Reference no: EM132381613

Discussion 1: The idea that came is that of monitoring the taxes paid in the city, to enhance accountability and ensure that all businesses pay tax as expected of them. The new system will help in monitoring the registered businesses that will not have paid their taxes and forward their names for further actions by the authorities (Waller & Weerakkody, 2016). The system will work on an auto-update idea where the authorities will be notified whenever a business license expires and the tax to be paid is overdue. This will inform the authorities to follow up with the matter and prompt the business owner to pay taxes, failure to which, they are held liable for tax avoidance. The city policy to be created will ensure that all the businesses and individuals who avoid paying taxes or get late in their tax compliance are fined by the authorities.

I will use EMA workbench in coming up with a simple code for the system that I want in Vensim (Emmaworkbench, 2018). When creating the model of the idea, I will use three main features: ‘import', ‘from' and ‘with'. Here, the use of ‘import' will be for the retrieval of information from the city database where the information of all registered individuals and businesses is listed. ‘From' will specify the exact database or location within the feeder system where the data needed can be obtained from. In this case, it will be from the registry of businesses and the tax department of the city. ‘With' will be used to list out those individuals and businesses who have a tax liability in the city. The listing will help the authorities in knowing who to go after in getting taxes avoided. The tutorials at EMA workbench are a vital guide on how to create a simple code that will run and produce actual results. The system will help the city in implementing the new policy of recovering taxes from those who have boycotted the payments.

Discussion 2: Smart cities are the future, and it is imperative for policy making to be more efficient and successful, which helps to save both money and time, the two most essential elements for humankind. To help achieve this, EMA Workbench will help us a lot in making the right decisions as the situations for policy making can be unpredictable and as the tool is equipped with sampling simulation, data management, and visualization helps to simulate the outcomes with the help of time classification and machine learning techniques in a robust and successful way (Janssen, 2015).

The policy that I would be trying to create would be the number of taxi licenses issued as this is a problem that needs to be focused on to avoid the traffic issues in smart cities. I would go for data analytics which helps to gather the number of licenses issued, the number of applicants that are waiting for their license and the number of people that are using taxis every day. The second would be data visualization to study the data that is gathered to control the traffic, which is a significant issue in smart cities.

As all of this is unpredictable and can change with time EMA Workbench will help us to use the adaptive policy with the use of its data processing and visualization features as these are needed for our policy which involves a massive amount of data gathering.

Discussion 3: Exploratory Modelling and Analysis

Perhaps the most doubtless way to implement a policy is through informed decision making. Having reliable and accurate information enables you to consider all the factors in play and helps you make long-lasting solutions that work effectively. With the steady improvement in technology and computational prowess, there are many ways to acquire actionable information. One such way is the exploratory modelling and analysis workbench. This method is designed to compute experiments that analyze systems known to be very uncertain or very complex such that normal methods are not effective (Pruyt, 2013).

Smart cities are being developed even as we speak. It is important to know that the policies that we operate by now will not work for these smart cities because they are more advanced. EMA can be used to develop and implement polices such as traffic light placement which will enable efficient traffic control for our smart cities. The key features for this model include strategic placement of the traffic lights and well interconnected traffic light for remote operation (Alvin et al., 2012). The outcome should be specified as smooth traffic flow in majority of road routes and uncertain factors could include the interconnection of these traffic lights via the internet. EMA will analyze our simulated system and hence produce results that will guide the decisions when designing the policy.