Springer Dissecting Discrimination: Identifying Its Various Faces and Their Sources (Entscheidungs- und Organisationstheorie)

Product Description This Open-Access-book examines the phenomenon of discrimination using a descriptive approach. Discrimination is omnipresent, whether it is people who discriminate against other people or, more recently, also machines that discriminate against people. The first part of the analysis employs decision theory on discrimination, leading to two fundamental subtypes: taste-based discrimination and statistical discrimination. The second part links taste-based discrimination to social identity theory, demonstrates that not all taste-based discrimination is ultimately statistical discrimination, and reveals the evolutionary origins of our tastes. The third part surveys how people get their beliefs for statistical discrimination and thereby shows that they often deviate from Bayesianism: they have inherent prior beliefs and do not exclusively update their beliefs according to Bayes’ law. Additionally, the analysis of belief formation highlights the importance of the learning environment. The last part reassembles the previously dissected aspects of discrimination, presents a new descriptive model of discrimination, and lists five implications for a normative theory of discrimination. From the Back Cover This Open-Access-book examines the phenomenon of discrimination using a descriptive approach. Discrimination is omnipresent, whether it is people who discriminate against other people or, more recently, also machines that discriminate against people. The first part of the analysis employs decision theory on discrimination, leading to two fundamental subtypes: taste-based discrimination and statistical discrimination. The second part links taste-based discrimination to social identity theory, demonstrates that not all taste-based discrimination is ultimately statistical discrimination, and reveals the evolutionary origins of our tastes. The third part surveys how people get their beliefs for statistical discrimination and thereby shows that they often deviate from Bayesianism: they have inherent prior beliefs and do not exclusively update their beliefs according to Bayes’ law. Additionally, the analysis of belief formation highlights the importance of the learning environment. The last part reassembles the previously dissected aspects of discrimination, presents a new descriptive model of discrimination, and lists five implications for a normative theory of discrimination. About the author: Daniel Villiger is a research fellow at the Center for Ethics at the University of Zurich and a lecturer at the University of St. Gallen. He is currently involved in a project about transformative experiences.  .   About the Author Daniel Villiger is a research fellow at the Center for Ethics at the University of Zurich and a lecturer at the University of St. Gallen. He is currently involved in a project about transformative experiences.