convergent behavior of rankings under simulated annealing

55 Pages·2016·1.16 MB·English

FUNDAÇÃO GETULIO VARGAS
ESCOLA BRASILEIRA DE ADMINISTRAÇÃO PÚBLICA E DE EMPRESAS
MESTRADO EM ADMINISTRAÇÃO

In the heat of the moment:
convergent behavior of rankings under simulated annealing

BEATRIZ ABREU FOSS DE OLIVEIRA
RIO DE JANEIRO
2016
Ficha catalográfica elaborada pela Biblioteca Mario Henrique Simonsen/FGV

Oliveira, Beatriz Abreu Foss de

In the heat of the moment: convergent behavior of rankings under
–
simulated annealing / Beatriz Abreu Foss de Oliveira. 2016.
53 f.

Dissertação (mestrado) - Escola Brasileira de Administração Pública e de
Empresas, Centro de Formação Acadêmica e Pesquisa.
Orientador: Alexandre Linhares.
Inclui bibliografia.

1. Processo decisório. 2. Comportamento humano. 3. Ranking.
4. Simulated annealing (Matemática). I. Linhares, Alexandre. II. Escola
Brasileira de Administração Pública e de Empresas. Centro de Formação
Acadêmica e Pesquisa. III. Título.

CDD – 658.403

BEATRIZ ABREU FOSS DE OLIVEIRA

In the heat of the moment:
convergent behavior of rankings under simulated annealing

Dissertation delievered to Fundação Getulio
Vargas – FGV, as part of the
requirements to obtain the title of Master in
Business Administration, advised by
Professor Ph.D. Alexandre Linhares.

RIO DE JANEIRO
2016
ACKOWLEDGEMENTS

To my advisor, Alexandre Linhares, for supporting me not only in the dissertation process, but also
in my life.
To Celene Melo and Kaillen Givigi, for always helping me and being so kind.
To my friends Andréia Sodré, André Luiz Anselmo, Fernanda Concatto and Marcelo Salhab, for the
time they took in helping me with my dissertation and making me smile.
To all my friends from the Master course, with whom I shared difficult and joyful moments.
Finally, to my family and school friends, for cheering me up from the beginning to the end.
ABSTRACT
Rankings are a recent tool being used in several fields, including in management. Their pervasive
use is associated to the fields of behavior and decision making. Despite their constant use, few research
have tried to define the concept of ranking and the parameters to judge whether it is acceptable. In the
absence of a more precise understanding of what the term ranking means, its power is diminished as
well as its purpose. Thus, in this work I present the characteristics, advantages and disadvantages of
rankings. Further, I analyze patterns of behavior elicited in real rankings. To this end, I propose the use
of simulated annealing to study the quality of convergence of a ranking’s chosen dimensions. The
graphical analyses suggest, at least, two different patterns of convergence for rankings. The categories
were named well behaved and poorly behaved rankings.

Key words: rankings; simulated annealing; behavior; decision making.
DEFINITIONS
In order to facilitate the reading, there are some definitions that ought to be taken into consideration.
They are:
Convergence: Is the process of getting close to, and finding, a solution.
Delta (Δ): The finite difference between two functions.
Dimensions: Refers to a broader term, intangible aspect. It summarizes and can be break into
tangible aspects (indicators) that can be measured.
Entities: It is dependent on the subject of the ranking. For example, in a Chess Ranking, the entities
can be players. So entities are a general form of expressing the subject under analysis (e.g., players,
universities, countries).
Local minima: It is a point in space that has the lowest value, within a subset (i.e., neighborhood;
region) of the whole set of possible solutions.
Optimal Solution: It is the smallest overall value of a set of solutions.
TABLE OF FIGURES
Figure 1: Simulated Annealing applied to a ranking created through uniform distribution ........................................... 25
Figure 2: Simulated Annealing applied to ranking created based on a normal distribution ........................................... 27
Figure 3: Simulated Annealing applied to World Chess Federation Ranking ................................................................ 29
Figure 4: Simulated Annealing applied to World Bank Logistics Performance Index ................................................... 30
Figure 5: Simulated Annealing applied to Human Development Index of 2013 ............................................................ 31
Figure 6: Simulated Annealing applied to Financial Times Rankings of 2013 .............................................................. 33
Figure 7: Simulated Annealing applied to Failed States Index ....................................................................................... 34
Figure 8: Simulated Annealing applied to Global Peace Index ...................................................................................... 35
Figure 9: Simulated Annealing applied to Global Innovation Index of 2013 ................................................................. 36
Figure 10: Simulated Annealing applied to Doing Business Ranking of 2013............................................................... 37
Figure 11:Simulated Annealing applied to Academic Rankings of World Universities of 2012 ................................... 38
SUMMARY
1. INTRODUCTION: PEEKING REALITY ............................................................................................................... 8
2. LITERATURE REVIEW ....................................................................................................................................... 10
2.1 Theoretical Background ................................................................................................................................. 10
2.2 Gaming ........................................................................................................................................................... 13
2.3 Importance of studying rankings .................................................................................................................... 14
2.4 The way rankings change the world around them .......................................................................................... 15
2.5 The hidden problems ...................................................................................................................................... 17
3. METHODOLOGY ................................................................................................................................................. 19
3.1 Simulated Annealing ...................................................................................................................................... 19
3.2 Configuration & parameters of simulated annealing for the analysis of rankings ......................................... 21
3.3 Data Collection .............................................................................................................................................. 23
4. RESULTS............................................................................................................................................................... 24
4.1 Idealized rankings .......................................................................................................................................... 24
4.1.1 Synthetic Ranking #1: Uniform Distribution ......................................................................................... 24
4.1.2 Synthetic Ranking #2: Binomial/Normal Distribution .......................................................................... 26
4.2 Real Rankings: “well-behaved” cases ............................................................................................................ 28
4.2.1 Case 1: ................................................................................................................................................... 28
4.2.2 Case 2: ................................................................................................................................................... 30
4.2.3 Case 3: ................................................................................................................................................... 31
4.3 Real Rankings: transition to “poorly-behaved” cases .................................................................................... 32
4.3.1 Case 1: ................................................................................................................................................... 32
4.3.2 Case 2: ................................................................................................................................................... 34
4.3.3 Case 3: ................................................................................................................................................... 35
4.3.4 Case 4: ................................................................................................................................................... 36
4.3.5 Case 5: ................................................................................................................................................... 37
4.3.6 Case 6: ................................................................................................................................................... 38
5. DISCUSSION ........................................................................................................................................................ 40
5.1 Limitations ..................................................................................................................................................... 41
5.2 Future Research ............................................................................................................................................. 41
REFERENCES ............................................................................................................................................................... 43
APPENDIX .................................................................................................................................................................... 47
ANNEX: Simulated Annealing Code ............................................................................................................................. 50
1. INTRODUCTION: PEEKING REALITY
Mr. Richard Zavala, the director of parks and community services of Fort Worth, Texas, is
preoccupied with his position on the annual ParkScore ranking from The Trust for Public Land. He
does feel that it's somehow an inadequate measure, though: “Once again, those cities that have a higher
density of population have a tendency of scoring higher, simply because their population is more
compact, versus the city of Fort Worth that is more dispersed and spread over 350 square-miles” (Hirst,
2015).
Ben Harder, chief of health analysis for U.S. News & World Report, affirms that even for a routine
surgery the choice of the hospital is a life-changing experience, and rankings permit that the patients,
along with their doctors, research for the hospitals best suited for their necessity. Novant Health Inc, a
healthcare provider, also stated that rankings are helpful to guide patients in their healthcare decisions
(Craver, 2015).
Juniper Research created the top tech leadership ranking by assessing “vision, innovation and
personal capital” of leaders. In this ranking the first position goes to Nadella, the guy who created
“Windows-as-a-service”, the second position was taken by Jony Ive who created Apple’s smart watch
and in the eighth position, which seems ironical, there is Elon Musk, Tesla CEO, which has endeavored
in problems such as energy-efficient cars as well as space travel (Darrow, 2015).
Andrew Chamberlain, Glassdoor’s chief economist, discusses about his company’s new ranking
report about best city for jobs. He informs that according to the statistics, some cities, like Boston and
other metropolis, are being penalized by house pricing and suggest that if the mayors and officials
aspire for a better score in this ranking, they should address housing availability and high costs
(Salomon, 2015).
From recreational space to healthcare, the bottom line present in these excerpts is that rankings are
ubiquitous, even in unconventional topics. Some, as Juniper Research top tech leadership ranking,
prompt us to think what are the prerequisites someone has to have in mind to create a ranking. More
than that, raise questions about what are the goals being pursued by each ranking, providing evidence
that not only the positioning of the entities matter.
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