The following model shows us the fictional city in Ontario a municipality called Omnicity with fictional energy values and the relationship between all the energy types used within the city, how it affects the energy grid, with the inflows from the various types of energy the city produces. The power usage coming from Businesses and Residential, whilst the energy produced comes from Wind, Solar, Hydro, Nuclear, Natural Gas, and Micro-generated Solar Electricity from residential housing.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
Note: The following model is purely designed by ChatGPT with very limited human intervention.
This model simulates the tradeoff between the costs of AI resource allocation and the resulting benefits in terms of increased efficiency and effectiveness. It demonstrates how resources are invested in AI, how efficiency gains accumulate, and how diminishing returns impact the overall effectiveness over time. Key variables, such as resource cost, return on investment, and diminishing returns, illustrate the dynamic balance between resource consumption and productivity improvements.
This model explores the relationship between AI resource investment and the resulting gains in efficiency and effectiveness. The purpose is to illustrate how allocating resources to AI can drive improvements in performance but may also reach a point of diminishing returns. The model helps users understand how different investment rates impact AI-driven improvements in efficiency and effectiveness.
By adjusting the Investment Rate and observing changes in Efficiency Benefit and Effectiveness Benefit, users can gain insight into the tradeoffs of AI investment, observing points where added investment yields smaller returns. This visualization is valuable for decision-makers seeking to optimize resource allocation for maximum performance.
Tanjiopolis is a unique municipality located in northern Canada, known for its extreme seasonal climate where there is six months of very hot summers followed by six months of very cold winters, with no transitional seasons. This distinct environment has driven Tanjiopolis to innovate and thrive, harnessing its natural resources to achieve energy independence.
The municipality has invested heavily in a robust infrastructure of solar and wind generators, complemented by a few nuclear power facilities. The nuclear plants operate at only 10% of their maximum capacity during the summer, as the abundant solar energy meets the municipality's power needs. In contrast, during the winter, the nuclear facilities ramp up to 100% capacity to compensate for the reduced solar output due to limited sunlight.
Tanjiopolis takes pride in its commitment to sustainability, reinforced by a government-mandated policy that requires 2 solar panels per residential building, 4 solar panels per small business building, and 6 solar panels per large business building. This ensures that the municipality can sustain a population of 3 million people entirely through renewable energy sources, maintaining a self-sufficient power grid that operates independently from external systems.
The dynamics of human population in Oshawa are influenced by density dependent growth rates. When the population exceeds carrying capacity, the city's resources become overextended, leading to a decline in growth rates. Death rates and emigration rise, while birth and immigration rates drop, causing a population decrease. When population falls below its carrying capacity, the birth and immigration rates surpass death and emigration, resulting in population growth due to the availability of ample resources.
This model graphs the general idea of the inflow and outflow of energy in a small town in Ontario's power grid. The main sources of energy come from Nuclear being one of Ontario's biggest sources of energy, Wind, and Solar. The graph shows energy generation vs energy consumption and the demand in energy in the town.
The power dynamics of a fictitious rural community (Bobville). The dynamics illustrate the power needs of Bobville residents and businesses throughout the day and how they change when microgeneration, residential/commercial daytime use, and nighttime use are considered.
This model simulates human population growth on both a global scale and a local scale for Ajax, Ontario. The global population starts at 8 billion and Ajax starts at 121,780 Carrying capacities are set to 12 billion for Earth and 150,000 for Ajax. The model demonstrates the growth, showing how populations grow and stabilize as they approach their environmental limits.
This is the Logistics model for the country Nigeria over 25 years. Using a density-dependent rate,
At carrying capacity: Birth rate = Death rate. This is why at this point the population at reached a constant (a plateau) because the two rates equate themselves.
Below carrying capacity:Birth rate > death rate. There are enough resources for so the population so max birth rate is reached and more people are being birthed or are migrating into the country
Above carrying capacity: The birth rate < death rate. Nigeria's ecosystem have depleted and not enough to support its population so max death rate is reached.
Using this model, we see how population replenished per person
(Population per capita) decreases as the population nears carrying
capacity.
This model simulates the growth of human populations at a global level and a local level (e.g., Oshawa) using logistic growth principles. It includes components for population size, birth rates, death rates, migration for local population, and carrying capacity. Each stock and flow is described with units and explanations.
This model stimulates the growth of the human population at a large scale, ranging from global to local growth. It is modeled using logistic growth, where the carrying capacity (maximum sustainable population) limits the exponential growth due to available resources.
Brooklin is a small town that is developing quickly and has a younger average population than places like Whitby and Oshawa, therefore giving it a slightly higher birth rate. From when I was a child (Around 2008) it had a population of 15,000, and was estimated at >25,000 in 2018. This simple model is specifically for births and deaths, and doesn't take the fast development of neighbourhoods (i.e. people moving in from other places).
The dynamics of household water management with constant water supply and usage rates.
This model simulates the management of water in a household. The “Water Reservoir” stock represents the total amount of water available. The “Water Supply” inflow adds water to the reservoir based on the “Supply Rate.” The “Water Usage” outflow removes water from the reservoir based on the “Usage Rate.” By adjusting the “Supply Rate” and “Usage Rate,” users can see how conservation efforts impact water availability over time.
The dynamics of the human population in Oshawa are influenced by the carrying capacity.
When the population is below the carrying capacity, the birth rate exceeds the death rate, leading to population growth.
At the carrying capacity, the birth rate equals the death rate, and the population stabilizes.
If the population exceeds the carrying capacity, the death rate surpasses the birth rate, causing a population decline.
This model demonstrates how population growth is controlled by the availability of resources and space.
People (Human): Used for stocks like population and carrying capacity. It represents the number of individual humans.
People per person per year (Human / Human / Year): Used for rates like birth and death rates, indicating the number of people added or subtracted per person in the population each year.
People per year (Human / Year): Used for flows like population change, representing the total number of people added or subtracted from the population annually.
Star production was greater at the beginning of the universe, and has slowed down as lots of gases are now trapped in living stars. While this isn't true for every galaxy, this is a model simulating a galaxy where star formation rate is decreasing.
Everything is divided by 1 billion as to not have to simulate huge numbers.
This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
A model demonstrating the differences in productivity and cost in an arbitrary workforce (this could be one company/institution or an entire area). I used AI to assist me in creating this model, by explaining to it what I would like to have as my main stocks and what I'd like to model, and then had it suggest variables, flows, and links (and thus equations for the flows and variables as well). The actual initial values were also suggested by the AI.
The dynamics of the human population in Oshawa with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
