i Why is it important to test each of your sub models independently? Explain how you tested the sub-components of the main model using images and text to illustrate your methods and explain how your diagnostics conformed (or not) to your expectations.
Since any system relies on a connection of parts, principles or procedures working in harmony to achieve an outcome, testing sub-components independently lets the system architect confirm function of, and if need be – diagnose issues, and perform some sort of corrective action to rectify faults in the sub-system. Also, if the system is extremely complex, pinpointing bugs within the completed system would be very difficult.
Figure 1
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Sub models were compared to the provided code and instruction from the class PDF, and visually tested by seeing that when the initial conditions could be set for ‘distance to service’ and ‘utility’ values to initial center off. The attraction patches were showing the highest aesthetic/utility quality with the highest pixel gradient and verifying that the sub-systems were working correctly.
ii. The methods for calculating the components of utility are deterministic in this model. How is stochasticity implemented in the model? What parameter influences the degree of stochasticity?
Deterministic behavior is seen by the concentration of homes that always cluster around the two attraction patches as seen in the (image 4) below. However I believe that determinism and stochasticity for the model comes from the number placed in the ‘n-test’ input box. The higher the ‘n-test’ number, the more impact the preference variables have on the agent when making a choice on which patch to settle on based off the aesthetic quality of the patch (image 4) , the lower the number the less impact the preference variable have on agent settlement – thus a positive impact on the level of stochastic agent settlement. (image 5)
Image 4
Image 5
iii. How is initial environmental heterogeneity implemented in this model and what parameter or parameters determine its importance? Do you think that greater environmental heterogeneity corresponds to greater variability among replicate model runs, and why? Support your argument with evidence from replicate simulations with and without initial environmental heterogeneity.
Environmental heterogeneity is applied throughout the model by the preference inputs of distance from services, and aesthetic quality. These preference settings impact where the agent is going to settle around the attracting points or the center service point (if turned on). When these two inputs are set to zero, there is complete homogeny over the modeling environment. (Figure 6) I believe that the parameters that determine the importance of heterogenetic
‘Distance service preference’ and ‘aesthetic quality preference’ have the most impact on heterogeneity they determine how close one agent wants to be to a service center, and aesthetic quality or how many of the preferences are met in one single patch. (Figure 7)
Figure 6
Figure 7
iv. Explain the roles of feedbacks and path dependence in this model. How are the two concepts related? How do the three parameters for preference relate to feedbacks and path dependence? What parameter settings would you use to eliminate any effect of feedbacks or path dependence?
Feedbacks and path dependence in this model determine the locations of clustered patterns of settlement and services within the model. Utility, distance to services and aesthetics change over time due to the changes in settlement of homes and services and can be controlled by the ‘n-test’ input. Services for example are grown after a number of homes are built in an area, which in turn draw in more homes, and those homes grow more services – a positive feedback loop which would continue forever, except we limit the amount of homes.
Feedbacks could be reduced by lowering the preferences on aesthetics and service distance.
v. Describe at least two of the model assumptions or simplifications and how they could influence your interpretation of model results. Despite these assumptions and limitations, what can we learn from this model?
I think that this model over-generalizes, aesthetics and services as they’re related to what choices actually go into relocating your family into a neighborhood. I think that one over-simplification comes from the assumption that each household has the same income level, and that every single person has the same preference as far as distance from utility, services, and aesthetic desire.
