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.

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.

The dynamics of population growth and decline are influenced by a balance between birth rates and death rates, which are affected by various social, economic, and environmental factors. One key concept in population dynamics is the idea of carrying capacity, which refers to the maximum population size that an environment can sustain indefinitely given the available resources like food, water, shelter, and medical care.

Canadian population dynamics where growth rate (birth rate plus immigration rate) depends on density ...... When the population is at carrying capacity, the growth rate is equal to the mortality rate; when the population is below carrying capacity, the growth rate is greater than the mortality rate; when the population is above carrying capacity, the growth rate is lower than the mortality rate.

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 simulates the global human population's growth and decline over a 50-year timeline, factoring in birth rates (density-dep), death rates, and Earth's carrying capacity (K). The model illustrates how population dynamics shift as the population approaches or moves away from K: when the population reaches K, the birth rate matches the death rate, causing no net change; when the population is below K, births exceed deaths, leading to growth; and when the population exceeds K, deaths surpass births, causing the population to shrink. Through this simulation, the model offers insights into possible population trends like stabilization, growth, or decline, and highlights the relationship between reproduction, mortality, and environmental limits. Clear units reflect these shifts per year.

This model demonstrates how the population of trees fluctuates and changes when factors such as how much trees being planted and how many trees being harvested/torn down come into play! 

This model simulates the financial dynamics of an organic strawberry farm, illustrating how different variables impact the cumulative net income. Key inputs include the strawberry yield, sale price, worker hours, machine costs, and organic pesticide costs, all of which influence either the farm's revenue or expenses. The model allows adjustments to these variables, showing how factors like production levels, labor costs, and initial capital affect overall profitability. By visualizing these relationships, the model helps in exploring strategies to optimize income while managing costs effectively.

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.

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 complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

**NOTE: Kindly note we have used 10 sources in total that we have referenced - 2 are listed below as these 2 sources were used for the above description - the other references are located in the variables where the sources were used. 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

Group 8 Members: Leda Alizada (100821720), Pritika Lally (100867821), Mahad Rashid (100779108), Rileigh Rodych (100515185), Sami Siddique (100460897)

This complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

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.

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.

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.

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).

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.

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.

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. 

The dynamics of a moose population with constant birth and death rates.