Judgement and Decision Making (49001)
Assessment 2: Decision Making Modelling
Weight: 50%
Type: Individual
Description:
For this assessment task, students will build upon the real case study analyzed in Assessment Task One and develop decision-making models to address the identified decision-making situations. They will create cognition-driven, model-driven, and data-driven decision-making models, each tailored to address specific aspects of the case study. These models should integrate theoretical frameworks, cognitive processes, and relevant data to support decision making.
Instructions:
1. Select the real case study analyzed in Assessment Task One.
2. Develop TWO decision making models for two decision making situations. Select two of three below models:
- Develop a cognition-driven decision-making model tailored to address one of the identified decision-making situations from the case study. This model should emphasize the cognitive processes involved in decision making and incorporate relevant psychological theories and concepts.
- Create a model-driven decision-making model designed to address another decision making situation from the case study. This model should rely on established decision making models or frameworks such as rational decision making, bounded rationality, or prospect theory.
- Construct a data-driven decision-making model for addressing the remaining decision making situation from the case study. This model should utilize data analysis techniques, statistical methods, or machine learning algorithms to derive insights and support decision making.
3. Provide a rationale for the selection of each decision-making model and explain how it addresses the respective decision-making situation.
4. Justify the inputs, assumptions, and limitations of each decision-making model.
5. Present the decision-making models in a clear and structured manner, supported by relevant evidence and references.
6. Ensure in-text referencing and formatting are IEEE style.Faculty of Engineering and IT
Marking Criteria:
1. Decision making models (each 30%, total 60%)
➢ Cognition-Driven Decision-Making Model
- Appropriateness of the cognition-driven model for addressing the selected decision making situation.
- Integration of relevant psychological theories and concepts into the model.
- Clarity and coherence of the model presentation.
➢ Model-Driven Decision-Making Model
- Relevance and suitability of the model-driven approach for addressing the chosen decision-making situation.
- Effective utilization of established decision-making models or frameworks.
- Demonstration of understanding of theoretical foundations and practical implications.
➢ Data-Driven Decision-Making Model
- Selection and application of appropriate data analysis techniques or algorithms for addressing the specified decision-making situation.
- Robustness of the data-driven model in generating insights and supporting decision making.
- Consideration of data quality, reliability, and relevance in model development.
2. Rationale and justification (20%)
- Clarity and coherence of the rationale for selecting each decision-making model.
- Justification of inputs, assumptions, and limitations of the models.
- Insightful discussion of how each model addresses the respective decision-making situation.
3. Structure and presentation (20%)
- Well-structured and well-presented without grammatical errors.
- In-text and IEEE style referencing.