Before addressing the specific case of the smallpox epidemic among the Aztecs, it is important to introduce the basic epidemiological model known as SIR (Susceptible – Infected – Recovered). This model helps explain how an infection spreads through a population over time by classifying people into three main categories:
Susceptible (S): Individuals who have not yet been infected but are at risk.
Infected (I): Individuals who have contracted the disease and can transmit it.
Recovered (R): Individuals who are no longer contagious, either because they have developed immunity or have died.
One of the most powerful principles of systems thinking is that the structure of a system determines the patterns of behavior we observe over time. Trying to change behavior without altering structure is like treating symptoms without curing the cause.
The prey reproduces exponentially (αx) unless subject to predation. The rate of predation is the chance (βxy) with which the predators meet and kill the prey.
Predator
dy/dt = δxy - γy
The predator population growth δxydepends on successful kills and the reproduction rate; however, delta is likely be different from beta. The loss rate, an exponential decay, of the predators {\displaystyle \displaystyle \gamma y}γy represents either natural death or emigration
One of the most powerful principles of systems Este artículo explora el principio fundamental del pensamiento sistémico que establece que la estructura de un sistema determina su comportamiento. A través del caso del programa estadounidense TANF (Asistencia Temporal para Familias Necesitadas) —que ofrece apoyos económicos, alimentos básicos y servicios asociados— se ilustran tres configuraciones estructurales distintas que generan comportamientos radicalmente diferentes. Este análisis está orientado a propósitos educativos y muestra cómo una comprensión profunda de las estructuras permite intervenir de manera más efectiva tanto en sistemas sociales como organizacionales.
This article explores how the technique of normalization enables system dynamics models to begin in equilibrium, allowing hidden feedback structures to surface clearly. Using the story of People Express, it shows how the clash—or harmony—between visible decisions and invisible dynamics can define a company’s destiny. Financial results are revealed as deeply tied to service capacity, reputation, and employee engagement. Scaling wisely, it argues, requires more than growth—it requires systemic clarity.
This model attempts to understand the behavior of average lifetime of companies in the S&P500 index. The reference mode for the model is a graph available at this link: https://static-cdn.blinkist.com/ebooks/Blinkracy-Blinkist.pdf (page 5) which was discussed in the System Thinking World Discussion forum.
Mergers & Acquisitions can be one of the reasons for older companies to be replaced with newer companies in the Index. With M&A of older companies, the empty slots are taken over by newer companies. However, overtime, these new companies themselves become old. With steady M&A, the stock of older companies decreases and stock of newer companies increases. The result is that average age of the companies in the S&P Index decreases.
The oscillations in the diagram, according to me, is due to oscillations in the M&A activity.
There are two negative feedback loops in the model. (1) As stock of new companies increases, the number of companies getting older increases which in turn decreases the stock. (2) As M&A increases, stock of older companies decreases which in turn decreases M&A activities.
Limits of the model
The model does not consider factors other than M&A in the increase in number of new companies in the Index. New companies themselves may have exceptional performance which will result in their inclusion in the Index. Changes in technology for example Information Technology can usher in new companies.
Assumptions
1. It is assumed that M&A results in addition of new companies to the Index. There could be other older companies too, which given the opportunity, can move into the Index. Emergence of new technologies brings in new companies.
Overview This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.
How the model works
The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to increased visitors using the forest of mountain biking. Accompanying variables effect the tourism income that flows from use of the bike trails. On the right side, the forest flow begins with tree growth then a demand for timber leading to the logging production. The sales from the logging then lead to the forestry income. The model works by identifying how the different variables interact with both mountain biking and logging. As illustrated there are variables that have a shared effect such as scenery and adventure and entertainment.
Variables The variables are essential in understanding what drives the flow within the model. For example mountain biking demand is dependent on positive word mouth which in turn is dependent on scenery. This is an important factor as logging has a negative impact on how the scenery changes as logging deteriorates the landscape and therefore effects positive word of mouth. By establishing variables and their relationships with each other, the model highlights exactly how mountain biking and forestry logging effect each other and the income it supports.
Interesting Insights The model suggests that though there is some impact from logging, tourism still prospers in spite of negative impacts to the scenery with tourism increasing substantially over forestry income. There is also a point at which the visitor population increases exponentially at which most other variables including adventure and entertainment also increase in result. The model suggests that it may be possible for logging and mountain biking to happen simultaneously without negatively impacting on the tourism income.
