Matching Items (3)
Filtering by

Clear all filters

150669-Thumbnail Image.png
Description
Deoxyribonucleic Acid (DNA) evidence has been shown to have a strong effect on juror decision-making when presented in court. While DNA evidence has been shown to be extremely reliable, fingerprint evidence, and the way it is presented in court, has come under much scrutiny. Forensic fingerprint experts have been working

Deoxyribonucleic Acid (DNA) evidence has been shown to have a strong effect on juror decision-making when presented in court. While DNA evidence has been shown to be extremely reliable, fingerprint evidence, and the way it is presented in court, has come under much scrutiny. Forensic fingerprint experts have been working on a uniformed way to present fingerprint evidence in court. The most promising has been the Probabilistic Based Fingerprint Evidence (PBFE) created by Forensic Science Services (FSS) (G. Langenburg, personal communication, April 16, 2011). The current study examined how the presence and strength of DNA evidence influenced jurors' interpretation of probabilistic fingerprint evidence. Mock jurors read a summary of a murder case that included fingerprint evidence and testimony from a fingerprint expert and, in some conditions, DNA evidence and testimony from a DNA expert. Results showed that when DNA evidence was found at the crime scene and matched the defendant other evidence and the overall case was rated as stronger than when no DNA was present. Fingerprint evidence did not cause a stronger rating of other evidence and the overall case. Fingerprint evidence was underrated in some cases, and jurors generally weighed all the different strengths of fingerprint testimony to the same degree.
ContributorsArthurs, Shavonne (Author) / McQuiston, Dawn (Thesis advisor) / Hall, Deborah (Committee member) / Schweitzer, Nicholas (Committee member) / Arizona State University (Publisher)
Created2012
155983-Thumbnail Image.png
Description
This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective

This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.

This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.

Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
ContributorsPeng, Dening (Author) / Mirchandani, Pitu B. (Thesis advisor) / Sefair, Jorge (Committee member) / Wu, Teresa (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2017
141320-Thumbnail Image.png
Description

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,

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.

ContributorsNeal, Tess M.S. (Author) / Hight, Morgan (Author) / Howatt, Brian C. (Author) / Hamza, Cassandra (Author)
Created2017-04-30