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.

By: Roman Ahmad Zeia, Rhythm Alam, Hailey Uhlman-O’Conner, Ahmer Khan, Zain Siddiqui

This model estimates the annual emissions from Toronto's multiple transit options including Trains, Airplanes, Gas & Electric personal vehicles and Buses. The resulting chart reports in the in the amount of kilograms of emissions  over years.

This model exemplifies the simple system of a bathtub. The total water volume in the bathtub (lWater) at any given time is determined by the water added from the faucet and the water removed through the drain. The volume of water added from the faucet is affected by the water inflow rate, and the volume of water draining out of the bathtub is affected by the outflow rate. In this model, the total water volume (lWater) increases over time because the inflow rate (0.5L/s) is greater than the outflow rate (0.2L/s).

This population dynamics model simulates the growth and decline of the Javan Rhinoceros population over time (over course of 50 years). The model focuses on the balance between births and deaths within the population, providing insight into how different factors affect the species' survival and growth rates. By using constant fertility and death rates, the model reflects the natural dynamics of the Javan Rhinoceros population, illustrating the interplay between reproduction and mortality.

Ayman Town is a fictional municipality located within Ontario, Canada. This model visually represents the balance between energy supply and demand, and consolidates energy from various sources—nuclear, hydroelectric, natural gas, solar, and wind—into a Total Power Supply that feeds into the town’s power grid. On the demand side, energy consumption is divided into residential and business sectors, factoring in micro-generation (e.g., solar panels) to calculate net demand, or the remaining load that must be met by the grid.

This model highlights the dynamics between traditional and renewable energy sources while showing how decentralized energy generation can reduce grid dependence. By understanding the flow of energy and identifying key drivers of demand, this framework helps guide infrastructure planning, energy policy, and sustainability efforts, ensuring the town’s energy needs are met efficiently and reliably.

This is the flow of electricity in homes and business of the fictional town Wilton. The values were based on the projected power plan of the town Milton. In this stimulation, we see how much electricity is coming in from each energy source and how the total energy on the town's power grid is distributed between residents and corporate buildings in Wilton.

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

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 a 100 acre organic strawberry farm. The net income of this farm is determined through several factors such as its yield and sales and its harvesting and packaging and labour as well. Adjusting the value of acres can increase farm size and see cost differences and net income differences.

This model represents the population of ancient Rome in 200 BC with a density-dependent birth rate...the birth rate does not equal the death rate when the population is at carrying capacity because immigration and emigration are also factors. All values are based on historical estimates, as precise figures are not available.

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.

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 population dynamics of rabbits (prey) and foxes (predator) under different birth and death rates for both 

A model demonstrating the proportion of different power sources due to the residential and business sector demand in one day, as well as the carbon footprint in kg of CO2 produced. 

The UC Davis study gives yields for California, but the yields in Ontario are much less due to the less favourable growing conditions and season length (source:https://www.ontario.ca/page/dayneutral-strawberries). The revenue streams are different for the June-bearing and day-neutral strawberries depending on how many acres of each we have due to seasonal and off-season price differences and different yearly yields. The startup cost is the sum of the equipment, irrigation, and plant establishment costs, all of which depend on acres and set rates from the UC Davis study or the "Ontario Berry Crops Establishment and Production Costs 2022 Economic Report". The yearly expenses are labour costs, water cost, and fuel cost, which all depend on the number of acres and price rates for these aspects of the farm.

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).

Group 4:

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.

Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.

This model demonstrates the tradeoff between the resource costs of deploying AI systems—specifically electricity and water consumption—and the benefits gained from increased efficiency and effectiveness. It simulates how the deployment of AI systems grows over time and quantifies both the cumulative costs and the cumulative efficiency gains.
The following link will show how I created the model using AI:
https://chatgpt.com/share/671ff44a-912c-8008-b1ef-6535fe0ae0b1

This complex system models an organic strawberry farm in Northumberland County, ON. The model aims to highlight the various factors that influence revenue and expenses of the farm, and overall can predict the cumulative net income over a 25 year period. 

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 changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

In this model, I will be demonstrating my understanding of Modelling a Human Population by using Oshawa as a current example. For this model, we begin with a current population of 170,000. However, with Oshawa's "theoretically new" (just to demonstrate my understanding) neighbourhoods being built the population will change to reflect that and the city's appropriate carrying capacity! By using variable factors such as "Moving in/inflow rate" and "Moving out/outflow rate" to reflect the number of current residents residing within Oshawa (nResidents).