Vlerick HRday 2013: Men versus machine, who wins? - Prof. Marc Buelens

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Vlerick HRday 2013: Men versus machine, who wins? - Prof. Marc Buelens

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Vlerick HRday 2013: Men versus machine, who wins? - Prof. Marc Buelens

  1. 1. MAN VERSUS MACHINEPROF DR MARC BUELENS
  2. 2. © Vlerick Business SchoolOVERVIEW1. Big data & HR: the hype has started.If you can keep your head, when all around you are losingtheirs.2. Some promising applications (social sciences, HR…)If you can dream and not make dreams your master.3. Man versus Machine. Who’s the winner?Lose and start again at your beginnings …4. And what about intuition?Fill the unforgiving minute with sixty seconds worth ofdistance run.5. Some conclusionsYours is the Earth and everything thats in it.2
  3. 3. © Vlerick Business School1. BIG DATA, ANALYTICS & HRTHE HYPE HAS STARTED“There is an even bigger opportunity to applyBig Data to Human Resources” (Bershin, 2013)“When Big Data meets HR” (The New York Times,April 20, 2013)“Human capital management is entering thecorporate mainstream as new tools make itpossible for business people without advancedanalytical training to manipulate Big Data”(Brenon Daly, June 2, 2013)3
  4. 4. © Vlerick Business SchoolOf late, growing numbers of academics andentrepreneurs are applying Big Data to humanresources and the search for talent, creating afield called work-force science (NY Times,8/6/2013)Work-force science will increasingly be appliedacross the spectrum of jobs and professions,building profits, productivity, innovation andworker satisfaction (NY Times, 20/4/2013)Robot recruiters. Algorithms and big data arepowerful tools (The Economist, 6/4/2013)4
  5. 5. © Vlerick Business School5
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  8. 8. © Vlerick Business School2. WHY?So much wasted talent …Biases, discrimination, stupidityEfficiency, economies of scale …A ‘learning’ attitude8
  9. 9. © Vlerick Business SchoolPOTENTIAL APPLICATIONS ARE ENDLESSWhat drives performance?Can we predict whether a candidate will reallyperform?Can we predict who will leave the company?The vast majority of recruitment, selection,promotion, rewards, training, career planningare made on gut feel, ‘”experience”, “beliefs”9
  10. 10. © Vlerick Business SchoolWHAT DOES CORRELATE WITH SALES PERFORMANCE IN ALARGE FINANCIAL SERVICES COMPANY?1. No typos, errors, grammatical mistakes onresume2. Where they went to school3. What grades they had4. Did not quit school before obtaining some degree5. Had experience selling real-estate or autos6. The quality of their references7. Demonstrated success in prior jobs8. Ability to succeed with vague instructions9. Experience planning time and managing lots oftasks10. Sign of the Zodiac10
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  12. 12. © Vlerick Business SchoolWHOM TO AVOID FOR CUSTOMER SUPPORT INCALL CENTRES?Job hoppersJob candidates with a criminal recordPeople who live nearbyPeople who can get to work easilyPeople who had joined one or two socialnetworks?People who belong to four or more socialnetworks?Honest people?12
  13. 13. © Vlerick Business SchoolPREDICTING JOB PERFORMANCETHE MONEYBALL FACTOR13
  14. 14. © Vlerick Business School14
  15. 15. © Vlerick Business School3. Man versus MachineWhen should we use our headinstead of formula?
  16. 16. © Vlerick Business SchoolPHILIPPE TETLOCK284 expertsMade predictions (yes-no) inside or outsidetheir domain82 361 predictionsCompared toSimple statistical modelsOpinions of non-informed non-expertsOpinions of informed non-experts16
  17. 17. © Vlerick Business School“Tetlock contends that the fox--the thinker whoknows many little things, draws from an eclecticarray of traditions, and is better able to improvisein response to changing events--is more successfulin predicting the future than the hedgehog, whoknows one big thing, toils devotedly within onetradition, and imposes formulaic solutions on ill-defined problems.”“He notes a perversely inverse relationship betweenthe best scientific indicators of good judgement andthe qualities that the media most prizes in pundits--the single-minded determination required toprevail in ideological combat.”Source: Armstrong, 200517
  18. 18. © Vlerick Business SchoolTHE SEERSUCKER THEORY OF PREDICTIONS(ARMSTRONG)“No matter how muchevidence exists that seersdo not exist, suckers willpay for the existence ofseers.”A SeersuckerManifesto27 Apr, 2012 - Kevin GosaNo more dangerous fabrichas ever been woven,washed, and worn in thehistory of mankind thanseersucker.18
  19. 19. © Vlerick Business SchoolGROVE & MEEHL, 1996Mechanical method wins: 64 studiesNo significant difference: 64 studiesClinical judgment wins: 8 studies19
  20. 20. © Vlerick Business School“THE BROKEN LEG”“A HIP-CASTED PROFESSOR WILL NOT GO TO THEMOVIES”Should be an objective fact, determinable withhigh accuracyThe correlation with ‘immobilization’ is nearperfectNo interaction effects between broken leg andother factors that influence going to the moviesThe prediction does not use doubtful theories20
  21. 21. © Vlerick Business SchoolTHE FINE ART OF WRONG PREDICTIONS(MAKRIDAKIS)The future is never exactly like the past. This means that theextrapolation of past patterns or relationships cannot provideaccurate predictions.Statistically sophisticated, or complex, models fit past data well butdo not necessarily predict the future accurately.“Simple” models do not necessarily fit past data well but predict thefuture better than complex or sophisticated statistical models.Both statistical models and human judgment have been unable tocapture the full extent of future uncertainty. People who have reliedon these methods have been surprised by large forecasting errorsand events they did not consider.Expert judgment is typically inferior to simple statistical models.Forecasts made by experts are no more accurate than those ofknowledgeable individuals.Averaging the predictions of several individuals usually improvesforecasting accuracy.Averaging the forecasts of two or more models improves accuracywhile also reducing the variance of forecasting errors.21
  22. 22. © Vlerick Business SchoolBASIC RULES – (FIRST CONCLUSIONS)Try to develop simple modelsKnow that only very few runners have brokenlegsGo for many independent expertsAvoid ‘convincing’. Embrace ‘informing’Combine human and non-human predictions.Combine it in a mechanical, not in a clinicalway!Feed your simple model with good data andlearn ‘the errors of your ways’22
  23. 23. © Vlerick Business School4. AND WHAT ABOUT INTUITION?Half a Minute: Predicting Teacher Evaluations From Thin Slices of Nonverbal Behavior and Physical Attractiveness.Ambady, Nalini; Rosenthal, RobertJournal of Personality & Social Psychology. 64(3):431‐441, March 1993.23
  24. 24. © Vlerick Business SchoolRED FLAGYOUR INTUITION IS UNLIKELY TO BE VALIDWhen you did not have a varied,direct, frequent exposureWhen you did not ‘learn theerrors or your ways’When it is not a warning signal.When you are in the Bermudatriangle of hope, anxiety andgreedWhen we are not talking ‘thinslices of behaviour’24
  25. 25. © Vlerick Business School5. CONCLUSIONSMen occasionally stumble across the truth, butmost of them pick themselves up and hurry offas if nothing has happened.Winston Churchill25
  26. 26. © Vlerick Business SchoolUNFORTUNATELY…It is not always “and … and…”; in many cases it seemsto be “or”.We reap only what has beensown.In some areas it seems thatwe never learn : “Thechecklist manifesto”.Let us at least practice whatwe know.26

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