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Here is a sample of public Insights made by Insight Maker users. This list is auto-generated and updated daily.

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Spreadsheets as 
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
villela.paulo@gmail.com
linkedin.com/in/paulovillela/

Storytelling: Spreadsheets as System Dynamics Language
Profile photo Paulo Villela
3 3 weeks ago
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Understanding relationships is the foundation of effective problem solving. Though relations exists on multiple levels.

Video

theWay/Understanding
Profile photo Gene Bellinger
5
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SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
SARS-CoV-19 model
Profile photo Lucia Vega Resto
77
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Westley, F. R., O. Tjornbo, L. Schultz, P. Olsson, C. Folke, B. Crona and Ö. Bodin. 2013. A theory of transformative agency in linked social-ecological systems. Ecology and Society 18(3): 27. link

Transformative Agency in Social-Ecological System
Profile photo Geoff McDonnell
5
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

SEIR Infectious Disease Model for COVID-19
Profile photo Rolf Häsänen
677 4 months ago
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World4 is a predictive model for world population. Population has grown hyper-exponentially in the last millenium, with the doubling time decreasing from 900 years  in 1000 CE to a minimum of ~35 years in 1963 CE. Technology is defined as that which decreases the death rate and/or increases the effective birth rate (i.e. by decreasing infant mortality). Technology grows exponentially, therefore population fits a hyper-exponential (exponent within an exponent). Models for the end of growth are explored using equations that express the ways humans are depleting Earth's biocapacity, the nature of resource depletion, and the relationship between natural resources and human carrying capacity. This simple model, containing just two closed systems, captures the subtle shifts in the population trajectory of the last 50 years. Specifically, hyperexponential growth has given way to subexponential growth. A peak is predicted for the time around 2028.  [Bystroff, C. (2021). Footprints to singularity: A global population model explains late 20th century slow-down and predicts peak within ten years. PloS one, 16(5), e0247214.]
World4.5
Profile photo Christopher Bystroff
22 2 months ago