Applications of artificial intelligence (AI) models for management decision making under uncertainty - Festus Oderanti

Business and Decisions under Uncertainty
Examples of AI in Business
Artiﬁcial Intelligence (AI) Techniques
Experiential Teaching and Learning Strategies for AI
Conclusion
Further Reading
Applications of Artiﬁcial Intelligence (AI)
Models for Management Decision Making
under Uncertainty
Festus Oderanti
April 2014
Festus Oderanti AI for Management Decision Making under Uncertainty

Conclusion
Further Reading
Outline
1 Business and Decisions under Uncertainty
2 Examples of AI in Business
Success Stories of AI in Business
3 Artiﬁcial Intelligence (AI) Techniques
What is AI Artiﬁcial Intelligence
Examples of AI: Fuzzy Logic
Difference between binary and fuzzy sets
4 Experiential Teaching and Learning Strategies for AI
ELT and AI: A Case Study of BMG204
Resistance to AI
ETL as Motivating Strategies
Outcomes
5 Conclusion
6 Further Reading

Conclusion
Further Reading
Introduction: Business and Decisions
Effective and efﬁcient strategic
decision making is the backbone
for the success of a business
organisation among its
competitors.
Results of these decision making
processes grossly determine
whether the business will
continue to survive or not.
NTRODUCTION: BUSINESS AND DECISIONS
Effective and efficient strategic
decision making is the
backbone for the success of a
business organisation among
its competitors.
Results of these decision
making processes grossly
determine whether the
business will continue to
survive or not. Source: www.the-ceo-coach.com

Conclusion
Further Reading
Business and Decisions Cont..
In an uncertain economy, everybody has a responsibility to
update and enhance their skills.
Therefore management students needs to be aware of the
complexity of the business decision processes.
There are several uncertainties in business that
entrepreneurs struggle with when making decisions.

Conclusion
Further Reading
Research has proven that these
uncertainties could be effectively
and efﬁciently grasped using
artiﬁcial intelligence (AI)
techniques.
These quantitative decision
techniques are also quicker and
cheaper than the qualitative
methods!
Source: watchdog.org

Conclusion
Further Reading
Examples of AI in Business: Finance Mgmt
Bank use AI systems to organize
operations, invest in stocks, and
manage properties.
In August 2001, robots beat
humans in a simulated financial
trading competition.
Financial institutions have long
used artificial neural network
systems to detect charges or
claims outside of the norm,
flagging these for human
investigation.
XAMPLES OF AI IN BUSINESS: FINANCE
MGMT
Bank use AI systems to organize
operations, invest in stocks, and
manage properties.
n August 2001, robots beat humans
n a simulated financial trading
competition.
Financial institutions have long used
artificial neural network systems to
detect charges or claims outside of
the norm, flagging these for human
nvestigation. Source: deweymonster.com

Conclusion
Further Reading
Online and Telephone Customer Service
AI is implemented in automated
online assistants that can be
seen as avatars on web pages.
It can avail for enterprises to
reduce their operation and
training cost.
A major underlying technology to
such systems is natural language
processing.
AI used in answering machines
of call centres e.g speech
recognition software
EXAMPLES IN BIZ CONT: ONLINE AND
TELEPHONE CUSTOMER SERVICE
AI is implemented in automated
online assistants that can be seen as
avatars on web pages.
It can avail for enterprises to reduce
their operation and training cost.
A major underlying technology to
such systems is natural language
processing.
AI used in answering machines of call
centres e.g speech recognition
software
Source: wikimedia.org

Conclusion
Further Reading
Examples of AI in Biz: Hospital Mgmt
Many clinics use AI to organize
bed schedules, staff rotation, and
provide medical information.
Artiﬁcial neural networks are
used as clinical decision support
systems for medical diagnosis,
Other tasks include:
Computer-aided interpretation
of medical images
Heart sound analysis
MPLES OF AI IN BIZ: HOSPITAL
MGMT
inics use AI to organize bed
es, staff rotation, and provide
information.
l neural networks are used
al decision support
s for medical diagnosis,
asks include:
uter‐aided interpretation of
cal images
sound analysis
Source: casaresforeignresident.blogspot.com

Conclusion
Further Reading
Examples in Biz Cont: Others.
Websites like LinkedIn and Google use AI and analytics to
make behind-the-scenes connections among proﬁles.
Tech companies like IBM, Intel, and HP use AI and
analytics to strengthen management,
improve the way they manufacture, and reduce resource
use.
The U.S. Army uses AI and analytics to plan the delivery of
supplies and to ﬁght terrorism.
Relief agencies use AI and analytics to plan for disasters
When disaster strikes, to rush food and medicine to those
in need

Conclusion
Further Reading
Other Application of AI in Biz.
Data Mining
Automation of Distribution
Marketing Analysis
Pattern Classiﬁcation
Sales forecasting
Social media
Environmental work,
Product development
Advertising/marketing
Others

Conclusion
Further Reading
Success Stories of AI in Business.
One of the great success stories
involves work done by the
operations research (OR) group
at American Airlines (AA).
In 1982, Thomas Cook joined a
group of 12 OR analysts
Today the OR group is called
Sabre and employs 10,000
professionals worldwide.
SUCCESS STORIES OF AI IN
BUSINESS.
One of the great success stories
nvolves work done by the
operations research (OR) group
at American Airlines (AA).
n 1982, Thomas Cook joined a
group of 12 OR analysts
Today the OR group is called
Sabre and employs 10,000
professionals worldwide. Source: flytobarcelona.org

Conclusion
Further Reading
Success Stories of AI in Business Cont.
The group used AI techniques to
determine how many seats to sell
at discounts and full fare.
This revenue management
system at AA generates nearly
$1 billion annually in incremental
revenue.
Today, virtually every airline uses
some sort of revenue mgmt
system
The cruise, hotel, and car rental
industries etc
SUCCESS STORIES OF AI IN BUSINESS
CONT.
The group used AI techniques to
determine how many seats to sell at
discounts and full fare.
This revenue management system
at AA generates nearly $1 billion
annually in incremental revenue.
Today, virtually every airline uses
some sort of revenue mgmt system
The cruise, hotel, and car rental
industries etc Source: holidayhypermarket.co.uk

Conclusion
Further Reading
What is artiﬁcial intelligence?
AI is the human-like intelligent
models for decision making.
It deals with making intelligent
models
Examples of AI techniques:
Fuzzy logic models
Neural networks models
Bayesian networks
Game theory
Others AI techniques
Any combination of the above
ARTIFICIAL INTELLIGENCE (AI)
TECHNIQUES .
hat is artificial intelligence?
AI is the human‐like intelligence models
for decision making.
It deals with making intelligent models
amples of AI techniques:
Fuzzy logic models
Neural networks models
Bayesian networks
Game theory
Others AI techniques
Any combination of the above
Source:wordpress.com

Conclusion
Further Reading
Examples of AI: Fuzzy Logic concepts
Workshop:
How would you rate the present level of inﬂation in the
country?
e.g Very high, high, medium, low, very low etc?
Similarly, how would you rate the Interest rate?
Also how would you rate the room temperature?
How many people would you classify as “tall" in this room?
Please take a note and write down you answer and the
basis for your judgment

Conclusion
Further Reading
Example of AI Techniques: Fuzzy Logic Concepts
Definition
Fuzzy logic is (FL) a problem solving technique introduced to
deal with vague or imprecise problems
Definition
A fuzzy set is a set containing elements that have varying
degree of memberships in the set
Definition
Provides a framework that attempts to define a natural way of
dealing with problems

Conclusion
Further Reading
A key difference between crisp and fuzzy sets is their
membership functions.
Binary logic only admits the opposites of true and false
It does not admit degrees of truth in between the extremes
However, uncertainty permeates all scientiﬁc endeavours
E.g we can easily assess when someone is over 1.8m tall
For example, if “tall" is a set deﬁned as heights (h) ≥ 1.8m

Conclusion
Further Reading
Difference between Binary and Fuzzy Sets cont
For example, if “tall" is a set deﬁned as heights (h) ≥ 1.8m
In a binary sense, the person either is or is not: [0, 1], T/F
A computer would not recognize Mr B with h = 1.78 as
being a member of the set “tall"
The degree to which Mr B approaches h of 1.8m is fuzzy
Classical set objects satisfy precise properties of
membership
Fuzzy set objects satisfy imprecise properties of
membership

Conclusion
Further Reading
Difference between binary and fuzzy sets cont.DIFFERENCE BETWEEN BINARY AND
FUZZY SETS CONT.
Degree of
membership of “tall
men”
Name Height
(m)
Crisp
(binary)
Fuzzy
Harry 2.2 1 1.00
Bill 2.1 1 1.00
Barrack 1.9 1 1.00
Bush 1.8 1 1.00
Peter 1.7 0 0.94
Kate 1.2 0 0.66
William 1.1 0 0.61
Festus 0.2 0 0.11
• The elements of the fuzzy set “tall men” are all men, but their degrees
of membership depend on their height as shown in the table.
Source: www.doc.ic.ac.uk
DIFFERENCE BETWEEN BINARY AND
FUZZY SETS CONT.
Degree of
membership of “tall
men”
Name Height
(m)
Crisp
(binary)
Fuzzy
Harry 2.2 1 1.00
Bill 2.1 1 1.00
Barrack 1.9 1 1.00
Bush 1.8 1 1.00
Peter 1.7 0 0.94
Kate 1.2 0 0.66
William 1.1 0 0.61
Festus 0.2 0 0.11
Source: www.doc.ic.ac.uk
The elements of the fuzzy set “tall men" are all men, but their degrees
of membership depend on their height as shown in the table.

Conclusion
Further Reading
Difference between binary and fuzzy sets cont.
Instead of just tall or not, true or
false, 0 or 1 in binary logic, fuzzy
logic permits degree of tallness
E.g: very very tall, very tall, tall,
medium, slighly short, short, very
short, very very short, etc
DIFFERENCE BETWEEN
BINARY AND FUZZY SETS CONT.
Instead of just tall or
not, true or false, 0 or
1 in binary logic, fuzzy
logic permits degree of
tallness
E.g very very tall, very
tall, tall, medium, slighly
short, short, very short,
very very short, etc
• The elements of the fuzzy set
“tall men” are all men, but their
degrees of membership depend
on their height.
Source: metafilter.com

Conclusion
Further Reading
Difference between binary and fuzzy sets cont.
Fuzzy decision making system
(FDMS) uses a collection of fuzzy
membership functions and
decision rules
Rules solicited from experts in
the ﬁeld to reason about data.
Examples of fuzzy rules will be
shown in next slide
FUZZY DECISION MAKING
SYSTEM
Fuzzy decision making
system (FDMS) uses a
collection of fuzzy
membership functions and
decision rules
Rules solicited from experts
in the field to reason about
data.
Examples of fuzzy rules will
be shown in next slide
Figure: Fuzzy decision making system
Input
(crisp)
Fuzzific
ation
process
Fuz
zy
Deci
sion
Defuzzif
ication
process
Output
(crisp)
Fuzzy
Rule
Base

Conclusion
Further Reading
Example of Fuzzy Rules in Wage Increase Negotiation
Decisions
IF (Inflation is Very Low) AND (Profit is Low) THEN (Wage
Increase is Very Low)
IF (Inflation is High) AND (Profit is Medium) THEN (Wage
Increase is High)
IF (Inflation is High) AND (Profit is High) THEN (Wage
Increase is Medium)

Conclusion
Further Reading
Resistance to AI
Outcomes
Experiential Teaching and Learning (ETL) Strategies of AI to Mgmt. Students
The word experiential essentially
means learning & development
are achieved through personally
determined experience and
involvement rather than
“chalk-and-talk"
(businessballs.com).
ETL also regarded as growing a
person from the inside, whereas
conventional teaching and
training is the transfer of
capability into a person from
outside.
EXPERIENTIAL TEACHING AND
LEARNING (ETL) STRATEGIES OF AI
TO MGMT. STUDENTS
The word experiential essentially
means learning & development
are achieved through personally
determined experience and
involvement rather than 'chalk‐
and‐talk' (businessballs.com).
ETL also regarded as growing a
person from the inside, whereas
conventional teaching and
training is the transfer of
capability into a person from
outside.
Figure: The Experiential Leaning Cycle
and Basic Learning Styles
Source: (Kolb, 1984).

Conclusion
Further Reading
Resistance to AI
Outcomes
Experiential Teaching and Learning (ETL) Strategies of AI to Mgmt. Students Cont.
Most mgmt students have natural
tendency to resist AI.
They believe that AI is too
technical for them and therefore
should be restricted to Science
and Eng. students
ELT helps to motivate mgmt
students in learning and
developing AI skills
EARNING (ETL) STRATEGIES FOR
INTRODUCING AI TO MGMT.
STUDENTS
ost mgmt students have
atural tendency to resist AI.
hey believe that AI is too
chnical for them and
herefore should be restricted to
cience and Eng. students
LT helps to motivate mgmt
udents in learning and
eveloping AI skills
Source: www.rottenecards.com

Conclusion
Further Reading
Resistance to AI
Outcomes
ETL Strategies of AI to Mgmt. Students: A Case Study of BMG204
Sample AI assignment
(coursework):
To prepare a business report
on how fuzzy logic and its
concepts could be used to
capture economic uncertainty
in the nation’s budget
preparation for the year 2015.
This involve other AI tasks and
models development
Other information and
necessary details were given to
the student.
STRATEGIES OF AI TO MGMT. STUDENTS: A
CASE STUDY OF BMG204
AI assignment/coursework:
epare a business report on how
y logic and its concepts could
ed to capture economic
rtainty in the nation’s budget
aration for the year 2015.
involve other AI tasks and
ls development
r information and necessary
ls were given to the student.
Source: rakadd.wordpress.com

Conclusion
Further Reading
Resistance to AI
Outcomes
Sample Complaints from the Students
“..most people in professional ﬁeld don’t use
the fuzzy logic concept because it is too hard
for them to understand, so I am concerned to
how business students are supposed to
understand it.." Student A.
“..I am slightly worried the coursework we
have been assigned is seeming problematic.."
Student B
“..I don’t really know how fuzzy logic or AI is
relevant to business because I was told that it
is only used in engineering.." Student C
“..I feel that Festus has a very high
expectation of our work and I m worried I will
not achieve my desired grade..".
“Many of us feel slightly overwhelmed with the
level of knowledge expected from us.."
SAMPLE COMPLAINTS FROM THE
STUDENTS
..most people in professional field don’t
use the fuzzy logic concept because it is
oo hard for them to understand, so I am
oncerned to how business students are
upposed to understand it.. Student A”.
…I am slightly worried the coursework we
have been assigned is seeming
problematic.. Student B”
…I don’t really know how fuzzy logic or AI
s relevant to business because I was told
hat it is only used in engineering..
Student C”
…I feel that Festus has a very high
expectation of our work and I m worried I
will not achieve my desired grade..”.
…Many of us feel slightly overwhelmed
with the level of knowledge expected from
us.."
Source:www.neatorama.com

Conclusion
Further Reading
Resistance to AI
Outcomes
RE: Students Resistance to AI
Workshop:
In your opinion, what are the best strategies to address the
students’ concerns in the previous slide?
Strategy 1?
Strategy 2?
Strategy 3?
Strategy 4?
Strategy 5?

Conclusion
Further Reading
Resistance to AI
Outcomes
The Workable ETL Strategies to Motivate Mgmt Students on AI
Learner is central
Facilitation must be light and subtle
Find/create experiential learning opportunities
Reactions to experiences vary so don’t pre-judge
Single events can enable several different learning effects
The activity must be real and engaging - not based on
artiﬁcial impact
Ensure activities allow adequate and meaningful reviews
Use stimulating questions in reviews, especially for groups
discussions
Resist temptation to give answers - ask questions only
Have faith in people’s ability to learn for themselves
Getting started

Conclusion
Further Reading
Resistance to AI
Outcomes
Sample Students Feedback after above ETL Strategies
“..I just wanted to take this opportunity to thank you for
spending time last Wednesday and putting on an extra
tutorial. It was really helpful.." Student A
“..Thank you very much for organising the tutorial tonight, it
clariﬁed things up.." Student B.
“..Thank you for making time for your extra tutorial it was
very useful and cleared up the uncertainties that I was
having.." Student C..
“..Thank you for organising the tutorial this evening, my
colleagues and I are currently in the library with a much
better understanding.." Student D.

Conclusion
Further Reading
Resistance to AI
Outcomes
Results!
More than 10% of students
scored above 70% mark
Four of them scored 80%
How? Why?
Because despite UG, the reports seem
qualify (though with slight modiﬁcation)
for presentation at relevant AI
conferences!
Many performed beyond my
expectation.
Majority of the grades fall within
55% to 65%
Less than 12% scored between
40% and 50%
RESULTS!
More than 10% of students
scored above 70% mark
Four of them scored >= 80%
How? Why?
Because despite UG, the reports
seem qualify (though with slight
modification) for presentation at
relevant AI conferences!
Many performed beyond my
expectation.
Majority of the grades fall within
55% to 65%
Less than 12% scored between
Source: carmarthenshireyouth.co.uk

Conclusion
Further Reading
Resistance to AI
Outcomes
Examples of BMG204 Students’ Models
EXAMPLES OF BMG204 MODELS
• Examples of AI models developed by BMG204 students on budget
preparation using Matlab.
EXAMPLES OF BMG204 MODELS
• Examples of AI models developed by BMG204 students on budget
preparation using Matlab.
Examples of AI models developed by BMG204 students on
budget preparation using Matlab.

Conclusion
Further Reading
Conclusion
There is a strong evidence that business mgmt students
may attempt to resist AI techniques and related concepts.
However, adopting appropriate experiential teaching and
learning strategies would help to motivate them to embrace
the concepts.
The management students could perform better than their
science students and engineering counterparts
They have proved to be more competent in applying AI in
exciting ways to real-world situations and everyday
business decision making.

Conclusion
Further Reading
Further Reading
Festus Oderanti, Feng Li and Philippe De Wilde, (2012),
Application of Strategic Fuzzy Games to Wage Increase
Negotiation & Decision Problems, Jour. of Expert Systems
with Applications, 2012 39(12), 11103-11114 .
Festus Oderanti and Philippe De Wilde (2011); Automatic
Fuzzy Decision Making System with Learning for
Competing & Connected Businesses, Expert Systems with
Applications 2011, 38(12), 14574-14584.
Festus Oderanti and Philippe De Wilde (2010); Dynamics
of Business Games with Management of Fuzzy Rules for
Decision Making, 2010, International Journal of Production
Economics, 128 (1): 96-109.
www.businessballs.com

Conclusion
Further Reading
The End
Thank you!
Any Questions?
THANK YOU!
Source: orlanisheaghdha.com

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