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Attitudes, personality, and behavior [[electronic resource] /] / Icek Ajzen
| Attitudes, personality, and behavior [[electronic resource] /] / Icek Ajzen |
| Autore | Ajzen Icek |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Maidenhead, Berkshire, England ; ; New York, : Open University Press, 2005 |
| Descrizione fisica | xii, 178 p. : ill |
| Disciplina | 155.2 |
| Collana | Mapping social pschology |
| Soggetto topico |
Human behavior
Social psychology Attitude (Psychology) Personality Prediction (Psychology) |
| ISBN |
1-280-95055-2
0-335-22400-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910784697503321 |
Ajzen Icek
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| Maidenhead, Berkshire, England ; ; New York, : Open University Press, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Attitudes, personality, and behavior [[electronic resource] /] / Icek Ajzen
| Attitudes, personality, and behavior [[electronic resource] /] / Icek Ajzen |
| Autore | Ajzen Icek |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Maidenhead, Berkshire, England ; ; New York, : Open University Press, 2005 |
| Descrizione fisica | xii, 178 p. : ill |
| Disciplina | 155.2 |
| Collana | Mapping social pschology |
| Soggetto topico |
Human behavior
Social psychology Attitude (Psychology) Personality Prediction (Psychology) |
| ISBN |
1-280-95055-2
0-335-22400-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover -- Half Title -- Title -- Copyright -- Dedication -- Contents -- Preface -- Chapter 01 ATTITUDES AND PERSONALITY TRAITS -- FROM ACTS TO DISPOSITIONS -- Inferring personality traits from behavior -- Inferring attitudes from behavior -- Attitudes versus traits -- Explicit measures of attitudes and personality traits -- Overcoming self-presentation biases -- Implicit measures of attitudes and personality traits -- FROM DISPOSITIONS TO ACTIONS -- Dimensions of personality -- A hierarchical model of attitude -- SUMMARY AND CONCLUSIONS -- NOTES -- SUGGESTIONS FOR FURTHER READING -- Chapter 02 CONSISTENCY IN HUMAN AFFAIRS -- PSYCHOLOGICAL FOUNDATIONS OF CONSISTENCY -- Preference for consistency -- Functional consistency -- Inherent consistency -- EMPIRICAL EVIDENCE -- Behavioral consistency -- Predictive validity -- Implications -- SUMMARY AND CONCLUSIONS -- NOTES -- SUGGESTIONS FOR FURTHER READING -- Chapter 03 FROM DISPOSITIONS TO ACTIONS -- THE MODERATING VARIABLES APPROACH -- Situational factors as moderators -- Individual differences as moderators -- Secondary characteristics of the disposition -- THE MODE MODEL: AN INTEGRATIVE THEORETICAL FRAMEWORK -- Strength of behavioral dispositions -- MODERATING VARIABLES AND THE QUESTION OF CONSISTENCY -- Limitations of a moderating variables approach -- SUMMARY AND CONCLUSIONS -- NOTES -- SUGGESTIONS FOR FURTHER READING -- Chapter 04 THE PRINCIPLE OF COMPATIBILITY -- THE LOGIC OF AGGREGATION -- Consistency of behavioral aggregates -- Predictive validity for behavioral aggregates -- Aggregation and the question of consistency -- PREDICTION OF SPECIFIC BEHAVIORAL TENDENCIES -- The principle of compatibility -- PERSONALITY TRAITS AND SPECIFIC RESPONSE TENDENCIES -- Routines and habits -- Perceived behavioral control -- ATTITUDES AND SPECIFIC RESPONSE TENDENCIES -- Attitude toward a behavior.
SUMMARY AND CONCLUSIONS -- NOTES -- SUGGESTIONS FOR FURTHER READING -- Chapter 05 FROM INTENTIONS TO ACTIONS -- THE CASE OF WILLFUL BEHAVIOR -- Predicting behavior from intention -- THE CASE OF INCOMPLETE VOLITIONAL CONTROL -- Control factors -- Perceived behavioral control -- SPONTANEOUS INTENTIONS -- SUMMARY AND CONCLUSIONS -- NOTES -- SUGGESTIONS FOR FURTHER READING -- Chapter 06 EXPLAINING INTENTIONS AND BEHAVIOR -- A THEORY OF PLANNED BEHAVIOR -- Predicting intentions -- Predicting behavioral goals -- The informational foundation of behavior -- Background factors -- BEHAVIORAL INTERVENTIONS -- Theoretical considerations -- Illustrations -- SUMMARY AND CONCLUSIONS -- NOTE -- SUGGESTIONS FOR FURTHER READING -- Chapter 07 CONCLUSION -- BEHAVIORAL CONSISTENCY -- GENERAL DISPOSITIONS AND SPECIFIC ACTIONS -- VERBAL AND NONVERBAL RESPONSES -- References -- Author Index -- Subject Index -- Back cover. |
| Record Nr. | UNINA-9910807169603321 |
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Ajzen Icek
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| Maidenhead, Berkshire, England ; ; New York, : Open University Press, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Foresight / / edited by Lawrence W. Sherman and David Allan Feller [[electronic resource]]
| Foresight / / edited by Lawrence W. Sherman and David Allan Feller [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2016 |
| Descrizione fisica | 1 online resource (xi, 179 pages) : digital, PDF file(s) |
| Disciplina | 303.49 |
| Collana | The Darwin College lectures |
| Soggetto topico |
Forecasting
Prediction (Psychology) |
| ISBN |
1-316-78442-8
1-316-78634-X 1-316-78666-8 1-316-78698-6 1-316-22530-5 1-316-78730-3 1-316-78826-1 |
| Classificazione | SCI000000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foresight in ancient civilisations / Geoffrey Lloyd -- Foresight in journalism / Bridget Kendall -- Foresight and fiction / Robert Sawyer -- Foresight in scientific method / Hasok Chang -- Foresight in music / Nicholas Cook -- Foreseeing in space weather / Jim Wild -- Foresight and self-control / Terrie Moffitt -- Foresight in ancient Mesopotamia / Francesca Rochberg. |
| Record Nr. | UNINA-9910150192803321 |
| Cambridge : , : Cambridge University Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Personality characteristics of air traffic control specialists as predictors of disability retirement [[electronic resource] /] / Carolyn Dollar, Dana Broach, David J. Schroeder
| Personality characteristics of air traffic control specialists as predictors of disability retirement [[electronic resource] /] / Carolyn Dollar, Dana Broach, David J. Schroeder |
| Autore | Dollar Carolyn |
| Pubbl/distr/stampa | Washington, DC : , : U.S. Dept. of Transportation, Federal Aviation Administration, Office of Aerospace Medicine, , [2003] |
| Descrizione fisica | 1 online resource (12 unnumbered pages) : digital, PDF file |
| Altri autori (Persone) |
BroachDana
SchroederDavid J |
| Soggetto topico |
Air traffic controllers - United States - Psychology
Disability retirement - United States Prediction (Psychology) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910699699103321 |
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Dollar Carolyn
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| Washington, DC : , : U.S. Dept. of Transportation, Federal Aviation Administration, Office of Aerospace Medicine, , [2003] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Prediction in second language processing and learning / / edited by Edith Kaan, Theres Grüter
| Prediction in second language processing and learning / / edited by Edith Kaan, Theres Grüter |
| Pubbl/distr/stampa | Amsterdam ; ; Philadelphia : , : John Benjamins Publishing Company, , [2021] |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina | 418.0071 |
| Collana | Bilingual processing and acquisition |
| Soggetto topico |
Second language acquisition
Cognitive grammar Prediction (Psychology) |
| Soggetto genere / forma | Essays. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Prediction in Second Language Processing and Learning -- Editorial page -- Title page -- Copyright page -- Table of contents -- Acknowledgments -- List of contributors -- Chapter 1. Prediction in second language processing and learning: Advances and directions -- Introduction -- What is prediction and what is it good for in L2 learning and processing? -- What is prediction? -- What is prediction good for? -- A brief history of prediction in language processing research -- Research on prediction in L1 sentence processing -- Research on prediction in L2 sentence processing -- Prediction in L1 and L2 processing: The role of utility -- Cue reliability and utility in L1 predictive processing -- Cue reliability and utility in L2 predictive processing -- A note about proficiency -- Prediction and learning -- Insights from L1 speakers -- Insights from L2 speakers -- Again: Utility -- Concluding remarks, synopses of chapters, and future directions -- Acknowledgments -- Funding -- References -- Chapter 2. Automaticity and prediction in non-native language comprehension -- Introduction -- Graded view of automaticity -- A production-based model of prediction -- Automaticity of prediction in L1 -- Automaticity of prediction in L2 -- Conclusion -- References -- Chapter 3. Second language prediction ability across different linguistic domains: Evidence from German -- Introduction -- Semantic prediction -- Morphosyntactic prediction -- The current study -- Method -- Results -- Discussion -- Acknowledgments -- Funding -- References -- Chapter 4. Influence of syntactic complexity on second language prediction -- Introduction -- Prediction in comprehension -- Prediction in L2 comprehension -- Variation in prediction and mediating factors -- The current study -- Method -- Participants -- Materials -- Procedure -- Results -- Behavioral task accuracy.
Eye-tracking data analysis -- Discussion -- References -- Chapter 5. Language prediction in second language: Does language similarity matter? -- Introduction -- Cross-linguistic influence and language processing in L2 -- Cross-linguistic influence and prediction in L2 -- Do cross-linguistic differences in features and rules affect L2 prediction? -- Can L2 speakers use features that do not exist in their L1 as prediction cues? -- Is the use of the sentence context in L2 prediction affected by CLI? -- Is the use of the discourse context in L2 prediction affected by CLI?: First steps -- Cross-linguistic influence and models of language prediction -- Cross-linguistic influence and error-based implicit learning -- Cross-linguistic influence and prediction-by-production -- Conclusions and directions for future research -- References -- Chapter 6. Prediction in bilingual children: The missing piece of the puzzle -- What is prediction? -- How are prediction and proficiency related? -- What do we know about prediction skills in monolingual children? -- What do we know about prediction skills in adult L2 speakers? -- What do we (not) know about bilingual children? -- How can research with bilingual children inform L2 predictive processing accounts? -- What's next? -- Acknowledgements -- References -- Chapter 7. Code-switching: A processing burden, or a valuable resource for prediction? -- Introduction -- Illustrative Study 1: Can code-switching signal less expected upcoming lexical information? -- Illustrative Study 2: Can code-switching ease the processing of taboo or negative information? -- General discussion -- Potential mechanisms underlying the CS effect -- Factors modulating how CS affects prediction -- Conclusions -- Acknowledgments -- Funding -- References -- Chapter 8. Prediction and grammatical learning in second language sentence processing. Introduction -- Grammatical learning and prediction -- Learning and prediction in L2 acquisition -- Learning to predict in an L2 -- Learning to predict due to exposure and structural priming -- Predicting to learn in an L2 -- Syntactic adaptation and the consequences of prediction error -- Conclusions and outlook -- References -- Chapter 9. The role of prediction in second language vocabulary learning -- Overview -- Prediction and L1 vocabulary acquisition in children -- Prediction and vocabulary learning in adults -- Prediction and motivation in L2 vocabulary learning -- Summary and open questions -- Acknowledgments -- Funding -- References -- Chapter 10. Forcing prediction increases priming and adaptation in second language production -- Introduction -- Structural priming and learning from prediction error -- Prediction in L2 processing and structural priming -- The role of L2 proficiency in predictive processing and structural priming -- This study -- Methods -- Participants -- Materials -- Procedure -- Guessing game (GG) condition -- Control condition (CC) -- Data annotation and analysis -- Results -- Production of predicted primes in the GG group -- Proficiency -- Exit interview -- Discussion -- Acknowledgments -- References -- Index. |
| Record Nr. | UNINA-9910794529303321 |
| Amsterdam ; ; Philadelphia : , : John Benjamins Publishing Company, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prediction in second language processing and learning / / edited by Edith Kaan, Theres Grüter
| Prediction in second language processing and learning / / edited by Edith Kaan, Theres Grüter |
| Pubbl/distr/stampa | Amsterdam ; ; Philadelphia : , : John Benjamins Publishing Company, , [2021] |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina | 418.0071 |
| Collana | Bilingual processing and acquisition |
| Soggetto topico |
Second language acquisition
Cognitive grammar Prediction (Psychology) |
| Soggetto genere / forma | Essays. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Prediction in Second Language Processing and Learning -- Editorial page -- Title page -- Copyright page -- Table of contents -- Acknowledgments -- List of contributors -- Chapter 1. Prediction in second language processing and learning: Advances and directions -- Introduction -- What is prediction and what is it good for in L2 learning and processing? -- What is prediction? -- What is prediction good for? -- A brief history of prediction in language processing research -- Research on prediction in L1 sentence processing -- Research on prediction in L2 sentence processing -- Prediction in L1 and L2 processing: The role of utility -- Cue reliability and utility in L1 predictive processing -- Cue reliability and utility in L2 predictive processing -- A note about proficiency -- Prediction and learning -- Insights from L1 speakers -- Insights from L2 speakers -- Again: Utility -- Concluding remarks, synopses of chapters, and future directions -- Acknowledgments -- Funding -- References -- Chapter 2. Automaticity and prediction in non-native language comprehension -- Introduction -- Graded view of automaticity -- A production-based model of prediction -- Automaticity of prediction in L1 -- Automaticity of prediction in L2 -- Conclusion -- References -- Chapter 3. Second language prediction ability across different linguistic domains: Evidence from German -- Introduction -- Semantic prediction -- Morphosyntactic prediction -- The current study -- Method -- Results -- Discussion -- Acknowledgments -- Funding -- References -- Chapter 4. Influence of syntactic complexity on second language prediction -- Introduction -- Prediction in comprehension -- Prediction in L2 comprehension -- Variation in prediction and mediating factors -- The current study -- Method -- Participants -- Materials -- Procedure -- Results -- Behavioral task accuracy.
Eye-tracking data analysis -- Discussion -- References -- Chapter 5. Language prediction in second language: Does language similarity matter? -- Introduction -- Cross-linguistic influence and language processing in L2 -- Cross-linguistic influence and prediction in L2 -- Do cross-linguistic differences in features and rules affect L2 prediction? -- Can L2 speakers use features that do not exist in their L1 as prediction cues? -- Is the use of the sentence context in L2 prediction affected by CLI? -- Is the use of the discourse context in L2 prediction affected by CLI?: First steps -- Cross-linguistic influence and models of language prediction -- Cross-linguistic influence and error-based implicit learning -- Cross-linguistic influence and prediction-by-production -- Conclusions and directions for future research -- References -- Chapter 6. Prediction in bilingual children: The missing piece of the puzzle -- What is prediction? -- How are prediction and proficiency related? -- What do we know about prediction skills in monolingual children? -- What do we know about prediction skills in adult L2 speakers? -- What do we (not) know about bilingual children? -- How can research with bilingual children inform L2 predictive processing accounts? -- What's next? -- Acknowledgements -- References -- Chapter 7. Code-switching: A processing burden, or a valuable resource for prediction? -- Introduction -- Illustrative Study 1: Can code-switching signal less expected upcoming lexical information? -- Illustrative Study 2: Can code-switching ease the processing of taboo or negative information? -- General discussion -- Potential mechanisms underlying the CS effect -- Factors modulating how CS affects prediction -- Conclusions -- Acknowledgments -- Funding -- References -- Chapter 8. Prediction and grammatical learning in second language sentence processing. Introduction -- Grammatical learning and prediction -- Learning and prediction in L2 acquisition -- Learning to predict in an L2 -- Learning to predict due to exposure and structural priming -- Predicting to learn in an L2 -- Syntactic adaptation and the consequences of prediction error -- Conclusions and outlook -- References -- Chapter 9. The role of prediction in second language vocabulary learning -- Overview -- Prediction and L1 vocabulary acquisition in children -- Prediction and vocabulary learning in adults -- Prediction and motivation in L2 vocabulary learning -- Summary and open questions -- Acknowledgments -- Funding -- References -- Chapter 10. Forcing prediction increases priming and adaptation in second language production -- Introduction -- Structural priming and learning from prediction error -- Prediction in L2 processing and structural priming -- The role of L2 proficiency in predictive processing and structural priming -- This study -- Methods -- Participants -- Materials -- Procedure -- Guessing game (GG) condition -- Control condition (CC) -- Data annotation and analysis -- Results -- Production of predicted primes in the GG group -- Proficiency -- Exit interview -- Discussion -- Acknowledgments -- References -- Index. |
| Record Nr. | UNINA-9910825239103321 |
| Amsterdam ; ; Philadelphia : , : John Benjamins Publishing Company, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Predictive analytics : the power to predict who will click, buy, lie, or die / / Eric Siegel
| Predictive analytics : the power to predict who will click, buy, lie, or die / / Eric Siegel |
| Autore | Siegel Eric <1968-> |
| Edizione | [Revised and Updated Edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (338 pages) : illustrations |
| Disciplina | 303.49 |
| Soggetto topico |
Social sciences - Forecasting
Prediction (Psychology) Economic forecasting Social prediction Human behavior |
| ISBN |
1-119-15365-4
1-119-14568-6 |
| Classificazione | BUS016000BUS021000BUS043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Foreword Thomas H. Davenport xiii Preface to the Revised and Updated Edition What's new and who's this book for--the Predictive Analytics FAQ Preface to the Original Edition xv What is the occupational hazard of predictive analytics? Introduction The Prediction Effect 1 How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die? Chapter 1 Liftoff! Prediction Takes Action (deployment) 17 How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Chapter 2 With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets (ethics) 37 How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death? Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever, and can the agency use machine learning supercomputers to fight terrorism without endangering human rights? Chapter 3 The Data E ffect: A Glut at the End of the Rainbow (data) 67 We are up to our ears in data. How much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? Chapter 4 The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) 103 What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis? Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 133 To crowdsource predictive analytics--outsource it to the public at large--a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds? Chapter 6 Watson and the Jeopardy! Challenge (question answering) 151 How does Watson--IBM's Jeopardy!-playing computer--work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible? Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward--but that can be predicted in advance? Afterword 218 Eleven Predictions for the First Hour of 2022 Appendices A. The Five Effects of Prediction 221 B. Twenty Applications of Predictive Analytics 222 C. Prediction People--Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293 . |
| Record Nr. | UNINA-9910137489003321 |
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Siegel Eric <1968->
|
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| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Predictive analytics : the power to predict who will click, buy, lie, or die / / Eric Siegel
| Predictive analytics : the power to predict who will click, buy, lie, or die / / Eric Siegel |
| Autore | Siegel Eric <1968-> |
| Edizione | [Revised and Updated Edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (338 pages) : illustrations |
| Disciplina | 303.49 |
| Soggetto topico |
Social sciences - Forecasting
Prediction (Psychology) Economic forecasting Social prediction Human behavior |
| ISBN |
1-119-15365-4
1-119-14568-6 |
| Classificazione | BUS016000BUS021000BUS043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Foreword Thomas H. Davenport xiii Preface to the Revised and Updated Edition What's new and who's this book for--the Predictive Analytics FAQ Preface to the Original Edition xv What is the occupational hazard of predictive analytics? Introduction The Prediction Effect 1 How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die? Chapter 1 Liftoff! Prediction Takes Action (deployment) 17 How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Chapter 2 With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets (ethics) 37 How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death? Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever, and can the agency use machine learning supercomputers to fight terrorism without endangering human rights? Chapter 3 The Data E ffect: A Glut at the End of the Rainbow (data) 67 We are up to our ears in data. How much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? Chapter 4 The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) 103 What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis? Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 133 To crowdsource predictive analytics--outsource it to the public at large--a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds? Chapter 6 Watson and the Jeopardy! Challenge (question answering) 151 How does Watson--IBM's Jeopardy!-playing computer--work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible? Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward--but that can be predicted in advance? Afterword 218 Eleven Predictions for the First Hour of 2022 Appendices A. The Five Effects of Prediction 221 B. Twenty Applications of Predictive Analytics 222 C. Prediction People--Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293 . |
| Record Nr. | UNINA-9910818183603321 |
|
Siegel Eric <1968->
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| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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