Different Types of Judgement(UCTI SLIDE)

8/4/2019 Different Types of Judgement(UCTI SLIDE)

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FORECASTINGJUDGEMENTAL


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Module Code and Module Title Title of Slides

Methods Based on Judgment

Unaided judgment It is common practice to ask experts what will happen. This is a

good procedure to use when

experts are unbiased

large changes are unlikely

relationships are well understood by experts (e.g., demandgoes up when prices go down)

experts possess privileged information

experts receive accurate and well-summarized feedback abouttheir forecasts.


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Prediction markets

Prediction markets, also known as bettingmarkets, information markets, and futuresmarkets have a long history.


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Between the end of the US Civil War and WorldWar II, well-organized markets for betting onpresidential elections correctly picked the winner

in every case but 1916; also, they were highlysuccessful in identifying those elections thatwould be very close. More recently, in the fourelections prior to 2004, the Iowa Electronic

Markets (IEM) has performed better than polls inpredicting the margin of victory for thepresidential election winner.


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Contd

In the week leading up to the election,these markets predicted vote shares forthe Democratic and Republicancandidates with an average absolute errorof around 1.5 percentage points. The finalGallup poll, by comparison, yielded

forecasts that erred by 2.1 percentagepoints.


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Delphi

The Delphi technique wasdeveloped at RAND Corporation

in the 1950s to help capture theknowledge of diverse expertswhile avoiding the disadvantages

of traditional group meetings. Thelatter include bullying and time-wasting.


7/27Module Code and Module Title Title of Slides

To forecast with Delphi theadministrator should recruit betweenfive and twenty suitable experts and

poll them for their forecasts andreasons. The administrator thenprovides the experts with anonymoussummary statistics on the forecasts,and experts reasons for theirforecasts.



The process is repeated until there islittle change in forecasts betweenrounds two or three rounds are

usually sufficient. The Delphi forecastis the median or mode of the expertsfinal forecasts. Software to guide youthrough the procedure is available atforecasting principles.com.



Structured analogies

The outcomes of similar situations from the past(analogies) may help a marketer to forecast the outcomeof a new (target) situation. For example, the introductionof new products in US markets can provide analogies for

the outcomes of the subsequent release of similarproducts in other countries.

People often use analogies to make forecasts, but theydo not do so in a structured manner. For example, theymight search for an analogy that suits their prior beliefsor they might stop searching when they identify oneanalogy. The structured-analogies method uses a formalprocess to overcome biased and inefficient use ofinformation from analogous situations.



The outcomes of similar situations fromthe past (analogies) may help a marketerto forecast the outcome of a new (target)situation. For example, the introduction ofnew products in US markets can provideanalogies for the outcomes of the

subsequent release of similar products inother countries.



People often use analogies to makeforecasts, but they do not do so in astructured manner. For example, they

might search for an analogy that suits theirprior beliefs or they might stop searchingwhen they identify one analogy. Thestructured-analogies method uses a formalprocess to overcome biased and inefficientuse of information from analogoussituations.



To use the structured analogies method,an administrator prepares a description ofthe target situation and selects expertswho have knowledge of analogoussituations; preferably direct experience.



The experts identify and describeanalogous situations, rate their similarity tothe target situation, and match theoutcomes of their analogies with potentialoutcomes in the target situation. Theadministrator then derives forecasts from

the information the experts provided ontheir most similar analogies.



Game theory

Game theory has been touted in textbooksand research papers as a way to obtainbetter forecasts in situations involvingnegotiations or other conflicts. A Googlesearch for game theory and forecasting

or prediction identified 147,300 sites.

Despite a vast research effort, there is noresearch that directly tests the forecastingability of game theory.



However, Green (2002, 2005) tested theability of game theorists, who were urgedto use game theory in predicting theoutcome of eight real (but disguised)situations. In that study, game theoristswere no more accurate than university

students.



Judgmental Decomposition

The basic idea behind judgementaldecomposition is to divide the forecastingproblem into parts that are easier toforecast than the whole. One thenforecasts the parts individually, usingmethods appropriate to each part. Finally,

the parts are combined to obtain aforecast.



One approach is to break the problemdown into multiplicative components. Forexample, to forecast sales for a brand, onecan forecast industry sales volume, marketshare, and selling price per unit. Thenreassemble the problem by multiplying the

components together.



Empirical results indicate that, in general,forecasts from decomposition are moreaccurate than those from a globalapproach (MacGregor 2001). In particular,decomposition is more accurate wherethere is much uncertainty about the

aggregate forecast and where largenumbers (over one million) are involved.


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Judgmental bootstrapping

Judgmental bootstrapping convertssubjective judgments into structuredprocedures. Experts are asked what

information they use to make predictionsabout a class of situations. They are thenasked to make predictions for diversecases, which can be real or hypothetical.For example, they might forecast nextyears sales for alternative designs for anew product.


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The resulting data are then converted to amodel by estimating a regression equationrelating the judgmental forecasts to theinformation used by the forecasters. Thegeneral proposition seems preposterous. Itis that the model of the man will be more

accurate than the man. The reason is thatthe model applies the mans rules more

consistently.


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Judgemental bootstrapping models aremost useful for repetitive complexforecasting problems where data on thedependent variable are not available (e.g.demand for a new telecommunicationsdevice) or data does not vary sufficiently

for the estimation of an econometricmodel.


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Judgmental bootstrapping also allows experts tosee how they are weighting various factors. Thisknowledge can help to improve judgmental

forecasting. For example, with respect topersonnel selection, bootstrapping might revealthat some factors, such as height, weight orlooks, are used, even though they are notrelevant for the job. Bootstrapping also allows forestimating effects of changing key variableswhen historical data are not sufficient to allow forestimates.


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Expert systems

As the name implies, expert systems arestructured representations of the rulesexperts use to make predictions or

diagnoses. For example, iflocalhousehold incomes are in the bottomquartile, thendo not supply premiumbrands. The forecast is implicit in theforegoing conditional action statement:i.e., premium brands are unlikely to makean acceptable return in the locale..


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Rules are often created from protocols,whereby forecasters talk about what theyare doing while making forecasts. Where

empirical estimates of relationships fromstructured analysis such as econometricstudies are available, expert systemsshould use that information. Expertopinion, conjoint analysis, andbootstrapping can also aid in thedevelopment of expert systems


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Expert systems forecasting involves identifyingforecasting rules used by experts and ruleslearned from empirical research. One shouldaim for simplicity and completeness in the

resulting system, and the system should explainforecasts to users.

Developing an expert system is expensive andso the method will only be of interest in

situations where many forecasts of a similar kindare required. Expert systems are feasible whereproblems are sufficiently well-structured for rulesto be identified.


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Simulated interaction

others. It is especially useful when thesituation involves conflict. For example,one might wish to forecast how best tosecure an exclusive distributionarrangement with a major supplier.


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To use simulated interaction, an administratorprepares a description of the target situation,describes the main protagonists roles, and

provides a list of possible decisions. Roleplayers adopt a role and read about thesituation. They then improvise realisticinteractions with the other role players until theyreach a decision; for example to sign a trial one-year exclusive distribution agreement. The roleplayers decisions are used to make theforecast.

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Different Types of Judgement(UCTI SLIDE)