ISE 5830
Transcript Abbreviation:
Decision Analysis
Course Description:
Introduction to decision analysis, modern utility theory and risk modeling, Bayesian inference, value of information, multiattribute decision modeling, and application to engineering decisions under uncertainty.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: (ISE 2040.01 or ISE 2040.02) and Stat 3470, or Graduate Standing
Electronically Enforced:
Yes
Exclusions
(N/A)
Course Goals / Objectives:
Have a broad understanding of decision modeling.
'Solve' and analyze a decision problem.
Develop an ability to assess people?s attitudes towards risk, conflicting objectives, and subjective probability assessments.
Use software tools to model and analyze decision problems.
Use Monte Carlo simulation techniques for decision modeling and assessment.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Structuring and modeling decisions | 4.5 | 0.0 | 0.0 | 0 |
| Expected-value decision making | 3.0 | 0.0 | 0.0 | 0 |
| Sensitivity analysis and value of information | 4.5 | 0.0 | 0.0 | 0 |
| Risk and utility modeling | 12.0 | 0.0 | 0.0 | 0 |
| Modeling multiattribute decisions | 7.5 | 0.0 | 0.0 | 0 |
| Subjective probability assessments | 3.0 | 0.0 | 0.0 | 0 |
| Software tools | 4.5 | 0.0 | 0.0 | 0 |
| Monte Carlo simulation | 3.0 | 0.0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Problem sets | 35% |
| Case Study | 15% |
| Midterm Exam | 25% |
| Final Exam | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Making Hard Decisions with Decision Tools | R. Clemens and T. Reilly |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Significant contribution (7+ hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Some contribution (1-2 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Substantial contribution (3-6 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 5 | Some contribution (1-2 hours) | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| 6 | Substantial contribution (3-6 hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
Embedded Literacies Info:
Advanced Writing
1.1 Investigate and integrate knowledge of the subject, context and audience with knowledge1.2 Use of genres, conventions and rhetorical choices to advance a particular writing objective2.2 Reflect on how they adapt rhetorical and research strategies they have learned to contexts2.3 Develop scholarly, creative or professional products that are meaningful to them and their audience
Data Analysis - Quantitative
1.1A Explain basic concepts of statistics and probability1.2A Apply methods needed to analyze and critically evaluate statistical arguments1.3A Recognize the importance of statistical ideas
Data Analysis - Qualitative
This course does not have a Data Analysis – Qualitative Embedded Literacy
Technology
This course does not have Technology – Qualitative Embedded Literacy
Attachments
(N/A)
Additional Notes or Comments
(N/A)