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This model is an attempt to simulate what is commonly
referred to as the “pesticide treadmill” in agriculture and how it played out
in the cotton industry in Central America after the Second World War until
around the 1990s.  

 The cotton industry expanded dramatically in Central America
after WW2,
This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.

The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides.

The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions.

The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998).

Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.

 The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors. THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST W

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

A simple Susceptible - Infected - Recovered disease model.
A simple Susceptible - Infected - Recovered disease model.
      Planilhas como uma Linguagem de Dinâmica de Sistemas: Desbloqueando a Tradução de Modelos com IA   As planilhas estão em toda parte. No entanto, raramente pensamos nelas como  linguagens de modelagem .   Nesta apresentação , compartilho uma ideia central que vem moldando meu trabalho recente:

Planilhas como uma Linguagem de Dinâmica de Sistemas: Desbloqueando a Tradução de Modelos com IA

As planilhas estão em toda parte. No entanto, raramente pensamos nelas como linguagens de modelagem.

Nesta apresentação, compartilho uma ideia central que vem moldando meu trabalho recente:

👉 Quando rigorosamente estruturadas, as planilhas podem condificar estoques, fluxos, variáveis, parâmetros e relações causais.

Nesse sentido, elas podem ser entendidas como uma Linguagem de Domínio Específico (DSL) para a Dinâmica de Sistemas.

A partir dessa perspectiva, exploro como os Modelos de Linguagem de Grande Porte (LLMs) podem atuar não como caixas-pretas, mas como tradutores semânticos confiáveis, convertendo planilhas estruturadas em modelos formais de Dinâmica de Sistemas — preservando estrutura, significado e rastreabilidade.

Isso não é apenas uma contribuição técnica. Tem implicações mais amplas para:

  • a comunicação entre modeladores de sistemas,

  • a reprodutibilidade e a auditabilidade de modelos,

  • a educação,

  • e o diálogo entre pesquisa, políticas públicas e prática.

📎 Aqui está a apresentação e aqui a planilha com os dados originais.

Ficarei muito interessado em receber feedbacks, críticas e experiências relacionadas.

Isso parece um primeiro passo rumo a um futuro em que os modelos possam realmente “conversar” entre si, independentemente de sua linguagem original.

Prof. Dr. Paulo Villela
villela.paulo@gmail.com
linkedin.com/in/paulovillela/

yesterday
     Spreadsheets as a System Dynamics Language:   Unlocking Model Translation with AI    
   Spreadsheets are everywhere. Yet we rarely think of them as  modeling languages .   
    In this presentation , I share a core idea that has been shaping my recent work:     👉  When rigorously structured,

Spreadsheets as a System Dynamics Language: Unlocking Model Translation with AI

Spreadsheets are everywhere. Yet we rarely think of them as modeling languages.

In this presentation, I share a core idea that has been shaping my recent work:

👉 When rigorously structured, spreadsheets already encode stocks, flows, variables, parameters, and causal relationships.

In that sense, they can be understood as a Domain-Specific Language (DSL) for System Dynamics.

Building on this perspective, I explore how Large Language Models (LLMs) can act not as black boxes, but as reliable semantic translators, converting structured spreadsheets into formal System Dynamics models - while preserving structure, meaning, and traceability.

This is not just a technical contribution. It has broader implications for:

  • communication among system modelers,

  • model reproducibility and auditability,

  • education,

  • and dialogue between research, policy, and practice.

📎 Here is the presentation e here is the spreadsheet with original data.

I would be very interested in hearing feedback, critiques, and related experiences.

This feels like an early step toward a future in which models can truly “talk” to each other, regardless of their original language.

Prof. Dr. Paulo Villela

3 yesterday
      Hojas de Cálculo como un Lenguaje de Dinámica de Sistemas: Desbloqueando la Traducción de Modelos con IA   Las hojas de cálculo están en todas partes. Sin embargo, rara vez pensamos en ellas como  lenguajes de modelado . En esta presentación comparto una idea central que ha venido dando forma

Hojas de Cálculo como un Lenguaje de Dinámica de Sistemas: Desbloqueando la Traducción de Modelos con IA

Las hojas de cálculo están en todas partes. Sin embargo, rara vez pensamos en ellas como lenguajes de modelado.
En esta presentación comparto una idea central que ha venido dando forma a mi trabajo reciente:

👉 Cuando están estructuradas rigurosamente, las hojas de cálculo ya codifican niveles (stocks), flujos, variables, parámetros y relaciones causales.

En ese sentido, pueden entenderse como un Lenguaje Específico de Dominio (DSL) para la Dinámica de Sistemas.

A partir de esta perspectiva, exploro cómo los Modelos de Lenguaje de Gran Escala (LLMs) pueden actuar no como cajas negras, sino como traductores semánticos confiables, convirtiendo hojas de cálculo estructuradas en modelos formales de Dinámica de Sistemas, preservando la estructura, el significado y la trazabilidad.

Esto no es solo una contribución técnica. Tiene implicaciones más amplias para:

  • la comunicación entre modeladores de sistemas,

  • la reproducibilidad y auditabilidad de los modelos,

  • la educación,

  • y el diálogo entre investigación, políticas públicas y práctica.

📎 Aquí está la presentación y aquí la hoja de cálculo con los datos originales.

Me interesaría mucho recibir comentarios, críticas y experiencias relacionadas.

Esto se siente como un primer paso hacia un futuro en el que los modelos puedan realmente “hablar” entre sí, independientemente de su lenguaje original.

Prof. Dr. Paulo Villela
villela.paulo@gmail.com
linkedin.com/in/paulovillela/

yesterday
La situación modelada expresa el crecimiento de las ventas impulsadas por la motivación y productividad, pero es frenada por el tamaño del nicho de mercado.
La situación modelada expresa el crecimiento de las ventas impulsadas por la motivación y productividad, pero es frenada por el tamaño del nicho de mercado.
This model was converted from  this spreadsheet  to Insight Maker by importing a ModelJSON file generated by ChatGPT.  To see more details, see this presentation .    Spreadsheets as a System Dynamics Language: Unlocking Model Translation with AI       Spreadsheets are everywhere. Yet we rarely thin
This model was converted from this spreadsheet to Insight Maker by importing a ModelJSON file generated by ChatGPT. To see more details, see this presentation.

Spreadsheets as a System Dynamics Language: Unlocking Model Translation with AI

Spreadsheets are everywhere. Yet we rarely think of them as modeling languages.

In this presentation, I share a core idea that has been shaping my recent work:

👉 When rigorously structured, spreadsheets already encode stocks, flows, variables, parameters, and causal relationships.

In that sense, they can be understood as a Domain-Specific Language (DSL) for System Dynamics.

Building on this perspective, I explore how Large Language Models (LLMs) can act not as black boxes, but as reliable semantic translators, converting structured spreadsheets into formal System Dynamics models - while preserving structure, meaning, and traceability.

This is not just a technical contribution. It has broader implications for:

  • communication among system modelers,

  • model reproducibility and auditability,

  • education,

  • and dialogue between research, policy, and practice.

📎 Here is the presentation e here is the spreadsheet with original data.

I would be very interested in hearing feedback, critiques, and related experiences.

This feels like an early step toward a future in which models can truly “talk” to each other, regardless of their original language.

Prof. Dr. Paulo Villela
yesterday
 A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (healthy and temporarily immune).

A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (healthy and temporarily immune).

yesterday
 Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford). Here schedule pressure producing overtime is also added

Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford). Here schedule pressure producing overtime is also added

yesterday