Build Models in your Browser

Use Insight Maker to create rich pictures and causal loop diagrams. Then make shareable simulation models. All right in your browser, for free. Just sign up for a free account and start modeling now.

System Dynamics Models

System Dynamics is a powerful approach for modeling change within systems and Insight Maker is perfect for building System Dynamics models.

Agent Based Models

Explore the interactions between dynamic agents. Use Insight Maker to define agent behavior and simulate a population of agents interacting.

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.

From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)

Modelling human behaviour (MoHuB)

Geoff McDonnell

STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways.

(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).

(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.

(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:

Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Simple Terrestrial Ecosystem Model - Soil Moisture (STEM-SM)

Lisa Belyea

226

A Tragedy of the Commons situation exists whenever two or more activities, each, which in order to produce results, rely on a shared limited resource. Results for these activities continue to develop as long as their use of the limited resource doesn't exceed the resource limit. Once this limit is reached the results produced by each activity are limited to the level at which the resource is replenished. See also Archetypes.

Video

This model is part of

And? Understanding Relationships & Their Implications.

Tragedy of the Commons

Gene Bellinger

Este CLD é um remix dos conceitos de Joseph Pine e do ebook A Era da Transformação, de Edmour Saiani, compartilhado no Claude AI.

A Era da Transformacao a partir das ideias de Edmour Saiani e Joseph Pine

Paulo Villela

2 weeks ago

Simulating Hyperinflation for 3650 days.

If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings. We don't show the details of the central bank buying bonds here, just the net results.

See blog at http://howfiatdies.blogspot.com for more on hyperinflation, including a hyperinflation FAQ.

Hyperinflation Simulation

Vincent Cate

This model is a classic instance of an Erlang Queuing Process.

We have the entities:

- A population of cars which start off in a "cruising" state;

- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.

- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."

-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4

- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.

The model is set to run for 24 hours and all times are given in hours (or fraction thereof)

WIP:

- Calculate the average waiting time;

- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)

A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.

Electric Car Parking

Ray Madachy