IM-1175 with computable arguments, based on ideas from Micropublications  paper  about Claims, Evidence, Representations and Context Networks

IM-1175 with computable arguments, based on ideas from Micropublications paper about Claims, Evidence, Representations and Context Networks

10 months ago
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  Experiment with adjusting the initial number of moose and wolves on the island.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the initial number of moose and wolves on the island.
Attempts to model in the social dynamics of returning players
Attempts to model in the social dynamics of returning players
3 5 months ago
 Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.      With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.     We start with an SIR model, such as that featured in the MAA model featured
Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:
 Documentation       The Insight shown demonstrates how demand and supply in a real estate market can affect pricing.      Demand, Supply and Price have been represented by stocks. Each has an inflow where it has an increase in stock, and a corresponding outflow where stock is decreased.      Linkin
Documentation

The Insight shown demonstrates how demand and supply in a real estate market can affect pricing. 

Demand, Supply and Price have been represented by stocks. Each has an inflow where it has an increase in stock, and a corresponding outflow where stock is decreased. 

Linking each stock and flow is a variable that changes that which it is linked to. These have been labelled appropriately. Each variable takes a decimal value and multiplies it with that it is linked to, such as the rate of demand affecting the price set as 0.001*Demand. This is to generate the loops required to show the rise and fall in price, demand and supply.

Adjustments can be made to the price, supply and demand stocks to simulate different scenarios. Price can be between 400 (400,000) and 1000 (1,000,000) in accordance to average housing prices. Demand and supply can be between 0 (0%) and 100 (100%), although having these set as realistic figures will demonstrate the simulation best. 

Each simulation can be focused on how either demand and price interact over time or supply and price. These are shown in different tabs. 

When the simulation is carried out, the way in which demand and supply rates affect pricing can be seen. Demand and supply are shown with price following shortly after with a slight delay, since changes in market behavior does not immediately affect prices of housing. 

It should also be noted that the lines that represent each stock do not directly reflect the prices of housing in reality. Prices do not fluctuate so rapidly from 400 to near 0 like they do on the graph, however these are just representations of the interactions between each stock in a marketplace.
​ The Model      The model displayed depicts the interaction that the youth of Bourke has with the justice system and focuses on how factors like policing and community development affect the crime rate within this area. Bourke is a rural town that has a significant crime rate among youth. Local com
The Model

The model displayed depicts the interaction that the youth of Bourke has with the justice system and focuses on how factors like policing and community development affect the crime rate within this area. Bourke is a rural town that has a significant crime rate among youth. Local community members call for action to be taken in regards to this, meaning that steps must be taken to reduce the crime rate. This simple model explores how the amount of police and the investment of community development can have an effect on the town in regards to its issue of crime among youth.


Assumptions
  • Bourke's youth population is 1200, with 700 in town, 200 committing crimes and 300 already in jail
  • The amount of police, the expenditure on community development, and the domestic violence rate are the factors which have the potential to influence youth to commit crimes. The domestic violence rate is also influenced by the expenditure on community development.
  • Sporting clubs, interpersonal relationships between youth and police, and teaching trade skills all make up community expenditure
  • Activities relating to expenditure on community development run throughout the year, indicating that there is no delay where youth are not involved in these activities.
  • Every 6 months, only 60% of jailed youth are released. This may be for various factors such as committing crime in jail or being issued with lengthier sentences due to the severity of the crime(s) committed
  • 10% of youth who agree that domestic violence is an issue at home will commit crime
  • There is a delay of 1 month before youth go to jail for crime(s) committed. This model assumes that youth who have committed crime either return home (by decision or by not being caught) or go to jail. It also assumes that other punishments such as community service refer to returning back home.
  • The simulation takes place over a duration of 5 years (60 months)
  • Adults have little effect on the youth. Only where domestic violence is concerned do they play a factor within this model

How the Model Works

The model begins with the assumptions previously stated. Youth have the potential to commit a crime. 3 main variables influence this decision, including the amount of police, expenditure on community development, and domestic violence rate (which is influenced by the previous variable). These 3 variables are able to be adjusted using the relevant sliders with 0.5 indicating a low investment and 0.9 indicating a high investment. Police also have an influence on this decision. This variable is also able to be adjusted by a slider. Last of all, the domestic violence rate also contributes to this decision and this variable is negatively influenced by community development.

Once a youth has committed a crime they are either convicted and sent to jail or return back to town. The conviction rate is also influenced by the amount of police in town, as youth are more likely to get caught and thus jailed. Once again, the Police variable is able to be adjusted via the slider. This process takes a month.

From here, youth typically spend 6 months in jail. After this time period 60% are released while the remaining 40% remain in jail either due to lengthier sentences for more severe crimes or due to incidents within jail. The process then repeats.


Parameter Settings and Results
  • Initially there is a state of fluctuation within this model. It may be a good idea to ignore it and pay attention to how variables change over time from their initial state
  • Increasing the amount of police will raise the amount of people jailed and decrease crime
  • Increasing the community development variables from a minimal investment (i.e. set at 0.5) to a high investment (i.e. set at 0.9) will reduce both the crime rate and the conviction rate. It is worth noting that the community development variable also influences the domestic violence rate variable which also has an effect on the results
  • If only 2 of the 3 community development variables have a high investment then there is not much effect on the crime rate or jail rate. All 3 variables should be given the same level of investment to give us a desired outcome
  • The model does allow for a maximum of 40 police (as we do not want to spend more money on police than we already have in the past), as well as the maximum investment for community development. When choosing settings it may be necessary to ponder if it is financially realistic to maintain both a large number of police as well as investing heavily into community development
 Modified from Sterman (2006)  article  and Gene Bellinger's Assumptions  IM-351  by Dr Rosemarie Sadsad UNSW See also  Complex Decision Technologies IM  and  IM-63975

Modified from Sterman (2006) article and Gene Bellinger's Assumptions IM-351 by Dr Rosemarie Sadsad UNSW See also Complex Decision Technologies IM and IM-63975

3 9 months ago
Summary mostly of Cheryl Misak's 2004  Book  Truth and the End of Inquiry: A Peircean Account of Truth See also broader history of  Pragmatism insight , mostly  from Cheryl Misak's other works and reviews
Summary mostly of Cheryl Misak's 2004 Book Truth and the End of Inquiry: A Peircean Account of Truth
See also broader history of Pragmatism insight, mostly  from Cheryl Misak's other works and reviews

10 months ago