universidade federal de pernambuco decision theory - ppgep-ufpe

Transcription

UNIVERSIDADE FEDERAL DE PERNAMBUCO
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE PRODUÇÃO
DECISION THEORY IN THE AUTOMOTIVE MARKET
RAFAELLA AZEVEDO DE LUCENA SARMENTO
Supervisor: Fernando Menezes Campello de Souza, PhD.
RECIFE, FEBRUARY/2011
UNIVERSIDADE FEDERAL DE PERNAMBUCO
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE PRODUÇÃO
DECISION THEORY IN THE AUTOMOTIVE MARKET
DISSERTATION SUBMITTED TO UFPE
IN SUPPORT OF MASTER’S DEGREE
BY
RAFAELLA AZEVEDO DE LUCENA SARMENTO
Supervisor: Fernando Menezes Campello de Souza, PhD.
RECIFE, FEBRUARY/2011
“Not to make a decision is already a decision. Not to make a choice is already a choice.”
Kierkegaard
“Dedico esta dissertação a minha família e aos amigos e professores que me incentivaram e encorajaram nesta empreitada.”
Agradecimentos
Agradeço, primeiramente a Deus, meu amor maior, por ter me permitido desfrutar de
uma das coisas que mais gosto na vida, que é estudar. Por ter me dado força e consolo
nos momentos difíceis, e por ter colocado pessoas boas no meu caminho, as quais me
ajudaram a conduzir este mestrado e a torná-lo mais leve e prazeroso.
À minha família, Adeildo, Inês e Victor e a minha avó, tios e primos, pelo apoio e
incentivo, pela compreensão dos momentos que tive que me ausentar, e pela alegria e
afagos proporcionados quando estive presente. Ao meu noivo Jamaci pela paciência e
oportunos conselhos aos meus desabafos, e eterno carinho.
Ao Prof. Fernando Menezes Campello de Souza, PhD, meu orientador, pela oportunidade de amadurecer e aprender a lutar pelos meus objetivos. Pelo empenho e paciência
com minhas limitações intelectuais e acima de tudo pelo exemplo de pessoa que ama o
que faz, e que por isso faz muito bem feito!
A todos os meus amigos, em especial a Cassiano Henrique pela grande ajuda no aprendizado com o LATEX e outras ferramentas computacionais, as quais foram essenciais para a
construção deste trabalho, e pelo companheirismo prestado em todos os momentos deste
mestrado. E a Eduardo Maia, primeiramente pelo exemplo de pesquisador e acima de
tudo cristão, e pelas palavras de consolo e tranquilidade passadas nos momentos mais
estressantes.
Aos funcionários do PPGEP, e em especial a Juliane e Bárbara, secretárias do PPGEP,
pela simpatia e pela ajuda nos momentos difíceis.
Ao Prof. Roderick Kay pelos ensinamentos de inglês, pela simpatia e carisma nas
nossas aulas no CTG às quintas-feiras.
À CAPES - Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
pelo financiamento do meu curso de pós-graduação.
Muito obrigada,
Rafaella Azevedo de Lucena Sarmento
Resumo
Esta dissertação faz referência à importância de se utilizar modelos matemáticos na
tomada de decisões não estruturadas. O contexto utilizado é o de consumidores de automóveis, os quais possuem diferentes preferências de acordo com seus respectivos estilos
de vida, além de muitas variáveis com as quais precisam lidar no momento da compra
de um carro. Uma análise foi feita sobre os perfis e preferências dos consumidores. As
características dos carros, por sua vez, foram agrupadas em atributos, chamados de payoffs. A modelagem se utilizou da Teoria Decisão. Além disso, protocolos foram utilizados
para eduzir a Função Utilidade dos consumidores, através do uso de questionários. O
modelo construído se utiliza de varáveis dicotômicas, as quais permitem a aplicação da
distribuição binomial para edução da Função Consequência. O problema prosposto apresenta a decisão com o menor risco para três consumidores diferentes. A partir dos
resultados obtidos, pode-se afirmar que o estilo de vida do indivíduo interfere em suas
preferências e escolhas. Além disso, o modelo é avaliado por uma análise de sensibilidade
que mostra a eficiência do modelo para o problema proposto.
Abstract
This dissertation describes the importance of using mathematical models to take nonstructured decisions. The context used is that of car consumers, who have different
preferences in accordance with their life style and have many variables to deal with when
they go to buy a car. An analysis was made of consumers’ profiles and preferences. The
cars’ features were grouped into attributes, called payoffs. The modeling problematic used
Decision Theory. Besides this protocol foundations were used for educing the consumers’
Utility Function, using questionnaires. The model made use of dichotomic variables that
permit binomial distribution for educing the Consequence Function to be applied. The
proposed problem presents the decision with the lowest risk for three different consumers.
From the results obtained, it can be argued that the lifestyle of a person reflects his
preferences and choices. Moreover, the model is validated by a sensibility analysis that
shows the model to be effective for the problem proposed.
List of Figures
1.1
Cars produced in Brazil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2
Cars sold in Brazil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1
People distributed in clusters. . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2
Cluster analysis- Non-dichotomic variables. . . . . . . . . . . . . . . . . . . 54
4.3
Cluster analysis - dichotomic variables. . . . . . . . . . . . . . . . . . . . . 54
4.4
Consumers’ preferences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.5
Preferences of the clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.6
Social Analysis of Decision-maker 1. . . . . . . . . . . . . . . . . . . . . . . 70
4.7
4.8
List of Tables
1.1
The evolution of the profit rate in Brazil. . . . . . . . . . . . . . . . . . . . 14
1.2
Forecast of vehicle production. . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.3
Forecast of vehicle sales. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1
The Formation of Payoffs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2
Correlation Matrix of Car Aspects. . . . . . . . . . . . . . . . . . . . . . . 38
4.3
Total Price Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.4
Insurance Value Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5
Composition of Maintenance. . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.6
Maintenance Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.7
Fuel Consumption Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.8
Durability Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.9
Warranty Conditions Score. . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.10 Resale Value Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.11 Financial Terms Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.12 Economy Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.13 Appearance and Beauty Score. . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.14 Status and Prestige Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.15 Workmanship Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.16 Comfort and Ergonomics Score. . . . . . . . . . . . . . . . . . . . . . . . . 48
4.17 Accessories and Optional Extras Score. . . . . . . . . . . . . . . . . . . . . 48
4.18 Comfort Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.19 Safety Optional Extras Score. . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.20 Performance Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.21 Safety Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.22 Example of consumers’ payoffs. . . . . . . . . . . . . . . . . . . . . . . . . 59
4.23 Likelihood Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.24 Prior Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.25 Consequence Function (in blank). . . . . . . . . . . . . . . . . . . . . . . . 65
4.26 Consequence Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.27 Questionnaire divided into two scales to facilitate educing. . . . . . . . . . 68
4.28 Example of a questionnaire applied to decision-makers. . . . . . . . . . . . 69
4.29 The ranking of payoffs for the Decision-maker 1. . . . . . . . . . . . . . . . 71
4.30 Decision-maker 1’s Utility. . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.31 The ranking of payoffs for Decision-maker 2. . . . . . . . . . . . . . . . . . 72
4.33 The ranking of payoffs for Decision-maker 3. . . . . . . . . . . . . . . . . . 73
4.35 Decision rules: decision-maker 1. . . . . . . . . . . . . . . . . . . . . . . . . 75
List of Abbreviations
ABS
Anti-lock Braking System
ANFAVEA Associação Nacional dos Fabricantes de Veículos Automotores
BCWS
Best Cars Web Site
CPI
Consumer Price Index
DPDC
Departamento de Proteção e Defesa do Consumidor
ESP
Electronic Stability Program
GDP
Gross Domestic Product
GM
General Motors
IPI
Imposto sobre Produtos Industrializados
IPVA
Imposto sobre a Propriedade de Veículos Automotores
SUV
Sport Utility Vehicle
UFPE
Universidade Federal de Pernambuco
USA
United States of America
Contents
1 INTRODUCTION
13
1.1
Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2
Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3
1.2.1
General Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2.2
Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Organization of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 THEORETICAL BACKGROUND
2.1
19
Decision Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.1
Decision Theory Sets . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.2
Probabilistic Mechanisms
2.1.3
Decision Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1.4
Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
. . . . . . . . . . . . . . . . . . . . . . . 22
3 LEARNING A LITTLE ABOUT CARS
3.1
3.2
28
History of Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.1
Craft Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.2
Mass Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1.3
Lean Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Automotive Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1
Power Steering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.2
Air Bag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.3
Anti-lock Braking System . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.4
Rear wheel traction vs front wheel traction vs four wheel drive . . . 32
4 DECISION THEORY IN THE AUTOMOTIVE BUSINESS
4.1
4.2
34
The representation of payoffs . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.1
The Construction of Payoffs . . . . . . . . . . . . . . . . . . . . . . 35
4.1.2
Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Choosing a car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.1
Set of Payoffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.2
Set of States of Nature (Scenarios) . . . . . . . . . . . . . . . . . . 59
4.2.3
Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.4
Set of Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.5
Choosing the best decision based on the best decision rule . . . . . 64
4.2.6
Utility Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.7
A Sensibility Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 76
5 COMMENTS, CONCLUSIONS AND SUGGESTIONS
78
5.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2
Limitations of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3
Suggestions for future research . . . . . . . . . . . . . . . . . . . . . . . . . 80
REFERENCES
81
A Appendix
86
Chapter 1
1
INTRODUCTION
INTRODUCTION
Ever since the car was invented, it has been regarded as a very important form of
transport all over the world. First the car became a tool which transformed people’s
lives, because it offered a comfortable way to travel around, besides being quicker than
other means of transport. And secondly, the car changed production methods for ever.
The advances in the industry came from competition between car factories that used more
and more ideas to produce more cars at less cost and better quality and to meet their
client’s needs.
When the car was first invented, people thought it was only a new machine for rich
people, since it was built by hand, it was expensive. Twenty years later, Henry Ford
invented a new car that became the most popular car in history, the model-T. According
to Womack, Jones and Roos (2004), Ford introduced mass production, which meant
producing a large volume of products that were similar but at the cost of not being able
to cope with diversity. So, prices tell, and more people could buy a car, which made car
ownership a reality for many more people than in the past. Since then, growth in the use
of the car has constantly increased. In the 60s, most Japanese companies gained a large
advantage over other countries which enjoyed in mass production. In 20 years Japan saw
its share in worldwide automobile production rise due to the Toyota Production System,
or Lean Production, which allowed their companies to produce large volumes combined
with a large mix, while seeking to educe costs and yet having the flexibility to increase
production.
Car use is continually increasing and nowadays the car is one of the most important
items in urban life. Not only in developed countries, but also in emerging and poor
countries we can see many cars on the streets. There are factories in some of these
emergent countries, too, and this shows the spread of car use. In Brazil, for example, there
are 25 companies (associated with ANFAVEA) which manufacture vehicles, agricultural
machinery, engines and components (Associação Nacional dos Fabricantes de Veículos
Automotores, 2010a). This spread has many consequences for society in general and the
worldwide economy. One of these consequences is the division of work philosophy that
led to the management of the factory floor being controlled by other sectors. So in the
20th century the activity of car distribution has not been determined by car factories, but
13
Chapter 1
INTRODUCTION
by a specific sector: dealers. During this period, several sectors have appeared to offer
support to the automotive market (Moura Filho, 2007).
Car factories are responsible for a large part of the general economy. This was seen
recently, when the worldwide economy suffered a major crisis. One of the first things that
the government did was to lend money to car factories so as to continue to make the economy move. Globalization has brought about the development of factories, technological
innovation and new ways to organize and to manage production. Nowadays companies
direct greater attention to productivity, products and process quality, services, clients
and to flexibility in production, and always seek competitive advantages of low costs or
product differentiation. According to Souza (2004), cost is not a tool to estimate stock
and to determine the sale price anymore, but it is a very important instrument to help in
the competitive market since it establishes the size of the profit.
On the other hand, dealers are responsible for distributing the cars produced in factories. But not only for distributing. This sector offers, after sale services, such as
mechanical maintenance, spare parts and other car services. The large evolution in dealers’ approaches has caused fierce competition in the automotive market. But automakers
are powerful and the dealers are dependent on them. Therefor automakers demand a lot
of services from the dealers yet keep the largest part of the profit and squeeze the dealers’
share. According to Souza (2004), in Brazil the profit rate has been significantly reduced,
as can be seen in Table 1.1:
Table 1.1: The evolution of the profit rate in Brazil.
Year
Profit Rate
1959
39.0%
1990
13.6%
1991
5.9%
1993
2.1%
1995
1.3%
1997
1.0%
1999
0.6%
2000
0.7%
Source: ANFAVEA, 2003 apud Souza, 2004.
Consumers are the raison d’être of factories and dealers. They are the reason for the
transformations in technologies, car models, and methods of payment, since factories and
dealers need to be competitive. Nowadays one can find a large variety of car models,
brands, services and many different ways to pay. On the other hand, car manufactures
14
Chapter 1
INTRODUCTION
must tackle bad news too, such as the increase in the number of recalls (Redação, 2010).
Brands that consumers have trusted because of their quality, nowadays present defects in
safety items, for example. The number of recalls in 2009 was the highest compared with
other years. According to specialists, this was a reflection of the sales record registered
in September 2009 stimulated by ending the IPI (Imposto sobre Produtos Industrializados) reduction. The legal coordinator of DPDC (Departamento de Proteção e Defesa do
Consumidor) claims that the increase in the number of recalls shows that the Brazilian
consumer today is much more demanding than in the past (Redação, 2010). In other
words, currently clients are seeking to choose the best car and services for the best price,
and try to increase the cost/benefit ratio.
The graphs below, in Figures 1.1 and 1.2, see the continuous growth of car production
and sales, in Brazil, in the period 1999-2009.
Figure 1.1: Cars produced in Brazil.
Source: ANFAVEA, 2010b
Figure 1.2: Cars sold in Brazil.
Source: ANFAVEA, 2010b
Despite all these questions raised by consumers, research shows that the production
and sale of cars will tend to increase in coming years, as can be seen in Tables 1.2 and
1.3 .
1.1
Justification
The continuous increase in the number of car owners has caused the automotive market
to grow consistently, consumers’ demands to increase, competition to increase, and there
are only some among many other challenges to be met. Clients want to choose a car that
meets most of their objectives, their needs and that they can afford. So, what attitudes
15
Chapter 1
INTRODUCTION
Table 1.2: Forecast of vehicle production.
February (Million units)
Region
2009 2010
2011
NORTH AMERICA
8.6
10.6
12.3
United States
5.6
6.8
8.1
EUROPE
16.9 17.6
18.3
West Europe
12.0 12.4
13.0
Germany
5.1
5.2
5.3
United Kingdom
1.1
1.2
1.3
France
2.0
2.2
2.4
Italy
0.8
0.8
0.8
Spain
2.2
2.2
2.1
East Europe
4.8
5.2
5.4
Russia
0.7
1.1
1.1
ASIA
28.9 32.4
35.2
Japan
7.7
9.1
10.1
China
12.9 13.9
14.8
South Korea
3.5
3.7
3.9
India
2.4
2.9
3.1
MIDDLE EAST AND AFRICA
1.8
1.8
2.0
SOUTH AMERICA
3.7
3.9
4.1
Brazil
2.9
3.1
3.2
Total World
59.1 65.6
71.2
Source: Automotive Business, 2010
January (Million units)
2009 2010
2011
8.6
10.3
12.0
5.6
6.7
8.0
16.8 17.6
18.5
12.0 12.4
13.0
5.1
5.2
5.3
1.1
1.2
1.3
2.0
2.2
2.5
0.8
0.8
0.9
2.2
2.2
2.1
4.8
5.2
5.5
0.7
1.1
1.1
28.4 31.4
34.2
7.7
8.9
10.1
12.5 13.4
14.3
3.4
3.5
3.8
2.3
2.7
2.8
1.8
1.8
2.0
3.7
3.9
4.0
3.0
3.1
3.1
58.6 64.3
70.1
Table 1.3: Forecast of vehicle sales.
February (Million units)
Region
2009 2010
2011
NORTH AMERICA
12.7 13.8
16.3
United States
10.4 11.5
13.8
EUROPE
18.2 16.9
17.8
West Europe
14.9 13.5
14.1
Germany
4.0
3.0
3.3
United Kingdom
2.2
2.0
2.1
France
2.6
2.4
2.4
Italy
2.3
2.2
2.2
Spain
1.1
1.1
1.1
East Europe
3.3
3.4
3.7
Russia
1.5
1.6
1.6
ASIA
24.1 25.9
27.5
Japan
4.6
4.9
4.9
China
12.9 14.0
14.8
South Korea
1.4
1.4
1.4
India
2.1
2.4
2.7
MIDDLE EAST AND AFRICA
3.1
3.2
3.6
SOUTH AMERICA
4.1
4.3
4.4
Brazil
3.0
3.2
3.3
Total World
63.3 65.4
70.9
Source: Automotive Business, 2010
January (Million units)
2009 2010
2011
12.6 13.6
16.2
10.3 11.3
13.8
18.1 16.9
17.8
14.8 13.5
14.1
4.0
2.9
3.3
2.2
2.0
2.1
2.6
2.4
2.4
2.3
2.2
2.1
1.1
1.1
1.1
3.2
3.4
3.7
1.4
1.6
1.6
23.3 24.8
26.5
4.4
4.6
4.9
12.4 13.5
14.3
1.3
1.2
1.3
2.0
2.2
2.4
3.1
3.2
3.6
4.1
4.2
4.4
3.0
3.1
3.2
62.3 64.1
69.8
do these people need to have to attain these objectives? This is not an easy question
to answer. Taking a non-trivial decision should not be made intuitively, but rather a
16
Chapter 1
INTRODUCTION
rational method is needed. Using a mathematical model it is possible to obtain, with
consistency, the various options of decisions, what risks are inherent from each decision
and the intensity of these risks.
1.2
Objectives
Given the above, this dissertation sets out to meet the objectives described below.
1.2.1 General Objectives
To develop decision-making support models to help car consumers on how to act when
deciding what car to buy.
1.2.2 Specific Objectives
1. To raise the most important questions for decision-making that car consumers need
to answer;
2. To analyze car consumers’ profile and preferences;
3. To educe the decision-makers utility function;
4. To construct and validate a mathematical model to help car consumers on how to
act with regard to buying a car ;
1.3
Organization of Work
This introductory chapter sets out the justification, the objectives and the main academic approach. Besides, it includes a discussion of the methodology and a brief review
of the literature.
Chapter 2 presents the theoretical background and summarizes Decision Theory, the
mathematical model that underpins this study, and some statistical techniques, used to
analyze the data.
The third chapter presents a summary of the history of production. This shows how
the car influenced the progress of technology and production methods, besides some information on automotive technology..
17
Chapter 1
INTRODUCTION
Chapter 4 describes all the development work. This includes the problem of contextualizing the reality of car consumers and what can be done. In addition, it sets out the
research on decision-making by consumers, the consumer’s profile, preferences and the
construction of payoffs. And finally, this chapter presents the model with its variables
(states of nature, actions, observations), probabilistic mechanisms, consumer utility, that
lead to the best decision.
Chapter 5 presents comments and conclusions and makes suggestions for future studies.
18
Chapter 2
2
THEORETICAL BACKGROUND
Presented below in summary form are some areas of study that were exploited to better
develop this study. The terms, phrases and concepts explained in this section, and used
in this dissertation will contribute to a better understanding of how the research was
undertaken.
2.1
Decision Theory
All people must make decisions every day. There is no way to avoid making decisions.
However, most decisions are made instantaneously. It is not necessary to think very much
before taking them. For example, if a man is walking on a street and suddenly he sees a
mad dog running towards him, barking furiously, he would not think twice, he would run
to a safe place. But, when we are confronted with a life-altering decision, this is a good
opportunity to analyze formally the options available in order to select the one that will
bring the most favorable consequences.
Currently, most students in elementary, secondary, college and graduate schools do
not learn how to think in an uncertain world. According to Howard (2007), in the 1970s
the work of Tversky, Kahneman, and others proved that people who made decisions by
trusting only in their intuition were the victim of many errors that they would recognize,
upon reflecting on what they had done. This emphasized the need to make use of a formal
procedure to support the making of important decisions. Such analysis can be made from
Decision Theory.
“Since uncertainty is the most important feature to consider in making decisions, the ability to represent knowledge in terms of probability, to see how
to combine this knowledge with preferences in a reasoned way, to treat very
large and complex decision problems using modern computation, and to avoid
common errors of thought have combined to produce insights heretofore unobtainable” (Howard, 2007).
According to Stein (2010) to learn decision theory is like learning mathematics. Decision theory does not use mathematical conventional meaning, but it uses many principles
19
Chapter 2
and concepts from mathematics. It is a subject that deals with the practical problem
of rational decision-making. Decision Theory works on the problem of being logical in
uncertain situations (Campello de Souza, 2007). According to North (1968) decision theory can be considered a formalization of common sense. Mathematics uses unambiguous
language that can represent a decision problem. So, decision theory is a collection of tools
for structuring and choosing among a finite number of different alternatives. “In essence,
what the decision theory formalizes is the common sense idea that an individual should
take the best action based upon what he or she wants, knows, and can do.” (Lessa, 2008).
Since the middle of the 20th century, modern Decision Theory has been developed
through contributions from several academic disciplines. Decision theory has been used
by economists, statisticians, psychologists, political and social scientists or philosophers
(Hansson, 1994).
2.1.1 Decision Theory Sets
“What exactly is a decision problem? A decision problem arises when an
agent believes herself to have a number of distinct possible actions open to her.
She believes each action to have a range of possible outcomes. These outcomes
may vary according to certain facts about the world that may or may not be
within the agent’s control. Let us say, then, that a decision problem has three
components.” (Bermúdez, 2009)
Set of Payoffs
According to Stein (2010) payoffs are quantified results. Quantifying results is an
important step in making good decisions; and many decisions come down to how to
maximize favorable payoffs or minimize adverse ones. Payoffs are simply numbers on
a numerical scale, associated with this scale these numbers, such as physicists, dollars,
whatever are determined, and therefore many decisions become clear-cut. There must be
alternatives that can actually be selected. Intuitively, what the decision-maker needs to
do is rank the outcomes and choose the action that reaches the payoff with the highest
rank. Bermúdez (2009) claims ranking a set of outcomes at a minimum requires there to
be some preference relation R such that, for any pair of outcomes a and b, it is possible
20
Chapter 2
to define two further relations of strict preference and a relation of indifference by using
relation R. This permits all the outcomes to be organized into a linear ranking with the
most preferable at the top and the least favored at the bottom. Payoffs are represented
by P∗ = {p} and they can be money, happiness, satisfaction, based on the context of
problem.
Set of states of nature
According to Campello de Souza (2007) a state of nature is a description of the world
(nature) which leaves no relevant aspect without description. The states of nature are
what happen and can interfere in the payoffs, but the decision-maker cannot control
them. At one extreme it maybe assumed that agents have a probability distribution
for the different possible states. In some cases this distribution may be of objective
probabilities, but in other cases it might be a complete subjective probability distribution
over the relevant possible states. The subjective probability distribution is responsible for
degrees of belief or confidence, that respects the basic laws of the probability calculus. If
the relevant possible states are mutually exhaustive then the degrees of confidence that
were assigned to them must sum to 1; if a degree of confidence p is assigned to one of
them, then degree of confidence 1 − p must be assigned to its not occurring; and so on
(Bermúdez, 2009). The climate, an increasing growth rate in a country, the failure rate
of a machine are examples of states of nature. They are represented by Θ = {θ}.
Set of possible courses of action
Actions are what it is possible to do, when there are some states of nature and there
is a decision to make. The consequences of these actions are the payoffs. If the decision
problem is to be resolved by solving, then what should been chosen is the action whose
outcome has the highest utility. For any agent there will always be at least one such
action. If there is more than one, the decision-maker is oriented by decision theory not
only that he must choose one of the bound actions, but also that there can be no rational
grounds for choosing between them. Courses of actions are represented by A = {a}.
21
Chapter 2
Set of observations
In many cases it is not possible to see states of nature directly, so it is necessary to
work with variables that are related to θ. For example, it is possible to know something
about the weather from meteorological services. These observations are represented by
X = {x}.
2.1.2 Probabilistic Mechanisms
Several sources of uncertainty appear in decision problems, so it is necessary to describe
and to represent them. They are represented by probabilistic mechanisms. The main
probabilistic mechanisms are described below.
Consequence Function
According to Campello de Souza (2007) a consequence function is the probability
of winning a payoff p, when nature is in the state θ and the decision-maker chooses
action a: P (p|θ, a). In the continuous case, the consequence function is represented by
FP |Θ,A (p|θ, a). It is important to remember that a decision maker chooses an action which
increases the probability of his obtaining his favorite payoff, so he has a preference for a
payoff.
Likelihood Function
This is the probability distribution about the relation between the observations and
the states of nature. It is represented by: P (x|θ). In the continuous case, the likelihood function is represented by FX|Θ (x|θ). According to Campello de Souza (2007) the
problem of inverse probability, based on observed events, about the likelihood of causes,
was first solved in Bayes’ work. Bayes proposed the principle that all information that
a professional acquires over the years of experience can be treated quantitatively in the
decision problem. This a priori likelihood reflects the degree of belief that the specialists
have in the chances of what could happen. Horvitz, Breese and Henrion (1988) claimed
Bayesian or subjectivist model have the notion of probability of an event as a measure of
a person’s degree of belief in the event; probabilities are seen as properties of the state of
22
Chapter 2
knowledge of an individual rather than properties of a sequence of events. The attribution of a subjective probability should be founded on all information available to a person,
even those things that are known to be true or deducible in a logical sense, as well as
empirical frequency information. And this a priori distribution, which represents expert
knowledge, can be recorded on the achievements of the company formally, systematically
and computationally. There are two kinds of probability, that from the statistical (relative
frequency) or that epistemologically linked to the degree of belief of the province. The
Bayesian approach assumes importance in situations where there are few data or no data.
In these situations it makes no sense to ignore a priori knowledge that an expert has on
the variable in question, the result of his familiarity with the structure, constraints and
aspects of the problem and its details, which enable him to explain this knowledge in the
form of a probability distribution.
A priori distributions of the States of Nature
This means what is known about nature, before conducting any experiment or observations on θ. It is represented by: π(θ). It is not known which state nature will choose,
but what is necessary is to have at least a piece of vague information about the states of
nature in order to formulate the decision problem.
2.1.3 Decision Rules
According to Campello de Souza (2007), a decision rule is a procedure that allows a
course of action to be chosen, between the available ones, according to what the decisionmaker wants and what he knows. A decision rule is a function that associates each
observation with an action. So, nature chooses a state, which is observed by the decisionmaker, and then he chooses an action, with a view to making a decision on achieving
the best consequence for him. There are two kinds of decisions rules: randomized and
non-randomized.
Non-randomized decision rules are deterministic. Each observation x is associated with
an action a, so they are characterized by the function:
23
Chapter 2
d : X −→ A
x7→d(x)=a
(2.1.1)
Randomized decision rules are chosen arbitrarily according to a distribution δ. In
this kind of randomized rule, one needs, first to turn the non-randomized rule chosen d
into a randomized one and thereafter observe x and then adopt action a. So δ(d) is the
probability of choosing the rule d, and the act of all distributions δ is D∗ = {δ}.
In the decision-making process, the payoff does not depend on the choice of the decision
rule. The motives for choosing an action are not important, but rather the action itself
(Campello de Souza, 2007). So, when the decision-maker chooses an action and nature
chooses a state, one needs to obtain the consequence function and the other functions
that are necessary for calculating the best decision: Loss Function and Risk Function.
According to Lessa et al. (2008), the risk function strikes a balance between the decision
rules. The process for obtaining the probability of a certain result related to the “state
of nature” defines the consequence function. So, from the risk function, it is possible to
establish the smallest risk, which is related to the best rule.
Also in accordance with Campello de Souza (2007), decision theory follows the steps
given below:
• Analyzing current and past conditions;
• Identifying uncertainty and chance from the past, present and future;
• Drawing up a mathematical model to describe and calculate the system with the
precision desired;
• Educing the decision-maker’s values and preferences about the system;
• Identifying alternatives of actions for obtaining the state desired;
• Logical-mathematical combination of options (actions alternatives), utilities (preferences and values) and probabilities (chances and uncertainty) with the system
mathematical model, to identify the most advantageous performance line;
• Implementing the actions chosen;
• Returning to the first step to detect mistakes and correct them.
24
Chapter 2
It is important to understand that, from the standpoint of rationality, the correct
decision is not always a successful decision. “Life’s winners, more often than not, are the
product of good decisions, and life’s losers are frequently the result of a bad decision.”
(Stein, 2010).
2.1.4 Utility
It is not possible to tackle Decision Theory without understanding the importance of
Utility. According to Campello de Souza (2007), utility focuses on the decision-maker’s
preferences about the possible consequences of an action. It is necessary to extract from
the decision-maker’s consciousness his preferences. This is called to educe or elicitation.
But it is not a simple task. Einhorn and Hogarth (1981) claimed that psychologists
can best contribute to decision research by elucidating the basic psychological processes
underlying judgment and choice. In their work Einhorn and Hogarth have tried to place
behavioral decision theory within a broad psychological context, and in doing so they
have emphasized the importance of attention, memory, cognitive representation, conflict,
learning, and feedback.
Indeed, the objective of the utility function is to develop a mathematic model that
represents the decision-maker’s preferences. So the idea is to quantify desires by associating goods with a value that shows a criterion of choice. In order to determine if one
consequence is preferred to another, it is necessary to establish a conceptual structure
by imposing some restrictions on different kinds of preferences, rationality restrictions.
Rationality is characterized by two things:
1. The objectives desired have to be consistent;
2. The actions have to converge to obtain the objectives.
The Utility is represented by:
u(p) = Ep v(p) =
∑
p∈X
25
P (p)v(p)
(2.1.2)
Chapter 2
Preference Relations
The basic concept for defining utility is preference relations, which are described below.
Definition 1 For all P, Q ∈ P ∗ ,
• P % Q: P is at least as much favored as Q;
• P ≻ Q: P is more favored than Q;
• P ∼ Q: P and Q are equivalents;
Definition 2 P ≻ Q if P % Q and false that P - Q;
Definition 3 P ∼ Q if P % Q and Q % P ;
Preference Axioms
Dubois and Prade (1995) claimed standard approaches to decision making under uncertainty are based on maximum expected utility theory. The expected utility criterion
is particularly appealing since it can be justified on the basis of an axiomatic approach.
According to Horvitz, Breese and Henrion (1988), axioms are rules for the consistent
combination of probabilities for related events.
Axiom 1 Completeness: P % Q or Q % P it is equivalent to saying P ≻ Q or Q ∼ P or
Q ≻ P;
Axiom 2 Transitivity:
• P ≻ Q and Q % R ⇒ P ≻ R;
• P ∼ Q and Q ∼ R ⇒ P ∼ R;
Axiom 3 Dominance:
• If P ≻ Q, 1 ≥ λ > 0, then for all R ∈ P ∗ had
λP + (1 − λ)R ≻ λQ + (1 − λ)R;
• If P ∼ Q, 1 ≥ λ > 0, then for all R ∈ P ∗ had
λP + (1 − λ)R ∼ λQ + (1 − λ)R;
Axiom 4 Archimedean: If P ≻ Q ≻ R, then there are numbers λ and µ that 1 > λ >
µ > 0 and that λP + (1 − λ)R ≻ Q ≻ µP + (1 − µ)R.
26
Chapter 2
Given a set of preferences expressed as an utility function, beliefs expressed
as probability distributions, and a set of decision alternatives, a decision maker
should choose that course of action that maximizes expected utility. The power of
this result is that it allows preferences for complex and uncertain combinations
of outcomes with multiple attributes to be computed from preferences expressed
for simple components. Thus, it may be used as a tool to help people think about
complex choices by decomposing them into simpler choices (Horvitz et al., 1988).
27
Chapter 3
3
LEARNING A LITTLE ABOUT CARS
When the car was invented, people thought it was only a new machine for rich people,
because it was made by hand and was so expensive. The evolution in the way of producing
cars totally changed the concept about cars, when we remember that cars were made
manually by one man and one by one. So, besides this progress in car technology all this
change caused huge differences in the industry in general. In the following sections a little
of the evolution of the production process is described and some interesting information
about automotive technology is given.
3.1
History of Production
“No lean producer may have achieved perfection and none ever will. But the
endless quest for perfection, on the part of lean producers, continues to generate
surprising results.” (Womack et al., 2004)
3.1.1 Craft Production
Craft production is the process of manufacturing by hand with or without the aid of
automated tools. The term craft production refers to a manufacturing technique applied
to the hobbies of handicraft but was also the most common method of manufacture in
the pre-industrialized world. A result of the craft manufacturing process is that the final
product is unique. While the product may be of extremely high quality, the uniqueness
can be detrimental as seen in the case of early automobiles.
It was impossible to produce two identical cars, because the company that produced
the cars ordered the pieces from different suppliers that did not have a metrology system
and precision machines to cut steel. So, several pieces of a car which arrived at the manufactures had specifications that were close to each other, but they were not to precisely
the same specification.
According to Womack, Jones and Roos (2004) this kind of production had the following
characteristics:
• Workers: highly skilled;
28
Chapter 3
• Tools: simple and flexible, they could perforate, cut; other operations in metal and
wood;
• Production: one by one;
• Costs: high;
• Organization: decentralized.
3.1.2 Mass Production
Mass production is a production method which produces large quantities at low cost
per unit. Despite the low cost, there was a quality in the products that was acceptable
for that time. The products are standardized and the parts are interchangeable: they fit
all products. The mass production process is characterized by mechanization to produce
large volumes of the flow of materials, organization through many stages of manufacturing
and the division of work from the shop-floor to engineering. On the shop-floor, assembly
lines are used very much to allow this kind of organization. The key concept of mass
production is not the idea of a continuous line, as many think, but the complete and
consistent interchangeability of parts for easy assembly (Wood Jr., 1992).
Mass production appeared after the 1st War (1908) when Henry Ford was trying to
solve craft production problems. Ford obtained a car that was user-friendly to drive and
anyone was able to fix: Model T Ford. These two characteristics were the base for the
changes. Ford stated that more production results in less costs for the consumers. In
1920 he obtained his largest production, 20 million equal cars in one year.
According to Womack, Jones and Roos (2004) mass production has the following characteristics:
• Workers: very specialized
• Tools: produce only one product
• Production: large volume, no variety;
• Costs: low;
• Organization: centralization, all the raw-material for cars was produced in the Ford
factories.
29
Chapter 3
After 30 years of success, mass production started to decrease in the USA. By 1955,
other countries were producing cars and capturing ever larger slices of the U.S. automotive
market. The three giant firms (Ford, GM and Chrysler) were losing their competitive
advantage. Mass production had become the most common way to produce cars around
the world. By the 50s, firms like Volkswagen, Renault, and Fiat were producing as much
as Detroit.
But the production systems of Europe were simple copies of the American ones. In
time, workers lay claim, through trade unions, to shorter working hours, as the monotony
of the factories was unbearable. This might have continued had it not been for the
emergence of a new industry in Japan. This form of industry was not a replica of mass
production. The Japanese were developing a new way to produce, which they called lean
production.
3.1.3 Lean Production
The World 2nd War, destroyed the manufacturing industries of the defeated countries.
Due to the lack of employees, the small number of consumers and the difficulty of obtaining
raw material, the Ford model could not be applied to Japanese reality. It was then that
Taiichi Ohno, who is considered the creator of the Toyota Production System and Kanban
System father, appeared. Ohno was born in China, in 1912, and graduated in Mechanical
Engineering. He was an employee first of the Toyoda family’s Toyoda Spinning, and
moved to the motor company in 1943, and gradually rose through the ranks to become
an executive. Early in his career he expanded the ideas developed by Toyoda to reduce
losses in production, by starting to experiment and develop production methods that
reduce the time of manufacture of major products and to create sub-assembly lines that
would support the final production line. Therefor, to adapt to the situation of that period,
Toyota adopted a leaner system with “just-in-time” suppliers.
The new model made for a more flexible organization with smaller stocks. The advantages in economies of scale, rentability and less waste, led to the Toyota System Production spreading, and within a few years, it was adopted by most automotive factories.
According to Moreira and Fernandes (2001) it can be said that the Toyota production
system seeks to achieve in the setting of product variety, lean production (high yield and
30
Chapter 3
quality) in the same way that the system of Ford production created an environment for
high-volume production of identical products.
In summary, lean production is characterized by:
• Workers: multi-qualified;
• Tools: automated;
• Production: just-in-time;
• Costs: lower than mass production;
• Organization: decentralization, the raw-material for the cars produced by different
suppliers.
3.2
Automotive Technology
The car was only a means of transportation but nowadays it can be considered an object
desired by most Brazilians. The new technology leads to cars being very comfortable and
pleasurable to drive. Besides, this industry is trying to develop cars with lower fuel
consumption and at ever greater competitive prices.
3.2.1 Power Steering
Power steering is intended to alleviate the stress of the driver, who thus controls the
mechanical part with greater ease, since most at the work is done hydraulically. This
reduction in stress allows the driver to save 80% of the energy that would be employed
to turn the steering wheel. “The steering system in general is an essential part of the
interface between the driver and his car, providing him the possibility of lateral vehicle
guidance” (Odenthal et al., 2002). Some cars have progressive power steering, which is
nothing more than a mechanism that makes the wheel stiffer as the speed increases. The
steering wheel of the electronic type gives the speed and then that reading activates a
valve that manages the flow of oil through the system. The direction of flow gets heavier
or lighter.
31
Chapter 3
3.2.2 Air Bag
Air bag design can vary according to brand, model or year of car. But, typically, an air
bag system consists of one or more sensors. These sensors are responsible for detecting
the longitudinal change in velocity of the vehicle during a crash. Moreover, there is an
electronic unit that monitors the system and a module that houses the inflator and the
bag (Segui-Gomez, 2000). They are expected to protect the driver and occupants in
combination with lap and shoulder safety belts.
While safety belts offer more security than the air bags, this occupant protection
reduces the risk by more than 8% of deaths in car accidents (Cummings et al., 2002).
According to Segui-Gomez (2000) the effectiveness of an air bag changes according to the
type of accident, for example, in head-on frontal or near-head-on crashes, the probability
of having severe and fatal injuries is decreased.
3.2.3 Anti-lock Braking System
The Anti-lock System for wheels is a device combined with the conventional brakes
system, which prevents the complete blockage of the wheels, thereby improving the control
and stability of the vehicle during braking, thus optimizing, in some cases, the area of
braking and taking full advantages of the adherence of each tire (Toresan Jr., 2010). The
anti-lock braking system reduces the chance of accident on stopping a car suddenly on a
slippery road. A wheel slipping (the tread of tire skids on the road) has less grip than
a wheel that is not slipping. So, by avoiding the sliding of the wheels during braking,
anti-lock brakes bring two benefits: stopping is faster and the trajectory of the car can
be changed while braking.
3.2.4 Rear wheel traction vs front wheel traction vs four wheel drive
Over the past 25 years a radical change in trend has been seen: while in the 70s most of
the cars used the classic (traditional front engine/rear-wheel) traction, from the late 90s
until now cars without rear traction are no longer manufactured. All use the engine/front
traction, and the overwhelming majority use a transverse engine. Formerly, to have a car
with front-wheel traction was unreliable for large angles, such as how best to steer the
32
Chapter 3
wheels. Therefor, the car had very stiff front wheel steering, which made a driver feel
that the car had front wheel traction. In this case, the rear wheel traction gave way more
smoothly. With the popularization and the cheaper costs of constant velocity joints, this
is no longer a problem and car traction has become a matter of choosing which would be
most suited to the car proposed.
There is another kind of car traction, the four wheel drive. Cars of this type are called
4x4 or off-road vehicles which means the ability to work off the asphalt (Costa et al., 2010).
It is divided into two ways of working. One way, the temporary system, is designed only
for low adherence, such as off road or on snow or ice. The other one is called permanent
four-wheel drive and all wheel drive. These systems are designed for use on all surfaces,
both on the road and off-road. Most of them cannot be disabled. The system of temporary and permanent four-wheel drive can be assessed using the same criteria. The best
system will send exactly the right amount of torque to each wheel and is the maximum
amount of torque that will slop the tire from slipping.
33
Chapter 4
4
DECISION THEORY IN THE AUTOMOTIVE BUSINESS
The problem of how to choose a car is too complex to be solved intuitively and in a
single way. The needs, demands and wishes of the potential buyer of the car, whether a
new or second-hand one, can be so diversified that it becomes very difficult to establish
a list of important points of view and build criteria on which everybody would agree. A
very delicate criterion, for example, is price since how much money the buyer can afford
depends on his financial circumstances. Besides the relative importance of the criteria
depends on the personal characteristics of the buyer: for instance there are people who
like driving sports cars, or large comfortable cars or reliable cars or cars that are cheap
to run.
As to choosing a car, in more complex situations there is a limited amount of money
that is involved in the decision process. So to optimize the decision-maker’s gain within
his budget, it may appear necessary to use formal methods of decision analysis. In other
words, methods will depend on an explicit mathematical model of the decision-maker’s
preferences. This kind of reasoning, which includes examining of differences in evaluations, constitutes the main goal of the task of modeling preferences and aggregating them
in order to have an informed decision process (Bouyssou et al., 2000). There are many
different methods for modeling the decision process, but the one used in this dissertation
was Decision Theory since it is concerned primarily with making decisions in the face of
uncertainty.
In some decision-making methods, all the sources of imprecision have an effect on the
accuracy of determining the best decision. Very often, there is not even probabilistic
information on the precision of the judgments. As a consequence, the apparently clear
decision-choice of the alternative with the highest value might not be considered. “The
usual way out is extensive sensitivity analysis, which could be described as part of the
validation of the model” (Bouyssou et al., 2000). Thus, given many uncertainties that
are present in a car purchase, such as the economic environment, the quality control of
manufacture, the reliability of the car, price variations, the costs of fuel and insurance,
Decision Theory deals with them by probability so as to offer the decision maker the best
decision, and the chance of giving the best outcome.
34
Chapter 4
4.1
The representation of payoffs
There are many things that need to be considered when someone buys a car. To the
consumer it is difficult to choose between a comfortable car, a big car, those that have
optional extra accessories or safety accessories, or a cheap car, for example. Therefor,
given this challenge, it is necessary to simplify the number of variables, by reducing
them in number, by means of proper statistical techniques, so as to form payoffs for the
consumers.
As stated in the preceding paragraphs, consumers’ preferences vary according to their
financial circumstances. Lemos (2007) claims, for example, that the foreign origin of a
car for some consumers is their preferred choice because they believe other countries have
more advanced technologies. On the other hand, other people value domestic cars because
they feel the cost of maintenance and the availability of spare parts are more accessible.
Also according to Lemos (2007), consumers in high-value cars take into consideration in
the purchase, first, the comfort that the car provides, but are nonetheless concerned about
the cost benefit that it will offer, since price is the second item in the order of importance
at the time of purchase, Sampaio (2004) also claims this. In the following sections how the
payoffs were constructed for better representing consumer’s preferences will be presented.
4.1.1 The Construction of Payoffs
A variable is a non-specify element of a set that can assume any value within a value
set. On the other hand, a combination of these variables for the same individual is
an attribute. An attribute is a combination according to a rule or a formula. It is
an indicator that is translated by a formula involving the variables. Any formula that
makes sense is valid in constructing the attribute. But an attribute can be used as a
variable that will be useful in designing more connected constructs: aspects. It is an
art to model these attributes and aspects, thereby transforming subjective preferences
into numerical results. According to Campello de Souza (2007) variables, attributes and
aspects are important items in the decision making process. After all if it is subjective, it
is measurable (Campello de Souza, 2007). In many study areas the art of modeling is an
important step to advance research. For instance Siqueira (2002) argues that a subject
which is independent and scientifically structured, like Organizational Behavior, depends
35
Chapter 4
on procedures being applied to measure variables and that nowadays it is a challenge for
researchers to develop measures that are valid, precise and current.
A survey was conducted in 2009 by students at the Universidade Federal de Pernambuco, and aimed to analyze the profile and preferences of automobiles’ consumers in Recife
(Pernambuco-Brazil). In this survey, a questionnaire was used which contained a large
variety of questions, since questions about age, address or income, as well as questions
about preferences for car brands, opinions about car’s services or what features that they
consider when they buy a car (the completed questionnaire can be seen in appendix). So,
this data were useful in 2 ways. First, it was possible to make a statistical analysis that
characterized the surveyed population, and second, some variables, that were extracted
from a question, were grouped in attributes. According to what was said, in the previous paragraph, the attributes permit the situation to be better characterized and better
information to be gathered.
So, the variables chosen are in question 31 of the questionnaire and they relate the
importance of each factor when buying a car. For the questionnaire, the higher the value
given for the variable, the more important the feature related is for decision-maker. An
ordinal scale was used. These variables are described below.
• Q31A: Accessories and Optional extras (Acces)
• Q31B: Appearance and Beauty (Appear)
• Q31C: Financial Terms (Fin)
• Q31D: Performance (Perf)
• Q31E: Durability (Dur)
• Q31F: Fuel consumption (Fuel)
• Q31G: Maintenance (Maint)
• Q31H: Total Price (Price)
• Q31I: Status and Prestige (Stat)
• Q31J: Resale value (Res)
• Q31K: Insurance value (Ins)
36
Chapter 4
• Q31L: Safety (Saf)
• Q31M: Comfort and Ergonomics (Comf)
• Q31N: Workmanship (Work)
• Q31O: Warranty conditions (War)
A Spearman Correlation using Statistica© software was used to discover what variables are connected with others. Table 4.2 shows the Correlation Matrix.
The correlation matrix result permitted all the variables to be grouped into only three
attributes that can be called coordinates payoffs. For each variable it was identified which
variables have a significant correlation coefficient, which was considered greater than or
equal to 0.60, and listed. For each list (15 in total, as it has 15 variables) a name was
assigned, which was a feature of that set of variables. Names were reduced to a minimum,
so that it had the smallest number of attributes possible. So for example, the first variable, accessories/optional extras, had the variables: appearance/beauty, status/prestige,
workmanship and comfort/ergonomics with correlation coefficients greater than 0.60, so
it was put on the list for Comfort. Other listings that have these or other variables with
similar characteristics were also calls for comfort. Completed listings, all those that had
included the word comfort, have been consolidated into one list through the amalgamation of all the variables relating to listings of comfort. The same procedure was conducted
for the other attributes. Table 4.1 shows what car characteristics comprise each payoff’s
coordinates and how they are denominated. It is important to mention that the three
payoffs coordinates formed are not strictly independent. Some variables may be part of
more than one coordinate payoff.
Table 4.1: The Formation of Payoffs.
Payoff 1-Economy Payoff 2-Comfort
Total Price
Appearance and Beauty
Insurance value
Status and Prestige
Maintenance
Workmanship
Fuel consumption
Comfort and Ergonomics
Durability
Accessories and Optional extras
Warranty conditions Resale value
Financial terms
Source: The author, 2010.
37
Payoff 3-Safety
Safety (Optional)
Warranty conditions
Workmanship
Performance
-
Var.
Acces Appear
Acces
1,000
0,966
Appear 0,966
1,000
-0,355
-0,370
Fin
Perf
0,598
0,574
Dur
-0,330
-0,373
Fuel
-0,554
-0,598
-0,439
-0,512
Maint
Price
-0,525
-0,559
0,936
0,926
Stat
Res
-0,032
-0,113
Ins
-0,047
-0,086
Saf
0,260
0,184
Comf
0,782
0,765
Work
0,797
0,770
War
0,331
0,265
Source: the author, 2010
Fin
-0,355
-0,370
1,000
-0,096
0,426
0,696
0,797
0,887
-0,279
0,515
0,642
0,211
-0,292
-0,228
0,169
Perf
0,598
0,574
-0,096
1,000
0,380
-0,020
-0,037
-0,248
0,613
0,142
0,025
0,672
0,858
0,828
0,718
Dur
-0,331
-0,373
0,426
0,380
1,000
0,828
0,699
0,544
-0,328
0,463
0,279
0,564
0,127
0,076
0,490
Fuel
-0,554
-0,598
0,696
-0,020
0,828
1,000
0,912
0,860
-0,490
0,608
0,520
0,385
-0,228
-0,225
0,311
Maint
-0,439
-0,512
0,797
-0,037
0,699
0,912
1,000
0,926
-0,417
0,654
0,691
0,502
-0,162
-0,137
0,373
Price
-0,525
-0,559
0,887
-0,248
0,544
0,860
0,926
1,000
-0,488
0,549
0,672
0,262
-0,368
-0,323
0,176
Stat
0,936
0,926
-0,279
0,613
-0,328
-0,490
-0,417
-0,488
1,000
0,093
0,005
0,213
0,723
0,755
0,360
Res
-0,032
-0,113
0,515
0,142
0,463
0,608
0,654
0,549
0,093
1,000
0,784
0,566
0,140
0,203
0,652
Table 4.2: Correlation Matrix of Car Aspects.
Ins
-0,047
-0,086
0,642
0,025
0,279
0,520
0,691
0,672
0,005
0,784
1,000
0,583
0,135
0,196
0,602
Saf
0,260
0,184
0,211
0,672
0,564
0,385
0,502
0,262
0,213
0,566
0,583
1,000
0,716
0,716
0,939
Comf
0,782
0,765
-0,292
0,858
0,127
-0,228
-0,162
-0,368
0,723
0,140
0,135
0,716
1,000
0,983
0,735
Work
0,797
0,770
-0,228
0,828
0,076
-0,225
-0,137
-0,324
0,755
0,203
0,196
0,716
0,983
1,000
0,752
War
0,331
0,265
0,169
0,718
0,490
0,311
0,373
0,176
0,360
0,652
0,603
0,939
0,736
0,752
1,000
Chapter 4
38
Chapter 4
When dealing with multi-dimensional judgments of payoffs, the basic and almost natural attitude is to try to construct a one-dimensional combination, which would represent
the value of the payoffs. This method may be inherited from school practice where all
other performance evaluations of the children have long been summarized into a single
figure, a weighted standard of their grades in all subjects.
But numbers do not always represent what they appear to be. It makes no sense to
work with raw judgments without taking the context into account. There are basically four
scales of measurement: nominal, ordinal, interval and ratio (Campello de Souza, 2007).
The numeric scales need to be able to exhaust all possibilities and at the same time be
mutually exclusive. The nominal scale, or Taxonomy, is the simplest of the scales and
allows the most basic of representations. It is based on grouping and sorting elements
for the formation of distinct sets. In studies using this scale, records are essentially
qualitative. The ordinal scale allows the representation of the values of one variable in
terms of where it stands in relation to other values. The basic notion is that of order. On
an interval scale, apart from sorting the categories of one characteristic, it can be said
that the interval is worth exactly the differences between categories. However, because
the zero in the interval scale does not occur naturally and is determined arbitrarily, we
can not say as a category it is worth more than the other. A classic example of magnitude
of greatness in an interval scale is temperature. It is always possible to set the zero point
on the scale arbitrarily. One can then proceed to the values of variables, all arithmetic
operations. This scale is then a of ratio scale. The ratio scale is the most comprehensive
and sophisticated of numerical scales. On a ratio scale, all operations are valid. There is
the possibility of transforming data that were recorded on a type of numerical scale on
data with another type of scale, provided that they respect the hierarchy and the basic
attributes of each. That is, data from a ratio scale can be transformed into data intervals,
the interval in ordinal and the ordinal in nominal. These transitions involve some loss of
information.
“We have also suggested that the significance of a number may be intermediate
between ordinal and cardinal; in that case, the interval separating two evaluations
might be given an interpretation: one might take into consideration the fact that
intervals are e.g. large, medium or small” (Bouyssou et al., 2000).
39
Chapter 4
Preference modeling is the activity that specifically deals with the meaning of data in
a decision context. As was said before, an attribute is a synthesis of variables derived by a
formula. So, after knowing what variables are connected with each other, it is necessary to
create formulas that will represent all the variables in few attributes. These are explained
in the following sections.
Payoff 1 - Economy
According to Table 4.1 the coordinate payoff Economy consists of the total price of a
car, its insurance value, maintenance costs, fuel consumption, durability, warranty conditions, resale value and the financial terms. To transform all these variables into only one
coordinate payoff it is necessary to group them together by a formula.
The first step of this process is to define the variables and what this means in numbers
or values. Then give them a graded score according to how good the car is for the
consumer. For example a score of 2 is better than a score of 1 or 0. Analyzing them one
by one is a better way of understanding how each is made up.
Total Price
Setting the range limits for car prices, was made possible by consulting the
table that contained the base values for calculating the Brazilian tax IPVA set by Minas
Gerais (2010). This table contains all brands of cars and different models such that an
overview of prices of cars is obtained. Thus the price levels of cars was divided into three
ranges:
Table 4.3: Total Price Score.
Variable
Total Price
Description
≤ R$50.000, 00
R$50.000, 00 − R$100.000, 00
> R$100.000, 00
Score
2
1
0
According to Resende and Scarpel (2009) the factor that most impacts on the price
of a car in Brazil is engine power, followed by degree of luxury and make. This division
permits the observation that cars priced ≤ R$50.000, 00 are popular cars, those between
R$50.000, 00 − R$100.000, 00 may be sedans and SUVs (Sport Utility Vehicle) and finally
luxury cars that can cost more than R$100.000, 00.
40
Chapter 4
Insurance Value
The insurance value also has three ranges. According to Wiltgen (2010)
there are many factors that influence insurance value: the driver’s profile, the car model
and type, the place where the driver uses the car, if the car has optional and safety
accessories. So it is difficult to know if someone is paying a fair price for insurance, but
knowing the ratio between the insurance value and the car value is a way to analyze the
price of car insurance.
Table 4.4: Insurance Value Score.
Variable
Insurance Value
Description
≤ 6% of car price
6% − 9% of car price
> 9% of car price
Score
2
1
0
So, according to the Table 4.4 the highest score is for cars that have the cheapest
insurance price and the lowest score is for cars that have the most expensive insurance
price. So, the lower the price, the better for the consumer, thus the higher the score.
Maintenance
It was not possible to define maintenance directly. It was necessary to know
what car maintenance consists of to give a score to these variables and then to set different
ranges for maintenance. So, the two variables considered to construct maintenance were
the number of years given for warranty (ranging from 1 to 5 years) and the origin of
spare parts, i.e. whether they are national or imported. Them more years of warranty
given to the consumer, the better for him, because if the car suffers from a mechanical
problem, the company is responsible for repairing it, so it does not become a cost for the
consumer. And if the spare parts are national, this is better for the consumer than if
they are imported, because the supply of national pieces is larger than that for imported
ones. In addition, national parts are restored more quickly and are cheaper too. Table 4.5
shows how the variable of maintenance was defined and constructed.
With the total score from the composition of maintenance it is possible to define
maintenance with values and to know in which range this is or is not favorable for the
consumer or not.
Fuel Consumption
Fuel consumption is one of the most important variables when talking
about automotive economy. After all, fuel is necessary for a car to work, so it is an expense
41
Chapter 4
Table 4.5: Composition of Maintenance.
Variable
Warranty Years
Origin of spare parts
Maximum Score
Minimum Score
Description Score
5 years
5
4 years
4
3 years
3
2 years
2
1 year
1
National
1
Imported
0
6 points
0 points
Table 4.6: Maintenance Score.
Variable
Maintenance
Description
≥4
<4
Score
1
0
while someone has a car. To know how economical a car is regarding fuel consumption,
the author’s experience of fuel consumption was used which made it possible to estimate
the ranges. Despite the lack of information on when a car is economical or not, Teixeira Jr
(2008) gives some rules that were established in the USA, that helped confirm the author’s
estimate. Car factories had were instructed by legislation to produce more efficient cars,
so they have to produce cars with a minimum efficiency of 14,7 km/l until 2020, to try
to free themselves from dependency on petrol. Thus a car can be classified as economical
when consumption is a minimum of 10 km/l. If it does not have this efficiency it is
classified as a non-economic car.
Table 4.7: Fuel Consumption Score.
Variable
Fuel Consumption
Durability
Description
≥ 10 km/l
< 10 km/l
Score
1
0
The more time a car spends without having mechanical problems, the better
is car durability. Factories estimate the warranty period based on the mean time their
cars run without having mechanical problems. So, according to Vasconcellos (2006) the
warranty period depends totally on the reliability of the product. If the product is reliable,
a longer warranty period can be offered. It is important to remember, however, that
within the warranty period the auto maker calls for scheduled maintenance to be carried
42
Chapter 4
out with the frequency it lays down. Therefore, in this case, the variable durability takes
into account that the scheduled maintenance is performed, as recommended by the auto
maker. Based on this, the durability score was constructed based on the warranty period.
Table 4.8: Durability Score.
Variable
Durability
Description
Repair more than 1 year after the warranty period
Repair just after the warranty period
Repair within the warranty period
Score
2
1
0
If the factories know how much time their cars can go without needing repair (so they
can guarantee this with the warranty period) and a car runs for longer than this time,
it can be considered a durable car (with a high score) because it outlasts the factory
guarantee.
Warranty Conditions
According to Vasconcellos (2006) there are different kinds of war-
ranty conditions for products. Summarizing, there is one in which the company pays for
all costs from fixing or repairing the car if it fails before the expiry date of the warranty
period, called Free Substitution Warranty, and another that combines a period when the
company is responsible for any repair or fix and after this period the consumer is responsible, called a Combined Policy. Of course the first, where the company pays for the
damage is better for the consumer. Besides, the longer the warranty period, the better it
is for the consumer. Table 4.9 summarizes the warranty conditions ranges.
Table 4.9: Warranty Conditions Score.
Variable
Warranty Conditions
Free
Free
Free
Free
Free
Free
Description
Substitution Warranty + 5 warranty years
Combined Policy + 5 warranty years
43
Score
1+5=6
1+4=5
1+3=4
1+2=3
1+1=2
1+0=1
0+5=5
0+4=4
0+3=3
0+2=2
0+1=1
0+0=0
Chapter 4
Resale Value
There are some cars that have a better resale value than others, because the
depreciation rate (the proportional difference between the resale value and the purchase
value, when compared at the same time) is greater than for the others. Then the resale
∑
value depends directly on the depreciation rate, which is given by the formula [Value
year(n − 1)/Value year(n)]/n. So, still using the data from Minas Gerais (2010) it was
possible to calculate the depreciation rate of many car makers and models, and then
stipulate the ranges for the resale value. The higher the value of the depreciation rate,
the higher the resale value of the car, which is good for the decision maker who wants to
sell his car.
Table 4.10: Resale Value Score.
Variable
Resale Value
Financial Terms
Description
Depreciation Rate ≥ 0, 864427
Depreciation Rate < 0, 864427
Score
1
0
There are many variables that influence financial terms. They are the
cash purchase value of the car, the value of the loan to finance the car, the number of
instalments, the interest rate and the value of the instalments. According to Abrita (2009)
among the important points for consumers of cars, within the context of financial terms,
the most notable are maintenance and that he rates offered are attractive, in line with the
competition, which has influence on the decision of the respondents. But to attract the
customer, these companies are ready to negotiate ways of paying the values. Therefore
consumers look for the best terms and choose the one that is most appropriate for their
profile. No variable was found to define financial terms in monetary values, because this
depends on the consumer’s circumstances, so descriptive ranges as in Table 4.11 were set.
Table 4.11: Financial Terms Score.
Variable
Financial Terms
Economy final score
Description
Very good terms
Good terms
Bad terms
Score
2
1
0
Finally, by grouping all the score for variables in only one table it
is possible to discover what maximum score the attribute and coordinate payoff Economy
44
Chapter 4
has. The formula used to define this payoff is simple: the maximum score for the coordinate payoff Economy was obtained by adding the maximum values of each variable,
ie by adding 2 for the total price, adding 2 for insurance value and so on. A car with
the highest score would be the most economical one. By dividing the total score in half,
it is possible to obtain the minimum score that is or is not good for the consumer, see
Table 4.12.
Table 4.12: Economy Score.
Variable
Total Price
Insurance Value
Maintenance
Fuel Consumption
Durability
Warranty Conditions
Resale Value
Financial Terms
Maximum Score
Good situation for consumer
Bad situation for consumer
Description
≤ R$50.000, 00
R$50.000, 00 − R$100.000, 00
> R$100.000, 00
≤ 6% of car price
6% − 9% of car price
> 9% of car price
≥4
<4
≥ 10 km/l
< 10 km/l
Repair more than 1 year after the warranty period
Repair just after the warranty period
Repair into the warranty period
Free Substitution Warranty + 5 years warranty
Combined Policy + 5 years warranty
Depreciation Rate ≥ 0, 864427
Depreciation Rate < 0, 864427
Very good terms
Good terms
Bad terms
17 points
≥ 9 points
< 9 points
Score
2
1
0
2
1
0
1
0
1
0
2
1
0
1+5=6
1+4=5
1+3=4
1+2=3
1+1=2
1+0=1
0+5=5
0+4=4
0+3=3
0+2=2
0+1=1
0+0=0
1
0
2
1
0
Half of the total score (17 points) is 8.5, so a score greater than or equal to 9 represents
an economical car, which is good for the consumer, and a score less than 9 means that
the car is not economical.
45
Chapter 4
Payoff 2 - Comfort
Coordinate payoff 2, called Comfort, consists of the variables Appearance and Beauty,
Status and Prestige of the car, Workmanship, Comfort and Ergonomics, and Accessories
and Optional extras. The way to establish the score is conducted as before. Every variable
is explained and has a score, which is added up with the others to give the Comfort score.
The variable Appearance and Beauty is defined by the car mod-
els, so the more luxurious the car is, the higher the score, and the more popular the car,
the smaller the score.
Table 4.13: Appearance and Beauty Score.
Variable
Status and Prestige
Description
Sports: the best performance
Luxury: expensive, sophisticated, with many accessories
Utility: aimed at people and cargo transportation
Sedan: more sophisticated than popular ones
Popular: cheaper and aimed the mass
Score
4
3
2
1
1
According to Machado, Estrehlau and Fasolo (2007) colors are an
integral part of everyone’s life and are important for marketing activities, since the aim
is to maintain capabilities: convey meaning, to draw consumers’ attention and lead a
consumer to enjoy a product or not, promoting or preventing its sale. Samy (2008) claims
that the most popular car colors in Brazil are silver, grey and black, respectively. The
first reason is the good resale value that these colors give to the car. Discrete colors are
favored in big cars. As a result of their size, they attract attention, so the color needs to
be sober.
There are many features that can define this variable Status and Prestige, like the car
model, the number and kinds of optional extras it has, how ergonomic it is, how good
its workmanship, its color and etc. As most of these features are mentioned in other
variables, we will take into consideration only the color to set the variable Status and
Prestige. Those colors that give status and prestige to a car have a score, the other ones
do not have a score.
46
Chapter 4
Table 4.14: Status and Prestige Score.
Variable
Status and Prestige
Workmanship
Description
Black
Silver
Grey
Others colors
Score
1
1
1
0
To define the variable Workmanship, some items were listed that a car
may have and thus be characterized as a car with good workmanship. The more such
items a car has the better its workmanship. So this variable can have a maximum score
of 5 points.
Table 4.15: Workmanship Score.
Variable
Workmanship
Maximum Score
Minimum Score
Description
Leather seat
Leather steering wheel
Protective rubber grips
Imitation wood dashboard
Metallic pedals
5 points
0 points
Score
1
1
1
1
1
According to Ribeiro, Câmara and Engler (2007) cars change
from being a utility to an object desired by people when it is seen to give status and
power. Currently the car is more than a means of transport. It is fundamental for leisure
time. Therefore automotive companies have been trying to produce more comfortable
cars for more demanding customers that want and need to spend more time in their cars.
In the design of the passenger compartment of a vehicle, there is a need to consider
some features of being in a car, such as free interior space, the comfort of seats, practicable
controls, the adaptability of the driver’s seat, thermal double glazing, external visibility
and dashboard visibility (Oliveira, 2009). In a similar way to workmanship, the Comfort
and Ergonomics score increases the more that features classified as good exist in a car.
So this variable can have a maximum score of 7 points.
Accessories and Optional Extras
And finally, the last variable that comprises the coor-
dinate payoff Comfort is that of Accessories and Optional Extras. The accessories and
optional extras of a car are items that make the car more comfortable, more expensive,
47
Chapter 4
Table 4.16: Comfort and Ergonomics Score.
Variable
Maximum Score
Minimum Score
Description
Free interior space
Comfort of Seats
Practicable controls
Adaptability of Driver’s seat
Thermal isolation windows
External visibility
Dashboard visibility
7 points
0 points
Good
Good
Good
Good
Good
Good
Good
Score
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
=
=
=
=
=
=
=
0
0
0
0
0
0
0
and with added value. In the same way as workmanship, these items have a score, so the
more items a car has, the higher the score. So this variable can have a maximum score of
7 points.
Table 4.17: Accessories and Optional Extras Score.
Variable
Accessories and Optional Extras
Description
Power windows
Electric lock
Alarm system
Power steering
Air conditioner
Stereo system
Automatic transmission
7 points
0 points
Maximum Score
Minimum Score
Comfort final score
Score
1
1
1
1
1
1
1
By grouping all the variables into Table 4.18 it is possible to discover
how the Comfort coordinate payoff has been graded. The formula used to define this payoff
is simple: the maximum score for the coordinate payoff Comfort was obtained by adding
the maximum values of each variable, ie by adding 4 for the appearance/beauty, adding
5 for workmanship and so on. A car with the highest score would be the one that is the
most comfortable. By dividing the total score in half, it is possible to obtain the minimum
score that is or is not good for the consumer.
Half of the total score (24 points) is 12, so a score greater than 12 points represents
a comfortable car, which is good for the consumer, and a score less than or equal to 12
means that the car is not comfortable.
48
Chapter 4
Table 4.18: Comfort Score.
Variable
Appearance/Beauty
Status/Prestige
Workmanship
Comfort/Ergonomics
Accessories/Optional Extras
Maximum Score
Description
Sports
Luxury
Utility
Sedan
Popular
Black
Silver
Grey
Other colors
Leather seat
Metallic pedals
Free interior space
Comfort of seats
Adaptability of the driver’s seat
External visibility
Power windows
Electric lock
Alarm system
Power steering
Air conditioner
Stereo system
Automatic transmission
24
> 12 points
≤ 12 points
Good
Good
Good
Good
Good
Good
Good
Score
4
3
2
1
0
1
1
1
0
1
1
1
1
1
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
= 1 Bad
1
1
1
1
1
1
1
=
=
=
=
=
=
=
0
0
0
0
0
0
0
Payoff 3 - Safety
According to Naschpitz (2010) Brazilian society does not give the same importance
to safety items, as in developed countries. This would cause industries to produce safe
cars, offering security enhancements as standard, not only as options, such as ABS brakes
and airbags. This lack of interest comes from the large share that popular cars have in
the automotive market. Some car makers produce models that do not have such safety
features even as optional extras (Rodrigues, 2008). On the other hand, luxury car owners
think differently. They are willing to pay a difference in price for safety. The installation
rate of airbags reaches 100% in luxury cars, 62% in medium cars, 20% in pick-ups and
only 18% in popular cars.
There are five variables to define payoff 3: safety, comfort and ergonomics, warranty
49
Chapter 4
conditions, workmanship and performance.
Safety Optional Extra
The first was characterized by the existence of safety optional
extras. Thus the more such optional extras a car has, the safer the car, so the score is
higher. So this variable can have a maximum score of 11 points, see Table 4.19.
Table 4.19: Safety Optional Extras Score.
Variable
Maximum Score
Minimum Score
Description
Airbag
Anti-lock Breaking System
Parking sensor
Electric lock
Lighted mirror
Rear fog light
Fog light
Alarm system
Blocker
GPS tracker
Electronic Stability Program (ESP)
11 points
0 points
Comfort and Ergonomics/Warranty Conditions/Workmanship
Score
1
1
1
1
1
1
1
1
1
1
1
The comfort and ergonomics
variable is equal to the payoff of Comfort (Table 4.16 refers). In the same way, the variables of warranty conditions is similar in payoff 1 - Economy (Table 4.9). Similarly, the
variable of workmanship is in payoff Comfort, see Table 4.15.
Performance
In assessing performance there are some important parameters that can
be considered: maximum speed, maximum slope, maximum acceleration and time and
distance for acceleration and pick up speed (Depetris, 2005). But such information is not
so easy to collect and is not so essential to the objective of this project. It is known that
the horsepowers (hp) of a car influences all these parameters, speed, acceleration, etc, so
it was decided to define performance based on a car’s horsepowers. The higher the car’s
horsepower, the better the performance.
Table 4.20: Performance Score.
Variable
Performance
Description
< 100 cv
100 − 150 cv
> 150 cv
50
Score
0
1
2
Chapter 4
Safety final score
Finally, all variables of coordinate payoff 3 - Safety are grouped in
Table 4.21. This shows what final score is good for the consumer and which is not. The
formula used to define this payoff is simple: the maximum score for the payoff Safety was
obtained by adding the maximum values of each variable, ie by adding 11 for the safety
optional extra, adding 6 for warranty conditions and so on. A car with the highest score
would be the safest one. By dividing the total score in half, it is possible to obtain the
minimum score that is or is not good for the consumer.
Table 4.21: Safety Score.
Variable
Warranty Conditions
Workmanship
Performance
Maximum Score
Free
Free
Free
Free
Free
Free
Description
Airbag
Anti-lock Breaking System (ABS)
Parking sensor
Electric lock
Lighted mirror
Rear fog light
Fog light
Alarm system
Blocker
GPS tracker
Electronic Stability Program (ESP)
Free interior space
comfort of Seats
Adaptability of driver’s seat
External visibility
Substitution Warranty + 4 years warranty
Leather seat
Metallic pedals
< 100 cv
100 − 150 cv
> 150 cv
31
≥ 16 points
< 16 points
51
Score
1
1
1
1
1
1
1
1
1
1
1
Good = 1 Bad
Good = 1 Bad
Good = 1 Bad
Good = 1 Bad
Good = 1 Bad
Good = 1 Bad
Good = 1 Bad
1+5=6
1+4=5
1+3=4
1+2=3
1+1=2
1+0=1
0+5=5
0+4=4
0+3=3
0+2=2
0+1=1
0+0=0
1
1
1
1
1
0
1
2
=
=
=
=
=
=
=
0
0
0
0
0
0
0
Chapter 4
Half of the total score (31 points) is 15,5, so a score greater than or equal to 16 points
represents a safe car, that is good for the consumer, and a score less than 16 means that
the car is not so safe.
Final thoughts on the payoffs
It is interesting to note that the Economy is in general negatively correlated to Comfort
and Safety, considering the same car type (one must pay extra for them).
Another important thing to comment on is that in the end, all the payoffs are dichotomic, in other words, they can be good or bad. There are no other levels of classification for them, but they are all possible. Therefore it is possible to represent the good
situation by 1, and the bad situation by 0.
4.1.2 Data Analysis
To better understand how people decide when to buy a car, there is a need to know
how these people live, how they think, what they like to do. A survey was conducted in
2009 by students from the Universidade Federal de Pernambuco (cited at the beginning
of the chapter) to understand a little about how people in Recife make decisions about
cars. From these data it was possible to conduct an analysis about people from Recife
who own or drive cars.
The Consumers’ Profile
There are data from 2.166 people interviewed. The statistical technique k − means
clustering, based on Euclidean distance, allowed all these people to be grouped together
in 6 clusters. This number of clusters was chosen by the author because of the homogeneity of characteristics that could be seen in each cluster, that permitted each cluster
to be adequately characterized for the study. The people in each cluster have similar
characteristics, but the groups are different from each other, so it is easier to understand
the different behaviors of all people interviewed. Figure 4.1 shows the result found by the
software Statistica© , after dividing the sample interviewed into clusters.
The variables considered to analyze the difference in people’s behavior were: education
level, marital status, number of children, family and individual incomes, if they have a
52
Chapter 4
Figure 4.1: People distributed in clusters.
savings account, health plan, if they go to the cinema, shopping centers, theater, museums,
beach, church. The differences between the clusters are caused by the differences between
the answers. In Figures 4.2 (non-dichotomic variables) and 4.3 (dichotomic variables) this
variability may be seen.
Cluster 1 for instance can be characterized by parents in the middle social class, because
people from cluster 1 are married and have a high education level. The majority have
a savings account, a health plan and go to places like the shopping centers, beach, pub,
museums, libraries. Cluster 1 can be called “middle class family”.
Cluster 2 represents people who are parents in a low social class. They are married,
have a larger number of children, but have the worst education level. Consequently,
they have the worst individual e family incomes and do not go frequently to places like
museums, cinemas, zoos or libraries. This cluster can be called “relatively poor family”.
Cluster 3 can be considered that formed by young people who have completed high
school and who have got married recently, because while on average most of them are
53
Chapter 4
Figure 4.2: Cluster analysis - Non-dichotomic variables .
Figure 4.3: Cluster analysis - dichotomic variables.
married, the mean number of children is not quite one. Despite most of them going to
places like the shopping centers, beach, pub, museums and having a good education level,
they have low individual and family incomes. Cluster 3 can be called “young adults”.
Cluster 4 is characterized by students from the low social level. They do not have
a high education level, are not married and their mean of number of children is almost
zero. Individual and family incomes are very low. This cluster can be called “penniless
students”.
54
Chapter 4
Cluster 5 consists of students from a high social level. As in Cluster 4, they do not
have a high education level, they are not married and the mean of number of children is
almost zero. Their individual income is also low, but the big difference is in the family
income, which is high. Cluster 5 can be called “wealthy students”.
And finally, Cluster 6 consists of parents from the high social classes. They have a high
education level, they are married, have 2 children on average, have very high individual
and family incomes and have higher cultural indicators too (going to cinemas, museums,
zoos, restaurants, libraries). Cluster 6 can be called “wealthy family”.
The Consumers’ Preferences
Another way to analyze the consumers’ profiles is by their preferences and what is
important to them about aspects of cars. So, using the data from the research commented
on above, it was possible to discover what variables are the most important for most
consumers, using the graph in Figure 4.4.
Figure 4.4: Consumers’ preferences.
Source: The author, 2010
Independently of cluster, the most important feature in a car, for most of the people
interviewed is fuel consumption. The highest columns are those related to Economy (fuel
consumption, total price, durability, maintenance). Secondly there are those that are in
55
Chapter 4
Safety (safety, comfort/ergonomics, warranty conditions) and finally, the smaller columns
are about Comfort (accessories/optional extras, appearance/beauty, status/prestige). But
making this same analysis for each cluster separately allows the differences in preferences
among the clusters to be understood.
Figure 4.5: Preferences of the clusters.
It can be seen from Figure 4.5, that most people from cluster 1 (parents from the
middle social class) give most importance to fuel consumption and total price. On the
other hand, compared with other clusters, they give more importance to performance,
maintenance, total price, resale value and safety. So, Brazilian parents from the middle
social class in general are worried about economic features when they buy a car.
Most people from cluster 2 (parents from a low social class) give more importance to fuel
consumption, but compared with other clusters, they are also worried about maintenance.
Therefore economic aspects are again observed in another cluster.
For cluster 3 (young people with a medium level of education and newly married)
durability and fuel consumption are very important to the majority, but accessories and
optional extras, financial terms, maintenance, status and prestige, resale value, insurance
value, comfort and ergonomics, workmanship and warranty conditions are the items focused on a car, compared with other clusters. These are typical of people at this age,
because young people are interested in appearance, but on the other hand, they do not
have enough money to buy a nice and expensive car, so they look for beauty and for
economy too.
56
Chapter 4
People from Cluster 4 are characterized by students at a low social level. Because of
their circumstances, they do not give importance to aspects like accessories and optional
extras, appearance and beauty, comfort and ergonomics or workmanship. In the same way
as the other clusters, the majority, more than 50%, of this cluster give more importance to
fuel consumption and it is the unique item in which they give most importance compared
to others clusters.
The most important aspect for most people from cluster 5 (students in the high social
level) is total price. But they prefer appearance and beauty more than other clusters. A
common worry that exists in society.
And finally, more than 50% of cluster 6 give importance to safety and comfort and
ergonomics. They display a greater preference than other clusters for accessories and
optional extras, appearance and beauty, performance and workmanship which is justified
by the cluster being characterized by parents from the high social class.
As to the preferences of different people, it can be recognized that cluster 1-4, people
from low and middle social classes, are a little similar since all focus on economy in general.
And clusters 5 and 6, who are rich people, worry about comfort and safety.
But it is interesting to mention some studies too, like that by Cunha (2004), which
shows in almost all products and stages of buying, reach on agreement the level of each
other participation in the decision to purchase, for example. On the other hand, according
to Artoni, Sofiato, Braga and Garcia (2010), for women, who have increased in number
as consumers, the seven factors that influence the decision to buy a car are: Technical
Aspects, Sensory Comfort, Perceived Value, Propaganda, Space-Size, Financial Value
and Post-Possession Costs. Despite the environmental factor be a matter of extreme
importance, Carvalho (2006) reported as a result of his work that the public gave the
environmental factor surveyed a poor perception of value, since 60% of respondents rated
this factor as having low and no importance in their decision to buy a car.
4.2
Choosing a car
Choosing the right car is never easy. What type of car will best suit the lifestyle? Then
there are the questions about emissions, fuel, security and does color make a difference?
There are many variables to consider when buying a car. In addition to the specifications
57
Chapter 4
of the automobile, there are external factors, over which one has no control, which can
also interfere with the purchase. Buying a car is not a trivial problem, so in this section
it will be treated on a systematic and formal way.
4.2.1 Set of Payoffs
In the previous section, a definition of coordinate payoffs was made. The payoffs that
are used in the calculus of this case are a vector consisting of these coordinate payoffs:


p
 1 


p⃗ =  p2 


p3
where:
• p1 - Economy: how economical the car is (≥ 9 is good, < 9 is bad);
• p2 - Comfort: how luxurious and comfortable the car is (> 12 is good, ≤ 12 is bad);
• p3 - Safety: how safe the car is (≥ 16 is good, < 16 is bad)
Once the coordinate payoffs are established, there will be 8 possible payoffs. A particularity from dichotomic models is that to determine the preference order of the variables
of the vector p, it is possible to perform sorting in lexicographic vector order of payoffs of
the problem (Oliveira, 2010). It is necessary only to define the coordinate payoffs order
of preference (that will depend on the decision-maker). The name lexicographic order is
derived from the order found in a dictionary, where series are compared in alphabetical
order, from left to right. Lexicographic order consists of an order function - a way of
sorting information. It is generally a useful and simple way to sort strings. Therefore
this reduces considerably the educing process of preferences for payoffs, which is part of
the modeling problems of Decision Theory. Remembering that the number 1 represents a
good situation for the consumer and the number 0 represents a bad situation, it is possible
to understand how these payoffs can be organized from Table 4.22 .
58
Chapter 4
Table 4.22: Example of consumers’ payoffs.
Payoffs
Economy
Comfort
Safety
p⃗8
p⃗7
p⃗6
p⃗5
p⃗4
p⃗3
p⃗2
p⃗1
1
1
1
1
0
0
0
0
1
1
0
0
1
1
0
0
1
0
1
0
1
0
1
0
4.2.2 Set of States of Nature (Scenarios)
States of nature are variables which the decision-maker cannot control. The real state
of nature will occur in a random way, and the decision-maker can only describe the possible
scenarios that can be assumed. For the problem proposed there are two variables acting
as states of nature:
• θa : The macroeconomic environment (growing or shrinking)
• θb : Quality control of car manufacturers (favorable or unfavorable for consumer)
The state θa represents the economy situation at the moment of buying a car. For
instance, a crisis could happen in the United States, like the one that began in 2008,
which hit car manufacturers hard. In this case, this was good for the consumer since car
prices decreased to keep the economy moving. But it could happen the other way that
car prices have to increase, and that could severely affect consumers’ preferences on car
purchase.
On the other hand, state θb represents the level of quality control in car manufacturers.
If a manufacturer does not have good quality control, probably their products present
defects. In a car, these defects can range from little details to safety problems that can
put the owner’s life at risk, such as brake defects, or problems with the horn, for example.
But, if the car manufacturer has good quality control, it will produce high quality cars
that will guarantee a good choice for the consumer.
With these two states of nature it is possible to construct three scenarios, that will be
the new states of nature adopted in the problem:
• θ1 : Shrinking Economy + Quality Control of car manufacturers unfavorable;
• θ2 : Shrinking Economy + Quality Control of car manufacturers favorable;
• θ3 : Growing Economy + Quality Control of car manufacturers favorable;
59
Chapter 4
State θ4 (growing economy + Quality Control of car manufacturers unfavorable)
was axed from the problem due to this state having low relevance for this problem, so it
can be set aside without big impacts on the problem of modeling. In this case, even if the
economy was growing, the state of nature would not be favorable for the consumer, since
manufacturers would be producing cars that are not safe or of sufficient quality, which
does not boost the purchase. This state of nature could resemble θ1 .
State θ1 represents the worst scenario, where in the economic view there is a shrinking
economy from a bank failing or a shake-up in the biggest economy, associated with the
situation of the car’s manufacturers which does not offer a quality control favorable for
consumers.
State θ2 represents an intermediate scenario, where in the economic view there is a
shrinking economy due to banks going bankrupt or a shake up in the biggest economy,
but they are associated with the situation of car manufacturers having a quality control
favorable for consumers.
State θ3 represents the best scenario, where in the economic view there is an expansion
in the economy from investments being approved in the region, or the country has found
another petroleum field or it is the headquarters of the next major sports event such as
the World Cup, associated with the situation of car manufacturers having quality control
favorable to the consumers, thus producing good cars.
It is important to remember that states θ1 , θ2 and θ3 will influence the payoffs probabilistically, besides the alternative actions that will be explained in the next section.
4.2.3 Actions
When the states of nature are known, it is necessary to make a decision or simply
act. From the Decision Theory point of view, there is no difference between making a
decision and acting, they are the same thing (Campello de Souza, 2007). In the same way
as states of nature, actions will influence payoffs probabilistically.
For the proposed problem the actions are:
• a1 To buy the most economical car, while thinking about comfort or safety that the
car can offer;
60
Chapter 4
• a2 To buy considering the variety of car prices, the comfort and safety that the car
can have;
• a3 To buy the most comfortable and safe car while thinking about price.
As to the actions listed above, a1 is the most practical and cheapest action, since the
consumer will not think of comfort, safety or other advantages that a car can give, only
of its price, so it will not be necessary to spend a lot of time looking for many kinds of
cars, but on the other hand, the car probably will not have with any comfort or safety.
Action a2 is an intermediate action, and maybe a harder one, because the consumer will
think about price, comfort and safety, so he/she will need to balance the advantages and
disadvantages of choosing between cars. And a3 is the most expensive action, since the
consumer will buy the most comfortable and safest car without worrying about price. In
other words, the consumer will buy the car of his/her dreams, that may come at a price
beyond his/her financial circumstances.
4.2.4 Set of Observations
Observations are the information that indicates an estimate of the states of nature.
Since the states of nature are implicitly unpredictable, through observations it is possible
to obtain data from other variables that are available and can bring useful information
about the tendency of the states of nature.
For the problem proposed, the observations for variables are:
• xa :The behavior of economic indicators (Gross Domestic Product, Inflation Rate,
Unemployment Rate) compared with the prior year (Favorable or Unfavorable);
• xb : The number of recalls of manufacturers compared with the prior year, including
all makes and models (High or Low).
It is interesting to emphasize that variable xa offers information that helps the decisionmaker to estimate θa . For instance, imagine that it can be observed that Gross Domestic
Product increases and the Inflation Rate and Unemployment Rate decreases. They are
strong indicators that the economy, represented by θa is growing. To estimate if xa is
Favorable or Unfavorable for economic growth we can look up the historical series of
61
Chapter 4
each indicator and verify the economic impacts that are associated with this variation in
the series. The observations of xa only help to estimate θa , so this cannot affirm that the
economy will advance, but that the probability of advance will be high, since the economic
indicators are favorable.
On the other hand, variable xb offers information that helps the decision-maker to
estimate θb . The lower the percentage of recalls in the last year, the more precise the
estimate of the state of nature.
The following scenarios were used for observations:
• x1 Economic Indicator Unfavorable + % recalls High;
• x2 Economic Indicator Unfavorable + % recalls Low;
• x3 Economic Indicator Favorable + % recalls Low;
It is interesting to emphasize that the variables x1 , x2 and x3 offer information that
helps the decision-maker to estimate θ. So, the observation x3 is an indicator that increases
the probability of θ3 , since the decision-maker do not have the control about the states of
nature!
The Economic Indicators
Macroeconomics means the economy as a whole. It seeks to answer questions such as:
“Why do some countries have high rates of inflation while others manage to keep prices
stable?” or “Why have some countries experienced a rapid growth in profits over the last
century, while others mired in poverty?”. Macroeconomists collect data on incomes, high
unemployment, and many other variables from different periods and countries. They try
to formulate general theories that help explain these data. Economists use many types
of data for measuring the performance of an economy. Three are especially important
macroeconomic variables: gross domestic product (GDP), the inflation rate and the unemployment rate.
Gross Domestic Product (GDP)
According to Mankiw (2004), real Gross Domestic Prod-
uct measures the total income of a country’s economy (adjusted for price level). GDP
is considered by many the best measure of economic performance. The aim of GDP is
62
Chapter 4
summarized in a single number, the dollar value of economic activity in a given period.
There are two ways to consider this statistic. One way is to see that GDP is the total
income of everyone in the economy. Another way is to think of GDP being the total
expenditure on the production of goods and services in the economy. GDP measures
something that people care about: their incomes. Moreover, an economy with a large
production of goods and services can better meet the demands of families, businesses and
government. The way GDP finds to measure both income and expenses is simple. In fact,
these two quantities mean the same thing because in the economy as a whole, income
must equal expense. Each transition that affects spending should affect earnings. Thus,
GDP measures the flow of dollars into the economy.
Economists describe the value of goods and services measured by nominal GDP in
current prices. A better measure of economic welfare would account for the production
of goods and services in the economy without any influence from price changes. For this
purpose, economists use real GDP, which is the value of goods and services measured by
a set of steady prices. In other words, real GDP shows what happened to spending on
production when the quantities changed, with prices remaining steady. Since prices are
constant, real GDP only varies from one year to another if the quantities produced vary.
As the capacity of a society to provide economic satisfaction to its members depends,
ultimately, on the quantities of goods and services produced, real GDP provides a better
measure of economic welfare than nominal GDP. The period during which real GDP falls
can be called a recession, if mild, or a depression, if more severe and longer-lasting.
Inflation Rate
The inflation rate measures speed of the increase in prices. It is one of
the basic worries of economists and economic policymakers. The most common measure
used to determine the cost of living is the consumer price index (CPI). By collecting the
prices of thousands of goods and services, the CPI converts the prices of many goods and
services into a single index which measures the general price level. So, CPI is the price
of the basket of goods and services compared to its price in a different base year. The
period in which prices are falling is called deflation.
Unemployment Rate
One aspect of economic performance is the efficiency with which
an economy uses its resources. As workers are the best feature of an economy, keeping
63
Chapter 4
them employed is a key concern of economic authorities. The unemployment rate is the
statistic which measures the percentage of people who want work but cannot find jobs.
4.2.5 Choosing the best decision based on the best decision rule
It is very important to emphasize the distinction between a good decision and a good
outcome. A good decision is one based on the uncertainties, values, and preferences of
the decision maker, so it is a logical decision, one which makes it possible to justify the
choice, taking all these aspects into account. A good outcome is one that is profitable or,
in other words, highly valued. Summarizing, a good outcome is one that we wish would
happen. Hopefully, by making good decisions in all the situations that face us, we are
likely to get good outcomes. Yet it is possible to be disappointed: a good decision can
produce a bad outcome.
The Likelihood Function
The observations X = {x} have a relation with the states of nature through a probability distribution represented by P (x|θ). So, when some observations are collected, it
is possible to estimate the probability of a state of nature occurring. For the problem
proposed, the likelihood function will be a matrix 3×3 that was educed from specialists.
In this case the specialist was a group of people who understood the subject and have
been questioned about these probabilities. Then the collected information was evaluated
and used to define the likelihood function, with the assistance of the supervisor, according
to the Table 4.23.
Table 4.23: Likelihood Function.
P (θ|x)
θ1
θ2
θ3
x1
0, 70
0, 10
0, 05
x2
0, 20
0, 85
0, 20
x3
0, 10
0, 05
0, 75
A Priori Distribution
A Prior distribution is an indicator about the states of nature in the light of historical
behavior. It is rare for there to be no knowledge about the behavior of θ. The probability
64
Chapter 4
about the prior distribution π(θ) can be from a data base with these registers or the
knowledge of a single specialist or a group of specialists on the subject.
In the problem proposed, educing prior knowledge was conducted by the same group
of specialists into likelihood, who obtained the values in Table 4.24.
Table 4.24: Prior Distribution.
θ
θ1
θ2
θ3
π(θ)
0, 1
0, 7
0, 2
The Consequence Function
Once an action a is adopted, a probabilistic mechanism is started that will choose
a consequence for the decision-maker. This probabilistic choice made by the system is
influenced by nature too. Nature chooses its state θ independently of the decision-maker.
So, the probability of receiving the payoff p will depend on θ and a.
The consequence function is represented by P (p|θ, a). The set of all probability distributions of payoffs is represented by P ∗ = {P } and these distributions P are the consequences. For the problem proposed, the consequence function will be a matrix 9×8, that
can be seen in Table 4.25.
Table 4.25: Consequence Function (in blank).
θ
θ1
θ1
θ1
θ2
θ2
θ2
θ3
θ3
θ3
a
a1
a2
a3
a1
a2
a3
a1
a2
a3
p⃗1
p⃗2
p⃗3
p⃗4
p⃗5
p⃗6
p⃗7
p⃗8
The next step is to define the probability of occurrence of the 8 payoffs for each one of
the 9 pairs (θ, a) for each decision-maker. In other words, with what probability will each
payoff occur on a scale that varies between 0 and 1 for each pair (θ, a). There are two
ways of doing this: one is through a data base using its information and the other is done
by specialists. In this case there is not a data base about the vast number of consumers,
65
Chapter 4
so it was necessary to obtain the probabilistic distribution from specialists. To facilitate
this activity, the binomial distribution used was as follows:
( )
n x
b(x; n; p) =
p (1 − p)n−x
x
(4.2.1)
Working with the binomial function helps in the process of obtaining the probability
distribution from the specialists, since the payoffs are already ranked. It is wished to
insert a coefficient between 0 and 1 to have the 8 payoffs in order of importance when
there is already a pair (θ, a). It is important to remember that the order of preference of
three coordinate payoffs depends on the decision-maker, thus with this information, there
could be 8 payoffs in order of importance by the lexicographic vectors order. On inserting
the binomial function in the left column of Table( 4.25), we have Table 4.26.
Table 4.26: Consequence Function.
Binomial
6%
15%
21%
41%
71%
60%
46%
80%
66%
θ
θ1
θ1
θ1
θ2
θ2
θ2
θ3
θ3
θ3
a
a1
a2
a3
a1
a2
a3
a1
a2
a3
p⃗1
0, 65
0, 32
0, 19
0, 02
0, 00
0, 00
0, 01
0, 00
0, 00
p⃗2
0, 29
0, 40
0, 36
0, 12
0, 00
0, 02
0, 08
0, 00
0, 01
p⃗3
0, 06
0, 21
0, 28
0, 25
0, 02
0, 08
0, 20
0, 00
0, 04
p⃗4
0, 01
0, 06
0, 13
0, 29
0, 09
0, 19
0, 29
0, 03
0, 13
p⃗5
0, 00
0, 01
0, 03
0, 20
0, 22
0, 29
0, 25
0, 11
0, 26
p⃗6
0, 00
0, 00
0, 01
0, 08
0, 32
0, 26
0, 13
0, 28
0, 30
p⃗7
0, 00
0, 00
0, 00
0, 02
0, 26
0, 13
0, 04
0, 37
0, 20
p⃗8
0, 00
0, 00
0, 00
0, 00
0, 09
0, 03
0, 00
0, 21
0, 05
4.2.6 Utility Theory
The goal of Utility Theory is to concentrate on the preferences that the decision-maker
has in the possible consequences of an action (Campello de Souza, 2007). The decisionmaker can be a person or a group of people whom have the power to make a final decision.
In our case there are individual decision-makers like car consumers in general or those
that need to buy many cars such as owners of taxi fleets, a car rental company, or a
company that needs many cars for its employees’ use. The final objective is to build a
mathematical model that permits the decision-maker’s preferences for goods that he could
obtain to be represented. The basic idea of Utility Theory is to quantify these preferences
by associating values to the goods values that represent a criterion of choice.
66
Chapter 4
A model can only exist when preferences satisfy rather demanding hypotheses. It thus
relies on having a theoretical base, which is very likely part of the intellectual appeal
of the method. Once the hypotheses of the model have been considered satisfactory or
made clear and are valid in a decision context and provided the process of educing all
parameters of the model has been directed correctly, the decision becomes obvious.
Various different kinds of studies have been developed on studying the utility of car
owners. In Rouwendal and Pommer (2004) for instance, the project allows to structure
the utility to be structured for multiple or single cars owners, when it is better or not to
have more than one car considering fixed costs, fuel costs and the decision-maker’s needs.
Another example is the study of Högberg (2007) in which the objective is to elicit the
value that car consumers place upon environmental concerns when purchasing a car.
Educing Car Consumers’ Utility
In our case, it is important to emphasize that it is necessary to know the order of preferences for the coordinate payoffs, which is based on the consumer’s profile. Considering
that there are six different consumer’s profiles, because of the six clusters, there will be six
different orders for preference. Thereafter, the payoffs will be ordered automatically for
each decision-maker and it is possible to get the Utility Function, through questionnaires,
which is the key point of Decision Theory.
Since there are six different kinds of preferences, there are six kinds of questionnaires.
Therefore, depending on the preference order of coordinate payoffs there is a specific
questionnaire. In the questionnaire two scales: u1 and u2 were considered. The first
includes the payoffs p1 to p5 and the second includes p4 to p8 . There are some elements
that are above other elements because of the error margin was corrected. In the first
scale the worst payoff is p1 e the best is p5 . Thus the decision-maker is asked, in this
scale, for which value of λ he is indifferent between receiving the payoff pj , j = 2; 3; 4 with
probability 1, or a lottery where he receives p5 with probability λ or p1 with probability
1 − λ. This has to be done on the other scale. Therefore the questionnaire consists of
asking successively in the two scales of values of u and converting them into a unique
scale by the method of “Overlay Bands”(Campello de Souza, 2007).
There is an example of questionnaire (Table 4.28) that was applied to car consumers.
67
Chapter 4
Table 4.27: Questionnaire divided into two scales to facilitate educing.
Scale 2
p⃗8
1
1
1
p⃗7
1
1
0
p⃗6
1
0
1
p⃗5
1
0
0
p⃗4
0
1
1
Scale 1
p⃗3 p⃗2
0
0
1
0
0
1
p⃗1
0
0
0
In this case a questionnaire for a decision-maker who prefers Economy ≻ Comfort ≻
Safety. The decision-maker has to answer to which probability (λ) he/she feels indifferent
between the fixed prize or to risk the game, that has a worse premium than the fixed one,
with probability (1 − λ) and a better premium than the fixed one, with probability λ.
68
Best prize with probability λ
p⃗ Economy Comfort Safety
p⃗4
Good
Bad
Bad
p⃗4
Good
Bad
Bad
p⃗4
Good
Bad
Bad
p⃗8
Good
Good
Good
p⃗8
Good
Good
Good
p⃗8
Good
Good
Good
Worse prize with probability 1 − λ
p⃗ Economy Comfort
Safety
p⃗1
Bad
Bad
Bad
p⃗1
Bad
Bad
Bad
p⃗1
Bad
Bad
Bad
p⃗5
Bad
Good
Good
p⃗5
Bad
Good
Good
p⃗5
Bad
Good
Good
Indifference λ
p⃗
p⃗2
p⃗3
p⃗5
p⃗4
p⃗6
p⃗7
Table 4.28: Example of a questionnaire applied to decision-makers.
Fixed prize
Economy Comfort
Bad
Bad
Bad
Good
Bad
Good
Good
Bad
Good
Bad
Good
Good
Safety
Good
Bad
Good
Bad
Good
Bad
Chapter 4
69
Chapter 4
The decision-maker’s utility function for the consequence function will be calculated
by:
u(P (p|θ, d)) =
∑
p
v(p)
∑
P (x|θ)P (p|θ, d(x))
(4.2.2)
x
Three different car consumers were chosen to represent the decision-makers. They are
from different clusters; in other words, they have different profiles. Because of this, they
have different preferences, too, so it is possible to analyze each one to understand how we
can educe utility and how this reaches the best decision.
Decision-Maker 1
The first decision-maker is characterized as a person that is in cluster
3; in other words, a young person who is married, but does not have children. He goes to
places like the shopping centers, beach, pub, museums and has a good education level, but
he has a low individual and family income. So, with this information, which is represented
in Figure 4.6, it is easier to understand the subsequent results .
Figure 4.6: Social Analysis of Decision-maker 1.
For this first decision-maker, the preference order for the coordinates payoffs is: Economy ≻ Comfort ≻ Safety. So, the order of the payoffs will be as in Table 4.29.
70
Chapter 4
Table 4.29: The ranking of payoffs for the Decision-maker 1.
Payoffs
Economy
Comfort
Safety
p⃗8
1
1
1
p⃗7
1
1
0
p⃗6
1
0
1
p⃗5
1
0
0
p⃗4
0
1
1
p⃗3
0
1
0
p⃗2
0
0
1
p⃗1
0
0
0
First, the questionnaire for utility educing was applied, similar to that presented in
Table 4.28. When λ values for each situation are obtained, these data are enter on
a spreadsheet (prepared by the author and her supervisor) that calculates the utility
function of the decision-maker. The result of the utility function for decision maker 1 is
in Table 4.30.
Table 4.30: Decision-maker 1’s Utility.
u
0,0000
p⃗1
Decision-Maker 2
0,0779
p⃗2
0,1169
p⃗3
0,2208
p⃗4
0,2597
p⃗5
0,6104
p⃗6
0,9221
p⃗7
1,0000
p⃗8
The second decision-maker is characterized as a person who is in cluster
5 because he is a student in the high social level who neither yet has a higher education
level nor is married. Individual income is also low, but the big difference is in family
income, which is high (Figure 4.7).
71
Chapter 4
Therefore, for this second decision-maker, the preference order for the coordinates
payoffs is: Safety ≻ Economy ≻ Comfort. Thus, the order of the payoffs will be as in
Table 4.31.
Table 4.31: The ranking of payoffs for Decision-maker 2.
Payoffs
Safety
Economy
Comfort
p⃗8
1
1
1
p⃗7
1
1
0
p⃗6
1
0
1
p⃗5
1
0
0
p⃗4
0
1
1
p⃗3
0
1
0
p⃗2
0
0
1
p⃗1
0
0
0
Applying the educing questionnaire and using the spreadsheet, the utility function for
decision-maker 2 is shown Table 4.32:
u
0,0000
p⃗1
Decision-Maker 3
0,4865
p⃗2
0,6908
p⃗3
0,7297
p⃗4
0,9730
p⃗5
0,9865
p⃗6
0,9973
p⃗7
1,0000
p⃗8
The third decision-maker is characterized as a person who is in cluster
6 because he is married, has 2 children, has a high education level and has a very high
individual and family income. It is possible to see this in Figure 4.8.
Therefore, for this third decision-maker, the preference order for the coordinates payoffs
72
Chapter 4
is: Safety ≻ Comfort ≻ Economy. Thus, the order of the payoffs will be as in Table 4.33.
Table 4.33: The ranking of payoffs for Decision-maker 3.
Payoffs
Safety
Comfort
Economy
p⃗8
1
1
1
p⃗7
1
1
0
p⃗6
1
0
1
p⃗5
1
0
0
p⃗4
0
1
1
p⃗3
0
1
0
p⃗2
0
0
1
p⃗1
0
0
0
Applying the educing questionnaire and using the spreadsheet, the utility function for
decision-maker 2 is shown in Table 4.34:
u
0,0000
p⃗1
0,3636
p⃗2
0,4091
p⃗3
0,5455
p⃗4
0,9091
p⃗5
0,9773
p⃗6
0,9909
p⃗7
1,0000
p⃗8
Decision Rules
A decision rule is a function that associates each observation with an action. Campello
de Souza (2007) claims that a decision rule is a procedure that permits an action course
to be chosen, among the available ones, which is appropriate to what the decision-maker
wants. Nature chooses θ, the decision-maker chooses a and the system chooses a consequence p with probability P . With some kind of information about how Nature acts and
how the system acts, the decision-maker adopts, into his possibilities, a course of action
that increases the probability of him getting the consequence that he most prefers. The
set of all possible decision rules is represented by D = {d} and the number of possible
decision rules is defined by:
||D|| = ||A||||X ||
(4.2.3)
In this case, there will be 27 decision rules, since there are 3 possible actions: a1 , a2
and a3 , and 3 observations: x1 , x2 and x3 . Each rule is compared with each other and the
one that has the lowest risk is the best decision.
Risk Function and Bayesian Risk
The risk represents the average loss when the true state of nature is θ and the decisionmaker uses rule d. The Risk Function is defined by:
73
Chapter 4
Rd (θ) = E(L|θ) =
∑
L(θ, d(x))P (x|θ)
(4.2.4)
x
The Risk Function is related to loss, that is defined by the negative of utility:
L(θ, d(x)) = −u(P (p|θ, d(x)))
(4.2.5)
On the other hand, Bayesian risk takes into account π(θ). It is asked then how more
likely p is since the decision rule d is chosen. This is shown by:
P (p|d) =
∑
P (p, θ|d) =
θ
∑
P (p|θ, d)P (θ|d) =
∑
P (p|θ, d)π(θ)
(4.2.6)
θ
θ
Note that P (θ|d) = π(θ) because it is assumed, of course, that nature selects θ no
matter how the action was select, ie, regardless of the decision rule. In this case, knowing
the prior distribution, we can talk about the probability of obtaining a good because of
a decision rule, not because of a decision rule and a state of nature. Therefore, the risk
of decision rule d is given by:
rd = −u(P (p|d)) = −
∑
π(θ)u(P (p|θ, d)) =
θ
∑
π(θ)Rd (θ)
(4.2.7)
θ
This is the risk when using the decision rule d, with π as a prior distribution. Campello
de Souza (2007) describes in full the methodology used in the process of the decision choice.
So, with the basic sets and the probabilistic mechanisms, we have a decision choice.
Decision-maker 1
For decision-maker 1 the decision-rules are set out in Table 4.35. All
the risks are listed for all possible decisions and the decision with lowest risk is indicated,
that is the best decision among the other possible solutions for the problem proposed.
It is important to emphasize that the risk calculation has a negative value. The best
decision is the d26 , which proposes that for any observation (x1 , x2 or x3 ), the best action
is a2 .
74
Chapter 4
Table 4.35: Decision rules: decision-maker 1.
Decision
d
x1
d1 a1
d2 a1
d3 a2
d4 a2
d5 a3
d6 a3
d7 a1
d8 a1
d9 a2
d10 a2
d11 a3
d12 a3
d13 a2
d14 a3
d15 a1
d16 a3
d17 a1
d18 a2
d19 a1
d20 a1
d21 a2
d22 a2
d23 a3
d24 a3
d25 a1
d26 a2
d27 a3
Decision-maker 2
Rules
x2 x3
a2 a3
a3 a2
a1 a3
a3 a1
a1 a2
a2 a1
a1 a2
a1 a3
a2 a1
a2 a3
a3 a1
a3 a2
a1 a1
a1 a1
a2 a2
a2 a2
a3 a3
a3 a3
a2 a1
a3 a2
a3 a2
a1 a2
a1 a3
a2 a3
a1 a1
a2 a2
a3 a3
θ1
0,034332
0,037
0,059
0,064
0,077
0,080
0,025
0,027
0,059
0,066
0,085
0,089
0,052
0,073
0,032
0,084
0,039
0,071
0,028
0,036
0,038
0,056
0,080
0,087
0,020722
0,063247
0,091615
Rd(θi )
θ2
0,510
0,393
0,276
0,418
0,265
0,524
0,237
0,226
0,546
0,560
0,396
0,421
0,262
0,240
0,521
0,549
0,382
0,432
0,496
0,396
0,443
0,287
0,253
0,538
0,211955
0,571571
0,409353
θ3
0,516
0,683
0,470
0,292
0,659
0,315
0,655
0,460
0,319
0,526
0,287
0,688
0,263
0,259
0,710
0,715
0,489
0,499
0,310
0,683
0,693
0,664
0,465
0,521
0,25386
0,719798
0,494056
Bayes
−rd
0,46849
0,42486
0,29914
0,35861
0,33468
0,43779
0,30983
0,25952
0,45256
0,50812
0,34384
0,44971
0,24358
0,22881
0,51881
0,54366
0,37454
0,41417
0,41294
0,42713
0,46178
0,34945
0,28436
0,49334
0,20396
0,55843
0,39939
decision is the d16 , which proposes that for the observation x1 the best action is a3 and
for the observations x2 or x3 the best action is a2 .
Decision-maker 3
decision is the d26 , which proposes that for any observation (x1 , x2 or x3 ), the best action
is a2 .
75
Chapter 4
Decision
d
x1
d1 a1
d2 a1
d3 a2
d4 a2
d5 a3
d6 a3
d7 a1
d8 a1
d9 a2
d10 a2
d11 a3
d12 a3
d13 a2
d14 a3
d15 a1
d16 a3
d17 a1
d18 a2
d19 a1
d20 a1
d21 a2
d22 a2
d23 a3
d24 a3
d25 a1
d26 a2
d27 a3
Rules
x2 x3
a2 a3
a3 a2
a1 a3
a3 a1
a1 a2
a2 a1
a1 a2
a1 a3
a2 a1
a2 a3
a3 a1
a3 a2
a1 a1
a1 a1
a2 a2
a2 a2
a3 a3
a3 a3
a2 a1
a3 a2
a3 a2
a1 a2
a1 a3
a2 a3
a1 a1
a2 a2
a3 a3
θ1
0,210932
0,219
0,324
0,348
0,389
0,405
0,154
0,168
0,326
0,367
0,427
0,454
0,283
0,362
0,197
0,432
0,233
0,389
0,170
0,218
0,223
0,310
0,403
0,446
0,127018
0,353328
0,46798
Rd(θi )
θ2
0,925
0,879
0,760
0,883
0,759
0,928
0,745
0,740
0,933
0,944
0,878
0,893
0,749
0,744
0,929
0,943
0,874
0,894
0,914
0,892
0,898
0,764
0,755
0,939
0,729283
0,948689
0,888754
θ3
0,929
0,960
0,899
0,814
0,938
0,822
0,932
0,891
0,824
0,937
0,812
0,966
0,786
0,784
0,970
0,976
0,919
0,927
0,816
0,960
0,968
0,940
0,897
0,935
0,777704
0,978474
0,924656
Bayes
−rd
0,86144
0,83719
0,75146
0,81958
0,76515
0,85753
0,73278
0,72201
0,85461
0,89089
0,82250
0,86956
0,71518
0,71810
0,87221
0,90458
0,82641
0,85586
0,82516
0,84607
0,85291
0,76223
0,75438
0,89381
0,68573
0,90166
0,85878
It can be seen that the course of action a1 has not appeared in any of the decision rules.
Probably because it does not fit the profile of the decision-makers mentioned here. Since
a1 is the decision to buy the most economical car possible, without regard for the minimum
of comfort or safety, this should not be a good option for most car consumers. Action
a3 (buy the most comfortable and safe car), in turn, appeared only for decision-maker
2, who falls in cluster 5, which is characterized by people who give more importance
to appearance and beauty than other clusters. Action a2 is the most common among
decision-makers, because, despite demanding more time, it is a more cautious action, and
one that takes account of all the variables of an automobile.
4.2.7 A Sensibility Analysis
A sensibility analysis is a way to validate the method used to solve a problem. So, it is
important to analyze if the method is robust or not. In this case, an increment of 1% and
76
Chapter 4
Decision
d
x1
d1 a1
d2 a1
d3 a2
d4 a2
d5 a3
d6 a3
d7 a1
d8 a1
d9 a2
d10 a2
d11 a3
d12 a3
d13 a2
d14 a3
d15 a1
d16 a3
d17 a1
d18 a2
d19 a1
d20 a1
d21 a2
d22 a2
d23 a3
d24 a3
d25 a1
d26 a2
d27 a3
Rules
x2 x3
a2 a3
a3 a2
a1 a3
a3 a1
a1 a2
a2 a1
a1 a2
a1 a3
a2 a1
a2 a3
a3 a1
a3 a2
a1 a1
a1 a1
a2 a2
a2 a2
a3 a3
a3 a3
a2 a1
a3 a2
a3 a2
a1 a2
a1 a3
a2 a3
a1 a1
a2 a2
a3 a3
θ1
0,179233
0,189
0,251
0,279
0,299
0,318
0,143
0,149
0,263
0,281
0,334
0,345
0,233
0,288
0,173
0,329
0,195
0,297
0,162
0,188
0,196
0,244
0,305
0,335
0,131636
0,27449
0,351611
Rd(θi )
θ2
0,877
0,798
0,637
0,824
0,630
0,895
0,605
0,602
0,906
0,913
0,813
0,823
0,631
0,620
0,880
0,905
0,795
0,831
0,871
0,803
0,834
0,640
0,627
0,902
0,595513
0,915517
0,820181
θ3
0,882
0,927
0,836
0,722
0,895
0,735
0,881
0,818
0,741
0,900
0,717
0,941
0,676
0,671
0,946
0,959
0,864
0,882
0,722
0,927
0,946
0,900
0,831
0,895
0,657941
0,964167
0,876825
Bayes
−rd
0,80148
0,75457
0,63066
0,74572
0,64113
0,80308
0,60404
0,59121
0,80545
0,84093
0,74335
0,79166
0,59518
0,59281
0,81430
0,85140
0,74174
0,78120
0,76599
0,75768
0,78359
0,64349
0,62830
0,83857
0,55572
0,85376
0,77883
2% was added that randomly would cause variations above or below in the probabilistic
mechanisms (likelihood function and a prior distribution) so as to observe if the best
decision would change or not.
For each decision-maker was made a perturbation 400 times of 1% and 2% respectively,
which means 800 perturbations in total for each decision maker. For decision-maker 1
the variations did not cause any change to the best decision in 800 disturbances tested of
variations, so the decision remains d26 . For decision-maker 2 it was the same situation,
for both kinds of variation (1% or 2%) the best decision did not change, decision d16
remaining the choice for all the 800 instances tested. However for decision-maker 3, it
was different. With the variation of 1% the best choice did not change from d26 in 100%
of the 400 times; on the other hand, with a variation of 2% the best choice was d16 but
did not change either in 100% of the 400 instances tested.
77
Chapter 5
5
COMMENTS, CONCLUSIONS AND SUGGESTIONS
According to Carvalho and Pedrozo (2011), there are many factors that limit the
decision maker in the process of decision making. Organizations to cope with uncertainty,
for example, create procedures (and even strategies) that are increasingly homogeneous.
Instead of fostering a creative destruction, they generate a maintenance of often separate
processes that trigger low efficiency and competitiveness. So in practice, although the
world is increasingly more complex, decision-makers often try to avoid this condition.
Other factors that limit decision making is access to incomplete and incorrect information
about the nature of the problem and its possible solutions, the lack of time, lack of
resources material and human resources to collect more complete information,distorted
perceptions, inability to recall the great amounts of information and the limits of human
intelligence.
As a consequence of this, decision makers are satisfied or accept the first satisfactory
decision that they discover, or instead of maximizing the results of this decision, they
seek to optimize the resources available in the process decision. In situations where no
information is available, or information is insufficient, intuition can become more appropriate since it comes from experiences and feelings. However, intuition can precipitate
the decision maker, who might skip stages of the decision-making process to reach a final
decision, often based on incomplete data and/or diagnoses. Therefore, its use is limited
and present risks.
Therefore, given that intuition alone is not able to provide better agreed decisions, a
logical structure is required to add some contribution. The global result of this study
was to model a decision problem for car consumers, by transforming subjective ideas
into values that could be calculated to get an objective result. Decision theory addresses
the problem of how to decide what to do when it is uncertain what will happen, since
uncertainty is an indelible mark of the universe (Campello de Souza, 2007).
The automotive market covers such a large scope, that it would be impossible to deal
with this in its entirety in this study alone. The payoffs chosen to model this problem
could vary in numerous ways, for example, they could be about new or used cars, a luxury,
sports, popular, utility or travel cars, or there could be numerous payoffs where each would
be a different model/make of car. A study could also have been made of, for example, the
78
Chapter 5
situation of roads and highways so as to take these taken into consideration when choosing
a car. The payoffs constructed attempted to cover as much information as possible within
the scope of time and information for modeling something as comprehensive as possible,
taking into account the majority of those characteristics in a car at the time of purchase.
The actions of the decision maker could be different too, for example, deciding between
either buying or not the automobile, or deciding how many cars to buy. But this study was
intended to work with the hypothesis that the decision maker would finalize his decision
to own a car.
Ultimately, the model presented can be complemented with more payoffs, states of
nature, observations or actions and it is automated by a spreadsheet that facilitates its
being adapted to other contexts. Dichotomic variables were used that enable the order of
payoff vectors to be ordered from an order of preference among variables in a automatic
way, following the binary logical of ordering, but this does not prevent the use of nondichotomic variables.
5.1
Conclusions
1. The proposed representation of the payoffs (mostly) and other variables of the problem proved to be adequate for the formulation of problem of choosing the car in
terms of Decision Theory;
2. The decision-maker’s profile (educational level, marital status, number of children,
individual and family income, whether they have savings, health insurance, if they
go to movies, malls, theaters, museums, beaches or church) directly interferes in their
preferences and choices;
3. By a sensibility analysis, it is possible to realize that the model developed is effective
for the problem proposed.
5.2
Limitations of the study
1. To educe utility is not an easy activity, because it is required that the decision-maker
knows what he really wants, and the methods that exist are not very didactic;
79
Chapter 5
2. Mathematical models are a simplification of reality; they use only the more relevant
variables;
3. There was no database to define the values of probabilistic mechanisms;
5.3
Suggestions for future research
1. Do field research with more decision-makers to educe utility, using the current model,
and using a different scale for payoffs that are not dichotomous;
2. Working with payoffs defined by the models/makes of cars, the existing types (luxury,
sports, popular, utility, leisure) or whether they are new or used;
3. Collect historical data, about θ and x, and make the interface between them and the
consequence function;
4. Examine the possibility of creating an authority to support the decision maker that
will have adequate data to assist in making a decision;
5. Perform a sensitivity analysis which will also cause variations in the utility function.
80
REFERENCES
Abrita, M. M. Efeitos da crise mundial na procura de financiamentos de veículos junto
à carteira de clientes exclusivo-pessoas físicas-na agência de Vila da Quinta (Rio Grande
do Sul-RS) do Banco do Brasil S.A. Dissertação de Mestrado, Universidade Federal do
Rio Grande do Sul, Porto Alegre, 2009.
Artoni, P. R. C. D; Sofiato, D.; Braga, R. P. F; Garcia, E. C. Análise dos
atributos que influenciam a compra de automóveis por mulheres. In: Seminários em
Administração-SEMEAD,13, setembro, 2010. (2177-3866).
Associação Nacional dos Fabricantes de Veículos Automotores. Anuário
da indústria automobilística brasileira.
ANFAVEA, 2010a.
Disponível em:
<http://www.anfavea.com.br/anuario.html>. Acesso em: 14 mar. 2011.
Associação Nacional dos Fabricantes de Veículos Automotores. Estatísticas. ANFAVEA, 2010b. Disponível em: <http://www.anfavea.com.br/tabelas.html>.
Acesso em: 20 fev. 2010.
Bermúdez, José Luis. Decision theory and rationality. New York: Oxford University
Press, 2009.
Bouyssou, D.; Marchant, T.; Pirlot, M.; Perny, P.; Tsoukiàs, A.; Vincke,
P. Evaluation and decision models. Boston: Kluwer Academic Publishers, 2000.
Campello de Souza, F. M. Decisões racionais em situações de incerteza. 2. ed., 2007.
Carvalho, D. M.; Pedrozo, E. Á. Caos, complexidade e tomada de decisão. Revista
Brasileira de Gestão e Desenvolvimento Regional, v. 7, n. 1, p. 203–230, 2011.
Carvalho, V. A. A influência do fator ambiental na decisão de compra de automóveis
premium. Dissertação de Mestrado, Universidade Federal Fluminense, 2006.
81
Costa, E. E.; Caldeira, A. R.; Magalhães, P. H. V.; Parreiras, L. L.; Santos,
M. O. Desenvolvimento tecnológico de um véiculo para deslocamento offroad de portadores
de necessidades especiais. Belo Horizonte: 7o. Seminário de Extensão da Universidade
FUMEC, novembro, 2010. (Relatório Técnico, n. 978-85-63372-03-1).
Cummings, P.; McKnight, B.; Rivara, F. P.; Grossman, D. C. Association of
driver air bags with driver fatality. BMJ, v. 324, May, p. 1119–1122, 2002.
Cunha, R. de A. N. Percepção da influência do adolescente no processo de decisão de
compra familiar. Universidade Nove de Julho, 2004.
Depetris, A. Desenvolvimento e aplicação de um programa em Matlab Simulink para
a simulação do desempenho de veículos rodoviários comerciais em movimento acelerado.
Dissertação de Mestrado, Escola Politécnica da Universidade de São Paulo, São Paulo,
2005.
Dubois, D.; Prade, H. Possibility theory as a basis for qualitative decision theory.
Morgan Kaufmann, 1995. p. 1924–1930.
Einhorn, H. J.; Hogarth, R. M. Behavioral decision theory. Annual Reviews, 53–88,
1981.
Hansson, S. O. Decision theory. Royal Institute of Technology (KTH), Stockholm,
August, 1994.
Högberg, M. Eco-driving? A discrete choice experiment on valuation of car attributes.
Dissertação de Mestrado, School of Economics and Management-Lund University, Sweden,
2007.
Horvitz, E. J.; Breese, J. S.; Henrion, M. Decision theory in expert systems and
artificial intelligence. Stanford University, 1988.
Howard, R. A. The foundations of decision analysis revisited. Advances: Foundations
of DA Revisited, v. 10, n. 060520, p. 1–44, 2007.
Lemos, R. F. Atributos de compra de automóveis de alto valor. Dissertação de Mestrado,
Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, 2007.
82
Lessa, P. S. et al . Decision theory applied to image quality control in radiology. BMC
Medical Informatics and Decision Making, v. 8, 2008.
Machado, D. D. P. N.; Estrehlau, S.; Fasolo, K. B. É possível agrupar consumidores de acordo com a preferência por cores? Revista de Negócios, v. 8, n. 1, 2007.
Mankiw, N. G. Macroeconomia. 5. ed. Rio de Janeiro: LTC, 2004.
Minas Gerais. Secretaria de Estado de Fazenda. Tabela de valores da base de
cálculo do imposto sobre a propriedade de veículos automotores - IPVA 2010. Minas
Gerais: Fundação Instituto de Pesquisas Econômicas - FIPE, 2010.
Moreira, M. P.; Fernandes, F. C. F. Avaliação do mapeamento do fluxo de valor
como ferramenta da produção enxuta por meio de um estudo de caso. In: Encontro
Nacional de Engenharia de Produção-ENEGEP, 21, 2001.
Moura Filho, E. M. Mercado automotivo. Dissertação de Mestrado, Universidade
Federal de Pernambuco, Recife, 2007.
Naschpitz, B. C.
Reflexões sobre sociedade do consumo e seu efeito na qual-
idade de produtos.
In:
Congresso nacional de estudantes de engen-
haria mecânica, 27, agosto, 2010.
Disponível em: <http://www.uff.br/petmec
/downloads/ativdownloads/artigocreem2010recall2.pdf>. Acesso em: 21 mar. 2011.
North, D. W. A tutorial introduction to decision theory. IEEE Transactions On Systems
Science And Cybernetics, v. 4, n. 3, p. 200–210, 1968.
Odenthal, D.; Bünte, T.; Heitzer, H-D; Eicker, C. How to make steer-bywire feel like power steering. In: Triennial world congress, 15, 2002. Disponível em:
<http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2002/data/content/
01883/1883.pdf>. Acesso em: 17 jun. 2010.
Oliveira, G. A. de. A teoria da decisão aplicada nas soluções de redes de telecomunicações. Dissertação de Mestrado, Universidade Federal de Pernambuco, Recife, 2010.
Oliveira,
L.
Ergonomia - dirigindo corretamente.
Street Customs, 2009.
Disponível em: <http://www.streetcustoms.com.br/revistas-carros/life-style
/ergonomia-dirigindo-corretamente.html>. Acesso em: 24 ago. 2010.
83
Projeção.
da produção de veículos sobe mais uma vez, 2010.
Disponível em:
<http://www.automotivebusiness.com.br/estatisticas.aspx>. Acesso em: 20 fev.
2010.
Redação, Da.
2010.
Número de recall em 2009 bateu recorde.
Disponível em:
Gazeta do Povo,
<http://www.gazetadopovo.com.br/automoveis/conteudo.
phtml?tl=1&id=960566&tit=Numero-de-recall-em -2009-bateu-recorde>. Acesso
em: 11 fev. 2010.
Resende, C. B. de; Scarpel, R. A. Importância das características na precificação
de veículos nacionais. Produção, v. 19, n. 2, p. 345–358, 2009.
Ribeiro, T. M.; Câmara, J. J. D.; Engler, R. C. Habitáculo veicular. Palermo:
Universidad de Palermo, 2007. (Relatório Técnico, n. 1850-2032).
Rodrigues, A.
Segurança ainda opcional.
AutoData, 2008.
Disponível em:
<http://www.autodata.com.br/modules/revista.php?m=reportagens&recid=4298>.
Acesso em: 4 nov. 2010.
Rouwendal, J.; Pommer, J. An indirect utility model of multiple car ownership and
use. Amsterdam: Tinbergen Institute Discussion Paper, 2004.
Sampaio, D. O. et al . Um estudo comparativo sobre o comportamento do consumidor
de automóveis novos. In: Seminário de Administração-semead,7. São Paulo: USP,
2004.
Samy, R. Veja a melhor cor para o seu carro. WebMotors, 2008. Disponível em:
<http://www.webmotors.com.br/wmpublicador/Mercado_Conteudo.vxlpub?hnid=
39337>. Acesso em: 16 nov. 2010.
Segui-Gomez, M. Driver air bag effectiveness by severity of the crash. AmJ Public
Health, v. 90, n. 10, p. 1575–1581, 2000.
Siqueira, M. M. M. Medidas do comportamento organizacional. Estudos de Psicologia,
v. 7, n. (número especial), p. 11–18, 2002.
Souza, L. B. O mercado automotivo do grande Recife. Dissertação de Mestrado, Universidade Federal de Pernambuco, Recife, 2004.
84
Stein, J. The right decision. 1. ed. New York: McGraw Hill, 2010.
Teixeira
Jr,
S.
Em busca do carro do futuro.
Exame.com,
2008.
Disponível em: <http://portalexame.abril.com.br/revista/exame/edicoes/0910
/tecnologia/m0149913.html>. Acesso em: 1o. nov. 2010.
Toresan Jr., W. Cálculo de velocidade para veículos equipados com sistemas de freios
ABS. Porto Alegre: Instituto Geral de Perícias-RS, 2010.
Vasconcellos, R. de G. Extensão do período de garantia para veículos populares.
Dissertação de Mestrado, Escola Politécnica da Universidade de São Paulo - USP, São
Paulo, 2006.
Wiltgen, J.
Saiba quais carros têm os seguros mais caros.
Exame.com,
2010. Disponível em: <http://exame.abril.com.br/seu-dinheiro/carros/noticias
/saiba-quais-carros-tem-seguros-mais-caros-561853?page=5>. Acesso em:
1o.
nov. 2010.
Womack, J. P.; Jones, D. T.; Roos, D. A máquina que mudou o mundo. Rio de
Janeiro: Elsevier, 2004.
Wood Jr., T. Fordismo, Toyotismo e Volvismo. Revista de Administração de Empresas,
v. 32, n. 4, p. 6–18, 1992.
85
A
Appendix
QUESTIONÁRIO SOBRE AUTOMÓVEIS
1. Sexo
(0) Feminino (1) Masculino
2. Data de nascimento
/
/
3. Maior nível de instrução concluído
(1) Até a 4a. Série (1o. Grau Menor)
(5) Curso Superior
(2) Da 5a. à 8a. Série (1o. Grau Maior)
(6) Especialização
(3) Da 1a. à 3a. Série do 2o. Grau
(7) Mestrado
(4) Curso Técnico
(8) Doutorado
4. Estado civil
(1) Solteiro (3) Desquitado
(2) Casado
(5) Uniã o Informal
(4) Viúvo
5. Quantos filhos você tem?
6. Qual a sua faixa de renda familiar?
(01) Até R$ 2.000,00
(06) De R$ 10.000,01 a R$ 12.000,00
(02) De R$ 2.000,01 a R$ 4.000,00
(07) De R$ 12.000,01 a R$ 14.000,00
(03) De R$ 4.000,01 a R$ 6.000,00
(08) De R$ 14.000,01 a R$ 16.000,00
(04) De R$ 6.000,01 a R$ 8.000,00
(09) Acima de R$ 16.000,00
(05) De R$ 8.000,01 a R$ 10.000,00
7. Principal ocupação (atual ou a última)
(01) Nenhuma
(05) Empresário
(09) Aposentado
(02) Empregado Público
(06) Estudante
(99) Outra
(03) Empregado Privado (07) Do Lar
(04) Autônomo
(08) Produtor Rural
86
8. Qual o setor no qual você ou sua organização atua?
(1) Nenhum
(4) Serviço
(2) Comércio
(5) Construção
(3) Indústria
(6) Agricultura
(9) Outro
9. Que tipo de posição ou cargo que você exerce atualmente?
(1) Nenhum
(2) Estagiário
(3) Administrativa (Agente Administrativo/Secretário/Assistente/Auxiliar/Atendente)
(4) Supervisão ou Gerência (Chefia de Setor ou Divisão/Direção de Departamento)
(5) Diretoria (Presidência/Direção Geral/Superintendente)
(9) Outro
10. Qual a sua faixa de renda individual?
(01) Até R$ 1.000,00
(06) De R$ 5.000,01 a R$ 6.000,00
(02) De R$ 1.000,01 a R$ 2.000,00
(07) De R$ 6.000,01 a R$ 8.000,00
(03) De R$ 2.000,01 a R$ 3.000,00
(08) De R$ 8.000,01 a R$ 10.000,00
(04) De R$ 3.000,01 a R$ 4.000,00
(09) De R$ 10.000,01 a R$ 12.000,00
(05) De R$ 4.000,01 a R$ 5.000,00
(10) Acima de R$ 12.000,00
11. Qual a área onde você reside?
87
(1) Recife - ÁREA 1: Aflitos, Apipucos, Casa Forte, Espinheiro, Graças, Jaqueira,
Parnamirim,Poço, Tamarineira, etc.
(2) Recife - ÁREA 2: Afogados, Areias, Barro, Bongi, Caxangá, CDU, Cohab, Cordeiro,
Curado, Engenho do Meio, Estância, Ibura, Iputinga, Ilha do Retiro, Ipsep,
Jardim São Paulo, Madalena, Mangueira, Mustardinha, Prado, Sancho, San Martin,
Tejipió, Torre, Torrões, Várzea, etc.
(3) Recife - ÁREA 3: Água Fria, Alto José Bonifácio, Alto José do Pinho,
Alto do Mandú, Alto Sta. Terezinha, Arruda, Beberibe, Cajueiro, Campina do Barreto,
Campo Grande, Casa Amarela, Dois Irmãos, Encruzilhada, Fundao, Linha do Tiro,
Macaxeira, Monteiro, Morro da Conceição, Nova Descoberta, Torreão,
Vasco da Gama, etc.
(4) Recife - ÁREA 4: Boa Vista, Cabanga, Derby, Ilha do Leite, Ilha Joana Bezerra,
Paissandú, Santo Amaro, Santo Antônio, São José, etc.
(5) Recife - ÁREA 5: Boa Viagem, Imbiribeira, Pina e afins.
(6) Olinda
(7) Jaboatão dos Guararapes
(8) Caruaru
(9) Outro município que não Recife, Olinda, Jaboatão ou Caruaru.
12. Como você costuma decidir sobre questões práticas e financeiras?
(1) Rapidamente (2) Tomando Algum Tempo
(3) Demoradamente
13. Que importância você dá à própria reputação/prestígio/status?
(1) Pouca (2) Alguma
(3) Muita
14. Quantos cartões de crédito você tem?
Cartões
15. Você tem poupança? (1) Sim (0) Não
16. Você tem seguro-saúde privado? (1) Sim (0) Não
17. Você paga previdência privada? (1) Sim (0) Não
88
18. Pelo menos uma vez por mês, você:
A) Vai a um Restaurante:
(1) Sim
(0) Não
B) Vai a um Cinema/Teatro:
(1) Sim
(0) Não
C) Vai a uma Boite/Danceteria:
(1) Sim (0) Não
D) Vai a um Shopping:
(1) Sim
E) Vai a Praia/Camping/Etc.:
(1) Sim (0) Não
F) Vai a um Bar com Amigos:
(1) Sim
G) Vai a um Parque/Zoológico:
(1) Sim (0) Não
H) Vai a um Museu/Exposição:
(1) Sim
(0) Não
I) Vai a uma Biblioteca/Livraria:
(1) Sim
(0) Não
J) Vai a uma Missa/Culto:
(1) Sim
(0) Não
(0) Não
(0) Não
19. Qual a origem do automóvel que você usa mais frequentemente?
(1) Próprio
(2) Alugado
(3) Cedido por familiares ou amigos
(4) Cedido pela empresa onde trabalha
(9) Outro
20. Como é o seu automóvel?
A) Fabricante:
(01) Audi
(08) Hyundai
(15) Subaru
(02) BMW
(09) Kia
(16) Suzuki
(03) Chevrolet (10) Mercedes-Benz
(17) Toyota
(04) Citröen
(11) Mitsubishi
(18) Volkswagen
(05) Fiat
(12) Nissan
(19) Outro
(06) Ford
(13) Peugeot
(07) Honda
(14) Renault
B) Cilindrada:
C) Ano de Fabricação:
D) Tipo de modelo:
89
(1) Popular: Do tipo mais despojado e barato, destinado às massas.
(2) Passeio: Mais sofisticado do que o popular, mas não utilitário ou de luxo.
(3) Utilitário: Transporte de carga ou passageiros, trabalhos específicos.
(4) Luxo: Caros, sofisticados e repletos de acessórios.
(5) Esportivo: Arrojado e de excelente desempenho.
E) Tem ar condicionado? (1) Sim (0) Não
F) Tem direção hidráulica? (1) Sim (0) Não
G) Tem vidro elétrico? (1) Sim (0) Não
H) Tem trava elétrica? (1) Sim (0) Não
I) Tem sistema de som? (1) Sim (0) Não
J) Tem alarme? (1) Sim (0) Não
21. Há quanto tempo você possui o automóvel?
A)
anos B)
meses
22. Uso freqüente do automóvel (uma vez por semana ou mais):
A) Ida pessoal ao trabalho/estudo
(1) Sim
(0) Não
B) Compras domésticas
(1) Sim
(0) Não
C) Viagens
(1) Sim
(0) Não
D) Transporte de parentes e amigos
(1) Sim
(0) Não
E) Transporte de carga
(1) Sim
(0) Não
F) Transporte de turistas
(1) Sim
(0) Não
G) Transporte de passageiros pagantes (1) Sim
(0) Não
H) Lazer
(0) Não
(1) Sim
23. Normalmente, quanto tempo você passa dirigindo o seu automóvel?
(1) 0 a 5 horas/semana
(5) 20 ou mais horas/semana
24. O seu carro se danificou num acidente de trânsito nos últimos 12 meses?
90
(Responda em relação ao acidente mais grave)
(0) Não
(1) Sim, com danos apenas na pintura e/ou lanternagem
(2) Sim, com danos atingindo o funcionamento de acessórios
(3) Sim, com danos atingindo motor ou suspensão
25. O seu carro tem seguro? Sim (1) Não (0)
26.
Com que freqüência você realiza atividades de manutenção do seu
automóvel, tais como verificação de água, óleo, pneus, etc.?
(1) Menos de uma vez a cada seis meses
(2) Semestralmente
(3) Mensalmente
(4) Quinzenalmente
(5) Semanalmente
(6) Diariamente
27. Qual o atual estado do seu carro?
A) Motor
(1) Ruim
(2) Mais ou Menos
(3) Bom
B) Lataria e Pintura (1) Ruim
(2) Mais ou Menos
(3) Bom
C) Acessórios
(1) Ruim
(2) Mais ou Menos
(3) Bom
D) Rodas
(1) Ruim
(2) Mais ou Menos
(3) Bom
E) Bancos
(1) Ruim
(2) Mais ou Menos
(3) Bom
F) Sistema Elétrico
(1) Ruim
(2) Mais ou Menos
(3) Bom
28. Nos últimos seis meses, quantas vezes você precisou ir à oficina automecânica para resolver um problema com o seu automóvel?
vezes
29. Você irá adquirir um outro automóvel nos próximos seis meses?
(1) Sim (0) Não
30. Você pretende adquirir agora (ou escolheria adquirir, caso não planeje
uma compra próxima) um automóvel:
A) Idade:
91
(1) Novo, isto é, 0 km
(2) Com até 02 anos de fabricação.
(3) Com 02 a 04 anos de fabricação.
(4) Com mais de 04 anos de fabricação.
B) Fabricante:
(01) Audi
(08) Hyundai
(15) Subaru
(02) BMW
(09) Kia
(16) Suzuki
(03) Chevrolet (10) Mercedes-Benz
(17) Toyota
(04) Citröen
(11) Mitsubishi
(18) Volkswagen
(05) Fiat
(12) Nissan
(19) Outro
(06) Ford
(13) Peugeot
(07) Honda
(14) Renault
C) Tipo de modelo:
(1) Popular: Do tipo mais despojado e barato, destinado às massas.
(2) Passeio: Mais sofisticado do que o popular, mas não utilitário ou de luxo.
(3) Utilitário: Transporte de carga ou passageiros, trabalhos específicos.
(4) Luxo: Caros, sofisticados e repletos de acessórios.
(5) Esportivo: Arrojado e de excelente desempenho.
31. Usando a escala abaixo, indique a importância de cada um dos fatores
listados a seguir na hora de você comprar um carro.
(1) Nenhuma (2) Pouca (3) Razoável (4) Muita
92
(5) Enorme
A) Acessórios e Opcionais
B) Aparência, Beleza, Estética
C) Condições de Financiamento
D) Desempenho
E) Durabilidade
F) Economia de Combustível
G) Manutenção
H) Preço Total
I) Status e Prestígio do Carro
J) Valor de Revenda
K) Valor do Seguro
L) Segurança
M) Conforto e Ergonomia
N) Acabamento
O) Condições de Garantia
32. Qual a importância das fontes de informação consultadas por você para
a compra de um automóvel?
A) Amigos e Parentes
(0) Não Consulto
(1) Pouca (2) Razoável (3) Muita
B) Anuncio em Jornal
(0) Não Consulto
C) Concessionárias/Revendas (0) Não Consulto
D) Revistas Especializadas
(0) Não Consulto
E) Sites da Internet
(0) Não Consulto
F) Outras Fontes
(0) Não Consulto
33. Você comprará ou compraria um automóvel de:
(1) Certamente Não
(2) Provavelmente Não
(3) Talvez Sim, Talvez Não
(4) Provavelmente Sim
(5) Certamente Sim
93
A) Concessionárias de Marcas de Automóvel Específicas
B) Lojas de Carros Novos e/ou Usados em Geral
C) Consórcios de Carros Novos e/ou Usados
D) Particulares (Pessoa Física)
E) Direto do Fabricante via Internet
F) Outros
34. Aproximadamente, qual seria o preço justo para você vender o seu atual
automóvel?
R$
35. Quanto você acha que custa o carro que você estará ou estaria disposto
a comprar?
R$
36.
Qual a sua opinião acerca dos serviços de manutenção das conces-
sionárias em geral em comparação com a maioria das outras oficinas?
A) São significativamente mais caras
(1) Sim
(0) Não
B) São mais seguras
(1) Sim
(0) Não
C) Oferecem mais garantias
(1) Sim
(0) Não
D) Usam sempre peças originais
(1) Sim
(0) Não
E) O ambiente e o atendimento são sempre melhores
(1) Sim
(0) Não
F) Têm os profissionais mais qualificados
(1) Sim
(0) Não
G) Devem ser sempre usadas enquanto o carro estiver na garantia (1) Sim
(0) Não
H) São inconvenientes por serem poucas e/ou distantes
(1) Sim
(0) Não
I) Usá-las sempre aumenta o valor de revenda do carro
(1) Sim
(0) Não
37. Que partes de seu veículo já necessitaram de manutenção?
94
A) Motor
(1) Sim
(0) Não
B) Lataria e Pintura (1) Sim
(0) Não
C) Acessórios
(1) Sim
(0) Não
D) Rodas
(1) Sim
(0) Não
E) Bancos
(1) Sim
(0) Não
F) Sistema Elétrico
(1) Sim
(0) Não
38. Que motivos você adota para a troca do seu automóvel?
A) Término da garantia
(1) Sim
(0) Não
B) Quebras excessivas do veículo
(1) Sim
(0) Não
C) Lançamento de novo modelo
(1) Sim
(0) Não
D) Problemas com serviços de pós-venda
(1) Sim
(0) Não
E) Ocorrência de acidente
(1) Sim
(0) Não
F) Oferta / promoção
(1) Sim
(0) Não
G) Idade / kilometragem do atual veículo
(1) Sim
(0) Não
H) Redução de impostos (IPI, IPVA, etc.)
(1) Sim
(0) Não
95

universidade federal de pernambuco decision theory - ppgep-ufpe

Transcription

Similar documents

compresor nebulizador pingüino compresseur nébuliseur penguin

www.cbradio.nl: Comparison Chart Galaxy DX

Warranty Reimbursement - Bellavia Blatt and Crossett, PC

Valplast Warranty - Ultimate Dental Laboratory

Surfit™ Table Leg Installation Instructions Surfit Table

Nationwide Limited Lifetime Warranty

the original dover drag strip 26th annual

The hiropractic nd assage Table from Lifetimer

RC3017

Warranty and Liability