2do Congreso Investigadores Venezolanos de la Comunicación 1er Encuentro Latinoamericano Investigadores Transdisciplinarios de la Comunicación
Comunicación, ciudadanía y complejidad en clave latinoamericana
21 al 25 de abril de 2009
http://www.invecom.org/eventos/2009/index.php
Nuevos métodos para la investigación de la comunicación social y los medios de comunicación
1. 11
Nuevos métodos para laNuevos métodos para la
investigación de lainvestigación de la
comunicación social y loscomunicación social y los
medios de comunicaciónmedios de comunicación
Prof. Tom JohnsonProf. Tom Johnson
Instituto de Periodismo AnalíticoInstituto de Periodismo Analítico
Santa Fe, Nuevo Mexico USASanta Fe, Nuevo Mexico USA
t o m @ j t j o h n s o n . c o mt o m @ j t j o h n s o n . c o m
Prof. Pedro SotolongoProf. Pedro Sotolongo
Presidente Fundador de la Cátedra“Presidente Fundador de la Cátedra“
de Complejidad” de La Habanade Complejidad” de La Habana
p e d r o . s o t o l o n g o @ i n f o m e d . i l d . c up e d r o . s o t o l o n g o @ i n f o m e d . i l d . c u
2. 22
Objetivos de esta mañanaObjetivos de esta mañana
Introducir los conceptos de laIntroducir los conceptos de la
teoría general de sistemas,teoría general de sistemas,
Sistemas Dinámicos, Teoría deSistemas Dinámicos, Teoría de
Complejidad Computacional yComplejidad Computacional y
AplicadaAplicada
Destacar el carácterDestacar el carácter
transdisciplinario (no inter-transdisciplinario (no inter-
disciplinario) de la complejidaddisciplinario) de la complejidad
Convocatoria para la creación deConvocatoria para la creación de
"medios de comunicación Proyecto"medios de comunicación Proyecto
Genoma" (Si hay tiempo)Genoma" (Si hay tiempo)
3. 33
Primeros ConceptosPrimeros Conceptos
DataesferaDataesfera
lugar conceptual en el que todos los datoslugar conceptual en el que todos los datos
existen en todos los formatosexisten en todos los formatos
La dataesfera es accesible para todas lasLa dataesfera es accesible para todas las
personas en diversos gradospersonas en diversos grados
DatosDatos AnálisisAnálisis la informaciónla información
Información = "Lo que nos ayuda a tomarInformación = "Lo que nos ayuda a tomar
una decisión"una decisión"
"Sistemas" y "sub-sistemas" en la"Sistemas" y "sub-sistemas" en la
Dataesfera funcionan para procesar datos,Dataesfera funcionan para procesar datos,
ayudar en el análisis y generar informaciónayudar en el análisis y generar información
4. 44
Teoría General de SistemasTeoría General de Sistemas
Definición:Definición:
"... un sistema es una configuración de partes"... un sistema es una configuración de partes
conectadas y unidas por una red de relaciones“conectadas y unidas por una red de relaciones“
La perspectiva analítica y el énfasis se desplazaLa perspectiva analítica y el énfasis se desplaza
de las partes a la organización de y a lasde las partes a la organización de y a las
relaciones entre las partes (ejemplo: variables /relaciones entre las partes (ejemplo: variables /
agentes / entidades)agentes / entidades)
Reconocimiento de que las interacciones no sonReconocimiento de que las interacciones no son
estáticas, sino dinámicasestáticas, sino dinámicas
5. 55
Atributos de TGSAtributos de TGS
1. Compuesta de variables, i.e.,
elementos que pueden ser definidos o
descritos, por separado.
2. Sub-variables. Árbol-a-rama-a-
hoja-a-célula
3. Existen relaciones horizontales
entre las variables y …
4. …relaciones verticales (i.e,
jerárquicas)
6. 66
Atributos de TSGAtributos de TSG
3. Un sistema tiene límites
Conceptual
jurídico: las empresas, la jurisdicción
geográfica
Cultural
4. Un sistema tiene metas auto-definidas o
con una definición impuesta por el
observador / investigador
Gana dinero
Proporcionar al grupo seguridad,
felicidad, procreación
8. 88
El Periódico como sistemaEl Periódico como sistema
Variable/agentsVariable/agents
Editorial
Publicidad
ProducciónCirculación
Administración
El sistema tiene variables / agentes /
entidades
9. 99
El Periódico como sistemaEl Periódico como sistema
Editorial
Publicidad
ProducciónCirculación
Administración
Variables are related to other variables
…y generalmente en una relación que puede
medirse.
Circulación
11. 1111
El periódico como un sistemaEl periódico como un sistema--
ScalingScaling
Un sistema - y el análisis del sistema(s) -
pueden ser "a escala"
Datasphere
Medios de
comun.
Periódicos
Editorial
Deportes
Futbol
Equipo local
Concepto alto
Baja
Concepto
Escalabilida
d
12. 1212
Ejemplo de la herramienta deEjemplo de la herramienta de
modelado de simulación SimVenturemodelado de simulación SimVenture
“Calidad de la
experiencia”
“Tráfico total”
13. 1313
Valor de los Sistemas de PensamientoValor de los Sistemas de Pensamiento
Exige definir y enfocarse exactamente en
el sistema del que se está hablando
Exige la consideración del nivel de
análisis, es decir, el "zooming" de niveles
de enfoque
Exige la definición de variables y, a
continuación, la importancia relativa de las
variables
Exige el examen de las relaciones entre
las variables
14. 1414
TGS como base para modelos deTGS como base para modelos de
simulación y análisis de la complejidadsimulación y análisis de la complejidad
TGS como base para modelos de
simulación y análisis de la
complejidad
15. 1515
Resumen
Datasphere oDatasphere o DataesferaDataesfera
DatosDatos AnálisisAnálisis InformaciónInformación
SistemasSistemas
– Límites de Sistemas losLímites de Sistemas los
– Los variables/agentes /entidadesLos variables/agentes /entidades
– Relaciones entre variablesRelaciones entre variables
– Objetivos del SistemaObjetivos del Sistema
– ““Feedback (bucle) / "aprendizaje"Feedback (bucle) / "aprendizaje"
Preparación para modelado de simulaciónPreparación para modelado de simulación
y Teoría de la Complejidad y Complejidady Teoría de la Complejidad y Complejidad
AplicadaAplicada
16. 1616
Herramientas para la ComplejidadHerramientas para la Complejidad
Complejidad y AplicadaComplejidad y Aplicada
Complejidad - como los fractales,Complejidad - como los fractales,
las estadísticas y SIG - es unlas estadísticas y SIG - es un
"transdisciplinario" método"transdisciplinario" método
analíticoanalítico
17. 1717
Herramientas para la ComplejidadHerramientas para la Complejidad
Complejidad y AplicadaComplejidad y Aplicada
FractalesFractales == A figuraA figura geométricageométrica que seque se
repite en virtud de varios niveles de aumento,repite en virtud de varios niveles de aumento,
una formauna forma irregularirregular que aparece en todas lasque aparece en todas las
escalasescalas dede longitudlongitud, por ejemplo, un, por ejemplo, un helechohelecho
18. 1818
Transdiscipline Tools : SIGTransdiscipline Tools : SIG
SIG:SIG: SSistemas deistemas de IInformaciónnformación
GGeográficaeográfica
20. 2020
Análisis visual de la Complejidad y RedAnálisis visual de la Complejidad y Red
diagramasdiagramas
21. 2121
Recursos en línea: Complejidad yRecursos en línea: Complejidad y
Análisis de Redes SocialesAnálisis de Redes Sociales
Complejidad y Redes SocialesComplejidad y Redes Sociales
BlogBlog
El objetivo de este blog es ofrecer un foro para laEl objetivo de este blog es ofrecer un foro para la
discusión de los temas interrelacionados de ladiscusión de los temas interrelacionados de la
red de análisisred de análisis yy teoría de sistemas complejos.teoría de sistemas complejos.
http://ComplexityYSocialNetBlog-Esp.notlong.comhttp://ComplexityYSocialNetBlog-Esp.notlong.com
Análisis de redes socialesAnálisis de redes sociales
Análisis de redes sociales se basa en el supuestoAnálisis de redes sociales se basa en el supuesto
de la importancia de las relaciones entre lasde la importancia de las relaciones entre las
unidades que interactúan.unidades que interactúan.
http://www.iq.harvard.edu/blog/netgovhttp://www.iq.harvard.edu/blog/netgov
24. 2424
Herramientas para la Complejidad YHerramientas para la Complejidad Y
Complejidad AplicadaComplejidad Aplicada
Complejidad - como los fractales, lasComplejidad - como los fractales, las
estadísticas y SIG - es unestadísticas y SIG - es un
"transdisciplinario" método analítico"transdisciplinario" método analítico
Red de análisisRed de análisis ((Social Network AnalysisSocial Network Analysis))
Leyes de PotenciaLeyes de Potencia
– GeografíaGeografía
– BiologíaBiología
– Forense de contabilidadForense de contabilidad
((la Ley de Benfordla Ley de Benford:: Benford’s LawBenford’s Law))
– UrbanizaciónUrbanización
25. 2525
Análisis de la Leyes de PotenciaAnálisis de la Leyes de Potencia
Leyes de Potencia: una relaciónLeyes de Potencia: una relación
matemática entre los aspectos de unmatemática entre los aspectos de un
tipo - tamaño de las piedras en untipo - tamaño de las piedras en un
montón de rocas - o dos cantidades.montón de rocas - o dos cantidades.
Si una cantidad es la frecuencia de un caso, la relación esSi una cantidad es la frecuencia de un caso, la relación es
una facultad de derecho de distribución, y la disminución deuna facultad de derecho de distribución, y la disminución de
las frecuencias muy lentamente como el tamaño del eventolas frecuencias muy lentamente como el tamaño del evento
aumenta.aumenta.
Otros tipos de relacionesOtros tipos de relaciones
– de la tasade la tasa
– metabólica de una especie y su masa corporalmetabólica de una especie y su masa corporal
(llamada la ley de Kleiber)(llamada la ley de Kleiber)
– Tamaño de una ciudad y el número deTamaño de una ciudad y el número de
patentes que producepatentes que produce..
26. 2626
Leyes de Potencia BiologíaLeyes de Potencia Biología
The “mouse-elephant” plot has the
exponent 7/6.
Source: http://www.bionik.tu-berlin.de/institut/xtutor1.htm
28. 2828
Contabilidad Forense - la Ley deContabilidad Forense - la Ley de
BenfordBenford
Source: http://paul.kedrosky.com/archives/2008/12/19/bernie_vs_benfo.html
Could you have applied Benford's Law to the distribution of most significant
digit in the monthly series of Madoff returns, spotted something awry, and
turned him in?
30. 3030
Leyes de Potenciazona urbanaLeyes de Potenciazona urbana
Source: 2007 Growth, innovation, scaling, and the pace of life in cities. Luís M. A. Bettencourt, José Lobo, Dirk Helbing,
Christian Kühnert, and Geoffrey B. West PNAS 104(17):7301-7306
The slope of Power Law plots do not always have to go
up
31. 3131
Inversa Ley de PotenciaInversa Ley de Potencia
20% de casos = 80% de total cuantidad
80% de total casos = 20%
32. 3232
Lista de los periódicos del mundo en circulaciónLista de los periódicos del mundo en circulación
(000s)(000s)
ley de potencia inversa
59 diarios necesario para 80%
de total
33. 3333
Clasificación jerárquica de la circulaciónClasificación jerárquica de la circulación
mundial de periódicosmundial de periódicos
3 periódicos representan el 20%
del total de la circulación
34. 3434
Clasificación jerárquica de la circulaciónClasificación jerárquica de la circulación
mundial de periódicosmundial de periódicos
3 periódicos representan el 20%
del total de la circulación
35. 3535
ResumenResumen
Ver los fenómenos como sistemasVer los fenómenos como sistemas
Utilice la teoría general de sistemas paraUtilice la teoría general de sistemas para
pensar en los fenómenospensar en los fenómenos
Definir las relaciones entre variables y laDefinir las relaciones entre variables y la
manera de medir las relacionesmanera de medir las relaciones
Aplicar métodos cuantitativos yAplicar métodos cuantitativos y
cualitativos para entender cualquiercualitativos para entender cualquier
fenómenofenómeno
Buscar colegas de otras disciplinas conBuscar colegas de otras disciplinas con
otras metodologías. Pruebe sus métodos.otras metodologías. Pruebe sus métodos.
36. 3636
Gracias.
11
NuevosNuevos métodos paramétodos para lala
investigacióninvestigación de lade la
comunicacióncomunicación social y lossocial y los
mediosmedios dede comunicacióncomunicación
Prof. Tom JohnsonProf. Tom Johnson
Instituto de Periodismo AnalíticoInstituto de Periodismo Analítico
Santa Fe, Nuevo Mexico USASanta Fe, Nuevo Mexico USA
t o m @ j t j o h n s o n . c o mt o m @ j t j o h n s o n . c o m
Prof. Pedro SotolongoProf. Pedro Sotolongo
Presidente Fundador de la Cátedra“Presidente Fundador de la Cátedra“
de Complejidad” de La Habanade Complejidad” de La Habana
p e d r o . s o t o l o n g o @ i n f o m e d . i l d . c up e d r o . s o t o l o n g o @ i n f o m e d . i l d . c u
GraciasGracias
37. 3737
IFIF time available….time available….
Tom can introduce News MediaTom can introduce News Media
Genome ProjectGenome Project
39. 3939
Genes y cancerGenes y cancer
26 de julio de 2007, 9:33 PM CT
UM investigadores identificar los genes implicados en el cáncer de
mama
Los científicos observaron que el gen, FOXP3, suprime el crecimiento
tumoral. FOXP3 se encuentra en el cromosoma X, lo que significa una única
mutación puede silenciar el gen de manera eficaz. Esto es inusual, ya que
sólo uno de otros genes asociados con el cáncer se ha encontrado en el
cromosoma X.
41. 4141
““Proyecto Genoma de MediosProyecto Genoma de Medios
Informativos”Informativos”
Objectives:Objectives:
– Develop methods to “map the genome”Develop methods to “map the genome”
of media institutionsof media institutions
– Develop methods to conduct an autopsyDevelop methods to conduct an autopsy
of media institutions that have recentlyof media institutions that have recently
dieddied
– Do this by looking at methods fromDo this by looking at methods from
other disciplinesother disciplines
42. 4242
““Proyecto Genoma de MediosProyecto Genoma de Medios
Informativos”Informativos”
– ArticulateArticulate initialinitial mission statementmission statement
Identify tools to conduct “condition of the institution”Identify tools to conduct “condition of the institution”
analysis (autopsy?) of existing mediaanalysis (autopsy?) of existing media
– Create int’l database of media organizationCreate int’l database of media organization
variablesvariables
– Create platform for collaboration on analysisCreate platform for collaboration on analysis
– Share knowledge of analytic tools fromShare knowledge of analytic tools from
multiple disciplinesmultiple disciplines
– Share evolution of methods and findingsShare evolution of methods and findings
43. 4343
““Proyecto Genoma de MediosProyecto Genoma de Medios
Informativos”Informativos”
Project processProject process
– Create Web 2.0 website with …Create Web 2.0 website with …
Methods for creating “standards” forMethods for creating “standards” for
metadata and data qualitymetadata and data quality
Web site objectivesWeb site objectives
Data source sitesData source sites
Analytic tool sitesAnalytic tool sites
Project management sub-sets andProject management sub-sets and
collaboration toolscollaboration tools
44. 4444
Proyecto Genoma de MediosProyecto Genoma de Medios
InformativosInformativos
– Define/create databaseDefine/create database
MetadataMetadata
–Trans-lingual definitions/code tagsTrans-lingual definitions/code tags
Who can submit/verify/edit data?Who can submit/verify/edit data?
Evaluation methods of data quality?Evaluation methods of data quality?
45. 4545
Clickstream Data Yields High-Resolution Maps ofClickstream Data Yields High-Resolution Maps of
ScienceScience
46. 4646
Gracias.
11
NuevosNuevos métodos paramétodos para lala
investigacióninvestigación de lade la comunicacióncomunicación
social y lossocial y los mediosmedios dede comunicacióncomunicación
Prof. Tom JohnsonProf. Tom Johnson
Instituto de Periodismo AnalíticoInstituto de Periodismo Analítico
Santa Fe, Nuevo Mexico USASanta Fe, Nuevo Mexico USA
t o m @ j t j o h n s o n . c o mt o m @ j t j o h n s o n . c o m
Prof. Pedro SotolongoProf. Pedro Sotolongo
Presidente Fundador de la Cátedra“Presidente Fundador de la Cátedra“
de Complejidad” de La Habanade Complejidad” de La Habana
p e d r o . s o t o l o n g o @ i n f o m e d . i l d . c up e d r o . s o t o l o n g o @ i n f o m e d . i l d . c u
GraciasGracias
Notas del editor
new methods for investigating social communications and media institutions
nuevos métodos para la investigación de la comunicación social y los medios de comunicación
Objectives this morning
Introduce concepts of General Systems Theory, Dynamic Systems, Complexity Theory and Applied Complexity
Present examples of Applied Complexity.
Emphasize the transdisciplinary nature (not inter-disciplinary) of Complexity
Call for creation of “News Media Genome Project”
First Concepts
Datasphere
Conceptual place where all data exists in all formats
Datasphere is accessible by all people to varying degree
Data Analysis Information
Information= “That which helps us make a decision”
“Systems” and “sub-systems” in Datasphere function to process data, assist in analysis and generate Information
General Systems Theory
Definition:“… system is a configuration of parts connected and joined together by a web of relationships”
Analytic perspective and emphasis shifts from parts to the organization of and relationships between the parts (i.e.variables/agents/entities)
Recognition that interactions are NOT static, but dynamic
================================================================================================================================
GST focuses on the system's structure instead of on the system's function. It proposes that complex systems share some basic organizing principles irrespective of their purposes, and that these principles can be measured and modeled mathematically.
Introduced by the Austrian biologist Ludwig von Bertalanffy (1901-72) and by the UK economist Kenneth Boulding (1910-93) around the year 1955.
Source: http://en.wikipedia.org/wiki/Systems_theory
“… system means a configuration of parts connected and joined together by a web of relationships”
“…recognizing the interdependence between groups of individuals, structures and processes that enable an organization to function”
“…The emphasis with systems theory shifts from parts to the organization of parts, recognizing interactions of the parts are not "static" and constant but "dynamic" processes.”
Source: http://en.wikipedia.org/wiki/Systems_theory
As a transdisciplinary, interdisciplinary and multiperspectival domain, the area brings together principles and concepts from ontology, philosophy of science, physics, computer science, biology, and engineering as well as geography, sociology, political science, psychotherapy (within family systems therapy) and economics among others. Systems theory thus serves as a bridge for interdisciplinary dialogue between autonomous areas of study as well as within the area of systems science itself.
Source: http://www.survey-software-solutions.com/walonick/systems-theory.htm
“A holist approach is to examine the system as a complete functioning unit. A reductionist approach looks downward and examines the subsystems within the system. The functionalist approach looks upward from the system to examine the role it plays in the larger system. All three approaches recognize the existence of subsystems operating within a larger system.”
Source: http://www.geocities.com/seaskj/glossary.html#G
Theory, created by von Bertalanffy, in which complex systems are viewed holistically, as amounting to more than the sum of the parts.*
Source: http://www.opbf.org/open-plant-breeding/glossary/g
There are many different kinds of system, such as solar systems, political systems, ecological systems (ecosystems), mechanical systems, legal systems, electrical systems, and so on.
The concept of the pathosystem is based on the general systems theory.
Systems theory is now divided into the general systems theory and complexity theory, which developed out of it. Systems theory is based on the concept of a pattern, and of systems levels, which are patterns of patterns.
Attributes of GST
Composed of variables, i.e. elements that can be defined, or described, separately.
Sub-variables. Tree-to-branch-to-leaf-to-cell
There are relationships between variables
Horizontal relationships
Vertical (i.e. hierarchical) relationships
Attributes of GST
A system has boundaries
Conceptual
Legal: corporate, jurisdiction
Geographic
Cultural
A system has goals, self-defined or with a definition imposed by observer/researcher
Make money
Provide for group security, happiness, procreation
Attributes of GST
A system learns from changes in its variables or environment
Newspaper as a system
System has variables/agents/entities
Editorial
Office
Advertising
Circulation
Production
Variables are related to other variables, and typically in a relationship that can be measured.
“Environmental” Models -- Datasphere
Newspaper as a system
SCALEABILITY
A system – and analysis of the system(s) – can be “scaled”
Screen shot of SIMVENTURE program
Indicates that we can also use both QUALITATIVE AND QUANTITATIVE variables and measures. I.e. Qualitative = “Quality of Experience” and Quantitative=“Total Traffic”
donde puede apreciarse que podemos utilizar tanto variables y medidas cualitativas como cuantitativas.
Cualitativa: “Calidad de la experiencia”
Cuantitativa: “Tráfico total”
Value of Systems Thinking
Demands definition/focus on exactly what system are you talking about?
Demands consideration of level of analysis, i.e. “zooming” levels of focus
Demands definition of variables and then the relative importance of those variables
Demands consideration of relationships between variables
GST as basis for simulation models & Complexity analysis
Summary
Datasphere
Data Analysis Information
Systems
Boundary
Variables/agents/entities
Relationships between variables
System goals
Feedback/”learning”
Preparation for simulation modeling & Complexity Theory and Applied Complexity
Tools for Complexity y Applied Complexity
Complexity – like fractals, statistics and SIG – is a “transdisciplinary” analytic method
Fractal: A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole,"[1] a property called self-similarity. The term was coined by Benoît Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion.[2]
A fractal often has the following features:[3]
* It has a fine structure at arbitrarily small scales.
* It is too irregular to be easily described in traditional Euclidean geometric language.
* It is self-similar (at least approximately or stochastically).
* It has a Hausdorff dimension which is greater than its topological dimension (although this requirement is not met by space-filling curves such as the Hilbert curve).[4]
* It has a simple and recursive definition.
http://en.wikipedia.org/wiki/Fractal
Social Network Analysis
An example of a social network diagram. The node with the highest betweenness centrality is marked in yellow.
Social network analysis (related to network theory) has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics as well as a popular topic of speculation and study.
A social network is a social structure made of nodes (which are generally individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, ideas, financial exchange, friendship, sexual relationships, kinship, dislike, conflict or trade.
Social network analysis views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
http://en.wikipedia.org/wiki/Social_network_analysis
Power Law(s)
A power law is a special kind of mathematical relationship between two quantities. If one quantity is the frequency of an event, the relationship is a power-law distribution, and the frequencies decrease very slowly as the size of the event increases. For instance, an earthquake twice as large is four times as rare. If this pattern holds for earthquakes of all sizes, then the distribution is said to "scale". Power laws also describe other kinds of relationships, such as the metabolic rate of a species and its body mass (called Kleiber's law), and the size of a city and the number of patents it produces. What this relationship means is that there is no typical size in the conventional sense. Power laws are found throughout the natural and manmade worlds, and are an active area of scientific research.
http://en.wikipedia.org/wiki/Power_law
Benford's law, also called the first-digit law, states that in lists of numbers from many real-life sources of data, the leading digit is distributed in a specific, non-uniform way. According to this law, the first digit is 1 almost one third of the time, and larger digits occur as the leading digit with lower and lower frequency, to the point where 9 as a first digit occurs less than one time in twenty. The basis for this "law" is that the values of real-world measurements are often distributed logarithmically, thus the logarithm of this set of measurements is generally distributed uniformly.
This counter-intuitive result has been found to apply to a wide variety of data sets, including electricity bills, street addresses, stock prices, population numbers, death rates, lengths of rivers, physical and mathematical constants, and processes described by power laws (which are very common in nature). The result holds regardless of the base in which the numbers are expressed, although the exact proportions change.
It is named after physicist Frank Benford, who stated it in 1938,[1] although it had been previously stated by Simon Newcomb in 1881.[2] Although many "proofs" of this law have been put forth (starting with Newcomb himself), none were mathematically rigorous[3] until Theodore P. Hill's in 1995.[4]
http://en.wikipedia.org/wiki/Benford%27s_law
Tools for Complexity y Applied Complexity
Fractals = A geometric figure that repeats itself under several levels of magnification, a shape that appears irregular at all scales of length, e.g. a fern
=================================================================================
Fractal: A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole,"[1] a property called self-similarity. The term was coined by Benoît Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion.[2]
A fractal often has the following features:[3]
* It has a fine structure at arbitrarily small scales.
* It is too irregular to be easily described in traditional Euclidean geometric language.
* It is self-similar (at least approximately or stochastically).
* It has a Hausdorff dimension which is greater than its topological dimension (although this requirement is not met by space-filling curves such as the Hilbert curve).[4]
* It has a simple and recursive definition.
http://en.wikipedia.org/wiki/Fractal
A figura geométrica [http://en.wiktionary.org/wiki/geometric ] que se repite en virtud de varios niveles de aumento, una forma irregular [http://en.wiktionary.org/wiki/irregular ] que aparece en todas las escalas [http://en.wiktionary.org/wiki/scale ]de longitud [http://en.wiktionary.org/wiki/length ] por ejemplo, un helecho [http://en.wiktionary.org/wiki/fern]
Social Network Analysis
An example of a social network diagram. The node with the highest betweenness centrality is marked in yellow.
Social network analysis (related to network theory) has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics as well as a popular topic of speculation and study.
A social network is a social structure made of nodes (which are generally individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, ideas, financial exchange, friendship, sexual relationships, kinship, dislike, conflict or trade.
Social network analysis views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
http://en.wikipedia.org/wiki/Social_network_analysis
Power Law(s)
A power law is a special kind of mathematical relationship between two quantities. If one quantity is the frequency of an event, the relationship is a power-law distribution, and the frequencies decrease very slowly as the size of the event increases. For instance, an earthquake twice as large is four times as rare. If this pattern holds for earthquakes of all sizes, then the distribution is said to "scale". Power laws also describe other kinds of relationships, such as the metabolic rate of a species and its body mass (called Kleiber's law), and the size of a city and the number of patents it produces. What this relationship means is that there is no typical size in the conventional sense. Power laws are found throughout the natural and manmade worlds, and are an active area of scientific research.
http://en.wikipedia.org/wiki/Power_law
Benford's law, also called the first-digit law, states that in lists of numbers from many real-life sources of data, the leading digit is distributed in a specific, non-uniform way. According to this law, the first digit is 1 almost one third of the time, and larger digits occur as the leading digit with lower and lower frequency, to the point where 9 as a first digit occurs less than one time in twenty. The basis for this "law" is that the values of real-world measurements are often distributed logarithmically, thus the logarithm of this set of measurements is generally distributed uniformly.
This counter-intuitive result has been found to apply to a wide variety of data sets, including electricity bills, street addresses, stock prices, population numbers, death rates, lengths of rivers, physical and mathematical constants, and processes described by power laws (which are very common in nature). The result holds regardless of the base in which the numbers are expressed, although the exact proportions change.
It is named after physicist Frank Benford, who stated it in 1938,[1] although it had been previously stated by Simon Newcomb in 1881.[2] Although many "proofs" of this law have been put forth (starting with Newcomb himself), none were mathematically rigorous[3] until Theodore P. Hill's in 1995.[4]
http://en.wikipedia.org/wiki/Benford%27s_law
Transdiscipline Tools : SIG
SIG: Sistemas de Información Geográfica
Fractals and Networks
Source: http://universe-review.ca/R10-35-metabolic.htm
Then in 1997, a couple of physicist and biologists successfully derive the 3/4 power-law using the concept of fractal. The theory considers the fact that the tissues of large organisms have a supply problem. That is what blood systems in animals and vascular plants are all about: transporting materials to and from tissues. Small organisms don't face the problem to the same extent. A very small organism has such a large surface area compared to its volume that it can get all the oxygen it needs through its body wall. Even if it is multicellular, none of its cells are very far from the outside body wall. But a large organism has a transport problem because most of its cells are far away from the supplies they need. Insects literally pipe air into their tissues in a branching network of tubes called tracheae. Mammals have richly branched air tubes, but they are confined to special organs, the lungs. Fish do a similar thing with gills. Trees use their richly dividing branches to supply their leaves with water and pump sugars back from the leaves to the trunk. The 3/4-power law is derived in part from the assumption that mammalian distribution networks are "fractal like" (Figure 03) and in part from the conjecture that natural selection has tended to maximize metabolic capacity "by maintaining networks that occupy a fixed percentage (6 - 7%) of the volume of the body".
Visual analysis of Complexity
Network diagrams
Just as we have learned to “read” a scatter or regression plot – look to the slope; look for outliers -- so too will be come to learn how to read visualizations of complex systems
Complexity and Social Networks Blog of the Institute for Quantitative Social Science and the Program on Networked Governance, Harvard University Welcome! The objective of this blog is to offer a forum for the discussion of the intertwined subjects of network analysis and complex systems theory. http://www.iq.harvard.edu/blog/netgov
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What is Social Network Analysis?
Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models, and applications that are expressed in terms of relational concepts or processes. Along with growing interest and increased use of network analysis has come a consensus about the central principles underlying the network perspective. In addition to the use of relational concepts, we note the following as being important:
* Actors and their actions are viewed as interdependent rather than independent, autonomous units
* Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial)
* Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action
* Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors
The unit of analysis in network analysis is not the individual, but an entity consisting of a collection of individuals and the linkages among them. Network methods focus on dyads (two actors and their ties), triads (three actors and their ties), or larger systems (subgroups of individuals, or entire networks.
Source: Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.
Network graphic examples
Source:
Power Laws, Scale-Free Networks and Genome Biology (Molecular Biology Intelligence Unit) (Hardcover)
by Eugene V. Koonin (Editor), Yuri I. Wolf (Editor), Georgy P. Karev (Editor)
Source: http://www.amazon.com/Scale-Free-Networks-Biology-Molecular-Intelligence/dp/0387258833#reader
AGNA stands for Applied Graph & Network Analysis.
Agna is a platform-independent application designed for social network analysis, sociometry and sequential analysis. This software can help you if you study communication relations in groups, kinship relations or the structure of animal behavior - to mention just a few realms where it can be used.
WHAT IS NETWORK ANALYSIS
Network analysis (or social network analysis) is a set of mathematical methods used in social psychology, sociology, ethology, and anthropology.
This methodology assumes that the way the members of a group can communicate to each other affect some important properties of that group (such as performance, leadership, work satisfaction etc.)
A network models generally a communication group. It consists of a number of nodes (each node corresponding to a member of the group) and a number of edges (each one being associated to a communication connection between two actors).
Network data is stored in the adjacency matrix (or the sociomatrix). Commonly, the [i,j] element of the adjacency matrix refers to the communication behavior of actor ‘i’ to actor ‘j’.
http://www.geocities.com/imbenta/agna/big_splash_1.gif
Tools for Complexity y Applied Complexity
Complexity – like fractals, statistics and SIG – is a “transdisciplinary” analytic method
Network analysis (Social Network Analysis)
Power laws
Geography
Biology
Forensic accounting (Benford’s Law)
Urbanization
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Power Law(s)
A power law is a special kind of mathematical relationship between two quantities. If one quantity is the frequency of an event, the relationship is a power-law distribution, and the frequencies decrease very slowly as the size of the event increases. For instance, an earthquake twice as large is four times as rare. If this pattern holds for earthquakes of all sizes, then the distribution is said to "scale". Power laws also describe other kinds of relationships, such as the metabolic rate of a species and its body mass (called Kleiber's law), and the size of a city and the number of patents it produces. What this relationship means is that there is no typical size in the conventional sense. Power laws are found throughout the natural and manmade worlds, and are an active area of scientific research.
http://en.wikipedia.org/wiki/Power_law
Benford's law, also called the first-digit law, states that in lists of numbers from many real-life sources of data, the leading digit is distributed in a specific, non-uniform way. According to this law, the first digit is 1 almost one third of the time, and larger digits occur as the leading digit with lower and lower frequency, to the point where 9 as a first digit occurs less than one time in twenty. The basis for this "law" is that the values of real-world measurements are often distributed logarithmically, thus the logarithm of this set of measurements is generally distributed uniformly.
This counter-intuitive result has been found to apply to a wide variety of data sets, including electricity bills, street addresses, stock prices, population numbers, death rates, lengths of rivers, physical and mathematical constants, and processes described by power laws (which are very common in nature). The result holds regardless of the base in which the numbers are expressed, although the exact proportions change.
It is named after physicist Frank Benford, who stated it in 1938,[1] although it had been previously stated by Simon Newcomb in 1881.[2] Although many "proofs" of this law have been put forth (starting with Newcomb himself), none were mathematically rigorous[3] until Theodore P. Hill's in 1995.[4]
http://en.wikipedia.org/wiki/Benford%27s_law
Power Law Analysis
Power law: a mathematical relationship between aspects of one type – size of stones in a pile of rocks – or two quantities. If one quantity is the frequency of an event, the relationship is a power-law distribution, and the frequencies decrease very slowly as the size of the event increases.
Other kinds of relationships
metabolic rate of a species and its body mass (called Kleiber's law)
Size of a city and the number of patents it produces.
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Power Laws
the metabolic rate R for all organisms follows exactly the 3/4 power-law of the body mass, i.e., R M3/4. This is known as the Kleiber's Law. It holds good from the smallest bacterium to the largest animal (see Figure 01). The relation remains valid even down to the individual components of a single cell such as the mitochondrion, and the respiratory complexes (a subunit of the mitochondrion) as shown in Figure 02. It works for plants as well, though with a different ratio. This is one of the few all-encompassing principles in biology. But the law's universality is baffling: Why should so many species, with their variety of body plans, follow the same rules?
Technical definition
A power law is any polynomial relationship that exhibits the property of scale invariance. The most common power laws relate two variables and have the form
f(x) = ax^k\! +o(x^k),
where a and k are constants, and o(xk) is an asymptotically small function of x. Here, k is typically called the scaling exponent, where the word "scaling" denotes the fact that a power-law function satisfies f(c x) \propto f(x) where c is a constant. Thus, a rescaling of the function's argument changes the constant of proportionality but preserves the shape of the function itself. This point becomes clearer if we take the logarithm of both sides:
\log\left(f(x)\right) = k \log x + \log a.
Notice that this expression has the form of a linear relationship with slope k. Rescaling the argument produces a linear shift of the function up or down but leaves both the basic form and the slope k unchanged.
Source: Personal communication. Jose Luis HernandezDirector - ITHavana Medical Group cacerjlh@infomed.sld.cu
Forensic Accounting – Benford’s Law
Could you have applied Benford's Law to the distribution of most significant digit in the monthly series of Madoff returns, spotted something awry, and turned him in, without knowing anything about "split strike conversion" strategies?
Source: http://paul.kedrosky.com/archives/2008/12/19/bernie_vs_benfo.html
The upshot is that Bernie's performance numbers tracked Benford's surprisingly closely. In other words, a straightforward numerical analysis of his performance numbers – without recourse to knowledge about "split strike conversion" option strategies – would not necessarily have shown up the (alleged) fraud here. Matter of fact, had you done this sort of quantitative analysis as an SEC employee, your tendency might have been to be somewhat skeptical about claims of fraud.
Now, this isn't meant to absolve Madoff. While he has been convicted of nothing, the allegations seem well substantiated, and he has apparently said some awfully incriminating things. Nevertheless, it is interesting to see that any fraud here was sufficiently sophisticated such that the proffered performance numbers were credible from a distributional point of view.
Taking it one step further, it almost certainly means Madoff's numbers would have been generated algorithmically. He didn't pluck them from air at the end of each month. That is, I think, interesting in that it shows that this (alleged) con was at least somewhat more sophisticated than some of the noisier critics out there have been saying.
Urban area Power Laws
Urban area Power Laws
Source: http://intersci.ss.uci.edu/wiki/index.php/Image:PaceOfLife.jpg and
A power law is a special kind of mathematical relationship between two quantities. If one quantity is the frequency of an event, the relationship is a power-law distribution, and the frequencies decrease very slowly as the size of the event increases. For instance, an earthquake twice as large is four times as rare. If this pattern holds for earthquakes of all sizes, then the distribution is said to "scale". Power laws also describe other kinds of relationships, such as the metabolic rate of a species and its body mass (called Kleiber's law), and the size of a city and the number of patents it produces. What this relationship means is that there is no typical size in the conventional sense. Power laws are found throughout the natural and manmade worlds, and are an active study of scientific research.
Source: http://commons.wikimedia.org/wiki/File:Long_tail.svg
Picture by Hay Kranen
Summary
See phenomena as systems
Use General Systems Theory to think about the phenomena
Define the relationships between variables and how to measure those relationships
Apply quantitative and qualitative methods to understand any phenomena
Seek out colleagues in other disciplines with other methodologies. Try their methods.
“Environmental” Models
Biosphere and scaling
The condition of the cell, the chromosome, the gene can tell us important things about the condition of the organism AND, increasingly,
Can tell us what can be expected in the future about the organism.
Genes and implications for cancer
U-M researchers identify gene involved in breast cancer
Scientists at the University of Michigan Comprehensive Cancer Center have identified a gene associated with the development of an aggressive form of breast cancer.
The scientists observed that the gene, FOXP3, suppresses tumor growth. FOXP3 is located on the X chromosome, which means a single mutation can effectively silence the gene. This is unusual, as only one other gene associated with cancer has been found on the X chromosome.
When one copy of the FOXP3 gene is silenced, the scientists found in studying mice, 90 percent of the mice spontaneously developed malignant tumors. The scientists also looked at FOXP3 in human breast tissue cells, comparing malignant and non-malignant cells. FOXP3 was found to be either deleted or mutated in a substantial portion of the cancer sample: about 80 percent of the cancer tissues studied did not express the gene at all.
In addition, the scientists found FOXP3 to be a repressor of HER-2, a protein that typically marks a more aggressive form of breast cancer. The scientists believe FOXP3 suppresses the HER-2 gene. HER-2 can be activated by a number of different factors, but the scientists observed that when FOXP3 is normal, it keeps HER-2 levels low; when FOXP3 is missing or mutated, HER-2 levels are likely to rise.........
“Environmental” Models -- Datasphere
“Proyecto Genoma de Medios Informativos”
Objectives:
Develop methods to “map the genome” of media institutions
Develop methods to conduct an autopsy of media institutions that have recently died
Do this by looking at disciplines and methods from other disciplines
“Proyecto Genoma de Medios Informativos”
Articulate initial mission statement
Identify tools to conduct “condition of the institution” analysis (autopsy?) of existing media
Create int’l database of media organization variables
Create platform for collaboration on analysis
Share knowledge of analytic tools from multiple disciplines
Share evolution of methods and findings
Project process
Create Web 2.0 website with …
Methods for creating “standards” for metadata and data quality
Web site objectives
Data source sites
Analytic tool sites
Project management sub-sets and collaboration tools
Define/create database
Metadata
Who can submit/verify/edit data
Evaluation methods of data quality
Source: Published online 9 March 2009 | Nature | doi:10.1038/458135a
Box: Usage mapped
From the article: web usage data outline map of knowledge
Original article: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004803#pone-0004803-g005
http://0-www.nature.com.opac.sfsu.edu/news/2009/090309/full/458135a/box/1.html
Online of images:
http://www.plosone.org/article/slideshow.action?uri=info:doi/10.1371/journal.pone.0004803&imageURI=info:doi/10.1371/journal.pone.0004803.t006