Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...

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Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Theoretical models of financial markets built on the assumption that some investors are overconfident yield one central prediction: overconfident investors will trade too much. We test this prediction by partitioning investors on the basis of a variable that provides a natural proxy for overconfidence--gender. Psychological research has established that men are more prone to overconfidence than women. Thus, models of investor overconfidence predict that men will trade more and perform worse than women. Using account data for over 35,000 households from a large discount brokerage firm, we analyze the common stock investments of men and women from February 1991 through January 1997. Consistent with the predictions of the overconfidence models, we document that men trade 45 percent more than women and earn annual risk-adjusted net returns that are 1.4 percent less than those earned by women. These differences are more pronounced between single men and single women; single men trade 67 percent more than single women and earn annual risk-adjusted net returns that are 2.3 percent less than those earned by single women.

https://papers.ssrn.com/sol3/Delivery.cfm/98110615.pdf?abstractid=139415&rulid=175370&mirid=3

Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment

Theoretical models predict that overconedent investors trade excessively We test this prediction by partitioning investors on gender Psychological research demonstrates that, in areas such as enance, men are more overconedent than women Thus, theory predicts that men will trade more excessively than women Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997 We document that men trade 45 percent more than women Trading reduces men’s net returns by 265 percentage points a year as opposed to 172 percentage points for women It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so Josh Billings, nineteenth century American humorist It is difecult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors Rational investors make periodic contributions and withdrawals from their investment portfolios, rebalance their portfolios, and trade to minimize their taxes Those possessed of superior information may trade speculatively, although rational speculative traders will generally not choose to trade with each other It is unlikely that rational trading needs account for a turnover rate of

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Boys will be Boys: Gender, Overconfidence, and Common Stock Investment

/pdf/research-design-qualitative-quantitative-and-mixed-methods-1m92m1x5gg.pdf

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

For Resilience Engineering, 'failure' is the result of the adaptations necessary to cope with the complexity of the real world, rather than a breakdown or malfunction. The performance of individuals and organizations must continually adjust to current conditions and, because resources and time are finite, such adjustments are always approximate. This definitive new book explores this groundbreaking new development in safety and risk management, where 'success' is based on the ability of organizations, groups and individuals to anticipate the changing shape of risk before failures and harm occur. Featuring contributions from many of the worlds leading figures in the fields of human factors and safety, Resilience Engineering: Concepts and Precepts provides thought-provoking insights into system safety as an aggregate of its various components, subsystems, software, organizations, human behaviours, and the way in which they interact. The book provides an introduction to Resilience Engineering of systems, covering both the theoretical and practical aspects. It is written for those responsible for system safety on managerial or operational levels alike, including safety managers and engineers (line and maintenance), security experts, risk and safety consultants, human factors professionals and accident investigators.

Resilience Engineering: Concepts and Precepts

The State of Human Reliability Analysis. Shortcomings of First-Generation HRA. Cognitive Reliability and Error Analysis Method. Background of the Book. The Need of HRA . The Ubiquity of Erroneous Actions. The Role of HRA in PSA. The Modelling of Erroneous Actions. The Conceptual Impuissance . The Classification of Erroneous Actions. Traditional Human Factors Approaches. Information Processing Approaches. The Cognitive Systems Engineering Perspective. Evaluation. The Schism between HRA and Psychology. A Conceptual Framework . Introduction. The Need to Predict. Method, Classification, Model. Modelling of Cognition. Standard Classification Schemes. Performance Shaping Factors and Common Performance Conditions. HRA - The First Generation . Reliability and Safety Analysis of Dynamic Process Systems. First-Generation HRA Approaches. Conclusions. HRA and Cognition: Extensions. CREAM - A Second Generation HRA Method . Principles of Cream. Models of Cognition. Basic Principles of the Classification Scheme. Classification Groups. Links Between Classification Groups. The Search For Causes: Retrospective Analysis . Analysis and Stop Rules. Overall Method. Example of Retrospective Analysis. Qualitative Performance Prediction . Principles of Performance Prediction. Predictive Use of the Classification Scheme. Principles of Qualitative Performance Prediction. The Quantification of Predictions . CREAM - Basic Method. CREAM Basic Method: an Example. CREAM - Extended Method. Extended CREAM Method: an Example.

Cognitive reliability and error analysis method : CREAM

PREFACE THE DRIVING FORCES Introduction On Terminology Computerization and Growing Complexity Self-Reinforcing Complexity Cycle Complexity and Unpredictability Conspicuousness of the Human Factor The Constraining Paradigm Input-Output Models The Shannon-Weaver Model Communication Model Prototypical Information Processing From Human-Machine Interaction to Joint Systems The Cognitive Viewpoint The Classical Human-Machine View The Disintegrated View Changing the Paradigm Definition of a Cognitive System The Scope of CSE THE EVOLUTION OF WORK Technological Systems as Amplification 'Examples of Amplification Amplification of Control Effects of Amplification Amplification and Interpretation Tools and Prostheses A Short History of Human-Machine Interaction The Conspicuousness of Cognition Changing Balance Between Doing and Thinking Loss of Work-Specific Information The Law of Requisite Variety Models of the Human as Controller The Joint Cognitive System (JCS) Control and Cognition Disjoint and Joint Systems Amplifying the Ability to Control THE BASICS OF A SCIENCE Model-Classification-Method Requirements to Measurements Theory-Driven Measurements Theory-Begging Measurements The Meaning of Measurements The Elusiveness of Cognition Cognition in the Mind Cognition in the Wild The Focus of CSE Cognition and Context Cognition and Control Modeling Cognition and Context Sequentiality in Reality and in Models The Threads of CSE Coping With Complexity Use of Artifacts Joint Cognitive Systems Merging the Threads COPING WITH COMPLEXITY Introduction About Coping Sources of Complexity Losing Control Lack of Time Lack of Knowledge Lack of Readiness or Preparedness (Competence) Lack of Resources Coping Strategies Information Input Overload Information Input Underload Designing for Simplicity Simplicity-Complexity Trade-Off Information Structuring How Should the Interaction Be Designed? Designing for Complexity Support for Coping Time Predictability Summary USE OF ARTIFACTS Introduction Phenomenology of Coagency Tools and Prostheses Artifacts in CSE Range of Artifacts Cognitive Artifacts The Substitution Myth Consequences of Technology Change Traffic Safety Typical User Responses to New Artifacts Failure Modes of an Artefact The Accidental User User Models and Accidental Users JOINT COGNITIVE SYSTEMS Introduction On the Nature of System Boundaries Automation and Joint Cognitive Systems Degrees of Human-Machine Dependency Automation Philosophies Ironies of Automation Lesons of Automation Function Analysis and Goal Achievement Goals and Means Simple Test-Operate-Test-Exit (TOTE) Recursive Goals-Means Descriptions CONTROL AND COGNITION Introduction Feedback and Control Feedforward and Control The Substance of Modeling Cognition Cognition Without Context Cognition in Context Mental Models and the Law of Requisite Variety COCOM-Contextual Control Model Model Constituents Control Modes ECOM-Extended Control Model Tracking Regulating Monitoring Targeting ECOM Structure and Parameters Interaction Between Loops Modeling the Loss of Control TIME AND CONTROL Orthodoxy in Modeling Model Minimization Two Neglected Issues Modeling Control Predictability Available Time The Modeling of Time Representation of Time in COCOM Time and Control Modes How to Enhance Control Technological and Organizational Solutions Human Solutions Conclusions CSE AND ITS APPLICATIONS What Should CSE Be About? Extensions to Human Factors/Ergonomics Design of Work (Cognitive Task Design) Making Work Easy Making Work Safe Problems in the Design of Work Control Rooms Adaptation Decision Support The Last Words BIBLIOGRAPHY AUTHOR INDEX SUBJECT INDEX

Joint Cognitive Systems: Foundations of Cognitive Systems Engineering

Accidents are preventable, but only if they are correctly described and understood. Since the mid-1980s accidents have come to be seen as the consequence of complex interactions rather than simple ...

Barriers and Accident Prevention

Performance, Reliability, And Unwanted Consequences. The Need for Models of the Reliability of Cognition. The Nature of Human Reliability Assessment. Reliability. Scientific Issues. The Fundamentals of the Model. The Dependent Differentiation Method. Chapter Summaries. Discussion. References. Index.

Erik Hollnagel

Papers

Resilience Engineering: Concepts and Precepts

Cognitive reliability and error analysis method : CREAM

Joint Cognitive Systems: Foundations of Cognitive Systems Engineering

Barriers and Accident Prevention

Human Reliability Analysis: Context and Control