IT728A - Driven Decision Making - Visualization - Motivate - Visual Interface - Assessment Answer

IT728A - Driven Decision Making - Visualization - Motivate - Visual Interface - Assessment Answer

Task:

the tasks
1. Identify a (real and teacher-accessible) data set of interest (examples of possible datasets and dataset sources can be found on the description page of Assignment 2 on Canvas).
2. Formulate a decision problem that can be addressed by using the information contained in the dataset, and motivate its importance. Identify your assumptions, and the profile of the target user, i.e. the individual who, supported by your system, will actually be addressing the decision problem (e.g. domain experts, experts in machine learning techniques, novices, etc.). This specification will have a crucial impact on the motivation behind several of your next choices.
3. Investigate and report sources of uncertainty affecting your dataset (e.g. accuracy, disagreement among multiple or heterogeneous sensors, incomplete data, inaccurate modeling, etc.).
4. Describe the techniques applied to the analysis of your dataset and select the fundamental results that will inform a solution to your decision problem. Determine how a relevant subset of data (i.e. specific features in the dataset) can be selected to inform a solution to your decision problem and justify your choice based on your analysis and results.
5. Organize the information on your visual interface for the DSS prototype, fine-tuning an appropriate trade-off between principles of transparency (explicitly including techniques for uncertainty visualization) and cognitive load on the target decision-maker. Motivate your choices and their impact on enabling users’ deep insight on the decision problem, i.e. supporting situation assessment and decision making. At the same time modulate the effect of possible cognitive biases in the decision process. Any graphic tool for a quick prototyping is allowed (e.g. Tableau, Spotfire, etc.).
6. Describe and discuss your solution, analyzing its strong and weak points, also with reference to general principles of normative and descriptive decision theory.

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