Managing Partner, Bayesia USA & Singapore
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Keynote by Olivier Sibony: Noise — A Flaw in Human Judgment
Abstract:
Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.
In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions.
Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.
About the Presenter:
Olivier Sibony is a professor, writer, and advisor specializing in the quality of strategic thinking and the design of decision processes. Olivier teaches Strategy, Decision Making, and Problem Solving at HEC Paris. He is also an Associate Fellow of SaĂŻd Business School in Oxford University.
Before he was a professor, Olivier spent 25 years with McKinsey & Company in France and in the U.S., where he was a Senior Partner. There, he was, at various times, a leader of the Global Strategy Practice and of the Consumer Goods & Retail Sector.
Olivier’s research interests focus on improving the quality of decision-making by reducing the impact of behavioral biases. He is the author of articles in various publications including “Before You Make That Big Decision”, co-authored with Nobel Prize winner Daniel Kahneman, which was selected as the cover feature of Harvard Business Review’s book selection of “10 Must-Reads on Making Smart Decisions”. In French, he also authored a book, Réapprendre à Décider.
Olivier builds on this research and on his experience to advise senior leaders on strategic and operational decision-making. He is a frequent keynote speaker and facilitator of senior management and supervisory board meetings. He also serves as a member of corporate, advisory, and investment boards.
Olivier Sibony is a graduate of HEC Paris and holds a Ph.D. from Université Paris-Dauphine.
https://youtu.be/C6CxJLo4O-Y
Abstract:
Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.
In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions.
Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.
About the Presenter:
Olivier Sibony is a professor, writer, and advisor specializing in the quality of strategic thinking and the design of decision processes. Olivier teaches Strategy, Decision Making, and Problem Solving at HEC Paris. He is also an Associate Fellow of SaĂŻd Business School in Oxford University.
Before he was a professor, Olivier spent 25 years with McKinsey & Company in France and in the U.S., where he was a Senior Partner. There, he was, at various times, a leader of the Global Strategy Practice and of the Consumer Goods & Retail Sector.
Olivier’s research interests focus on improving the quality of decision-making by reducing the impact of behavioral biases. He is the author of articles in various publications including “Before You Make That Big Decision”, co-authored with Nobel Prize winner Daniel Kahneman, which was selected as the cover feature of Harvard Business Review’s book selection of “10 Must-Reads on Making Smart Decisions”. In French, he also authored a book, Réapprendre à Décider.
Olivier builds on this research and on his experience to advise senior leaders on strategic and operational decision-making. He is a frequent keynote speaker and facilitator of senior management and supervisory board meetings. He also serves as a member of corporate, advisory, and investment boards.
Olivier Sibony is a graduate of HEC Paris and holds a Ph.D. from Université Paris-Dauphine.
https://youtu.be/C6CxJLo4O-Y
Cybersecurity Risk Assessment with Bayesian Networks
Risk assessment is challenging when data is unavailable, hard to obtain, or costly to process. Organizations often request estimates from experts instead. This talk demonstrates how to integrate cybersecurity data with expert estimates using Bayesian Networks. Cybersecurity analysts, resource managers, and executives can use Bayesian Network models to perform risk assessments, select security controls, and prioritize which suspicious events to investigate first. System administrators can configure autonomous sources of data including vulnerability scanners and cybersecurity event monitoring systems to automatically update these hybrid network models alongside inputs from risk analysts and executives.
About the Presenter
Corey Neskey has been providing analyses, architecting secure environments, and leading security program implementations in IT security and risk since 2011. His career started with informing executive decision-making using algebraic data analyses for explanation, simulation, and attribution (i.e., intelligence analysis, forensics, SOC, CIRT), and optimization. His toolset expanded to more descriptive and predictive methods (i.e., machine learning/AI for risk assessment, vulnerability prioritization, event correlation). He is now developing skills for integrating these analytical areas and expanding beyond algebraic methods and static probability calculus to using Bayesian network models.
https://youtu.be/JOBd4EX8cZ0
Risk assessment is challenging when data is unavailable, hard to obtain, or costly to process. Organizations often request estimates from experts instead. This talk demonstrates how to integrate cybersecurity data with expert estimates using Bayesian Networks. Cybersecurity analysts, resource managers, and executives can use Bayesian Network models to perform risk assessments, select security controls, and prioritize which suspicious events to investigate first. System administrators can configure autonomous sources of data including vulnerability scanners and cybersecurity event monitoring systems to automatically update these hybrid network models alongside inputs from risk analysts and executives.
About the Presenter
Corey Neskey has been providing analyses, architecting secure environments, and leading security program implementations in IT security and risk since 2011. His career started with informing executive decision-making using algebraic data analyses for explanation, simulation, and attribution (i.e., intelligence analysis, forensics, SOC, CIRT), and optimization. His toolset expanded to more descriptive and predictive methods (i.e., machine learning/AI for risk assessment, vulnerability prioritization, event correlation). He is now developing skills for integrating these analytical areas and expanding beyond algebraic methods and static probability calculus to using Bayesian network models.
https://youtu.be/JOBd4EX8cZ0
Dr. Lionel Jouffe presents the key innovations of BayesiaLab 10 at the 2021 BayesiaLab Conference.
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The Spanish TV coverage of the Bayesia-developed COVID-19 diagnostic app keeps going. Fast-forward to 2:01:00 to see our app on the Horizonte program.
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Bayesia's COVID-19 diagnostic app was featured on Spanish TV today. Check it out!
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Please join our group of physicians, epidemiologists, and data scientists to help improve the knowledge base for COVID-19 diagnostics.
Collaborative Knowledge Engineering
While there are no experts yet on COVID-19, many of you have clinical experience with all kinds of lung diseases. To help improve the COVID-19 knowledge base, we need to consolidate all your expertise quickly and efficiently.
Web-Based Expert Knowledge Elicitation
We are currently hosting a web-based knowledge elicitation session using the BEKEE framework. Upon registration, you will receive the credentials to log into the BEKEE server and can start sharing your expertise.
From Your Expertise to Worldwide Clinical Practice
Our objective is to further improve the Bayesian network that serves as the knowledge base for our COVID-19 Adaptive Questionnaire, i.e., an expert system that can differentiate COVID-19 from other flu-like diseases.
Collaborative Knowledge Engineering
While there are no experts yet on COVID-19, many of you have clinical experience with all kinds of lung diseases. To help improve the COVID-19 knowledge base, we need to consolidate all your expertise quickly and efficiently.
Web-Based Expert Knowledge Elicitation
We are currently hosting a web-based knowledge elicitation session using the BEKEE framework. Upon registration, you will receive the credentials to log into the BEKEE server and can start sharing your expertise.
From Your Expertise to Worldwide Clinical Practice
Our objective is to further improve the Bayesian network that serves as the knowledge base for our COVID-19 Adaptive Questionnaire, i.e., an expert system that can differentiate COVID-19 from other flu-like diseases.
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What is the distribution of distances between a million random points in a square? The answer requires either lots of complicated math, brute-force computing, or an incredibly simple Bayesian network. Watch our webinar on social distancing to see how a hard problem becomes trivial with BayesiaLab.
