditional Economics is that people agents are perfectly

rational; they think of every action, optimize, choose the best possible strategies, they

are fully-informed and so forth. The perfect rationality assumption allows economists to

construct models which can be solved analytically with exact results. Later in this work

we will discuss why this approach can limit our understanding. Beinhocker (2006, p.

122) describes some properties of human thinking and bounded rationality. People act

under framing biases, availability biases, decide under conditions of risk and

uncertainty, use mental accounting, superstitious reasoning or representativeness. For

example framing some issues can affect how we think about them. Under perfect

10

rationality condition, it not matter how we frame the situation. Or availability bias

means that people make decisions based on data they have instead of searching for data

they really need, to make a right decision. These all deviate from the assumption of

perfect rationality. And if we add bounded rationality to economic models, we will get

different results than from traditional models.

Agents do not have perfect information at their disposal. They consider just a

few alternatives while deciding their actions. They exploit from their knowledge.

Agents decide under the uncertainty and cognitive limitations.

Traditional Economics with an equilibrium approach look at what action,

strategies and expectations would be consistent with aggregate patterns agent caused,

but complexity economics asks how the agents would react to these patterns. (Arthur

2013, p. 3) While searching for behavior consistent with the equilibrium approach we

see traditional assumptions of perfect rationality, perfect information, and no diversity

among the agents behavior of all the agents can be treated as corresponding to the

average, representative agents. Thoughts, perceptions, mental states, and feelings are

processed in certain ways that vary amongst human beings, resulting in perceiving

reality differently. Because they react in different ways, this heterogeneity creates

complexity.

People are not endowed with perfect rationality and getting information is

costly. People are diverse and this diversity makes them guess the behavior of other

people. They create beliefs about their environment. (Arthur 2013) People are also not

as good in deductive reasoning as Traditional Economics assume. Instead, because of

their cognitive limitations, people have bounded rationality and tend to simplify the

complicated situations they get into by creating temporary internal models, using

heuristics and hypothesis, which they then apply to these complicated situations.

According to feedback people get from their environment, they strengthen their beliefs

or change them. In other words, people (agents) learn. They learn and evolve in their

strategies, beliefs, internal models in order to get better results next time. But what they

do not do is calculate, optimize and so forth. (Beinhocker 2006, p. 126-130)

1.4.2. Inductive reasoning

Induction reasoning is used in the complexity theory. It allows to make

generalizations from specific observations. Sometimes it is called the bottom up

11

approach. We begin with specific observations and measures, start to recognize some

patterns and regularities, formulate some hypotheses to explore, and finally end up

developing some general conclusions. (Crossman 2014) However, even if the premises

are true, the conclusion can be false. For example: Marry is a woman. Marry is 170

centimeters high. Therefore women are 170 centimeters high.

In complexity science the agent-based simulations are used for the research and

study of phenomena. This method permits to obtain particular observations from which

we can conclude something about reality. Axelrod (1997, p. 21-40) points out: Since

scientific explanations are generally defined as the derivation of general laws, which

are able to replicate the phenomena of interests simulations appear to be less scientific

than analytical models.

Induction works together with abduction. Induction is used to obtain knowledge

about some behavior of the model, but in the real world, we refer to the logical process

of abduction. Abduction is a method of reasoning which looks for the hypothesis that

would best explain the observed phenomena in the model. (Encyclopedia of Complexity

and System Science, p. 214)

This way of reasoning is adopted in the complexity theory because of the

bounded rationality of the people. People learn and create a mental model of particular

situations. If they end up in some similar situation, they will try to match their internal

model to this situation and solve the problem accordingly. They might adjust their

internal models as they face more and more similar situations. That means they will

adopt some general knowledge from the concrete cases.

Inductive reasoning is opposite to deductive reasoning which is used in Austrian

economics. Later in the work we will discuss if this is a big barrier in unifying

Complexity and Austrian approaches.

12

1.5. Interesting in-between

If we summarize the properties of agents, we can tell that they are connected

because they cooperate and are dependent on each others actions. These actions change

the current state of the world and since they cooperate or compete, it always affects

them. There is also some diversity among the agents (they are heterogeneous), but

agents are adaptive because they are able to perceive their environment and react to

changes. They also have bounded rationality because they do not have all information at

their disposal and do not optimize every action. These characteristics work to some

degree. What does it mean?

Agents are connected with each other but all agents in the system are not

connected. Usually agents interact locally with their neighbors, therefore all actions

taken in the world do not affect all agents. It also implies that there is not such a strong

interdependence in the system. Although agents are diverse and heterogeneous, they are

not completely diverse to the others. They are very similar, they differ only in specific

things. And what about adaptation and learning? Agents following fixed rules without

any adaptation or learning will remain in equilibrium. On the contrary, if everyone is

changing his decision and optimize in order to adapt to every change which comes,

there will be equilibrium again. Hence, the complex pattern will arise from little

learning and adaption. (Page 2009, p. 10-12)

Hence, complexity is always created in-between. According to the

aforementioned classes we can say that complexity is created between order and chaos

or at the edge of the chaos.

2. Review of Complexity Economics

In this sub-unit I would like to introduce a new approach to economic science

which has been slowly growing over the last 25 years. Later in this work I will discuss

why the rethinking of economics is needed, the crucial problems of a mainstream

approach and the differences between the mainstream and complexity approach.

At the beginning of his book Origin of wealth, Beinhocker (2006) talks about

problems of the current economic science, e.g. economics is not helpful in explaining

the economic crises, economic theories are often based on unrealistic assumptions and

mathematical models are often contradicted by real-world data and do not provide a

sufficient description of the world. But the important question is: What does this

13

different approach offer? And could the Complexity Economics replace the neoclassical

economics? Is the economic science in the middle of the paradigm-shift?

Complexity Economics offers a new way of thinking about economies. The key

change in this approach is seeing economy as a dynamic system without equilibrium.

The equilibrium approach is not necessarily wrong. But the problem is that this

approach places a very strong filter on what we can see in the economy technological

innovations and exploitations of market opportunities which implies constant

uncertainty etc. (Arthur 2013, p. 3-5) Agents (producers, consumers, firms) interact

with each other and these interactions create aggregate patterns in the system then

agents react to these patterns again. For example when new technology is introduced

to the market and it becomes broadly used, it might, for example, help consumers save

money because it allows them to use one device instead of many (e.g. the smartphones)

and they can use their saved money for something else. Or producers of notebooks

might decrease their prices so the consumers will buy more notebooks. When there is

some change in the system we will constantly react to it. But the economy is a

decentralized whole and all patterns are created spontaneously and without any central

control. How?

2.1. Self-organization in the economy

All interactions in the economy take place in the market. The market is a place

where trading occurs but it is not designed and it works in a decentralized way.

Individuals pursuing their own self-interest lead the society as a whole to greater

outcomes.

The merchant intends only his own gain, and he is in this, as in many other cases, led

by an invisible hand to promote an end which was no part of his intention By

pursuing his own interest he frequently promotes that of the society more effectually

than when he really intends to promote it. (Smith 1776, book 4, ch. 2, p. 485)

This famous quote of Adam Smith catches the main idea of self-organization in

the markets. Agents follow simple rules of utility or profit maximization and they

interact with each other. They cooperate, trade or compete and the competitive markets

emerge. These interactions are constrained locally but still all the participants of the

market are interdependent and connected through the price mechanism. Prices are the

14

linking mechanism between consumers and producers and give them necessary

information to adapt to the market changes.

Without any intention of these agents at the micro level, these interactions lead

the society at the macro level to better outcomes. "Every individual is continually

exerting himself to find out the most advantageous employment for whatever capital he

can command. (Smith 1776, p. 482) The well-being of the society and effective

allocation of resources are spontaneously created they are self-organized. Self-

organization is the invisible hand of Adam Smith. Now, I would like to talk more

about the markets - why and how did they emerge.

2.2. Emergence of the markets

The main goal of mankind was always to survive and man was equipped by the

instincts which allowed him to protect himself. But people lived in small tribes because

it was more favorable. Each member of the clan had his or her own role and tasks to

fulfil so that labor division and trading within the tribe could emerge. But for the tribe to

survive it needed to create rules everyone had to respect and follow in order to lower

risk and danger. Hence, the tribes which kept following rules had a greater chance of

survival. The rules had been set according to the current needs of the tribe - they were

constantly evolving. Working rules had survived and began to develop further into

traditions, culture and institutions. None of this was planned or designed, it emerged

spontaneously. These traditions and institutions also allowed not only people to survive,

but also cooperate with each other which was crucial for the further development of

society and economies. (Hayek 1988, p. 11-22)

The cooperation enabled the labor division and specialization. These had been

realized not only within the tribes but also between various tribes which could therefore

trade with each other. In this way market could emerge. Trading allowed labor division

between the tribes because they could specialize and focus on providing specific goods.

Adam Smith mentioned the example of a pin factory. He observed ten men at work,

each of whom specialized in one or two steps of the pin-making process. (Smith 1776,

book 1, ch. 1, p.4) The specialization and cooperation enabled the tribe to make 48,000

pins per day, or 4,800 pins per man. Without this labor division, the pin factory would

have only been able to make approximately twenty pins per man per day, or maybe

none. (Beinhocker 2006, p. 25)

15

2.2.1. Market competition

Competition is an important element in the market activity. It holds importance

for both consumers and producers. Lets focus on producers for now. Producers in the

market will offer opportunities4 which they consider at least comparably attractive as

the opportunities of other producers. But producers will offer these opportunities only if

they will yield them the greatest outcome. They follow the profit-maximization rule. If

the market considers these opportunities as non-attractive, these producers will be

rejected by the market and their competitors survive. This motivates them to provide

offered opportunities as efficiently as possible. It is the only way to gain profit on the

market. They adjust their business plans, strategies and so forth just as the tribes

adjusted their rules in order to survive. This is the driving force which enables the

development of new technologies and the decrease of costs and therefore increases the

profit which can be invested again. Schumpeter called this creative destruction. He saw

the growth of the economy in the entrepreneurs innovations which were internal,

endogenous factors crucial in the study of wealth creation. Since entrepreneurs must

adjust their business plans and strategies; we can consider this as some kind of

evolution in the market. And it actually is.

2.2.2. Evolution in the markets

I have indicated how the cooperation and competition affect the markets. There

are firms with business strategies being tested on the market. Markets provide fitness

function and a selection process that represent the needs of the population. Hence, this

function determines the conditions for which firm will survive or fail. It includes many

factors such as satisfaction of the demand, quality of the goods and services,

competition in the market and also institutional conditions in which the firm operates.

Markets provide a means of shifting resources toward the firms which make the best use

of them with minimal costs. The evolutionary algorithm of the markets also includes

replication. If there is a monopoly in the market because of introducing new product or

technology and it is successful, it is very likely to be replicated by other firms if there

are few or none barriers for entry to the market. These are the reasons why the markets

work so efficiently. Beinhocker (2006, p. 295) compares the traditional economics

4 Goods and services

16

view of the market-efficiency with the complexity economics view. He states: The

reason that markets are good at allocation has more to do with their computational

efficiency (they get the right signals to the right people), than with their ability to reach

a global equilibrium.

Traditional Economics considers markets as perfectly efficient under

equilibrium conditions but the equilibrium conditions are never met and markets do not

work perfectly. Hence, there is a consensus about efficiency of the markets both in the

traditional view as well as in the complexity view but there is a difference in how they

approach it.

2.3. Positive and negative feedbacks

When I talked about dynamics before, I was describing how the dynamics work

in the system and that we can observe many kinds of dynamics according to the classes

of behavior. But now, I would like to describe with a few examples, how the dynamics

are actually created.

We can notice that economy is a very dynamic and vivid entity and all taken

actions influence another people, their decisions and behavior. Often these actions have

some influence for other actions to take place and it can launch cascades of actions with

positive or negative feedback. Actions where positive feedback occurs produce more of

that action. Beinhocker (2006, p. 100-102) gives an example with a consumers drop in

confidence can lead to decreased spending, which leads to decreased production, which

leads to unemployment, which leads to even lower consumer confidence and thus a

further drop in spending, spiraling right down into a recession.

Positive feedback reinforces, accelerates, or amplifies whatever is happening, whether

it is a virtuous cycle or downward spiral. Systems with positive feedback can thus

exhibit exponential growth, exponential collapse, or oscillations with increasing

amplitude. (Beinhocker 2006, p. 101)

Negative feedback has an opposite effect. They tend to stabilize a system and

push in the opposite direction. They also tend to make a system self-regulating; they

produce stability and reduce the effect of fluctuations. In the traditional economics

approach we can consider the market-clearing price as the negative feedback which

leads the market to equilibrium.

17

Another constituent of the dynamics in the system are time delays. Time delays

are created by the opposite effect of the positive and negative feedback. Beinhocker

(2006, p 102) says: The positive feedbacks drive the system, accelerating it, but at the

same time the negative feedbacks are fighting back to dampen and control it. When time

delays are thrown in, the driving and damping can get out of balance, and out of synch,

causing the system to oscillate in highly elaborate ways.

Are the positive and negative feedbacks and time delays consistent with the

equilibrium approach of Traditional Economics? Or is the notion of non-equilibrium in

the economy more realistic?

2.4. Non-equilibrium

Lets recall the model of landscapes. We already know that if there were only

perfectly rational agents and they were all the same, they could reach only one global

peak, Mount Fuji single equilibrium. There is no space for exploration,

improvements and creation. We reach just this one point and we are done. But the real

world is full of rugged and dancing landscapes, especially in the economies. We do not

know how agents might react to the aggregate patterns they create and as I have stated,

agents do not even know how the other agents will behave.

Hence, this makes the landscapes dance. An agent must adapt to new situations

and this implicitly assumes non-equilibrium because equilibrium is a pattern that does

not change. (Arthur 2013, p. 3) Therefore the equilibrium system cannot endogenously

create novelty. It does not mean that we could not reach the equilibrium. On the

contrary, while climbing the hills we can get stuck at the local optimum, lets say in the

temporary equilibrium. When we go to the store, we want to buy tomatoes and they are

some price. We might think that they are very expensive. But we want them and we buy

them. If we do this, we agree with their price, we get to the equilibrium, to the local

optimum. Why is it temporary? Because it may happen that due to better conditions

next year there will be better harvest, more tomatoes and therefore they will cost less.

Again, conditions will change and it also changes our situation because we are better off

now with the lower price of the tomatoes. The landscape dances, new situations emerge

and we have to react to them.

This all puts the economy in non-equilibrium, it is the natural state of the

economy and it is always open to reaction. There does not exist an optimal solution for

18

each situation and we usually adapt to the upcoming situations. It allows us to explore

our way forward, we create strategies and actions that are tested for survival. For

example, if a firm uses wrong strategies it may lead to its loss and discharge from the

market. Evolution enters to the system but it arises in the natural tendency of strategies

to compete for survival. (Arthur 2013, p. 7) In the next sub-unit, I would like to briefly

introduce the basic concept of networks because their study is crucial for understanding

how economies work.

2.5. Networks

Before the science of networks was developed, the networks had been studied in

certain disciplines, for example in mathematics the networks were called graphs and the

graph theory developed, sociologists studied social networks and so forth. But scientists

had started to wonder if networks had some common properties and if we could

formulate some theory about their structures, evolution and dynamics. But why is so

important to focus on the study of networks? As Mitchell (2009, p. 233) states, network

thinking means focusing on relationships between entities. These relationships give us

the insight to how the complexity and emergent phenomena are created.

The complex systems as a whole are a set of networks which are in addition

composed of the nodes and the edges (or links) between them. We can measure the

degree of a node through the number of edges coming into or out of the node. If some

node has a high degree, it becomes a hub a node with many connections coming in or

out of it. We also measure the distance between the nodes. This allows us to observe for

example the shortest path between two certain nodes. Elimination of the hub in the

network may lead to the failure of the system because many nodes are connected and

dependent on it. The nodes can also form clusters which are fractions of pairs of

neighbors that are connected to one another. (Mitchell 2009, p. 234 238) The cluster is

for example when you have two friends who are also friends with each other. We can

again measure the clustering coefficient which is the average fraction over all the nodes.

The networks are often neither regular, nor completely random. The regular

networks have long average path length between the nodes and high average clustering.

The random networks, instead, have low average path length and low average

clustering.

19

Figure 4 Regular and Random network (source: GILBERT, HAMIL 2009)

But the real world complex networks have different properties. They have low

average distance between nodes and high average clustering. This is called the small

world property. These networks have relatively few long-distance connections but a

small average path-length. (Mitchell 2009, p. 238)

Figure 5 - Small-world network (source: GILBERT, HAMILL 2009)

All complex networks have the small-world property and as you can see in the

picture, there are many hubs and clusters which creates an interesting pattern of the

network. The study of networks allows us to understand how the structures of networks

are created and organized and also the robustness of the network or its cascade failures.

It could help us, for example, to better understand the bank failures during a financial

crises, how fast the crises will spread on the global market or how fast the technological

innovation will catch on. Hence, the study of networks is crucial for Complexity

Economics. Complex networks have also the long-tailed (power law, scale free)

distribution. These kinds of networks have a small amount of hubs, heterogeneity of

degree values, self-similarity and small-world structure. (Barbsi, Albert 1999, p. 2-4)

Every scale-free network has small-world property but it does not necessarily hold

reversely. In economies we can observe many power law phenomena, such as wealth

distribution or prices of assets on the financial markets.

20

Network science is an interesting field which is very important for studying

complex systems. The structures of networks can significantly influence the behavior of

complex systems. Also, including the concept into economic models could have

interesting implications for the economic science.

3. Summary

In this chapter we have introduced Complexity science and Complexity

Economics. We have listed key disciplines which create Complexity science and

explained a few concepts which we will need in our analysis later. Since Complexity

Economics is relatively new discipline with different approach, I would like to now pay

attention to comparison of Complexity, Austrian and Traditional Economics.

21

II. Comparative analysis of Complexity, Austrian and

Traditional Economics

Experience without theory is blind,

but theory without experience

is mere intellectual play.

Immanuel Kant

Complexity science has its foundations in Classical Political Economy based on

Adam Smith and his invisible hand which I talked about in the previous chapter. The

principles of the complex systems theory was later developed by Austrian School of

economics concretely by F. A. Hayek. His theory of spontaneous order and complex

phenomena created the basis of complexity science. Hence, I would say that Austrian

economics and Complexity economics might have much in common and I would like to

examine their similarities in this chapter. But we also need to look at the differences in

these two approaches and see if there is any problem which could cause some difficulty

in our analysis.

It is also important to focus on the Traditional Economics concepts, compare

them with Austrian and Complexity Economics and therefore analyze what these

approaches could possibly offer instead of the mainstream approach. Our gained

knowledge will help us with further analysis of business cycles.

1. Principles of Traditional Economics

By Traditional Economics I mean Mainstream or Neoclassical economics

which is generally accepted as a paradigm in economic science and the consensus

which is taught at universities nowadays. By Traditional Economics I will refer to

Mainstream/Neoclassical Economics.

Unfortunately, the models constructed in the traditional economics approach do

not correspond to empirical evidence (Beihnhocker 2006, p. 48) which is why this

should be sciences main task to explain how the world works, in our case, how the

economies work. In this unit, I would like to describe the main characteristics of

Traditional Economics and discuss its problems. General criticism of Traditional

Economics is about the unrealistic assumptions in models- mainly about the perfect

22

rationality of agents. I will also focus on the equilibrium approach which limits our

understanding of economic phenomena. This should lead to answer why, in my opinion,

complexity economics is a better approach to study economies.

1.1. Assumptions in economic models

As I mentioned above, the main criticism of traditional economics is held for its

assumptions. Milton Friedman advocates the unrealism of assumptions used for

constructing economic theories. He says that until the theories enable us to predict the

future and explain some phenomena according to reality, the significance of the realistic

assumptions is very low and, contrarily, the important hypotheses have assumptions that

inaccurately describe the reality. The assumptions do not have to be realistic since they

offer accurate approximation of reality. (Friedman 1966, p. 15)

Good models, indeed, are an approximation of reality and they should work like

a map show the streets and roads but without unnecessary details which would

encumber the map and lose its function. The problem is not the simplification of

assumptions, it is even desirable, but the contradiction of assumptions and reality.

Traditional Economics faces this problem. But the main task of science should be to

explain phenomena, not to predict it. Economics is often compared with meteorology.

Meteorologists are capable of explaining what and why something is happening in the

atmosphere, they can also offer some predictions about weather, but since the

atmosphere is a highly dynamical system facing thousands of changes over time, long

run predictions often do not work. The economy works similarly. It is a dynamical

system which creates complex phenomena and changes over time. Time also plays a

very important role in a systems dynamics and I will discuss this later. But I would like

to now describe the way economics has arrived to adopt these simplified, even

unrealistic assumptions.

It all began when Lon Walras imported the concept of equilibrium from physics

to economics. It allowed him to solve the problems with mathematical precision but it

required the making of a set of highly restrictive assumptions. (Beinhocker 2006, p. 48)

A lot of criticism came down but it was highly ignored by economists.8 Milton

Friedmans essay even helped to support this approach.

8 For further details see Beinhocker (2006, p. 45 48)

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1.1.1. Positivism in economics

Traditional Economics adopted the positivistic approach to the research.

According to Friedman (1966) the ultimate goal of a positive science is the development

of a theory that yields valid and meaningful (predictions about phenomena not yet

observed). Its function is to give a sense to empirical data and explain them. Theory is

to be judged by its predictive power; only factual evidence can show whether it can be

accepted or rejected. The way of testing the validity of a hypothesis or theory is by

comparing the predictions and experiences. The empirical evidence is obtained from the

statistical data or controlled experiments. The quantitative analysis is then performed by

econometrics which applies mathematics, statistics and computer science to economic

data. It practically allows economic theories to be empirically tested and it is also

utilized for making predictions. (Geweke et al. 2006, p. 2-6)

1.1.2. Equilibrium approach

Walrasian equilibrium or the general equilibrium approach assumes the

existence of two types of agents households and firms firms offer goods and demand

labor force and households offer labor force and demand goods. Firms are a profit

maximizer, meaning that they seek production function which yields to the greatest

returns. On the other hand, the households seek to maximize their utility by choosing a

set of goods that satisfy their needs the best under the budget constraint. Hence, we

consider two types of agents but the agents are homogenous.

Very often we encounter the term representative agent. It means that the agents

have the same properties and they are identical. It is often used in macroeconomics

because it is easier for the aggregation. The sum of their choices are mathematically

equivalent to the decision of one individual. Since we consider households and firms to

be identical, we arrive to perfectly competitive markets. Firms are price takers, price is

set by the demand function and there is practically no space for technological

innovation or change. This is because the economic profit of the firms is zero and there

is nothing left to invest. Both households and firms form the supply and demand sides

and the economics tries to figure out whether these two sides can reach trade agreement

based on single price they are willing to trade for on the market. When the supply and

demand match, market reaches the equilibrium. (Cardenete et al. 2012, p. 5-7)

24

In Walrasian equilibrium, economy is also assumed in that all markets are

interconnected and the change in price of one good affects all the prices of other goods.

Since all the markets are interconnected, we can also calculate the prices by a set of

equations. The interactions are held indirectly through the price mechanism and the

prices, matching the demand and supply, are set by an auctioneer who makes the

process of finding trading opportunities cost free. (Hahn, 2008)

Alfred Marshall developed the concept of partial equilibrium. It focuses only on

the single-product market when other conditions are being held fixed. In other words it

uses the ceteris paribus condition. It examines the effects of some actions in the

equilibrium of that particular market. The dynamical process in these models is the

adjustment of prices to supply and demand or the clearance of the market. This process

is considered in the short run where economy is out of equilibrium, the prices are not

clearing market, there is an excess of demand or supply, preferences of people and costs

of firms are also fixed. In the long run everything adjusts by change in prices, demand

equals supply and market is in equilibrium. Time in neoclassical models is not often

considered and it is being distinguished between the very short run, short run and long

run periods. (Salanti 1991, p. 73-75)

Nevertheless, it is not completely true that traditional models do not include

time. This has bearing on the static models only. Neoclassical economics also deals with

the dynamic models which we will pay attention later.

1.1.3. Perfect rationality and full information

I have already mentioned that adding the equilibrium approach into economics

requires many restrictions and simplifications of models, so that we could obtain nice

analytical solutions. The biggest critics came down on the assumption of perfect

rationality of people. They calculate, optimize and take into account all information that

is available for free. They think of every single action they are going to take.

Furthermore, they live in a simple world with linear relationships. They are also

self-interested in economic matters and there is no space for altruism. Since economics

is so called science of human action, these simplifications, almost unrealistic, do not say

much about human action. Of course, there is psychology which should tell us how

human behavior works and why. However, some pieces of knowledge from psychology

could be useful for economics as well because unrealistic assumptions in models could

25

lead us to unrealistic results. In fact, Herbert Simon, Daniel Khaneman and Amos

Tversky, who contributed to the development of behavioral economics, proved that the

concept of perfect rationality is misleading and it is not necessary for modelling

economic behavior. Instead, they constructed many theories on the basis of bounded

rationality. They used findings from psychology and used them to explain some

economic phenomena. For example the Prospect theory describes how people choose

between probabilistic alternatives that involve risk. People make decisions according to

their subjective evaluation of potential losses and gains based on certain heuristics

rather than the calculation of expected value of final outcome. (Kahneman, Tversky

1979, p. 267-272)

People also do not seek for the best solution and utility maximization but for the

satisfactory solution. During this process they sometimes, on purpose, skip searching

for additional information and are satisfied with the available solution. People neither

optimize nor calculate their actions. Instead, they use heuristics or mental models which

help them to act quickly and without much effort.

1.1.4. Exogenous shocks

If Traditional Economics does not include real time in the models and uses only

short run and long run periods to distinguish between non-equilibrium and equilibrium

state, how does it deal with the changes in economy? The changes in economy are

represented by moving from one equilibrium to another. These changes are embodied in

exogenous shocks. These shocks are exogenous because the effects which cause the

changes are not included in models. Hence, the dynamics are represented as

equilibrium, shock, new equilibrium and so forth. The exogenous shocks are created by

technological innovations, government actions or weather. But the problem here is that

the most interesting things which apparently effect economies are placed outside the

models. This could imply that business cycles in the economy are also exogenous and

random because they are caused by these exogenous shocks. (Beinhocker 2006, p. 54-

56)

It is obvious (and I have talked about it earlier) that good models should be a

simplification of the reality but they should also explain the phenomena. But traditional

economic models take the most interesting issues which fundamentally influence the

26

events in economy away and I ask myself the question, Is this the right way to model

economies and investigate the economic actions?

1.2. Comparative statics

In general equilibrium analysis, the economy itself is represented by a system of

equations. Each equation captures a relationship between the endogenous and

exogenous variables. It is often used as a linear approximation to the system of

equations that defines the equilibrium, under the assumption that the equilibrium is

stable. It permits easier understanding of particular relationships and nice analytical

solutions. (Kehoe 1987)

The number of endogenous variables should be equal to the number of

independent equations. When examining models constructed by others, it is not always

obvious which variables are endogenous and which variables are exogenous but it

should be explicitly stated. A solution to the system of equations is a set of values for

the endogenous variables, such that all of the equations of the system are

simultaneously satisfied. (Nachbar 2008)

Comparative statics is a technique used in Traditional economics which is used

to examine how the solution for the endogenous variables is affected when the

exogenous variables change, in other words, on the relationship between the variables.

It compares two states of the systems before the change and current state. This

analysis focuses on how output is changed rather than the dynamical process of

reaching a new equilibrium. (Nachbar 2008) An example of using this technique is in

the study of markets: the changes in demand and supply and how these changes affect

the equilibrium price and equilibrium quantity. Simply put, how the change of price of

good A, which is the substitute of good B, affects the price of good B.

This approach is different from that of Complexity and Austrian Economics, it

focuses on the outcome and relationship between two variables while others remain

fixed. Nevertheless, in the real world there are many variables and relationships

between them and we cannot simply illustrate it by the system of equations. A system of

equations might capture the state of the system in one single moment but the study of

the process can best explain why things happen in the particular way.

27

1.3. Dynamics in Traditional Economics

Every static equilibrium occurs in a given set of data. A change in the data

results in a new equilibrium position. As we discussed, comparative statics compares

these two points, two states before and after. We distinguish between static analysis of

static economy, static analysis of dynamical economy and so forth. In static analysis

there can be several equilibrium points and the process from moving from one to

another is a subject of the dynamic analysis. (Omay et al. 2004, p. 5) Therefore we can

say that dynamic analysis studies the path how the economic equilibrium is attained.

Economic variables change over time and thus the variables are considered as functions

of time. Time is therefore key factor in dynamic analysis.

Economic dynamics includes changes, time lags and external shocks. In a

dynamic equilibrium the variables may all be changing. Economic dynamics is

concerned with growth, business cycles or stability of equilibrium.

1.3.1. Neoclassical growth model Solow

The main interest of this model is to analyze how an economy reaches the steady

state equilibrium. In the steady state, various variables grow at the constant rate and

economy does not grow. However, at the beginning of the analysis, the state of

economy is unstable and economy converses to its equilibrium point which will not

change during the time. Hence, the dynamics in this model are the convergence to

equilibrium by balancing the growth rate of given variables which are different at the

beginning of analysis. (Mankiw 2010, p. 217)

1.3.2. Real business cycle theory

This theory is based in the Solow growth model and focuses on the causes and

propagation of business cycles in economy. According to this theory, business cycles

are an adequate response of optimizing agents to external technological shocks. These

shocks influence the production function and therefore strike the whole economy. The

dynamics in this theory are represented by external stochastic shocks which change the

potential product of the economy and therefore labor markets, market of loanable funds

and so forth. (Romer 2011, p. 189-237)

28

1.3.3. DSGE dynamic stochastic general equilibrium

Real business cycle model is an example of the DSGE model. DSGE modelling

creates one part of applied macroeconomics and is used in contemporary

macroeconomics for making predictions and understanding the structural changes in the

economy. (Roger, Farmer 2008) They study how the economy evolves over time and

reaches the equilibrium. DSGE models are based on micro-foundations, therefore they

study the aggregate level behavior of the economy by analyzing the interactions

between the agents and how they react to the fluctuations which the economy suffer

from. These fluctuations have random external character and that is why these models

are called also stochastic. (Farmer R., 2008)

1.4. Summary

In this unit we have discussed important characteristics of the Traditional

Economics approach, which is mainly focused on static equilibrium and dynamic

equilibrium achieving analysis. Reaching the equilibrium in the model requires certain

assumptions which also serve to simplify the analysis. We will return to these subjects

later when comparing the differences between Traditional and Complexity economics.

Now, lets focus on the tradition of the Austrian school of economics and its

methodology.

2. Austrian school of economics

Austrian school of economics is the school of economic thought which dates

back to the publication of the book Principles of Economics in 1871 by Carl Menger.

He was one of the three co-founders of marginal utility theory. However, Austrian

school differs from others by its rejection of mathematics; Menger did not use

mathematic tools in his analysis of marginal utility. Austrians are interested in the

individuals perspective of economic action and care that their theories are being

constructed with focus on subjectivism which is called agent-based reasoning.

(Rosser 2009, p. 394) Austrians also see market as a complex process that produce

order from interactions between the individuals (from bottom up).

Friedrich A. Hayek, another representative of Austrian school, strongly

contributed to the birth of Complexity Science with his works The Sensory Order

(1952) in which he described the central nervous system as a complex adaptive system

29

and Theory of complex phenomena (1964). Hayeks theory of complexity is related to

Santa Fe complexity. (Rosser 2009, p 395) He was openly and actively interested in

complexity ideas, system theory, cybernetics and evolution. The biggest characteristic

of Hayek is his notion of spontaneous order.

30

2.1. Principles of Austrian school of economics

Austrian school began research of economic phenomena through praxeology

science. Praxeology is built on the axiom that humans act. They act in order to satisfy

their need (reach their goals) and this behavior is then conditional. This axiom is

irrefutable. (Elgar 1997, p. 58) The first who came up with praxeology was Ludwig

von Mises in his book Human Action (1996, p. 103). At the beginning of the book

(1996, p. 103) At the beginning of the book he describes human action and sets the

purposefulness as an assumption of a conditional human action which is his main topic

of research. A priori could be deduced from praxeology- the economic theories and

explained economic phenomena. Hence, Austrian economics uses an axiomatic-

deductive method and is not empirical science. Theories cannot be verified or falsified

by empirical data but again by some deductive reasoning.

2.1.1. Methodological individualism

Praxeology is interested in the human action of individual beings. Mises (1996,

p. 41-43) states that all actions are performed by individuals. Collective actions are

always done through the intermediary (one of the members of the group). Social

institutions emerge spontaneously because of the individuals interests which motivate

them to unify. Since the individual is the one who acts, all economic phenomena can be

traced back to this individual. That is why Austrian school refuse macroeconomic

aggregates, such a GDP.

2.1.2. Methodological subjectivism

Methodological subjectivism means that people assign a value to the things. A

good does not contain the value on its own. But we cannot measure the value by some

numbers or units. The value is rather ordinal than cardinal. An individual values the

marginal unit of the good which is the additional unit of that good. The utility from

these additional units is called marginal utility. (Boettke 1998, p. 17-22) This is also an

explanation for the well-known paradox of the diamond which Adam Smith and other

classical economists have wondered about. It is obvious that water is more useful than

diamond. But still the diamond is way more expensive than water. People do not decide

about all diamonds of the world or about all the water but rather about 1 piece of

diamond and a bottle of water. And because people have a lot of water, the marginal

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utility from water is lower than the marginal utility from the first diamond and thus the

bottle of water is cheaper than this diamond.

Subjectivism is important to explain the development of exchange. For two

people to exchange goods with each other, they must value the good of other partner

higher than their own good. After exchanging, they both have to be better off otherwise

they will not exchange.

2.1.3. A priori deductive reasoning

Austrian economics derives from deductive logic a priori their economic

theories. For Mises, economic phenomena must be deduced from the first axiom that

humans act. If we assume this axiom true, conclusions logically deduced from it are

valid. According to Austrians, the deductive method is the key instrument in

understanding and describing economic patterns.

Austrians argue that the methods used by natural sciences could not work in

social sciences because there are no underlying constants in human behavior that can be

observed in natural sciences. Second, there is no way to conduct a truly controlled

experiment in the social sciences because people are aware of the experiment and their

actions are influenced by the environment and so forth. (Murphy 2003)

2.1.4. Time

The presence of time in human decision making seems to be clear but it is often

ignored in economic analysis. Time is an omnipresent scarce factor that individuals

must take into account in their human action. (Rothbard 2009, p. 13) Individuals prefer

their end to be achieved in the shortest possible time. The sooner it arrives, the better.

Time is a means to be economized and each individual valuates time differently. This is

given by time preferences which we will talk about later. Rothbard (2009, p. 4)

assumes:

All human life must take place in time. Human reason cannot even conceive of an

existence or of action that does not take place through time. At a time when a human

being decides to act in order to attain an end, his goal, or end, can be finally and

completely attained only at some point in the future.

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2.1.5. Evenly rotating economy

Austrian economics focuses on the study of the market process and how to reach

the equilibrium state. Instead of using the term equilibrium, Austrians use Evenly

Rotating Economy (ERE). But economy will never reach this state because the prices of

all products remain the same, all needs are satisfied and there is no space for money

(because there is no exchange) and therefore the human action also does not exist. This

concept is also used to illustrate the function of entrepreneurship and to demonstrate

meaning of profit and loss. (Mises 1996, p. 244-248)

The same market transactions are repeated again and again. The goods of the higher

orders pass in the same quantities through the same stages of processing until

ultimately the produced consumers' goods come into the hands of the consumers and

are consumed. No changes in the market data occur. Today does not differ from

yesterday and tomorrow will not differ from today...Therefore prices--commonly called

static or equilibrium prices--remain constant too. (Mises 1996, p. 247)

3. Agreements and possible disagreements of Austrian and

Complexity economics

We can observe that Austrian and Complexity economics have in common

certain characteristics in their approach and methodology. A deep theme of Austrian

economics has been that of spontaneous order or self-organization of the economy.

The origin of this theme dates to the putative founder of the Austrian School, Carl

Menger, with his theory of the spontaneous emergence of money for transactions

purposes in primitive economies. Menger drew this approach from the Scottish

Enlightenment figures of David Hume, Adam Ferguson, and Adam Smith, with the

latters Wealth of Nations (1776) holding particular importance. The most important

developer of this idea within the tradition after Menger was F.A. Hayek (1948), who

would identify this self-organization phenomenon with emergence, later expanding

upon this in the broader concept of complexity.

I would like to now focus on the substantive links between Austrian and

Complexity Economics and also underline possible differences.

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3.1. Agreements between Complexity and Austrian Economics

The crucial characteristic which connects Complexity and Austrian Economics

is the view of an economy as a dynamical complex system where subjective preferences

matter because they differentiate agents. Agents are heterogeneous. Austrians also

emphasize the process of learning. They hold onto the concept of rationality of people

but in a subjective way. People act the best they can according to the information and

knowledge they have in order to fulfill their goals. This concept of rationality is not in

contradiction of bounded rationality. Contrarily, Austrians embed the asymmetry of

information in their analysis and are aware of the cognitive limitation of peoples minds

However, what is the most substantial common characteristic of Complexity and

Austrian economics is the concept of emergence and complexity. Hayeks spontaneous

order is an analogy for the complexity. It all comes from the bottom up, from the

interactions among the heterogeneous individuals. From these interactions the

dynamical market emerges without any central planner or human design. Spontaneous

order in society is the result of human action, but not of human design. (Hayek 1967,

p. 11) Hayek sees society and its institutions as a network produced by the decisions of

many individuals, distributed over space and time, each seeking to solve her problem

and spontaneously create a societys structure. (Rosser 2009) Talking about society,

complexity theorists like Austrians emphasize that is it not possible to understand

aggregate behavior without recognizing what is going on at the micro level.

3.1.1. BRICE

In Handbook of research on complexity (2009, p. 397-400) the common

methodological overlaps of Complexity and Austrian Economics are summarized. Their

initial letters create an acronym BRICE.

BOUNDED RATIONALITY: The term bounded rationality was invented by Herbert

Simon. Hayek and Mises derived the impossibility of economic calculation in socialism

from bounded rationality. Hayek explains bounded rationality in The Sensory Order. He

notes that the mind has