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- Creators: Arizona State University
This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology pertains to the legal issue, such as in insanity cases, child custody hearings, and psychological injuries in civil suits. The legal system itself and many people involved, such as jurors, assume mental health experts are “objective” and untainted by bias. However, basic psychological science from several branches of the discipline suggest the law’s assumption about experts’ protection from bias is wrong. Indeed, several empirical studies now show clear evidence of (unintentional) bias in forensic mental health experts’ judgments and decisions. In this chapter, we explain the science of how and why human judgments are susceptible to various kinds of bias. We describe dual-process theories from cognitive neuroscience, cognitive psychology, and social psychology that can help explain these biases. We review the empirical evidence to date specifically about cognitive and social psychological biases in forensic mental health judgments, weaving in related literature about biases in other types of expert judgment, with hypotheses about how forensic experts are likely affected by these biases. We close with a discussion of directions for future research and practice.
Chapter 1 carries out a review of the mobility models found in the literature and sets the economic context of this dissertation. Chapter 2 explores a simple model that considers poor and rich classes and the impact that educational success may have on altering mobility patterns. The role of the environment is modeled through the use of a modified version of the invasion/extinction model of Richard Levins. Chapter 3 expands the socio-economic classes to include a large middle class to study the role of social mobility in the presence of higher heterogeneity. Chapter 4 includes demographic growth and explores what would be the time scales needed to accelerate mobility. The dissertation asked how long it will take to increase by 22% the proportion of educated from the poor classes under demographic versus non-demographic growth conditions. Chapter 5 summarizes results and includes a discussion of results. It also explores ways of modeling the influence of nonlinear dynamics of mobility, via exogenous factors. Finally, Chapter 6 presents economic perspectives about the role of environmental influence on college success. The framework can be used to incorporate the impact of economic factors and social changes, such as unemployment, or gap between the haves and have nots. The dissertation shows that peer influence (poor influencing the poor) has a larger effect than class influence (rich influencing the poor). Additionally, more heterogeneity may ease mobility of groups but results depend on initial conditions. Finally, average well-being of the community and income disparities may improve over time. Finally, population growth may extend time scales needed to achieve a specific goal of educated poor.