Decision Model - Group 4 - 2018/2019, Semester B, Quartile 3: Difference between revisions

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= Propositions =
= Propositions =
In this section, we consider propositions regarding each of the attributes coined earlier. Since we have three categories of solutions (detection, identification, and neutralisation).  
In this section, we consider propositions regarding each of the attributes coined earlier. Since we have three categories of solutions (detection, identification, and neutralisation).  
The individual stating whether or not they agree with the propositions should be as least restrictive as possible. That is, one should only agree with certain propositions when they really need to place those restrictions upon the solution.


== Propositions for all categories ==
== Propositions for all categories ==

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Introduction

In this section, we will describe our decision model. First, a description of what a decision model actually is will be given, to give a basic understanding of the concept. After that, we will explain what our decision model in fact does on a higher level, without all the details inside the decision model. After that, we will explain how the decision model is derived, and how our decision model works on a lower level.

What is a decision model?

A decision model is an intellectual template for perceiving, organizing, and managing the business logic behind a business decision[1]. An informal definition of business logic is it is a set of business rules represented as atomic elements of conditions leading to conclusions. A decision model is not simply a list of business rules or business statements. Rather, it is a model representing a structural design of the logic embodied by those statements. In our case, we modify the decision model such that it proposes questions and uses the answers given to those questions in order to label certain solutions with a certain score based on how well they fit to the answer of the question. If a certain solution fits better for a certain answer on a specific question, this solution gets a higher score than a solution that does not fit that answer at all. We elaborate more on this later. Then, when all attributes are scored, they are combined and the solution that has the most attribute scores in common often has the highest score. A list that contains the three best fitting solutions of each of the categories (detection, identification, neutralization) based on what solutions have the highest score are displayed to a user of the decision model.


As described before, our decision model gives as output the best solution for anti-UAV systems based on the input of the user. This user can be, for example, an airport seeking to improve on its anti-UAV systems. Due to the enormous growing list of solutions for this, airports may find it difficult to decide for themselves. After our thorough analysis on solutions and types of airports, we have seen that some solutions fit certain airports better than others, and thus we decide to give a systemized model to consult users in this difficult choice.

How does our decision model work?

The decision model that we will use, will for a large part be based on the Dutch StemWijzer[2], which is a website that helps people find the political party that fits best with their point of view/opinion. The way this works is as follows, the website presents the user thirty propositions, to which the user can answer to agree, disagree or either be neutral. Then, the user can choose whether there are some subjects that he or she might find more important than the others. StemWijzer[2] scores each political party by counting the number of times the user and the party have the same point of view for a proposition, where the more important propositions give double the score if the point of view aligns. In the end, the score for each political party adds up and the parties with the highest score fit best with the point of view of the user.

The reason for choosing this type of decision model is that it is easy and straightforward for the users since for each question the user only needs to fill out whether he agrees, disagrees or is neutral. Furthermore, the user can decide whether he/she finds certain subjects more important than others. This is really useful in our situation, as airports might find certain attributes of a solution a lot more important for their airport than others.

In our situation, where we would like to find an anti-UAV system that fits best with a certain airport, the decision model of StemWijzer needs a few minor changes in order to make it work. Instead of asking about the propositions of political parties, we will ask the user questions on the attributes a solution has and are based on the recommendation report. The attributes used for this, are explained in the next section. The questions asked on the attributes of the solutions will be based on the comparison of the attributes between the solutions. However, since a solution of an anti-UAV system can exist of three parts: detection, identification and neutralization, questions will be asked on all three of these 'sub-solutions'. Users might also indicate if they want a complete solution including detection, identification and neutralization, or whether they do not need one or more of these 'sub-solutions'. It might be the case that an airport might just only want a UAV detection system, in which case the questions on identification and neutralization will be skipped and only a 'sub-solution' for detection will be given. Then, the user will be asked to indicate the attributes that are most important to the airport. The scoring of the solutions will work in the same way as StemWijzer calculates the score for political parties, and will be explained in more detail below.

Attributes

As described above, we will create a decision model that airports can use to decide on which type of anti-UAV system to deploy. For this decision model, we have deconstructed the needs of the airports into concrete attributes. These attributes are based on the recommendation report. Here, we distinguished between three different types of airports and identified all the USE-stakeholders for each type. Furthermore, we did a risk analysis for each type of airport and a stakeholder analysis. Using this stakeholder analysis, we were able to set up a set of requirements, from which we have derived these core attributes. We will first summarise a list of these attributes to get a clear overview of what attributes are all taken into account when creating the decision model.

Airport specific attributes

These attributes are attributes that are intrinsic to an airport. They are attributes that cannot be changed based on preference. Although the airport cannot pick a preference here, they are important to keep into account when advising the best solution, since, e.g. the size of an airport can have a big influence on which type of solution fits the user's preferences best.

List of airport specific attributes:

  • Type of the airport (Commercial, Military, Recreational)
  • Size of the airport

Preference specific attributes

These attributes are not necessarily only dependent on the airport/user. For two airports with comparable sizes and type, one airport might decide to prioritise certain attributes over others. Since we want to centralize the user and give the user as much freedom of choice as possible, these preference specific attributes were added. Some preference attributes, like the safety of the solution for bystanders, may seem like an open door. However, our main goal of these attributes is to get a value to the priority of this attribute; some airports might prioritize safety moreover costs than other airports. To consult the airports, while giving these airports as much freedom in setting their own preferences, these open door attributes are included.

List of preference specific attributes:

  • Cost of the solution, this can be split up into two sub-attributes:
    • Initial costs (purchase)
    • Long term cost (maintenance)
  • Range of the solution
  • Deployment speed of the solution
  • Safety of the solution for bystanders
  • Reliability of the solution
  • Hindrance to the immediate environment of the solution
  • Types of drones that the solution can be used for
  • Scalability of the solution in terms of a growing airport

Scoring the solutions

The next step is for the decision model to rank or score these attributes so that the decision model can link the final outcome of the attributes to actual solutions. To score these solutions, multiple choice questions were used in the same way as StemWijzer[2] asks their users questions. An example of scoring the attributes based on the questions is as follows:

Q: "The budget for a UAV 'detection' system is 10.000 euro or less."

A:

  • Agree
  • Neutral
  • Disagree

Based on this question, all detection solutions that cost less than 10.000 euro will obtain a point (or two depending on the weight, which will be explained in the next section), if the user answers 'Agree'. On the other hand, all detection solutions that cost more than 10.000 euro will obtain a point (or two) if the user answers 'Disagree'. If the user answers 'Neutral', then none of the solutions will obtain a point for this question. All these questions are justified and all questions will be explained in greater detail (see section questions), so that each attribute can get a justified and well-calculated score. The main point of this example is to show how we are going to score solutions based on the questions that we ask.

Weighing the attributes

Now that our decision model has calculated the score of each attribute with respect to the preferences of the user, we must also appropriately weigh the attributes. In most cases, the emmision does not contribute equally to the choice in solution as the safety of the solution, to give an example. We will weigh these attributes as follows: We ask the user to indicate the attributes that they find more important than others. For these attributes, we will double the score for a solution that aligned with the answer of the user.

Translating the attributes to advised solutions

After the user finishing stating whether they agree, disagree, or feel indifferent towards all propositions, an actual combination of solutions for each of the chosen sub-systems can be proposed. Each of the solution proposed under the section solutions will be grouped in either the `agree', `disagree', or `neutral' category for each proposition. We consider three large categories for the propositions themselves, namely identification, detection, and neutralisation. Each of these categories considers propositions that relate to the most important attributes coined previously. Scoring the solutions for illegal drone activity will be done based on the answers that the individual using the decision model provides.

So, we now have a way of scoring and weighing the demands of airports based on the propositions given below. We also have a way to score the given solutions based on the attributes that we have deconstructed from the needs of the stakeholders. What now remains is linking the solutions for each category and the outcome of the prepositions together. For each proposition, if the user agrees, all solutions in the `agree' category for that proposition gain 1 point. If the user, however, disagrees, all solutions in the `disagree' category for that proposition gain 1 point. Furthermore, the user can also skip the proposition if they do not care about the attribute coined for that proposition. In the end, the user can also indicate which attributes are more important to them. All these attributes will gain a multiplier of 2. Additionally, the user can deselect solutions that they do not want to be taken into account during the final result presentation.

For example, let us consider a solution `x' for category `y' and the attributes: `cost', `scalability', and `safety'. Let us assume there is only a single proposition for each attribute. Let the user answer on the propositions such that solution `x' is the right solution for the attributes `cost' and `safety', but not on the `scalability'. Furthermore, the user has indicated that cost is more important than the other attributes. The final score the solution `x' then gets is: 2 (cost) + 0 (scalability) + 1 (safety) = 3. By scoring each of the solutions in this manner, we can, in the end, advise the solutions with the highest scores for each of the categories (detection, identification, and neutralisation) to fit best with the demands of the airport at stake. Note that these are not final descisions that the airport should blindly follow. Rather, we intend to provide a recommendation based on the needs of the airport.

Propositions

In this section, we consider propositions regarding each of the attributes coined earlier. Since we have three categories of solutions (detection, identification, and neutralisation).


The individual stating whether or not they agree with the propositions should be as least restrictive as possible. That is, one should only agree with certain propositions when they really need to place those restrictions upon the solution.

Propositions for all categories

Range of the solution

Q: "The diameter regarding the area consisting of the airport the anti-UAV mechanism should cover is more than 5km"

This question is proposed when it comes to the area that the anti-drone mechanism should cover. Of course, when one purchases an anti-drone mechanism, one wants to make sure that the whole airport is covered by this mechanism such that any illegal drone activity can be detected and dealt with appropriately.


Safety of the solution for bystanders

Q: "Bystanders must be protected from any illegal UAV activity at all cost"

For some airports, it is important that all bystanders are protected. For other airports, however, there might not really be any bystanders or only a very few. Then, it might be less important for the anti-drone mechanism to protect bystanders to a large extent.


Hindrance to the immediate environment of the solution

Q: "The solution should not be of any hindrance to the people living/working within 1km of the border of the airport"

Certain solutions might cause annoyance to their surroundings. Therefore, it is important to consider the people that either live or work within 1km of the border of the airport.


Q: "The solution must not emit CO2"

It might be important for some types of airports to minimise their CO2 output. For other airports, however, this output might not matter at all. This can be taken into account when offering a solution.

Scalability of the solution in terms of a growing airport

Q: "The anti-UAV mechanism must be able to scale when the airport grows"

Some airports are already quite established, while others are still growing every day. When these growing airports invest in an anti-drone mechanism, it could be important that this solution can scale with the size of the airport, such that no new anti-drone mechanism is needed once the airports have grown exponentially.

Detection

These questions will be asked if the user has chosen this category in the initial question. These questions will be on the detection sub-solutions.

Cost of the solution

Q: "How many euros would you be willing to spend on anti-drone mechanisms?"

A:

  • [1]: <100 euros,
  • [2]: 100-1.000 euros,
  • [3]: 1.000-5.000 euros,
  • [4]: 5.000-10.000 euros,
  • [5]: 10.000-100.000 euros,
  • [6]: >100.000 euros.

We propose this question as it captivates the main ideas regarding the costs that a company would be willing to spend on anti-drone mechanisms. It is important what the budget of the company is in order to provide an adequate solution that considers the right price class. We can specify a few different categories when we consider the price ranges. All of these price ranges are based on the solutions that have been proposed.

Q: "How many euros would you be willing to spend on the anti-drone mechanisms after the initial purchase? (Think about future updates)"

A:

  • [1]: <100 euros,
  • [2]: 100-1.000 euros,
  • [3]: 1.000-5.000 euros,
  • [4]: 5.000-10.000 euros,
  • [5]: 10.000-100.000 euros,
  • [6]: >100.000 euros.

We propose this question as it captivates the ideas regarding the costs that a company would be willing to spend on anti-drone mechanisms after the initial purchase has been made. This is important to consider as new technologies release at unfixed times. These price ranges are equal to the price ranges proposed for the initial purchase and can be seen as a form of extension.

Deployment speed of the solution

Q: "The detection system should reliably detect illegal UAV presence within 5 seconds"

This question is proposed when it comes to how fast the anti-drone mechanism can be deployed. For some airports, it is more important that mechanisms can be deployed immediately, and for others, it might be a bit less important.

Reliability of the solution

Q: "To what extent should the anti-drone mechanism be reliable?"

A:

  • [1]: Not at all,
  • [2]: To a minimum level,
  • [3]: If possible,
  • [4]: To a good extent,
  • [5]: At all cost.

When it comes to some airport, it is important that the anti-drone mechanism should be reliable. Under normal conditions, the mechanism should, of course, be reliable as things should simply work when deploying them, but we can still make a few distinctions based on the solutions offered.

Types of drones that the solution can be used for

Q: "What type of drone operations should the anti-drone mechanism be able to handle?"

A:

  • [1]: All operations under the open category (C1,C2,C3, and C4),
  • [2]: All operations under the specific category,
  • [3]: All operations under the certified category,
  • [4]: All operations under the open and specific category,
  • [5]: All operations.

Some airports expect more types of a certain drone to appear than others. It could be possible that some airports only wants to protect against certain types of drones due to a various number of reasons. Then, it is possible to offer different solutions to this airport.

Identification

These questions will be asked if the user has chosen this category in the initial question. These questions will be on the identification sub-solutions.

Neutralisation

These questions will be asked if the user has chosen this category in the initial question. These questions will be on the neutralization sub-solutions.

Q: "Within what time should the illegal drone activity be neutralised (if it has to be neutralised)?"