Suchergebnisse

Intro -- PSYCHOLOGY OF BIAS -- PSYCHOLOGY OF BIAS -- Library of Congress Cataloging-in-Publication Data -- CONTENTS -- PREFACE -- Chapter 1: BIASES IN MEDICAL DECISION MAKING -- ABSTRACT -- 1. INTRODUCTION -- 1.1. The Error in Medicine -- 1.2. The Dimension of Medical Error -- 2. COGNITIVE BASIS OF DIAGNOSIS -- 2.1. Diagnostic Errors -- 2.2. The Medical Choice: Beyond the Given Data(Toward Diagnosis) -- 2.3. Cognitive Heuristics and Biases -- 3. TWO WAYS OF THINKING -- 3.1. Is there Room for Intuition in Medicine? -- 3.2. Summing Up -- CONCLUSION -- REFERENCES -- Chapter 2: PERCEPTUAL BIASES FOR THREAT -- ABSTRACT -- BIASES FOR ANCIENT THREATS-AN EVOLUTIONARY MODEL -- BIASES FOR MODERN THREATS-A LEARNING MODEL -- BIASES FOR THREAT IN CHILDREN AND INFANTS-A DUAL-PATHWAY MODEL -- BEHAVIORAL IMPLICATIONS -- CONCLUSION -- REFERENCES -- Chapter 3: ON THE RELATIONSHIPBETWEEN STIMULUS VALENCE,EMOTIONAL BIAS, AND BEHAVIOR -- ABSTRACT -- INTRODUCTION -- RELEVANCE OF THE STIMULUSVALENCE TO SURVIVAL -- FROM ENVIRONMENT TO COGNITIONTHROUGH MOOD -- EMOTIONAL VALENCE:IS IT AN ARTIFICIAL DICHOTOMIZATION? -- THE POWER OF UNCONSCIOUSLY PERCEIVEDEMOTIONAL STIMULI -- THEORETICAL ORIENTING FRAMEWORKS -- REFERENCES -- Chapter 4: THE BIAS TOWARD CAUSE AND EFFECT -- THE BIAS TOWARD CAUSE AND EFFECT -- MAKING THE CASE FOR 'CAUSE' -- THE SAME UNDERLYING CONCEPT? -- CHALLENGES TO A SINGLE NOTION OF CAUSE -- A DIFFERENT APPROACH -- REFERENCES -- Chapter 5: RESPONDING TO GROUPBASEDDISCRIMINATION:WHEN THE NORMATIVE PROTECTIONOF THE (DISADVANTAGED)INGROUP MATTERS -- ABSTRACT -- RESPONDING TO GROUP-BASED DISCRIMINATION:WHEN THE NORMATIVE PROTECTIONOF THE (DISADVANTAGED) INGROUP MATTERS -- DISCRIMINATION: THE TARGET'S PERSPECTIVE -- OVERVIEW OF THE PRESENT RESEARCH -- METHOD -- RESULTS -- CONCLUSION -- REFERENCES.

During the Summer of 2015, the University of Washington eScience Institute ran an interdisciplinary summer internship program focused on urban informatics, civic engagement, and data-intensive social science. Borrowing elements from the successful Data Science for Social Good (DSSG) programs at the University of Chicago and Georgia Tech, and building on our own previous consulting and "incubation" programs for data-intensive projects in physical, life, and social sciences, we brought together teams of students (graduate, undergraduate, and high school), data scientists, project leads and stakeholders from the University of Washington and local NGOs to design, develop, and deploy new solutions to high-impact problems in the Seattle Metro Area. In this paper, we describe the inaugural offering of the eScience DSSG and reflect on the process of organizing and structuring the program. The DSSG attracted 144 graduate and undergraduate student applicants from over 10 different fields of study. The final DSSG fellow cohort included 16 students accepted from this pool of applicants. In addition, we included six high school students who joined us from a separate program designed to expose young people to research activities and an undergraduate student who had already started working on one of the projects through another summer research program. We solicited project proposals from research professionals across academic, non-profit, and government institutions. Ultimately, 4 projects were chosen out of 11 submitted proposals: two addressing transportation access for people with limited mobility, one identifying factors affecting whether homeless families find permanent housing, and one deriving new metrics of community well-being from social media data and other relevant data sources. All datasets were sourced from Seattle businesses, foundations, and agencies, with the exception of social media. The teams worked in a shared studio space designed in part for this purpose, and participated in tutorials on relevant tools ...