49001 Judgment and Decision Making
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
Judgment and decision making play pivotal roles across diverse domains, from engineering to business and our daily routines. This subject offers an exploration of fundamental theories and concepts, delving into biases, heuristics, decision analysis, decision making under uncertainty, and effective problem-solving strategies within the realm of judgment and decision making. Students gain insights into a spectrum of decision-making models: from cognition-driven to model-driven and data-driven approaches. These multifaceted models serve as vital tools for conceptualising, implementing, and operating modern engineering systems. Moreover, the subject examines principles underpinning individual, group, and strategic decision-making strategies. By exploring these diverse strategies, students develop a nuanced understanding of effective decision-making dynamics in various contexts.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify cognitive biases and heuristics that influence decision-making processes and mitigate their impact in various scenarios. (B.1) |
|---|---|
| 2. | Apply decision analysis tools and frameworks to evaluate and resolve complex problems. (C.1) |
| 3. | Implement diverse decision-making models, including model-driven, cognition-driven, and data-driven approaches, grasping their strengths, weaknesses, and optimal applications in real-world contexts. (D.1) |
| 4. | Utilise group and strategic decision-making strategies to solve complex decision situations across varied scenarios and organisational levels. (E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
Students enrolled in the Master of Professional Engineering should note that this subject contributes to the development of the following Engineers Australia Stage 1 competencies:
- 1.4. Discernment of knowledge development and research directions within the engineering discipline.
- 1.5. Knowledge of engineering design practice and contextual factors impacting the engineering discipline.
- 2.3. Application of systematic engineering synthesis and design processes.
- 3.1. Ethical conduct and professional accountability.
- 3.2. Effective oral and written communication in professional and lay domains.
- 3.4. Professional use and management of information.
- 3.5. Effective team membership and team leadership.
Teaching and learning strategies
Our teaching and learning strategy aligns with the University learning.futures strategy by integrating flipped learning, organising active learning experiences, fostering an interactive and collaborative environment, and providing feedback with time for personal reflection. Through exploring real-world scenarios each week, students apply concepts to practical decision-making challenges. Prior to workshops, individual preparation ensures active engagement during discussions. The interactive workshop structure, involving smaller group discussions, encourages collaborative analysis of case studies, linking theoretical concepts in judgment and decision-making to practical scenarios. Hands-on activities, such as decision modeling, enhance engagement and immerse students in decision-making contexts. Utilising tech tools facilitates dynamic discussions and resource sharing. Continuous learning checks, including post-class discussions that provide feedback and time for personal reflection, ensure alignment with learning objectives and the principles of the learning.futures strategy.
Content (topics)
- Mindful judgment and ethical decision making
- The psychology of judgment and decision making
- Models of decision making (descriptive, statistical, and optimization models will be covered)
- Heuristics and biases
- The social side of judgment and decision making
- Data-driven and AI-assisted decision making
Assessment
Assessment task 1: Case Study
| Intent: | To apply frameworks you have learned from class to analyze and address real-life decision-making problems effectively |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | 2500 to 3000 words |
Assessment task 2: Decision-Making
| Intent: | To enhance your understanding of the underlying logic and theory behind the frameworks introduced in class |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 40% |
| Length: | 4000 to 4500 words |
Assessment task 3: Group Decision-Making
| Intent: | To leverage your individual expertise in specific areas to make collaborative decisions as a part of a team |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Presentation |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
| Length: | 3000 to 3500 words |
| Criteria: | Demonstrated knowledge and understanding of subject content |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
References
[1] Carmody-Bubb, M. (2023). Cognition and Decision Making in Complex Adaptive Systems: The Human Factor in Organizational Performance. Springer Nature.
[2] Kochenderfer, M. J. (2015). Decision making under uncertainty: theory and application. MIT press.
Other resources
Canvas
The subject is supported by Canvas https://canvas.uts.edu.au
Substantive subject advice will be provided progressively throughout the study session via the subject’s Canvas site. Access to the site will be available to enrolled students at the beginning of the session. Students enrolling after the start of the session will experience a delay before access is granted.
The site is intended to be your resource and web interface. Please note that UTS prides itself as a place of learning and tolerance. The University will take action to protect its reputation in this regard. Student behaviour within the Canvas virtual portal should be in keeping with appropriate behaviour anywhere on the campus. Please be aware that the University and the lecturer monitor the site and that the software supports extensive traceability of activity.