Ningún departamento de marketing desea malgastar el tiempo atendiendo y desarrollando individuos que no tienen intenciones genuinas de comprar, pero es una tarea difícil y agotadora determinar rápidamente cuáles son realmente potenciales compradores, y cuáles no. La complejidad aumenta considerablemente cuando el número de aparentes oportunidades de negocio escala a valores imposibles de analizar humanamente.  

Cuando un negocio tiene aceitado el proceso de captación de oportunidades de negocio (leads), el siguiente paso es el manejo de los mismos para optimizar el uso de recursos y así desarrollar cada cliente según sus necesidades particulares. 

Lead Management

Lead Scoring es una técnica que permite calificar el nivel de interés y calidad de un potencial cliente (lead), y es una forma de valoración de las oportunidades de negocio. Esta calificación se lleva a cabo cuantificando las diferentes acciones directas e indirectas que realiza el lead durante su ciclo de vida.

En la práctica, el lead scoring viene a ayudar a responder preguntas como:

¿Cuáles son datos de calidad, y cuáles pueden ser ignorados?

¿Debo seguir trabajando sobre este lead, o debo descartarlo?

¿Qué clientes necesitan un seguimiento personalizado, y a cuáles puedo hacerles un seguimiento automático?

Con una correcta implementación de un sistema de Lead Scoring, se pueden responder estas preguntas en forma efectiva, dándole diferente peso a características y eventos que el potencial cliente realiza durante su ciclo de vida. Por ejemplo, podríamos pensar que para algunos productos que tienen entrega restringida a cierta área geográfica, el código de área del número telefónico será muy importante para determinar si un lead entrante debe ser desarrollado o directamente descartado. También podríamos deducir que una persona que hace click en un newsletter o que asiste a una conferencia o que hace un llamado para preguntar sobre algún producto, tiene un nivel de interés importante.

Si juntamos la información declarada por el usuario (ubicación, edad, interés declarado en un producto/servicio, etc.) y la combinamos con la información deducida de sus comportamientos (clicks, llenado de formularios, visitas a página web, etc.), tenemos una base de datos útil para encontrar los leads idóneos.

Para traducir esta información, que es no solo numérica sino también en gran medida categórica, en un “score“, debemos dar un puntaje a cada una de las variables que podamos identificar y desarrollar un modelo que permita, por ejemplo, pronosticar la probabilidad de compra de cada cliente potencial. Existen modelos simples (en general aditivos) y otros más complejos que capturan relaciones no lineales entre el comportamiento los usuarios y su compromiso con la compra. En definitiva, lo que buscan las técnicas de Lead Scoring es maximizar la rentabilidad aumentando las ventas y optimizando el uso de los recursos limitados a través de un análisis automatizado y sistemático del comportamiento de los leads.

En el desarrollo de un nuevo sistema de Lead Scoring, hay dos grandes desafíos. El primero es la generación del algoritmo, que sólo se logra estudiando finamente al cliente de cada negocio para identificar las conductas que revelan sus intenciones de compra. El segundo desafío es la implementación del sistema, que busca unificar todo el proceso de gestión de clientes del departamento de Marketing. Continente Siete desarrolla sistemas de Lead Scoring para sus clientes teniendo en cuenta tanto la generación del algoritmo como la implementación del mismo, y de esta manera los ayuda a crecer basándose en la inteligencia de la gestión de sus.oportunidades de negocio.

Al realizar un desarrollo ad-hoc para el cliente se genera una gran ventaja respecto de sistemas o algoritmos enlatados, dado que no es el cliente el que tiene que adaptarse en sus procesos e interpretaciones de los datos, sino que todo se integra con sus procesos actuales y con métricas que tienen sentido a la hora de la toma de decisiones.

Nicolás Oteiza

Continente Siete

 variEnAperturaInquieto, a los 23 años, junto a dos socios, Rodríguez Varela ya había fundado una empresa. Y no cualquiera, sino el primer taller de algoritmos de la Argentina.

¿Cómo pueden servir unos cálculos matemáticos a la comunidad? Esa es la respuesta que Continente Siete está dispuesta a dar. “Utilizamos la simulación, es decir, metemos el mundo real en la computadora y, a partir de esa información, podemos hacer predicciones y resolver todo tipo de problemas”, explica el ingeniero Industrial recibido en el ITBA, facultad donde nació esta iniciativa, en un grupo de investigación, en 2005.  Proponen extrapolar este tipo de fórmulas, usadas en el ambiente académico, al mundo de los negocios.

“La academia, generalmente, va mucho más avanzada. Nosotros aprovechamos esto, lo costumizamos y lo volcamos al contexto empresarial”, detalla Rodríguez Varela, quien está en el día a día. En 2013, espera facturar $ 5 millones. Así, C7 trabaja los problemas como una red neuronal, uniendo una neurona con otra. Un ejemplo típico es el de resolver el problema de stock de una empresa de consumo masivo (Unilever es uno de sus clientes del taller). “Cada
neurona contempla distinta información: venta del producto ayer, venta anteayer, consumo del producto, órdenes que se generaron, precio. Todas se conectan entre sí”, detalla.
El resultado: poder predecir con precisión cuánto se venderá en los próximos meses y, así, resolver el problema de stock y producción.

 

Nota realizada por Apertura (www.apertura.com) en la conferencia Think Thank 3.0 que realizó la selección sub 35 de los jóvenes líderes de la Argentina.

http://www.apertura.com/revista/La-seleccion-sub-35-de-los-jovenes-lideres-de-la-Argentina-20130716-0004.html

Son el único taller de algoritmos en el país, formado por jóvenes profesionales. Garantizan la resolución de problemas, tanto sociales como económicos, de grandes y pequeñas empresas, así como de organismos públicos.

Por Natalia Szydlowski | Toma Mate y Avivate

nysprensa@gmail.com

C7 Jams: Ciclo de Conferencias

En una moderna oficina de Vicente López, encontramos un grupo de jóvenes estudiantes y profesionales, creando cálculos matemáticos muy particulares denominados “algoritmos”. La empresa, Continente Siete, fue llamada así, porque justamente los algoritmos representarían un “séptimo continente virtual”, en el que se desarrolla un modelo matemático, que luego es “bajado” a la realidad. Al aplicar las medidas que surgen de estos algoritmos, siguiendo esta metodología propuesta, se puede solucionar apropiadamente la problemática en cuestión. Así, esta empresa constituye el primer Taller de Algoritmos de la Argentina.

Inicio en la Argentina

Nacieron como un “Laboratorio de Investigación” dentro del Instituto Tecnológico de Buenos Aires (ITBA), y en el año 2008 se organizaron como empresa privada. Integrada mayoritariamente por jóvenes Ingenieros de distintas ramas, Matemáticos, Diseñadores Gráficos y Marketing, se caracteriza por ser una empresa con un staff horizontal, es decir carece de una organización jerárquica. Esto promueve el dinamismo y comunicación intergrupal, características esenciales para este tipo de trabajo. Por otro lado, la diversidad de profesiones permite que cada uno seleccione las herramientas adecuadas para llegar al algoritmo más conveniente que posibilite la resolución del problema presentado.

Funcionamiento

Se organizan en varios grupos de trabajo encabezado por un líder. Cada uno toma un proyecto (problema a resolver), y se realizan varios encuentros con el cliente, quien les explica su/s inconveniente/s además de brindarle la información solicitada. La naturaleza de esta última variará según el caso a resolver, por lo que cada uno difiere tanto en el análisis como en la recolección de datos. Luego, en Continente Siete emplean sus conocimientos para encontrar el algoritmo adecuado al problema, lo prueban y es entregado al cliente. La forma de entrega es diversa, a través de un software, servicio, herramienta online, etc.

Asimismo, se encuentran constantemente investigando y desarrollando nuevas ecuaciones y cálculos de este tipo, para luego ir en búsqueda de mercados “aplicables”.

Desafío

Presenta un gran reto cada uno de los proyectos, debido a que todos difieren en su esencia. Continente Siete trabaja en proyectos que persiguen fines económicos, como la optimización de la venta de productos, publicidad, oferta y demanda. Pero, por otro lado, como los algoritmos son también aplicables a situaciones sociales, esto termina generando un nexo entre dos ciencias (matemáticas-sociales) donde también la empresa se desarrolla. Algunos ejemplos de lo trabajado son: el impacto de las adicciones que tendría en una organización, distribución óptima de francos laborales que brinden mayor grado de satisfacción, mejoras en la frecuencia de horarios del transporte público, etc.

Clientes posibles

Puede acceder desde cualquier individuo que tiene un negocio hasta una gran empresa. Es decir, cualquiera que presente una dificultad a resolver de carácter ambiental, cultural, económico como social.

Ideas a futuro

El taller crece día a día con cada uno de los proyectos que les llega, así como los que generan en forma voluntaria y ofrece al mercado. Es un deseo de la empresa desarrollarse no sólo a nivel privado, sino también estatal.

Quién lava los platos, resuelto con un algoritmo

Alguna vez le habrá pasado de faltar o irse vacaciones o simplemente no almorzar y justo tener designado que Ud. lavaba todos los platos de sus compañeros…Injusto ¿no? Esto en Continente Siete no ocurre, ya que diseñaron un algoritmo, implementado a través de un tablero de goma eva, que se encuentra colgado en una cartelera dentro del comedor. “El algoritmo” es como una “carrera a lavar los platos”. El que come avanza un casillero, el que lava es porque llegó a la meta, y el que lavó vuelve al punto de largada. Esto genera igual grado de satisfacción y equidad, fomentando el buen clima del equipo, sumado a las chistes “matemáticos-nerds” ¡infaltables a la hora de la comida!

Agradecemos a Continente Siete

Para más información:
www.continentesiete.com
info@continentesiete.com

Fuente: Toma Mate y Avivate

Toma Mate y Avivate permite la reproducción total o parcial de sus notas citando la fuente.

We are all different, right? Each person has its educational, contextual background, history, personal characteristics, hence a particular way to see the world and then, to act in our lives. You can decide to study mathematics, your friend could choose to go on holidays to Paris, and your neighbor could enjoy staying home the whole weekend watching TV. We are different.

However, have you noticed that many of your problems are the same as your friends’ ones?

  • Have you ever been on traffic jam on a highway and asked yourself: why are we stopped as if there were a traffic light when actually there is no one?
  • If you ever had the chance to see the market penetration ratio for a new product, didn´t you wonder why you get always the same penetrations curve no matter who deploys the marketing strategy?
  • Why all the countries have inflation? Why all the big cities have traffic problem?
  • Are you dealing with problems originated by a solution implemented some time ago? A solution that brought more problems instead of real solutions?
Here you have some cases of common problems; they happen all the time, everywhere and generally they are quite similar. But then, why do they still happen and we are unable to solve them definitely?
 
It turns out to be extremely hard to understand the systems in which we are involved in. And actually, you are part of the traffic system of your city, a member of the market that decides whether a new product will succeed or not, part of the economic system in your country, and part of a hundred of other systems. “System Dynamics” is a discipline focused on understanding the inter-relations among the different components of the system, identifying loops, letting you understand the originally unexpected results: political interventions that do not more than increasing poverty or business strategies aimed to gain new clients that lead to losing the old ones and eventually reducing the company’s opportunities to keep on growing.
 
“System Dynamics” focuses its attention on STRUCTURES. Structures end up conditioning the individual behaviors and the inter-relations among them, then defining the global results and behaviors. That is the reason why, no matter the people involved in the system, the results always are qualitatively equal.  And by structures I refer to the laws, rules of the system, the available information, the control mechanisms, the carrying capacities, the way of connecting the parts, etc. Structures lead the way you understand reality, and then, the way you act and react.
 
Life is somehow like a large and a little bit more complex game. In a game, you have clear rules that say what you can do or do not, the cases that you will be rewarded and the cases you will be punished, the information you will have available at the time of making a decision, and also, the time when you will be able to act. The game’s creator cannot anticipate which gamer will win, but he definitely knows the number of resulting winners, the ending conditions of the other participants, and also an idea of the possible evolutions and alternatives that could arise from the game. And why? Because he understands the structures behing it.
 
But don’t panic, you can still consider yourself a free person! “System Dynamics” do not look at individual behaviors and his particular way of acting. It studies the masses’ behavior, the global results. You are free and you will decide differently from others, but at the end the global results will be quite predictable by understanding the structures of the system you are part of.
 
In the book “Foundation” by Isaac Asimov, threre is an interesting dialogue between Hari Seldom and Gaal Dornick:
 
HS: – “What of psychohistory?”
GD: -“I haven’t thought of applying it to the problem.”
HS:- “Before you are done with me, young man, you will learn to apply psychohistory to all problems as a matter of course”
 
Without making comparison, and without going so extreme, I would say these last words to you but regarding “System Dynamics“. Once you start digging in the world of System Dynamics, you will see how useful it will be to understand a great part of the problems we face today. And remember that understanding the problem, you have 75% of the path to the solution completed!
 
“You will learn to apply System Dynamics to all problems as a matter of course”
 
Suggested bibliography: 
- “Business Dynamics” – by John D. Sterman.
- “The Fifth Discipline” – by Peter Senge.
- See other posts in our blog, related with structures: 
“Predator-Prey – Migration Adapted” & “Keep calm and win again”
  • The Show

During “La Semana de la Matemática” (“Math Week”), an event organized by the Buenos Aires University this past April 23 to 25, two members of C7 had the opportunity to make a presentation about Noise, “Procedural Generation of nth Dimensional Noise” specifically.

Math Week aims at delivering 4th and 5th year high-school students the Big Picture about studying and following a career in Mathematics.

With this in mind, we, Julia Picabea and Agustin Ramos Anzorena prepared a small talk on “Noise”, oriented towards the procedural generation of textures and maps used in 3D Animation, film and videoGame industry. This approach, we believe, softens the hard-algorithm impact that might scare the students away, by providing a familiar frame.

We started out by explaining how noise is, showed an example of a procedure using Voronoi Diagrams, and finally did a realTime demonstration inside a 3d Modelling Application, of how to create a realistic stone, from a simple cube, using nothing but procedural noises. This is what we learned in the process.

 

Also, take a look at the Poster we presented, where you can find some of the stuff explained here, plus q walkthrough of how to create a 3D Stone, in a 3D modelling application, starting from just a simple cube, using nothing but noises. It’s quite interesting.

Look at the Poster

 

  • What’s up with the noise

Noise can be thought of as an error imposed over a signal or measurement of data.

In Computer Graphics (CG), noise can help simulate naturally occurring phenomena that would be very difficult to generate otherwise.

Usually, random noise is no good, as Einstein said “God does not play dice with the universe”. Otherwise, we would have tropical trees scattered around polar glaciers.

This is why researchers seek ways to produce noises, that generate coherent values with parameters that make them highly controllable, while remaining “random” to the human eye. They fall into a category called “Procedural Generation”.

One of the most famous noises is the Perlin Noise, created by Ken Perlin, to produce organic textures for the movie “Tron”, in 1982. The film received an Academy Award for Technical Achievement (14 years later).

Nowadays, noises are widely used to create all sorts of special effects: clouds, fire, organic textures, terrain generation, object scattering and real-Time mesh wreckage in game physics engines.

This is simple Perlin Noise

Perlin Noise

This is simple Perlin Noise

 

This is a fractal Perlin Noise, where many scaled versions are applied on top of each other:

Perlin Noise Fractal

Perlin Noise Fractal

 

  • Voronoi Diagram and Worley Noise

Used in optimization, the Voronoi diagram can also be used to produce textures, and it’s also widely used in CG.

Let’s take a look at the algorithm implemented in a 2D plane in a screen:

  1. Generate random points across the area. These are the “feature points”.
  2. For each of the remaining points/pixels.
    2.1 – Measure the distance to each feature points.
    2.2 - Recognize which feature point is closer that is find the minimum distance.
    2.3 - Inherit/transfer properties of the nearest feature point to the point/pixel

That’s it!

This means that, if the pixel inherits the color of the feature point, we will end up with a vitraux-like plane, where each colored area represents all the pixels nearest to a certain feature point of the same color.

Voronoi Diagram

Voronoi Diagram


Voronoi Diagram (Area Edges)

Voronoi Diagram (Area Edges)

 

But it does not end here: we can tweak some parts of the algorithm to come up with totally different patterns.

For example:
What if we change the way we measure the distance in step 2.1?

The first image measures distances using the euclidean distance.
If A=(xa,ya) and B=(xb, yb) are 2-dimensional points, the euclidean distance between them is

d(A,B)=sqrt((xa-xb)2+(ya-yb)2)

But in CG you could use other distances, for example: Manhattan, Chebyshev, and Minkowski distances.
Let’s take a look at just one of them: Manhattan distance between the points A and B would be

d(A,B)= abs(xa-xb) + abs(ya-yb)

This distance is also called TaxiCab distance, since it would be the shortest distance that one could travel in a city, where it is not possible to move diagonally.

Voronoi Diagram (Manhattan Distance)

Voronoi Diagram (Manhattan Distance)

 

Usually, in CG, if we want to create a texture to control something other than the color of an object, we would create a grayscale image, because it represents an easy-to-handle 1-dimensional gradient. This means working with 1 range/scale of values at a time.

Different types of grayscale images can be generated by playing around with the Voronoi algorithm.

So, instead of inheriting the color, the pixel becomes a grayscale value, which is linearly interpolated between 0 and a specified maximum range.

Worley Noise (Distance: Linear Interpolation to closest Feature Point)

Worley Noise (Distance: Linear Interpolation to closest Feature Point)

Welcome to the Worley family of noises, where what matters is the type of Interpolation and the Feature Selection.

What does this mean?
You can interpolate the distance from the pixel to the feature in whatever way you like, for example:

Linear: sqrt(a2+b2)
Linear Squared: a2 + b2
Quadratic: (a2 + a*b + b2)

Or any other operation…

And what does Feature Selection mean?

All these distances are calculated from the pixel position towards the closest Feature Point. There are variations, called “F values”, which take into account the distance to the second closest Feature Point, or the third, or the nth.

Worley Noise (Distance: Linear Interpolation to second closest Feature Point)

Worley Noise (Distance: Linear Interpolation to second closest Feature Point)

Other variations include operations between F values

The following is F2 minus F1:

Worley Noise (Distance: Linear Interpolation to the difference between the second and the first closest Feature Point)

Worley Noise (Distance: Linear Interpolation to the difference between the second and the first closest Feature Point)

 

As you can see, a lot of these noises can be use to create textures of all sorts, such as leather, animal skin or scales, some plastics, or clouds, and a lot more with some creativity.

 

We hope you enjoyed this post!!

 

Music!

February 19, 2013 — Deja un comentario
At our office we spend many hours a day. Some of us more, some less, but it is common to have people around all day long. So, the problem since its beginings is how to maintain (us) entertained for such a long time.
The simplest answer? Music.
Music is known worldwide as a means to exchange tastes, and share with other people. It can even be considered a means of communication and identification.
The problem is, how do you share music in an office, without disturbing the people who work there? And, having fix this, how can you agree among so many people?
In our case, we have multiple music options, as explained below.

Jango

It is an easily accessible page which performs a first search according to a favorite artist, and then generates a list of tracks from similar artists, which can be rated as good or bad, according to the preferences of the user. Thus, selected artists lists are generated, and some unknown musics are discovered.

Grooveshark

In case of not wanting to explore new music, Grooveshark may be one of the best options. On this page you can find a variety of artists, songs and choose those you like best, assembling personal lists to your liking. In general, if you do not have a good internet connection is not recommended. However, being without video songs, is a good choice to put background music, without the risk of ending up listening to an artist who is not of your taste.

Youtube

It may happen that the connection to YouTube (since it is a page more developed than the previous ones) works better loading videos. In that case, you can create a list of your own, or use the suggested lists from the page.

Streema

If you are not convinced to listen to a particular artist, an interesting option is to connect to any online radio. Here, our favorite page is Streema, a platform that includes numerous radios from around the world, easy to use and very effective.

Server music

If we are not convinced with all of the above, or if the connection is working very slow, we have music on our servers, which are connected to the speakers of the office and we can access from our own computers. Thus, anyone in the office can change the music if it is not of its liking, or just prefer a change.
All previous cases follow a very particular dynamic that is generated and mutating over time. Sometimes the music is directly censored, and sometimes it is celebrated by most of the office. Also, there are times when it generates some discussion about what music to listen, while in other cases there are fights between two or more people entering the servers to simultaneously choose music, being favored in that case the fastest one with the cursor.
But, of course, the office always has music that entertains our work!

 

Continente Siete (C7) is not only a consulting firm, but also a partner for its clients, seeking high impact interventions (in quality and time duration) that would lead them to an upgraded level of performance.

Among all the projects and business areas where C7 has been working on, it can be mentioned the consulting service on S&OP process, Demand Planning and Forecasting. Based on the principle exposed before, the training and knowledge transferring to the client are part of the service given. This allows C7 develop methodologies and tools for specific use according to each particular case and the clients get not only the solutions but also the knowledge of how to obtain the best use from them.

The “Advanced Forecasting Course” developed for Unilever Brazil is a case of the relationship of knowledge sharing. The course had the aim of providing the participants with all the required know-how and techniques that would allow them to improve the demand planning and forecasting in Unilever. After two years of consulting relation with them, Continente Siete knew the opportunities and necessities that Unilever had, and the course was then designed to cover those gaps, also in alignment to the company’s objectives.

The course starts with some basic modules where issues regarding S&OP process and supply chain area are discussed, in order to let participants understand the context and impact of demand forecasting in business results. There are also modules regarding times series models where logics, strengths and weaknesses for each model are evaluated. Do you know when to use a weighted moving average model or the Holt’s method? Can you use the Winter’s method when the historical series shows a trend component?

The following modules include more complex issues, like the understanding and use of Key Performance Indicators (KPIs) and Key Behavior Indicators (KBIs) –a C7’s development of new kind of indicators-, to evaluate the forecasting performance and mainly to understand the type of series the user is dealing with: does they show trend? What about seasonality or volatility?. All these concepts then put together in a C7’s methodology development for forecasting, named: MAP (Model Assignment Process).

The final modules include more advanced issues like: methodologies to include price impact on the forecasting process, the impact of investments in different (type of) events, innovation forecast, range forecasting, Montecarlo Simulation, alternatives for “history cleaning process”, regressions and cannibalization effects. During the training days, C7 also introduces other possible approaches, such as Data Mining, Neural Networks, System Dynamics, Simulation and Artificial Intelligence. The course is complemented with a forecasting contest where participants compete in making the best forecast based on the knowledge brought during the training course.

Finally, it is sought a real application for all those trained issues by the participants on their day-to-day activities. To do that, they carry out an application project during three months where they study, define, develop, evaluate and suggest changes in the processes.

During 2012, C7 started, together with Unilever, the 2nd “Advanced Forecasting” Training Course that finished last January with great success. All Demand Planners have gone through the training allowing them count with technical and business knowledge that led to a significant improvement of the forecasting accuracy results for the company. At the same time, the application projects showed a great level of advance in methodology to be implemented in the Forecasting process.

C7’s objective is to be the client’s partner and to generate high leverage interventions. And the Advanced Forecasting Course for Unilever is a success case in that sense.

“Never regard study as a duty, but as an enviable opportunity to enter the wonderful and beautiful world of knowledge”. Albert Einstein.
click image to open the model

Predator-prey models are argubly the building blocks of the bio- and ecosystems as biomasses are grown out of their resource masses. Species compete, evolve and disperse simply for the purpose of seeking resources to sustain their struggle for their very existence. Depending on their specific settings of applications, they can take the forms of resource-consumer, plant-herbivore, parasite-host, tumor cells (virus)-immune system, susceptible-infectious interactions, etc. They deal with the general loss-win interactions and hence may have applications outside of ecosystems. When seemingly competitive interactions are carefully examined, they are often in fact some forms of predator-prey interaction in disguise.

Typical predator-prey models consist of 2 populations, where the predator affects the prey (through killing) and viceverse (no prey, no food). These dynamics can be represented with mathematical equations and run through simulation models (mainly System Dynamics).
In this adapted model, the predator does not die if there is not enough prey, they just migrate outside of the system. If there’s a lot of prey, they migrate into the system. This very small adaptation to the dynamics may lead to more structural possible outcomes.

Run the model with me and I’ll show you what I’m talking about!
When you first run the model, you get a transitional behavior, and a permanent, cyclic one, in which both populations have alternating peaks and valleys.



Overshoot and Collapse

Now what happens when you increase the Birth Rate (of prey)? You end with nothing! Why is this? Delays… You see that due to a high birth rate, prey population increases greatly, bringing a huge amount of predators into the system. This increase in predators makes the killing increase over births, reducing greatly the prey population. But by the time the predators realize that their food is depleting and leave the system… it is already too late.

Reduced Delays

Let’s try another thing now, let’s reduce the delay. Start the model over, and set the Migration Delay to the far left (0.01). You’ll immediately see that oscillations cease and equilibrium is found. Now try to increase birth rate, and see what happens. A new equilibrium is found, no collapse!

Play Around

There is another key behavior (the damped oscillator) which you can achieve by slightly reducing the delay. Anyhow, you can try out different scenarios, I’ll explain the controls. We already covered birth rate and migration delay. 
  • Desired Level of Attraction: is the amount of prey the predators see as “comfortable”. More than this, they inmigrate, less than this and they will emigrate.
  • Death Rate: is the natural death rate for prey (unaffected by predators).
  • Hunt Rate: is the rate at which predators “kill” prey.
  • Predator Perception Sensitivity: is an indication of how quickly or slowly predators are wanting to enter or leave the system. It is lightly different to Migration Delay. The first is the intention to migrate, the latter is the actual migration.


Outside Predator-Prey

Predator-prey structures can easily be extrapolated to other areas of application. The concept that delays make for oscillations and can potentially lead to overshoot-and-collapse behaviors has nothing to do with the predator-prey system. We can see the effect of delays, for example, in supply chain behaviors, where small shifts in consumer demand usually lead to huge shifts upstream. 
Whenever there is an action to be made, usually this action is based on some kind of input (which may take the form of gut-feeling, metrics, symptoms, etc… you name it). The thing is that there is a delay between the fact, and the moment in which we receive information for this input. How big or small the delay is depends on numerous factors, but for sure, there is a delay. 
After reading this, I hope that next time you take an action, you stop to think on delays and avoid over-reactions. 

The Market Behavior:

December 28, 2012 — Deja un comentario
 

What if you are a company competing in a challenging market. You would have to make decisions in order to increase your profit and the amount of sales.
 
You might consider that consumers will choose the company that brings them more benefit taking into account certain factors.
 
The questions would be: what decisions do I make as a company? Which factors are more relevant for consumers? How do I get an advantage from my competition? How do I react to my competitor´s moves?
 
 
So to answer these questions we developed a model that is a simplified representation of a dynamic market, with the following assumptions:
  • There is one product sold by multiple companies that are price-setters ( 0.1≤ Price≤1 ).
  • Each company produces according to demand, they don’t stock product.
  • Each company decides to produce the product with a certain level of quality (1≤ Quality ≤10  ).
  • A company´s initial price and quality is randomly assigned according to a uniform distribution within the range.
  • The company´s variable cost increases with the square of the quality it produces.
  • All companies have the same fixed costs.
  • All companies have a target market share according to:

                                   Target Mkt.Sh.= 1.15×(∑ consumer)/(∑company)

  • A company’s state is discretely defined by two variables: profit and market share.
  • There are four possible states for a company:
  • Star: Profit > 0 and Mkt. Sh. > Target Mkt. Sh.
  • Cash-Cow: Profit < 0 and Mkt. Sh. > Target Mkt. Sh.
  • Question Mark: Profit > 0 and Mkt. Sh. < Target Mkt. Sh.
  • Dog: Profit < 0 and Mkt. Sh. < Target Mkt. Sh.
  • Each company will follow one of two set strategies: price-driven competition and quality-driven competition.
  • Each user agent consumes only one product.
  • The product a user consumes is chosen to maximize his utility function.
  • The utility function for each consumer is:
  • U(P,Q)=P^α Q^(1-α)            P: price, Q: quality
  • Users’ α parameter are distributed according to a uniform distribution between [0,1]
  • Users continually reevaluate the utility of all products in the market and adjust their consumption decision.
  • In the model you may choose the amount of companies that are competing on the market.

While proving the model you might notice that:

  • Under the specified assumptions, the price strategy trumps the quality strategy.
  • There is a better chance of higher consumption diversity when the market has fewer companies.
  • The higher the competition the lower the profits.
You can try the model we developed here: http://www.runthemodel.com/models/934/

No, success is something I don´t wish on anybody. It´s like what happens to mountain climbers; they kill themselves to get to the top and when they get there, what do they do? Climb down, or try to do so, discreetly, with as much dignity as possible. - Gabriel García Márquez

The Formula 1 Grand Prix in Abu Dhabi was held on Sunday November 4. The final podium was conformed by Kimi Raikkonen, Fernando Alonso and Sebastian Vettel. If we look at the drivers’ championship to this date, this result is not surprising. However, if we look at the career development, the final positions could seem bewildering. How could such a result have occurred? Only these drivers seem to be able to accomplish a feat of this magnitude: the first one of them, winning with a less developed Lotus vehicle, the second starting sixth with a F2012 which in recent competitions does not meet his demands and the third, with all the opponents in front of him.
But is this really surprising? Honestly, no.
The reason for this deduction is based on the existence of an archetypal behavior which explains how a success can explain a new future success: the success of the successful.

Success to the Successful archetype

In this archetype there is competition between two or more actors who share a given limited resource among themselves, from the results achieved in the first instance, and then reuse this resource to generate a new competitive advantage with a direct result on the next competition. Thus, one who achieved the first victory has an advantage that may favor him in the next instance, with a forecast in his favor that, if carried out, will then extend his advantage, gaining momentum advantage at the expense of the other competitors.

To better understand this behavior we can rely on the following article which lists ten reasons justifying it, carried to the field of sports. Listed are some aspects such as the morale and self-determination, more difficult to quantify; but are also other factors, such as the press, the continuity and the invitation to best practices, reinforcing the theory of success.

Is this always the case? Clearly, not. In response, there is another article which warns of the possibility of falling into the obsession of success. In short, the author stresses the importance of not evaluating success by the success in itself, but to find ways to measure not only focused on the outcome. If so, would pose a possible solution to this archetype, avoiding entering the success to the successful behavior (and the consequent failure of failure).

What other options exist to overcome this vicious circle?
The best response is to always stay one step ahead: be proactive and take the initiative to carry out all necessary action to achieve a significant advantage over the competition. This means acting big.
Finally, the definitive way to avoid entering a bond of this type is to leverage on oneself: in order to have control over the desired success, and to escape from result-determining performances, it is necessary to start with a good attitude, and work in this internal communication, as described in this latter publication.

As a final conclusion, we can say that this type of behavior can be expected in any circumstances in which you compete for a common resource and that, if sustained, can be very detrimental to any of those affected and, ultimately, for the overall system. It is your own responsability to depart from this model with  cooperation and a global goal as the best solution in the long run.