Create an Insight Maker account to start building models. Insight Maker is completely free.


Start Now

Insight Maker runs in your web-browser. No downloads or plugins are needed. Start converting your ideas into your rich pictures, simulation models and Insights now. Features

Simulate

Explore powerful simulation algorithms for System Dynamics and Agent Based Modeling. Use System Dynamics to gain insights into your system and Agent Based Modeling to dig into the details. Types of Modeling

Collaborate

Sharing models has never been this easy. Send a link, embed in a blog, or collaborate with others. It couldn't be simpler. More

Free & Open

Build your models for free. Share them with others for free. Harness the power of Insight Maker for free. Open code mean security and transparency. More


Explore What Others Are Building

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

As initially proposed by Pr. William M White of Cornell University: http://www.geo.cornell.edu/eas/education/course/descr/EAS302/302_06Lab11.pdf http://www.eas.cornell.edu/
Environment
As initially proposed by Pr. William M White of Cornell University:
Global Carbon Cycle
France Caron
526
Jack Homer's model via ithink and Mark Heffernan.  From Jack's SDR   paper  Worker burnout: a dynamic model  See  IM-641  for the Causal loop diagram
Work Health Care Services Clinical Care

Jack Homer's model via ithink and Mark Heffernan.  From Jack's SDR  paper Worker burnout: a dynamic model  See IM-641 for the Causal loop diagram

Burnout Dynamics
Geoff McDonnell
3 5 months ago
Investigations into the relationships responsible for the success and failure of nations. This investigation was prompted after reading numerous references on the subject and perceiving that *Why Nations Fail: The Origins of Power, Prosperity, and Poverty* by Acemoglu and Robinson seem to make a gre
Society Roam Economics History Nations Fail Politics
Investigations into the relationships responsible for the success and failure of nations. This investigation was prompted after reading numerous references on the subject and perceiving that *Why Nations Fail: The Origins of Power, Prosperity, and Poverty* by Acemoglu and Robinson seem to make a great deal of sense.
Why Nations Fail
Gene Bellinger
14
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.
Arquetipo Sistémico
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.
Límite de Crecimiento
Marcelo A. Castro Iglesias
502 2 months ago
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
Disease Coronavirus Infection Corona Virus COVID-19 SARS-CoV-19 Pandemic
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
Lucia Vega Resto
63
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 effe
Environment Population Technology Demographics Limits To Growth
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
Christopher Bystroff
22 7 days ago