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| Preface | |
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| Introduction | |
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| Challenges and Opportunities with Optimal Decision Making and How Advanced Analytics Can Help | |
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| How We Make Decisions and What Gets in the Way | |
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| Intuition Versus Analytical Thinking | |
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| Poor Intuitive Statisticians | |
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| Understanding Human Nature | |
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| Biases and Decisions | |
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| Big Data and Information Overload | |
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| The Problem with Certitude | |
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| Advanced Analytics Does Not Care Who It Annoys | |
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| Types of Decision Making | |
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| Rise of the Machines: Advanced Analytics and Decision Making | |
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| Advanced Analytics | |
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| Predicting Outcomes | |
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| Improper Linear Models: Combining Expert Intuition with Analytics | |
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| Artificial Intelligence and Machine Learning | |
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| Human and Machine: The Ideal Decision-Making Team | |
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| A Word About AI Tools | |
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| Collaboration, Cooperation, and Reciprocity | |
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| Human Nature and Human Science | |
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| Reciprocity and Fairness | |
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| Selfish, Greedy, Lazy, and Dishonest | |
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| Human Nature 2.0 | |
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| Fierce Cooperation | |
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| Collaboration | |
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| Hard Wired to Share What We Know | |
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| Collective Intelligence | |
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| Asymmetric or Private Information | |
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| Game Theory 101 | |
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| The Power of Collaboration: The Scandinavian Model | |
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| What Kinds of Organizations Could Benefit from a High Degree of Collaboration? | |
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| The Benefits of Collaboration | |
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| The Bottom-Line Impact of Participative Decision Making | |
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| Organizational Culture | |
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| Optimal Incentive Contract for Collaboration: Sharing Control and Return Rights | |
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| Models of Collaboration | |
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| The SAS Institute | |
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| EMCOne | |
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| Boston Scientific | |
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| Advanced Analytics and Collaborative Decision Making | |
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| Challenges and Opportunities with Participative Decision Making | |
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| Software, Advanced Analytics, and Cooperation and Collaboration | |
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| Deep Q&A Expert Systems | |
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| Value Creation and Advanced Analytics | |
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| The Wealth of Organizations and What Advanced Analytics Can Do | |
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| Information Capital | |
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| Constant and Unrelenting Experimentation | |
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| Gold in Them There Databases: Human Capital Data | |
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| Not Only Human Experts Are Prone to Biases | |
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| Value and How to Create It: Intangible Capital | |
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| Who Really Holds the Keys to the Kingdom | |
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| The Nature of the Organization | |
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| The Cost of Employee Turnover | |
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| Strategic Choice and Advanced Analytics | |
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| HCM Practice Choice and Advanced Analytics | |
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| Business Intelligence Alignment of HCM Practices and Policies with Business Strategy | |
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| Decision Science, Business Intelligence, and Implications for HCM Decisions | |
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| Machine Learning and HR Practice Choice | |
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| Software Applications, Analytics, and HR Decisions | |
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| Software Options and Optimal HCM Practice | |
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| Enterprise Resource Planning Software | |
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| Talent Analytics | |
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| SAS Business Intelligence | |
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| Talent Scorecard | |
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| Talent Management Suites and Advanced Analytics | |
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| Human Science and Selection Decisions | |
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| Optimizing Selection and Promotion Decisions | |
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| Performance and Selection | |
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| Making the Unobservable Observable | |
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| Eliminating Biases from Selection Decisions | |
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| Human Science and Employee Selection | |
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| Skills Shortages | |
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| Workforce Planning, Talent Acquisition, and Decision Analytics | |
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| Workforce Planning and Predictive Analytics | |
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| When Is Workforce Planning Necessary? | |
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| Challenges with Forecasting | |
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| External Big Data and Employee Recruitment and Selection | |
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| Human Science and Selection and Promotions Decisions | |
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| What We Have to Learn from the Use of Advanced Analytics for Player Selection in Professional Sports | |
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| Biases and the Selection Decision | |
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| Selection Tools: Augmented Biographical Survey | |
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| Challenges with the Use of Bio Data | |
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| Applications of Human Science to Selection Decisions | |
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| The Application of Expert Intuition to Selection and Promotion Decisions | |
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| Applied Game Theory and Selection Decisions | |
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| Deep Q&A Expert Systems and Selection Decisions | |
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| Predictive Modeling and Selections Decisions | |
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| Applied Econometric and Machine Learning Techniques | |
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| Human Science and Incentives | |
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| Human Science and Incentives | |
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| Incentives, Motivation, and Human Science | |
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| Incentive Contracts | |
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| Collaboration and Tournament Compensation Do Not Go Together | |
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| We Get What We Pay For | |
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| Human Science and Motivation | |
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| Performance Management | |
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| Biases Impacting Performance Management and Compensation Decisions | |
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| Strategy Maps and Performance Management | |
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| Applying Human Science to Incentive Contracts | |
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| Irrational, Cooperative, and Looking for Meaning | |
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| Complexity Theory and Incentive Contracts | |
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| The Application of Expert Intuition to Incentive and Motivation Issues | |
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| Applied Game Theory and Incentive Contracts | |
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| Deep Q & A Expert Systems and Incentive Contract Decisions | |
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| Predictive Modeling and Incentive Contracts | |
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| Applied Econometric and Machine Learning Techniques | |
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| Application of Human Science to Specific Incentive Issues | |
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| Executive Compensation | |
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| Other Possible Human Science Incentive Applications | |
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| Conclusion | |
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| Garbage In | |
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| Our Argumentative Natures | |
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| Advanced Analytics and Diagnosis of HCM Issues | |
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| The Science (and Art) of Prediction | |
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| The Challenges with Being Empirically Declarative | |
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| Decision-Making Authority and Cooperation | |
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| Sharing Control and Return Rights | |
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| Individualization | |
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| Definitions (Appendix) | |
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| Endnotes | |
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| Index | |