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.