MULTI-CRITERIA DECISION ANALYSIS AND DECISION SUPPORT SYSTEMS

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MULTI CRITERIA DECISIONANALYSIS AND DECISIONSUPPORT SYSTEMSKEN BAGGETT TRAINING LINK.
HTTPS WWW JLAB ORG ACCELERATOR... THE AVERAGE MAN S JUDGEMENT IS SO POOR HE RUNS A RISK EVERYTIME HE USES IT EDGAR W HOWE NO DECISION IS A DECISION .
A NEED FOR A TRANSITION Technical World Decision Making Process CHOICE IMPORTANCE OF DECISION CONSEQUENCESOF A COMPLEXITY OF DECISION TASKDECISIO TIME PRESSURE.
N RULE SIMILARITY OF ALTERNATIVES RATING ON TWO DIMENSIONS DEPEND VALUE OF CONSEQUENCESS ON PROBABILITY OF THEIR OCCURENCE Accepting the rules in decision analysis requires a.
belief in the value of systematic logical thoughtsas a basis for decision making This cognitive style will not be natural to peoplewho prefer to be guided primarily by feelingsrather than thought .
Research based on the Myers BriggsType Indicator showshow people differ in the way they like to perceive and theway they like to judge Myers 1980 CONSIDERATIONS OF HUMAN NATURE DECISION ANALYSTS REALIZE THAT NOT.
EVERYONE SEES THE WORLD AS THEY DO DA INSIGHTS HELPS ELIMINATE THE BLIND SPOTS OF THOSE WHO RELY MAINLY ONCONSIDERATIO SOME WHO ARE NOT COMFORTABLE WITHNS OF HUMAN THE COGNITIVE STYLE OF DECISION ANALYSIS.
NATURE MAY AVOID IT IN ORGANIZATIONAL SETTINGS IT REQUIRES A WILLINGNESS TO BE OPEN ANDEXPLICIT IN DECISION MAKING THOSE WHO FEAR CRITICISM OR WHO WISH TOCONTROL BY CONCEALMENT WILL NOT FIND.
DECISION ANALYSIS TO THEIR TASTE USING DA TO MAKE DECISIONS USUALLYREQUIRES CHANGING THE WAY WE THINKABOUT THE WORLD CONCEPTUAL WE MUST NOT ONLY USE NEW.
CONSIDERATIO INFORMATION BUT CHANGE THE CONTEXTNS WITHIN WHICH WE PROCESS THISINFORMATION THIS MEANS CREATING NEW DISTINCTIONSTHAT ARE APPARENTLY NOT NATURAL BUT.
The most important distinction needed fordecision analysis is that between decisionand outcome A good outcome is a future state of the world thatwe prize relative to other possibilities .
A good decision is an action we take that is logicallyconsistent with the alternatives we perceive theCONCEPTUAL information we have and the preferences we feel CONSIDERATIO The world is not certain good decisions can lead to bad outcomes and vice.
This distinction is usually not observed If a bad outcome follows an action people say thatMaking theadistinctionthey made bad decision allows us toseparate action from consequence and.
hence improve the quality of action HEURISTICS AND COGNITIVE BIAS HEURISTICS IN DECISION COGNITIVE STRATEGIES PEOPLE USE TO QUICKLY FORM STRENGTH CHEAP INTUITIVE REQUIRES LITTLE.
PLANNING AND CAN BE USED QUICKLY EARLY WEAKNESS UNLIKE FORMAL ALGORITHMS HEURISTICSDO NOT GUARANTEE OPTIMAL OR EVEN FEASIBLE SOLUTIONS THE PROBLEM COGNITIVE BIAS.
THE COMMON TENDENCY TO ACQUIRE AND PROCESSINFORMATION BY FILTERING IT THROUGH ONE S OWNLIKES DISLIKES AND EXPERIENCES DECISION HEURISTICS THIS IS HOW MOST PEOPLE MAKE DECISIONS.
DAILY DECISIONS OFTEN A GOOD THING COMPLEX BUSINESS DECISIONS NOT ALWAYS A GOOD COGNITIVE BIAS CAN NEGATIVELY IMPACT DECISIONS TOO MANY SHORTCUTS SOCIAL PRESSURES.
INDIVIDUAL MOTIVATIONS EMOTIONS LIMITED HUMAN PROCESSING CAPACITY SOME WELL KNOWN COGNITIVE BIASES STUDIED INPSYCHOLOGY .
ANCHORING RELYING HEAVILY ON THE FIRST PIECE OFINFORMATION PROVIDED REPRESENTATIVENESS THE INCORRECT BELIEF THATSOMETHING BELONGS IN A CERTAIN CLASS OR GROUPCOGNITI BASED ON HOW SIMILAR IT IS TO SOMETHING IN THAT.
CLASS OR GROUP AVAILABILITY BELIEF THAT THE LIKELIHOOD OF SOMEEVENT IS BASED ON RECOLLECTION OF SIMILAR EVENTS 2 GROUPS OF STUDENTS ASKED TO ESTIMATE 1X2X3X4X5X6X7X8.
8X7X6X5X4X3X2X1 FIVE SECONDSTO ANSWER FIRST GROUP ESTIMATE 512 SECOND GROUP ESTIMATE 2 250 THE ANSWER TO BOTH QUESTIONS IS 40 320 .
WHAT S THE REASON BEHIND THIS BIG AG DIFFERENCE FACED WITH A TIME LIMIT PARTICIPANTSQUICKLY ANCHORED TO THE PRODUCT OF THEFIRST FEW NUMBERS.
1X2X3 6 8X7X6 336 14 PEOPLE RELY ON THE INITIAL CUE TO MA THE INITIAL PIECE OF INFORMATION IS CALLEDTHE ANCHOR .
ONCE THE ANCHOR IS SET STUDIES HAVESHOWED THAT WE BIAS OUR INTERPRETATIONANCHORIN OF OTHER INFORMATION AROUND THE EXPERIENCE AND COGNITIVE ABILITYSOMETIMES REDUCE BUT NOT ELIMINATE THE.
ANCHORING OTHER EXAMPLES A ROULETTE WHEEL THAT WAS PREDETERMINED TO STOP ON EITHER 10 OR 65 PARTICIPANTS WATCHED AND WERE THEN ASKED TO GUESS WHAT IS THE PERCENTAGE OF THE UNITED NATIONS THAT WERE AFRICAN NATIONS .
PARTICIPANTS WHOSE WHEEL STOPPED ON 10 GUESSED LOWER VALUES 25 ONAVERAGE THAN PARTICIPANTS WHOSE WHEEL STOPPED AT 65 45 ON AVERAGE DIFFICULTY OF AVOIDING ANCHORING EVEN WHEN GIVEN ANCHORS ARE OBVIOUSLY WRONG PHENOMENA OF ANCHORING STILL EXISTS .
ANCHORING WHAT OTHER EXAMPLES CAN YOU THINK A HEURISTIC THAT USES PAST EXPERIENCESOR MENTAL PICTURES BASED ON DATA TOFORMULATE A QUICK DECISION .
THE EXPERIENCES ARE REFERRED TO AS REPRESENTATIONS REPRESENTATIVEN USES BELIEFS OR PATTERNS TO INFORM CAN BE GOOD FOR QUICK DECISIONS BUT OFTEN INACCURACY AND IRRATIONAL DECISION MAKING .
CLOSE MINDEDNESS STEREOTYPING 18 LINDA IS 31 YEARS OLD SINGLE OUTSPOKEN AND VERY BRIGHT SHE MAJORED IN PHILOSOPHY AS A STUDENT SHE WAS DEEPLY CONCERNED.
WITH ISSUES OF DISCRIMINATION AND SOCIALJUSTICE SHE ALSO PARTICIPATED IN ANTI NUCLEAR DEMONSTRATIONS REPRESENTATIVENESS WHICH IS MORE PROBABLE .
CONJUNCTION1 LINDA IS A BANK TELLER 2 LINDA IS A BANK TELLER AND IS ACTIVE IN THE FEMINIST SIXTY FIVE PERCENT OF SUBJECTS PICKED 2 FOUND THE ILLOGICAL ARGUMENT MORE.
CONVINCING BREAKS FUNDAMENTAL LAWS OF PROBABILITY19 THE CONJUNCTION STATEMENT SHE IS X AND Y CANNOT APPLY TO MORE PEOPLE THAN THE GENERALSTATEMENT SHE IS X .
REPRESENTATIVEN PR LINDA IS A BANK TELLER 0 05 PR LINDA IS A FEMINIST 0 95 PR LINDA IS A BANK TELLER AND A FEMINIST 0 05X0 95 0 0475.
6 SIDED DIE 4 GREEN FACES 2 RED FACES DIE ROLLED 20 TIMES YOU WIN IF ONE OF THEFOLLOWING SEQUENCES COMES UP REPRESENTATIVEN 1 RGRRR.
ESS ROLL THE 2 GRGRRRDICE EXAMPLE 3 GRRRRR WHICH SEQUENCE WOULD YOU BET ON WHY 65 PICKED OPTION 2 BUT THE SEQUENCE IS ALREADY CONTAINED IN.
OPTION 1 21 A JUDGMENT ABOUT THE FREQUENCY OF AN EVENT BASED ON HOW EASILYTHEY CAN RECALL SIMILAR INSTANCES CAN BE EFFECTIVE WHEN A QUICK DECISION IS NECESSARY BUT IT CAN LEAD TO SYSTEMATIC ERRORS.
EXAMPLE ARE THERE MORE WORDS THAT START WITH K OR HAVE K AS THETHIRD LETTER PEOPLE CAN IMMEDIATELY THINK OF MANY WORDS THAT BEGIN WITH THELETTER K KANGAROO KITCHEN KALE AVAILABILI HARDER TO THINK OF WORD WHERE K IS THE THIRD LETTER.
ACKNOWLEDGE ASK TY PEOPLE ANSWER QUESTIONS LIKE THESE BY COMPARING THE AVAILABILITYOF THE TWO CATEGORIES AND ASSESSING HOW EASILY THEY CAN RECALLTHESE INSTANCES IT IS EASIER TO THINK OF WORDS THAT BEGIN WITH K THAN WORDS WITH.
K AS THE THIRD LETTER THUS PEOPLE JUDGE WORDS BEGINNING WITH A K TO BE A MORECOMMON OCCURRENCE THERE ARE THREE TIMES MORE WORDS WITH K IN THE THIRDPOSITION THAN WORDS THAT BEGIN WITH K 22.
ILLUSION OF INVULNERABILITYGROUP DECREASE OF MORAL CONSIDERATION MISJUDGEMENT OF UNANIMITYTHINK SANCTIONS AGAINST CRITICS IN THE GROUP DEVALUATION OF NON GROUP OPINIONS.
NEGATIVE CONSEQUENCES ON DECISIONGROUP QUALITYTHINK SIMPLISTIC DECISION STRATEGIES INCOMPLETE AND ONE SIDED INFORMATIONPROCESSING E G CONFIRMATION BIASES.
NO WORST CASE SCENARIOS HUMAN DECISION MAKING IS OFTEN NOT ATHOROUGHLY RATIONAL PROCESSTRANSITI DECISION HEURISTICS ARE USED BECAUSE OF LIMITEDCOGNITIVE RESOURCES.
ON OFTEN SUITABLE LEAD TO GOOD RESULTS BUT POSSIBILITY FOR SYSTEMATIC BIASESDECISION DECISION SUPPORT IS NEEDED WHERESUPPORT SYSTEMS ARE COMPLEX DATA IS EXTENSIVE.
DECISIONS HAVE IMPORTANT CONSEQUENCES OFFERS A SET OF STRUCTURED PROCEDURESTHAT ASSIST DECISION MAKERS TO DECISIO optionsN Quantify.
ANALYSIS ntypreferenceuncertaintpreference CHOICE OF A DECISION.
RULE DEPENDS ON IMPORTANCE OF DECISION CONSEQUENCES COMPLEXITY OF DECISION TASK TIME PRESSURE SIMILARITY OF ALTERNATIVES.
RATING ON TWO DIMENSIONS VALUE OF CONSEQUENCES PROBABILITY OF THEIR OCCURENCE DECISION ANALYSIS ALTHOUGH UNCERTAINTY CAN OFTEN BE REDUCED IT IS.
RARELY ELIMINATED WHETHER WE ARE DEALING WITH SCIENTIFIC ENGINEERING ORPERSONAL PROBLEMS WE ARE FORCED TO MAKE DECISIONS THATARE BASED ON INCOMPLETE KNOWLEDGE EVEN A DELIBERATION OF WHETHER MORE INFORMATION SHOULD.
BE COLLECTED BEFORE MAKING AN ACTUAL DECISION IS ITSELF ADECISION UNDER UNCERTAINTY DECISION MAKING UNDER UNCERTAINTY HAS BEEN ADDRESSEDIN MATHEMATICS BY PROBABILITY THEORY AND EXPECTEDUTILITY THEORY .
THESE TWO TOGETHER ARE KNOWN AS DECISION THEORY THE ART AND PRACTICE OF DECISION THEORY IS KNOWN ASDECISION ANALYSIS MAIN 1 DEFINE AND DESCRIBE THE PROBLEMSTAGES 2 CONSIDER AND DEFINE APPROPRIATE QUALITY.
ASSURANCES REQUIREMENTSOF A 3 FORMALIZE THE DESCRIPTIVE MODEL OF THE PROBLEMDECISION 4 OBTAIN THE NECESSARY INFORMATION FOR MODELLING5 ANALYZE IN ORDER TO DETERMINE THE SET OFANALYSIS ALTERNATIVES AND CRITERIA.
PROCESS 6 SELECT AN APPROPRIATE METHOD TO MAKE THE DECISION7 ESTABLISH A CLEAR RECORD OF THE PROCESS AND ANYDECISIONS TAKEN ORGANIZATIONALINTERESTING QUESTIONS .
WHAT STRATEGIES DECISION RULES ARE HOW ARE CONSEQUENCES RATED DEPENDING ONTHE CONTEXT HOW ARE RISK AND UNCERTAINTY TREATED ARE THERE SPECIAL INFLUENCES WHEN.
DECISIONS ARE MADE IN GROUPS PHILOSOPHY APPROACH BETTER TO BEMULTI APPROXIMATELY RIGHT THAN PRECISELY WRONG CARVETH READ DECISIO MCDA APPROACH .
N AIDING RELATIVELY SIMPLE MATHEMATICS BUT WITHASSUMPTIONS RETAINING AS MUCH AS POSSIBLE THEAPPROA COMPLEXITY OF THE REAL WORLDCH OPERATIONAL RESEARCH APPROACH COMPLEX MATHEMATICS BUT OVER SIMPLIFYING.
MODELS AND ASSUMPTIONS Strategic decision making in a sustainability framework is very seldom thedoing of one unique decision maker acting in an isolated wayMULTIPLE STAKEHOLDERS A variety of social actors or stakeholders each of them with their own.
value system have their say in the matter Traditional mathematical models Operations Research are notappropriate in multi criteria multi stakeholders decision situations whereit is hardly possible to account in unambiguous mathematical terms forcomplex interactions between actors constraints imposed by the.
environment etc A DIFFERENT APPROACH WHY A DIFFERENT APPROACH List all potential List all pertinentactions criteria.
a1 a2 an 1 2 k w1 w m k wm weightsevery actionProcess the.
evaluations todesignate the bestMulti criteria Relational Model General Scheme NUTS AND BOLTS Multiattributive.
Lexicographic RuleUtility MAU CONJUNCTIVE RULE Attach importance weights to attributes e g Voice quality 0 7 Price 0 2 Handling 0 1 Rank attributes.
Multiply attribute by importancevalues e g attributeby respective Voice quality and sum up DEFINE A MINIMAL THRESHOLD FOR EACH ATTRIBUTE E G 3 Price values .
benefit Handling A 0 7 6 alternativesCompare 0 2 5 0 1 2on 5 4 important attributeCHOOSE THE ALTERNATIVE B 0 7 6 0 2 3 0 1 7 5 5.
THAT EXCEEDS THE RESPECTIVE If no alternativeTHRESHOLD is dominatingVALUE Continue comparison 40C 0 7 4 0 2 10 ON EACH.
0 1 4 5 2ATTRIBUTE on next most important attribute Choose alternative with highest overall benefit value THERE ARE MANY TOOLS THAT CAN BE HELPFUL SOLVING MCDM PROBLEMS DECISION TREES EASILY VIEWED EVALUATES OVERALL POSSIBILITIES.
MULTI ATTRIBUTE UTILITY THEORY UTILITY VALUE IT IS AN ABSTRACTEQUIVALENT OF MEASURE THAT IS CONVERTED FROM VARIETY OF UNITS TO ASCALE FROM 0 TO 1 TOOL FUZZY SET ILL STRUCTURED VAGUE DATA ANALYTIC HIERARCHY PROCESS PAIRWISE COMPARISONS A AND B ARE.
EQUALLY IMPORTANT EQUALS TO 1 TO FIND EACH CRITERIA WEIGHT ANDMETHOD EVIDENTIAL REASONING THE DEGREE OF BELIEF 50 AVERAGE 40 GOOD 10 UNCERTAINTY SUITABLE FOR SITUATION WITH UNCERTAINTYTHERE IS NO UNIVERSAL METHOD THAT CAN SOLVE ALLTHE PROBLEMS.
DECISION TREES DECISION TREES CAN BE USED TO SELECT ALTERNATIVES AMONG DIFFERENTDISTINCTIONS THESE DISTINCTIONS HAVE ASSOCIATED PROBABILITIES ASSIGNED THAT MAY BE.
Quantify uncertainty. Quantify preferences. Combine uncertainty and preferences . Choice of A Decision Rule Depends On. Importance of decision consequences. Complexity of decision task. Time pressure. Similarity of alternatives. Rating on twodimensions: Value ofconsequences. Probabilityoftheiroccurence.

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