Applications of artificial intelligence (AI) models for management decision making under uncertainty...

Business and Decisions under UncertaintyExamples of AI in Business

Artificial Intelligence (AI) TechniquesExperiential Teaching and Learning Strategies for AI

ConclusionFurther 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




Outline1 Business and Decisions under Uncertainty2 Examples of AI in Business

Success Stories of AI in Business3 Artificial Intelligence (AI) Techniques

What is AI Artificial IntelligenceExamples of AI: Fuzzy LogicDifference between binary and fuzzy sets

4 Experiential Teaching and Learning Strategies for AIELT and AI: A Case Study of BMG204Resistance to AIETL as Motivating StrategiesOutcomes

5 Conclusion6 Further Reading





Introduction: Business and Decisions

Effective and efficient strategicdecision making is the backbonefor the success of a businessorganisation among itscompetitors.Results of these decision makingprocesses grossly determinewhether the business willcontinue to survive or not.

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. Source: www.the-ceo-coach.com





Business and Decisions Cont..

In an uncertain economy, everybody has a responsibility toupdate and enhance their skills.Therefore management students needs to be aware of thecomplexity of the business decision processes.There are several uncertainties in business thatentrepreneurs struggle with when making decisions.






Research has proven that theseuncertainties could be effectivelyand efficiently grasped usingartificial intelligence (AI)techniques.These quantitative decisiontechniques are also quicker andcheaper than the qualitativemethods!


Source: watchdog.org





Success Stories of AI in Business

Examples of AI in Business: Finance Mgmt

Bank use AI systems to organizeoperations, invest in stocks, andmanage properties.In August 2001, robots beathumans in a simulated financialtrading competition.Financial institutions have longused artificial neural networksystems to detect charges orclaims outside of the norm,flagging these for humaninvestigation.

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. Source: deweymonster.com






Online and Telephone Customer Service

AI is implemented in automatedonline assistants that can beseen as avatars on web pages.It can avail for enterprises toreduce their operation andtraining cost.A major underlying technology tosuch systems is natural languageprocessing.AI used in answering machinesof call centres e.g speechrecognition 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






Examples of AI in Biz: Hospital Mgmt

Many clinics use AI to organizebed schedules, staff rotation, andprovide medical information.Artificial neural networks areused as clinical decision supportsystems for medical diagnosis,Other tasks include:

Computer-aided interpretationof medical imagesHeart sound analysis

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 analysisSource: casaresforeignresident.blogspot.com






Examples in Biz Cont: Others.

Websites like LinkedIn and Google use AI and analytics tomake behind-the-scenes connections among profiles.Tech companies like IBM, Intel, and HP use AI andanalytics to strengthen management,

improve the way they manufacture, and reduce resourceuse.

The U.S. Army uses AI and analytics to plan the delivery ofsupplies and to fight terrorism.Relief agencies use AI and analytics to plan for disasters

When disaster strikes, to rush food and medicine to thosein need






Other Application of AI in Biz.

Data MiningAutomation of DistributionMarketing AnalysisPattern ClassificationSales forecastingSocial mediaEnvironmental work,Product developmentAdvertising/marketingOthers






Success Stories of AI in Business.

One of the great success storiesinvolves work done by theoperations research (OR) groupat American Airlines (AA).In 1982, Thomas Cook joined agroup of 12 OR analystsToday the OR group is calledSabre and employs 10,000professionals worldwide.

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. Source: flytobarcelona.org






Success Stories of AI in Business Cont.

The group used AI techniques todetermine how many seats to sellat discounts and full fare.This revenue managementsystem at AA generates nearly$1 billion annually in incrementalrevenue.Today, virtually every airline usessome sort of revenue mgmtsystemThe cruise, hotel, and car rentalindustries 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






Artificial Intelligence (AI) Techniques

What is artificial intelligence?AI is the human-like intelligentmodels for decision making.It deals with making intelligentmodels

Examples of AI techniques:Fuzzy logic modelsNeural networks modelsBayesian networksGame theoryOthers AI techniques

Any combination of the above

ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES .

What is artificial intelligence?AI is the human‐like intelligence 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

Source:wordpress.com






Examples of AI: Fuzzy Logic concepts

Workshop:How would you rate the present level of inflation in thecountry?

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 thebasis for your judgment






Example of AI Techniques: Fuzzy Logic Concepts

DefinitionFuzzy logic is (FL) a problem solving technique introduced todeal with vague or imprecise problems

DefinitionA fuzzy set is a set containing elements that have varyingdegree of memberships in the set

DefinitionProvides a framework that attempts to define a natural way ofdealing with problems






Difference between binary and fuzzy sets

A key difference between crisp and fuzzy sets is theirmembership functions.Binary logic only admits the opposites of true and falseIt does not admit degrees of truth in between the extremesHowever, uncertainty permeates all scientific endeavoursE.g we can easily assess when someone is over 1.8m tallFor example, if “tall" is a set defined as heights (h) ≥ 1.8m






Difference between Binary and Fuzzy Sets cont

For example, if “tall" is a set defined as heights (h) ≥ 1.8mIn a binary sense, the person either is or is not: [0, 1], T/FA computer would not recognize Mr B with h = 1.78 asbeing a member of the set “tall"The degree to which Mr B approaches h of 1.8m is fuzzyClassical set objects satisfy precise properties ofmembershipFuzzy set objects satisfy imprecise properties ofmembership






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

• 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.ukThe elements of the fuzzy set “tall men" are all men, but their degreesof membership depend on their height as shown in the table.






Difference between binary and fuzzy sets cont.

Instead of just tall or not, true orfalse, 0 or 1 in binary logic, fuzzylogic permits degree of tallnessE.g: very very tall, very tall, tall,medium, slighly short, short, veryshort, 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, slighlyshort, 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






Difference between binary and fuzzy sets cont.

Fuzzy decision making system(FDMS) uses a collection of fuzzymembership functions anddecision rulesRules solicited from experts inthe field to reason about data.Examples of fuzzy rules will beshown in next slide

FUZZY DECISION MAKING SYSTEM

Fuzzy decision making system (FDMS) uses a collection of fuzzy membership functions and decision rulesRules 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)

Fuzzificationprocess

FuzzyDecision

Defuzzificationprocess

Output(crisp)

Fuzzy Rule Base






Example of Fuzzy Rules in Wage Increase NegotiationDecisions

IF (Inflation is Very Low) AND (Profit is Low) THEN (WageIncrease is Very Low)IF (Inflation is High) AND (Profit is Medium) THEN (WageIncrease is High)IF (Inflation is High) AND (Profit is High) THEN (WageIncrease is Medium)





ELT and AI: A Case Study of BMG204Resistance to AIETL as Motivating StrategiesOutcomes

Experiential Teaching and Learning (ETL) Strategies of AI to Mgmt. Students

The word experiential essentiallymeans learning & developmentare achieved through personallydetermined experience andinvolvement rather than“chalk-and-talk"(businessballs.com).ETL also regarded as growing aperson from the inside, whereasconventional teaching andtraining is the transfer ofcapability into a person fromoutside.

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 StylesSource: (Kolb, 1984).






Experiential Teaching and Learning (ETL) Strategies of AI to Mgmt. Students Cont.

Most mgmt students have naturaltendency to resist AI.They believe that AI is tootechnical for them and thereforeshould be restricted to Scienceand Eng. studentsELT helps to motivate mgmtstudents in learning anddeveloping AI skills

EXPERIENTIAL TEACHING AND LEARNING (ETL) STRATEGIES FOR

INTRODUCING AI TO MGMT. STUDENTS

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

Source: www.rottenecards.com






ETL Strategies of AI to Mgmt. Students: A Case Study of BMG204

Sample AI assignment(coursework):

To prepare a business reporton how fuzzy logic and itsconcepts could be used tocapture economic uncertaintyin the nation’s budgetpreparation for the year 2015.This involve other AI tasks andmodels developmentOther information andnecessary details were given tothe student.

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 developmentOther information and necessary details were given to the student.

Source: rakadd.wordpress.com






Sample Complaints from the Students

“..most people in professional field don’t usethe fuzzy logic concept because it is too hardfor them to understand, so I am concerned tohow business students are supposed tounderstand it.." Student A.

“..I am slightly worried the coursework wehave been assigned is seeming problematic.."Student B“..I don’t really know how fuzzy logic or AI isrelevant to business because I was told that itis only used in engineering.." Student C“..I feel that Festus has a very highexpectation of our work and I m worried I willnot achieve my desired grade..".

“Many of us feel slightly overwhelmed with thelevel 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 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.."

Source:www.neatorama.com






RE: Students Resistance to AI

Workshop:In your opinion, what are the best strategies to address thestudents’ concerns in the previous slide?

Strategy 1?Strategy 2?Strategy 3?Strategy 4?Strategy 5?






The Workable ETL Strategies to Motivate Mgmt Students on AI

Learner is centralFacilitation must be light and subtleFind/create experiential learning opportunitiesReactions to experiences vary so don’t pre-judgeSingle events can enable several different learning effectsThe activity must be real and engaging - not based onartificial impactEnsure activities allow adequate and meaningful reviewsUse stimulating questions in reviews, especially for groupsdiscussionsResist temptation to give answers - ask questions onlyHave faith in people’s ability to learn for themselvesGetting started






Sample Students Feedback after above ETL Strategies

“..I just wanted to take this opportunity to thank you forspending time last Wednesday and putting on an extratutorial. It was really helpful.." Student A“..Thank you very much for organising the tutorial tonight, itclarified things up.." Student B.“..Thank you for making time for your extra tutorial it wasvery useful and cleared up the uncertainties that I washaving.." Student C..“..Thank you for organising the tutorial this evening, mycolleagues and I are currently in the library with a muchbetter understanding.." Student D.






Results!

More than 10% of studentsscored above 70% markFour of them scored 80%

How? Why?Because despite UG, the reports seemqualify (though with slight modification)for presentation at relevant AIconferences!Many performed beyond myexpectation.

Majority of the grades fall within55% to 65%Less than 12% scored between40% 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 40% and 50%

Source: carmarthenshireyouth.co.uk






Examples of BMG204 Students’ ModelsEXAMPLES 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 onbudget preparation using Matlab.





Conclusion

There is a strong evidence that business mgmt studentsmay attempt to resist AI techniques and related concepts.However, adopting appropriate experiential teaching andlearning strategies would help to motivate them to embracethe concepts.The management students could perform better than theirscience students and engineering counterpartsThey have proved to be more competent in applying AI inexciting ways to real-world situations and everydaybusiness decision making.





Further Reading

Festus Oderanti, Feng Li and Philippe De Wilde, (2012),Application of Strategic Fuzzy Games to Wage IncreaseNegotiation & Decision Problems, Jour. of Expert Systemswith Applications, 2012 39(12), 11103-11114 .Festus Oderanti and Philippe De Wilde (2011); AutomaticFuzzy Decision Making System with Learning forCompeting & Connected Businesses, Expert Systems withApplications 2011, 38(12), 14574-14584.Festus Oderanti and Philippe De Wilde (2010); Dynamicsof Business Games with Management of Fuzzy Rules forDecision Making, 2010, International Journal of ProductionEconomics, 128 (1): 96-109.www.businessballs.com





The End

Thank you!

Any Questions?THANK YOU!

Source: orlanisheaghdha.com


Applications of artificial intelligence (AI) models for management decision making under uncertainty...

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