The document describes decision analysis and decision making. It discusses identifying the problem, formulating a model, analyzing the model, testing results, and implementing solutions. It also discusses sources of errors like anchoring and framing biases. Anchoring occurs when people rely too heavily on the first piece of information when making decisions. Framing refers to how the options are presented, which can influence choices. The document provides examples to illustrate these concepts and emphasizes the importance of focusing on the consequences of choices rather than how problems are framed.
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
This presentation teaches the concept of Statistical Decision Theory.
Details of this is given here: http://kindsonthegenius.blogspot.hu/2017/12/basics-of-decision-theory.html
Watch the Video here: https://youtu.be/HSc31v67590
Table of Content
What is decision theory?
Application of Decision Theory – Cancer Diagnosis
Formal definition
False positives/False negatives
Minimizing misclassification
Reducing Expected Loss
Introduction to ROC
5.DECISION MAKING PROCESS :-
Recognizing & defining the situation
Identifying the alternatives
Evaluating the alternatives
Apply the model
Selecting the best alternatives
Conduct a sensitivity of the solution
Implementing the chosen alternatives
Following up & evaluating the result
6.TYPE OF DECISION MAKING ENVIRONMENT
Decision making under certainty
Decision making under uncertainty
Decision making under risk
23.DECISION TREE :
Instances describable by attribute-value pairs
e.g Humidity: High, Normal
Target function is discrete valued
e.g Play tennis; Yes, No
Disjunctive hypothesis may be required
e.g Outlook=Sunny Wind=Weak
Possibly noisy training data
Missing attribute values
Application Examples:
Medical diagnosis
Credit risk analysis
Object classification for robot manipulator (Tan 1993)
25.Bayesian analysis
26.Utility theory :
Step for determine the utility for money :
Develop a payoff table using monetary values
Identify the best and worst payoff value
For every other monetary value in the original payoff table
Convert the payoff table from monetary value to calculate utility value.
Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This presentation teaches the concept of Statistical Decision Theory.
Details of this is given here: http://kindsonthegenius.blogspot.hu/2017/12/basics-of-decision-theory.html
Watch the Video here: https://youtu.be/HSc31v67590
Table of Content
What is decision theory?
Application of Decision Theory – Cancer Diagnosis
Formal definition
False positives/False negatives
Minimizing misclassification
Reducing Expected Loss
Introduction to ROC
5.DECISION MAKING PROCESS :-
Recognizing & defining the situation
Identifying the alternatives
Evaluating the alternatives
Apply the model
Selecting the best alternatives
Conduct a sensitivity of the solution
Implementing the chosen alternatives
Following up & evaluating the result
6.TYPE OF DECISION MAKING ENVIRONMENT
Decision making under certainty
Decision making under uncertainty
Decision making under risk
23.DECISION TREE :
Instances describable by attribute-value pairs
e.g Humidity: High, Normal
Target function is discrete valued
e.g Play tennis; Yes, No
Disjunctive hypothesis may be required
e.g Outlook=Sunny Wind=Weak
Possibly noisy training data
Missing attribute values
Application Examples:
Medical diagnosis
Credit risk analysis
Object classification for robot manipulator (Tan 1993)
25.Bayesian analysis
26.Utility theory :
Step for determine the utility for money :
Develop a payoff table using monetary values
Identify the best and worst payoff value
For every other monetary value in the original payoff table
Convert the payoff table from monetary value to calculate utility value.
Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Page 1 of 7 Fin516 201830 Assessment 2 Value 30.docxkarlhennesey
Page 1 of 7
Fin516 201830 Assessment 2
Value: 30%
Due date: 17-April-2018
Submission method options: Turitin
Task – Answer all questions.
Question 1 45 marks
Corning Limited, one of the leading glass producers, is currently evaluating a potential new
product; a tough but light curved glass designed for unusually shaped tall buildings and office
blocks. The new product would cost more than their usual commercial exterior glasses and in
market research conducted by the company’s research department at a cost of $650,000 it
was judged superior to various competing products. Monica Andrews, the Chief Financial
Officer, must analyse this project, along with other potential investments, and then present
her findings to the company’s executive committee.
The project will require construction of a new plant that would have an annual capacity of
75,000 tons and will cost an estimated $18,500,000 to build. The estimated purchase cost of
machinery is $13,500,000 but shipping costs to move the machinery to the plant would total
$425,000 and the estimated installation charge is another $575,000.The land on which the
plant will be built has been vacant since it was bought three years ago at a cost of $750,000,
which has already been paid and expensed for tax purposes.
The company expects its new plant to produce 65,000 tons of glass per year and the
management of Corning anticipates they can sell the product at $350 per ton allowing the
company to gain 10% market share in the first year of operation. Fixed costs are expected to
average $12,500,000 annually while variable costs are estimated to be around $125 per ton.
The plant and machinery will be depreciated to zero on a straight-line basis over ten years,
with an estimated sale value of $1,000,000 after 10 years at the end of the project.
Corning requires 11.5% return on the project. The company tax rate is 30%.
Now assume that you are an assistant to Monica and she has asked you to analyse the project
and then to present your findings to her. Therefore,
a. Calculate the project’s incremental cash flow for each year and present in a tabular form.
15 marks
b. Using the incremental cash flows calculate the project’s:
i. Payback Period 1.5 marks
ii. Net Present Value 5 marks
iii. Profitability Index 2 marks
iv. Discounted Payback Period 1.5 marks
v. Internal Rate of Return using interpolation method 7 marks
c. Identify and discuss any further information that you may requ ...
You can use a calculator to do numerical calculations. No graphing.docxjeffevans62972
You can use a calculator to do numerical calculations. No graphing calculator is allowed. Please DO NOT USE ANY COMPUTER SOFTWARE to solve the problems.
1. (a) What is an assignment problem? Briefly discuss the decision variables, the objective function and constraint requirements in an assignment problem. Give a real world example of the assignment problem.
(b) What is a diet problem? Briefly discuss the objective function and constraint requirements in a diet problem. Give a real world example of a diet problem.
(c) What are the differences between QM for Windows and Excel when solving a linear programming problem? Which one you like better? Why?
(d) What are the dual prices? In what range are they valid? Why are they useful in making recommendations to the decision maker? Give a real world example.
Answer Questions 2 and 3 based on the following LP problem.
Let P1 = number of Product 1 to be produced
P2 = number of Product 2 to be produced
P3 = number of Product 3 to be produced
P4 = number of Product 4 to be produced
Maximize 80P1 + 100P2 + 120P3 + 70P4 Total profit
Subject to
10P1 + 12P2 + 10P3 + 8P4 ≤ 3200 Production budget constraint
4P1 + 3P2 + 2P3 + 3P4 ≤ 1000 Labor hours constraint
5P1 + 4P2 + 3P3 + 3P4 ≤ 1200 Material constraint
P1 > 100 Minimum quantity needed for Product 1 constraint
And P1, P2, P3, P4 ≥ 0 Non-negativity constraints
The QM for Windows output for this problem is given below.
Linear Programming Results:
Variable
Status
Value
P1
Basic
100
P2
NONBasic
0
P3
Basic
220
P4
NONBasic
0
slack 1
NONBasic
0
slack 2
Basic
160
slack 3
Basic
40
surplus 4
NONBasic
0
Optimal Value (Z)
34400
Original problem w/answers:
P1 P2 P3 P4 RHS Dual
Maximize
80 100 120 70
Constraint 1
10 12 10 8 <= 3200 12
Constraint 2 4 3 2 3 <= 1000 0
Constraint 3 5 4 3 3 <= 1200 0
Constraint 4 1 0 0 0 >= 100 -40
Solution
-> 100 0 220 0 Optimal Z-> 34400
Ranging Results:
Variable
Value
Reduced Cost
Original Val
Lower Bound
Upper Bound
P1
100
0
80
-Infinity
120
P2
0
44
100
-Infinity
144
P3
220
0
120
87.5
Infinity
P4
0
26
70
-Infinity
96
Constraint
Dual Value
Slack/Surplus
Original Val
Lower Bound
Upper Bound
Constraint 1
12
0
3200
1000
3333.333
Constraint 2
0
160
1000
840
Infinity
Constraint 3
0
40
1200
1160
Infinity
Constraint 4
-40
0
100
0
120
2. (a) Determine the optimal solution and optimal value and interpret their meanings.
(b) Determine the slack (or surplus) value for each constraint and interpret its meaning.
3. (a) What are the ranges of optimali.
7.12Chapter 7 Problem 12a). Complete the spreadsheet below by esti.docxalinainglis
7.12Chapter 7 Problem 12a). Complete the spreadsheet below by estimating the project's annual after tax cash flow.b). What is the investment's net present value at a discount rate of 10 percent?c). What is the investment's internal rate of return?d). How does the internal rate of return change if the discount rate equals 20 percent?e). How does the internal rate of return change if the growth rate in EBIT is 8 percent instead of 3 percent?Facts and AssumptionsEquipment initial cost $$ 350,000Depreciable life yrs.7Expected life yrs.10Salvage value $$0Straight line depreciationEBIT in year 128,000Tax rate38%Growth rate in EBIT3%Discount rate10%Year012345678910Initial cost350,000Annual depreciation50,00050,00050,00050,00050,00050,00050,000EBIT28,00028,84029,70530,59631,51432,46033,43334,43635,47036,534Net present value @ 10%Internal rate of return
7.13Chapter 7 Problem 13In many financial transactions, interest is computed and charged more than once a year. Interest on corporate bonds, for example, is usually payable every six months. Consider a loan transaction in which interest is charged at the rate of 1 percent per month. Sometimes such a transaction is described as having an interest rate of 12 percent per annum. More precisely, this rate should be described as a nominal 12 percent per annum coumpounded monthly.Clearly, it is desirable to recognize the difference between 1 percent per month compounded monthly and 12 percent per annum compounded annually. If $1,000 is borrowed with interest at 1 percent per month compounded monthly, the amount due in one year is:F = $1,000(1.01)12 = $1,000(1.1268) = $1,126.80 This compares to F = $1,000(1+.12) =$1,120.00 for annual compounding.Hence, the monthly compounding has the same effect on the year-end amount due as the charging of a rate of 12.68 percent compounded annually. 12.68 percent is referred to as the effective interest rate. To generalize, if interest is compounded m times a year at an interest rate of r/m per compounding period. Then,The nominal interest rate per annum, or the APR = m(r/m) = r.The effective interest rate per annum,or the EAR = (1+r/m)m - 1.Consider a $100,000, 30 year, fixed-rate, 9 percent, home mortgage requiring monthly payments.a. The monthly interest rate on the mortgage is 9%/12 months = .75%. What is the APR on the mortgage?b. What is the EAR on the mortgage?c. The borrower's payment book will look something like the following. Complete the entries for the first 6 months.Outstanding Balance Beginning of MonthMonthly paymentInterest duePrincipal paymentOutstanding Balance End of MonthDate01-31$100,00002-2803-3104-3005-3106-30d. After paying on this mortgage for 15 years, what will be the remaining principal outstanding? e. Suppose after 15 years the borrower has the opportunity to refinance the remaining principal on the mortgage with a new 15-year mortgage carrying an interest rate of 7 1/8%. Refinancing will involve $250 in costs and "points.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. 2
Decision Analysis
• Effective decision-making requires that we understand:
– The nature of the decision that must be made
– The values, goals, and objectives that are relevant to the decision
problem
– The areas of uncertainty that affect the decision
– The consequences of each possible decision
3. 3
A Decision-Making Model
Consider the following five-part decision-making model:
Adapted from Winning Decisions, by Russo and Shoemaker
Identify
Problem
Formulate
and
Implement
Model
Analyze
Model
Test
Results
Implement
Solution
Unsatisfactory Results
Most
important
4. Anchoring and Framing
• Errors in judgment arise due to what
psychologists term anchoring and framing
5. Anchoring
• A seeming trivial factor serves as a starting point
(anchor) for estimations
• Decision makers adjust their estimates but remain too
close to the anchor.
• 2 groups are asked to estimate the value of:
1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 or
8 X 7 X 6 X 5 X 4 X 3 X 2 X 1
• Median estimate of first series was 512
• Median estimate of 2nd series was 2,250
• The order, of course, is meaningless and the answer is
40,320
6. Framing
• Affects how a decision maker perceives the
alternatives in a decision – often involves a
win/loss perspective
• The way the problem is framed influences the
choices
7. Framing Example
• You have been given $1,000 but must choose between the following
alternatives:
a) Receive an additional $500 with certainty or
b) Flip a coin and receive an additional $1,000 if heads or $0 if tails
• a) is a sure win and the choice most people prefer
• Now suppose you are given $2,000 and must choose between
a) Give back $500 immediately or
b) Flip a coin and give back $0 if heads or $1,000 if tails
• When framed this way, alternative a) is a “sure loss” and many people who
previously preferred alternative a) now opt for alternative b) (because it
holds a chance of avoiding a loss)
• However it is clear that that in both cases the a) alternative guarantees a
total payoff of $1,500, whereas b) offers a 50% chance of a $2,000 total
payoff and a 50% chance of $1,000 total payoff.
• A rational decision maker should focus on the consequences of his/her
choices and consistently select the same alternative, regardless of how the
problem is framed
8. 8
A Framing Example
“Careful analysis at a major U.S. steel company showed it
could save hundreds of thousands of dollars per year by
replacing its hot-metal mixing technology, which required that
metal be heated twice, with direct-pouring technology, in
which the metal was only heated once. But the move was
approved only after considerable delay because senior
engineers complained that the analysis did not include the cost
of the hot metal mixers that had been purchased for $3 million
just a few years previously.”
How would you critique this decision?
Adapted from Winning Decisions, by Russo and Shoemaker
9. 9
Decision Trees and Decision Tables
• Often our problem solutions require decisions to be made
according to two or more conditions or combinations of
conditions
• Decision trees represent such decision as a sequence of steps
• Decision tables describe all possible combinations of
conditions and the decision appropriate to each combination
• Levels of uncertainty can also be built into decision trees to
account for the relative probabilities of the various outcomes
10. 10
Decision Tree Showing Possible
Outcomes
Outcome A
Outcome B
Sales Up 10%
Sales Up 15%
Sales Up 5%
Sales Even
A.1
A.2
B.1
B.2
Decision Made
Outcomes
Projected
Sales
Results
11. 11
Decision Tree of Outcomes -- Quantifying Uncertainties
Outcome A
Outcome B
Sales Up 10%
Sales Up 15%
Sales Up 5%
Sales Even
A.1
A.2
B.1
B.2
40%
60%
80%
20%
32%
8%
70%
30%
42%
18%
Decision Made
Projected
Sales
Results
13. Evaluating Your
Decision Tree
Start by assigning a cash value
or score to each possible
outcome. Estimate how much
you think it would be worth to
you if that outcome came
about.
Next look at each circle
(representing an uncertainty
point) and estimate the
probability of each outcome.
If you use percentages, the
total must come to 100% at
each circle. If you use
fractions, these must add up
to 1. If you have data on past
events you may be able to
make rigorous estimates of
the probabilities. Otherwise
write down your best guess. -
14. Calculating Tree Values
• Once you have worked out the value of the outcomes,
and have assessed the probability of the outcomes of
uncertainty, it is time to start calculating the values
that will help you make your decision.
• Start on the right hand side of the decision tree, and
work back towards the left. As you complete a set of
calculations on a node (decision square or uncertainty
circle), all you need to do is to record the result. You
can ignore all the calculations that lead to that result
from then on. –
15. Calculating The Value of
Uncertain Outcome Nodes
Calculating the value of uncertain
outcomes (circles on the
diagram): Multiply the value of
the outcomes by their probability.
The total for that node of the tree
is the total of these values. In the
example in Figure 2, the value for
'new product, thorough
development' is: 0.4 (probability
good outcome) x $1,000,000
(value) = $400,000 0.4 (probability
moderate outcome) x $50,000
(value) = $20,000 0.2 (probability
poor outcome) x $2,000 (value) =
$400 + $420,400 -
16. Calculating the Value
of Decision Nodes
When evaluating a decision node, write down the
cost of each option along each decision line. Then
subtract the cost from the outcome value that you
have already calculated.
This will give you a value that represents the benefit
of that decision. Note that amounts already spent do
not count for this analysis – these are 'sunk costs' and
(despite emotional counter-arguments) should not be
factored into the decision.
When you have calculated these decision benefits,
choose the option that has the largest benefit, and
take that as the decision made.
This is the value of that decision node.
Figure 4 shows this calculation of decision nodes in
our example:
In this example, the benefit we previously calculated
for 'new product, thorough development' was
$420,400. We estimate the future cost of this
approach as $150,000. This gives a net benefit of
$270,400. The net benefit of 'new product, rapid
development' was $31,400. On this branch we
therefore choose the most valuable option, 'new
product, thorough development', and allocate this
value to the decision node.
17. Result
By applying this technique we
can see that the best option is
to develop a new product. It is
worth much more to us to take
our time and get the product
right, than to rush the product
to market.
However, if we decide to
consolidate, It is better just to
improve our existing products
than to botch a new product
18. 18
Example of Using a Decision Tree or
Table to Capture Complex Business
Logic
Consider the following excerpt from an actual business
document:
If the customer account is billed using a fixed rate method, a minimum
monthly charge is assessed for consumption of less than 100 kwh.
Otherwise, apply a schedule A rate structure. However, if the account is
billed using a variable rate method, a schedule A rate structure will apply to
consumption below 100 kwh, with additional consumption billed according
to schedule B.
19. Articulating Complex Business Rules
Complex business rules, such as our example, can become
rather confusing
Capturing such rules in text form alone can lead to ambiguity
and misinterpretation
As an alternative, it is often wise to capture such rules in
decision tress or decision tables
The examples on the following slides will illustrate this
technique
20. 20
Decision Tree for this Example
fixed
rate
billing
variable
rate
billing
< 100 kwh
>= 100 kwh
< 100 kwh
>= 100 kwh
minimum charge
schedule A
schedule A
?
21. 21
Decision Tree for this Example
< 100 kwh
>= 100 kwh
< 100 kwh
>= 100 kwh
minimum charge
schedule A
schedule A
schedule A on
first 99 kwh
schedule B on
kwh 100 and above
fixed
rate
billing
variable
rate
billing
22. 22
Decision Table for Example – Version 1
Conditions 1 2 3 4 5
Rules
Fixed rate acct T T F F F
Variable rate acct F F T T F
Consumption < 100 kwh T F T F
Consumption >= 100 kwh F T F T
Minimum charge X
Schedule A X X
Schedule A on first 99 kwh, X
Schedule B on kwh 100 +
Actions
Is this a
valid
business
case? Did
we miss
something?
23. 23
Decision Table for Example – Version 2
Conditions 1 2 3 4
Rules
Account type fixed fixed variable variable
Consumption < 100 >=100 <100 >= 100
Minimum charge X
Schedule A X X
Schedule A on first 99 kwh, X
Schedule B on kwh 100 +
Actions
24. 24
Activity
Consider the following description of a company’s matching retirement
contribution plan:
Acme Widgets wants to encourage its employees to save for retirement.
To promote this goal, Acme will match an employee’s contribution to the
approved retirement plan by 50% provided the employee keeps the
money in the retirement plan at least two years. However, the company
limits its matching contributions depending on the employee’s salary and
time of service as follows. Acme will match five, six, or seven percent of
the first $30,000 of an employee's salary if he or she has been with the
company for at least two, five, or ten years respectively. If the employee
has been with the company for at least five years, the company will
match up to four percent of the next $25,000 in salary and three percent
of any excess. Ten-year plus workers get a five percent match from
$30,000 to $55,000. Long-term service employees (fifteen years or
more) get seven percent on the first $30,000 and five percent after that.
25. 25
Team Activity (cont’d)
1) Do one of the following tasks according to your instructor’s directions:
a) Create a decision tree that captures the business rules in this
policy.
b) Create a decision table that captures the business rules in this
policy.
2) Did your analysis uncover any questions, ambiguities, or missing
rules?
3) If so, do you think these would be as easy to spot and to analyze
using only the narrative description of this policy?
27. Developing More Complex Decision
Tables
In the 1950's General Electric, the Sutherland
Corporation, and the United States Air Force
worked on a complex file maintenance project,
using flowcharts and traditional narratives, they
spent six labor-years of effort but failed to define
the problem.
It was not until 1958, when four analysts using
decision tables, successfully defined the problem
in less than four weeks1.
_____________________________________
1Taken from “A History of Decision Tables” located at
http://www.catalyst.com/products/logicgem/overview.html
28. Steps to create a decision table
1. List all the conditions which determine which action to take.
2. Calculate the number of rules required.
– Multiple the number of values for each condition by each other.
• Example: Condition 1 has 2 values, Condition 2 has 2 values, Condition
3 has 2 values. Thus 2 X 2 X 2 = 8 rules
3. Fill all combinations in the table.
4. Define the action for each rule
5. Analyze column by column to determine which actions are
appropriate for each rule.
6. Reduce the table by eliminating redundant columns.
29. <cond-1> F T F T F T F T … T
<cond-2> F F T T F F T T … T
<cond-3> F F F F T T T T … T
… …
<cond-n> F F F F F F F F … T
<action-1> X X X X
<action-2> X X X X
<action-3> X X X X
… …
<action-m> X X X
Actions
Conditions
All possible combinations
Actions per combination
(each column represents a different state of affairs)
30. Example
• Policy for charging charter flight costumers for
certain in-flight services:2
If the flight is more than half-full and costs more than
$350 per seat, we serve free cocktails unless it is a
domestic flight. We charge for cocktails on all domestic
flights; that is, for all the ones where we serve cocktails.
(Cocktails are only served on flights that are more than
half-full.)
_____________________________________
2 Example taken form: Structured Analysis and System Specification, Tom de Marco,
Yourdon inc., New York,
31. List all the conditions that
determine which action to take.
Conditions Values
The flight more than half-full? Yes (Y), No (N)
Cost is more than $350? Y, N
Is it a domestic flight? Y, N
32. Calculate the space of combinations
Conditions Number of
Combinations
Possible Combinations/ Rules
1 2 Y N
2 4 Y
Y
N
Y
Y
N
N
N
3 8 Y
Y
Y
N
Y
Y
Y
N
Y
N
N
Y
Y
Y
N
N
Y
N
Y
N
N
N
N
N
… …
n 2n
33. Calculate the Number of Rules in Table
• Conditions in the example are 3 and all are
two-valued ones, hence we have:
All combinations are 23 = 8 rules OR
2 X 2 X 2 = 8 rules
34. Fill all rules in the table.
POSSIBLE RULESCONDITONS
more than half-full N N N N Y Y Y Y
more than $350 per
seat
N N Y Y N N Y Y
domestic flight N Y N Y N Y N Y
ACTIONS
35. Analyze column by column to determine which
actions are appropriate for each combination
POSSIBLE RULES
CONDITONS
more than half-full N N N N Y Y Y Y
more than $350 per seat N N Y Y N N Y Y
domestic flight N Y N Y N Y N Y
ACTIONS
serve cocktails X X X X
free X
36. Reduce the table by eliminating
redundant columns.
POSSIBLE COMBINATIONS
CONDITONS
more than half-
full
N N N N Y Y Y Y
more than $350
per seat
N N Y Y N N Y Y
domestic flight N Y N Y N Y N Y
ACTIONS
serve cocktails X X X X
free X
Note that some
columns are identical
except for one condition.
37. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N N N N Y Y Y Y
more than $350
per seat
N N Y Y N N Y Y
domestic flight N Y N Y N Y N Y
ACTIONS
serve cocktails X X X X
free X
Note that some
columns are identical
except for one condition.
Which means that
actions are
independent from
the value of that
particular condition.
38. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N N N N Y Y Y Y
more than $350
per seat
N N Y Y N N Y Y
domestic flight N Y N Y N Y N Y
ACTIONS
serve cocktails X X X X
free X
Note that some
columns are identical
except for one
condition.
Which means that
actions are
independent from
the value of that
particular condition.
Hence, the table
can be simplified.
39. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N N N Y Y Y Y
more than $350
per seat
N Y Y N N Y Y
domestic flight - N Y N Y N Y
ACTIONS
serve cocktails X X X X
free X
First we combine
the yellow ones
nullifying the condition.
40. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N N Y Y Y Y
more than $350
per seat
N Y N N Y Y
domestic flight - - N Y N Y
ACTIONS
serve cocktails X X X X
free X
First we combine the yellow
ones nullifying the condition.
Then the red ones.
41. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N N Y Y Y Y
more than $350
per seat
N Y N N Y Y
domestic flight - - N Y N Y
ACTIONS
serve cocktails X X X X
free X
First we combine the yellow
ones nullifying the condition.
Then the red ones.
Notice that yellow and red
columns are identical but by
one condition.
42. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N Y Y Y Y
more than $350
per seat
- N N Y Y
domestic flight - N Y N Y
ACTIONS
serve cocktails X X X X
free X
First we combine the yellow
ones nullifying the condition.
Then the red ones.
Notice that yellow and red
columns are identical but by one
condition.
So, we combine them.
43. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N Y Y Y
more than $350
per seat
- N Y Y
domestic flight - - N Y
ACTIONS
serve cocktails X X X
free X
First we combine the yellow
ones nullifying the condition.
Then the red ones.
Notice that yellow and red
columns are identical but by one
condition.
So, we combine them.
Then we combine the violet
colored ones.
44. Reduce the table by eliminating
redundant columns.
POSSIBLE RULES
CONDITONS
more than half-
full
N Y Y Y
more than $350
per seat
- N Y Y
domestic flight - - N Y
ACTIONS
serve cocktails X X X
free X
Notice that even when we observe that
the green columns are identical except
for one condition we do not combine
them:
A “NULLIFIED” condition is not the
same as a valued one.
What about this rule? Have we
over looked something?
46. Complex Decision Table Exercise
A company is trying to maintain a meaningful list of customers. The objective is
to send out only the catalogs from which customers will buy merchandise.
The company realizes that certain loyal customers order from every catalog and
some people on the mailing list never order. These customers are easy to
identify. Deciding which catalogs to send to customers who order from only
selected catalogs is a more difficult decision. Once these decisions have been
made by the marketing department, you as the analyst have been asked to
develop a decision table for the three conditions described below. Each
condition has two alternatives (Y or N):
1. Customer ordered from Fall catalog
2. Customer ordered from Christmas catalog
3. Customer ordered from Specialty catalog
The actions for these conditions, as determined by marketing, are described on
the next slide.
46
47. Complex Decision Table Exercise
Customers who ordered from all three catalogs will get the Christmas and
Special catalogs. Customers who ordered from the Fall and Christmas catalogs
but not the Special catalog will get the Christmas catalog. Customers who
ordered from the Fall catalog and the Special catalog but not the Christmas
catalog will get the Special catalog. Customers who ordered only from the
Fall catalog but no other catalog will get the Christmas catalog. Customers
who ordered from the Christmas and Special catalogs but not the Fall catalog
will get both catalogs. Customers who ordered only from the Christmas
catalog or only from the Special catalog will get only the Christmas or Special
catalogs respectively. Customers who ordered from no catalog will get the
Christmas catalog.
1. Create a simplified decision table (or tree) based on the above decision
logic.
47
48. Solution to Decision Table Exercise
Conditions and Actions 1 2 3 4 5 6 7 8
Order from Fall Catalog Y Y Y Y N N N N
Order from Christmas Catalog Y Y N N Y Y N N
Order from Special Catalog Y N Y N Y N Y N
Mail Christmas Catalog X X X X
Mail Special Catalog X X
Mail Both Catalogs X X
The four gray columns can
be combined into a single
rule. Note that for each,
there was NO order placed
from the Special Catalog.
In addition, Rules 1 & 5 and
Rules 3 & 7 can be combined.
Each pair produces the same
action and each pair shares two
common conditions.
49. Solution to Decision Table Exercise~
Final Version
Conditions and Actions 1 2 3
Order from Fall Catalog -- -- --
Order from Christmas Catalog Y -- N
Order from Special Catalog Y N Y
Mail Christmas Catalog X
Mail Special Catalog X
Mail Both Catalogs X
50. “A marketing company wishes to construct a decision
table to decide how to treat clients according to three
characteristics:
Gender, City Dweller, and age group: A (under 30), B
(between 30 and 60), C (over 60).
The company has four products (W, X, Y and Z) to test
market.
Product W will appeal to female city dwellers.
Product X will appeal to young females.
Product Y will appeal to Male middle aged shoppers who
do not live in cities.
Product Z will appeal to all but older females.”
Another Example:
51. The process used to create this decision table is the following:
1. Identify conditions and their alternative values.
There are 3 conditions: gender, city dweller, and age group. Put these into table as 3
rows in upper left side.
Gender’s alternative values are: F and M.
City dweller’s alternative values are: Y and N
Age group’s alternative values are: A, B, and C
2. Compute max. number of rules.
Determine the product of number of alternative values for each condition.
2 x 2 x 3 = 12.
Fill table on upper right side with one column for each unique combination of these
alternative values. Label each column using increasing numbers 1-12 corresponding to
the 12 rules. For example, the first column (rule 1) corresponds to F, Y, and A. Rule 2
corresponds to M, Y, and A. Rule 3 corresponds to F, N, and A. Rule 4 corresponds to M,
N, and A. Rule 5 corresponds to F, Y, and B. Rule 6 corresponds to M, Y, and B and so on.
3. Identify possible actions
Market product W, X, Y, or Z. Put these into table as 4 rows in lower left side.
4. Define each of the actions to take given each rule.
For example, for rule 1 where it is F, Y, and A; we see from the above example scenario
that products W, X, and Z will appeal. Therefore, we put an ‘X’ into the table’s
intersection of column 1 and the rows that correspond to the actions: market product W,
market product X, and market product Z.
52. 1 2 3 4 5 6 7 8 9 10 11 12
Gender F M F M F M F M F M F M
City Y Y N N Y Y N N Y Y N N
Age A A A A B B B B C C C C
Market
W
X X X
MarketX X X
MarketY X
MarketZ X X X X X X X X X X
53. 5. Verify that the actions given to each rule are correct.
6. Simplify the table.
Determine if there are rules (columns) that represent impossible situations.
If so, remove those columns. There are no impossible situations in this
example.
Determine if there are rules (columns) that have the same actions. If so,
determine if these are rules that are identical except for one condition and
for that one condition, all possible values of this condition are present in the
rules in these columns. In the example scenario, columns 2, 4, 6, 7, 10, and
12 have the same action. Of these columns: 2, 6, and 10 are identical except
for one condition: age group. The gender is M and they are city dwellers. The
age group is A for rule 2, B for rule 6, and C for rule 10. Therefore, all possible
values of condition ‘age group’ are present. For rules 2, 6, and 10; the age
group is a “don’t care”. These 3 columns can be collapsed into one column
and a hyphen is put into the age group location to signify that we don’t care
what the value of the age group is, we will treat all male city dwellers the
same: market product Z.
54. Final Decision Table
1 2 3 4 5 6 7 8 9 10
Gender F M F M F M F M F M
City Y Y N N Y N N Y N N
Age A A A B B B C C C
MarketW X X X
MarketX X X
MarketY X
MarketZ X X X X X X X X