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The Bayesian-optimality of decision-making behavior: across tasks, time, and psychopathology.
No full textUniversity of Minnesota Ph.D. dissertation. April 2025. Major: Psychology. Advisors: Iris Vilares, Vanessa Lee. 1 computer file (PDF); vii, 194 pages.From medical diagnosis to catching a baseball, people make decisions all the time. But on what basis are those decisions made and how optimal are they? This dissertation focuses on one theoretical approach to assess the optimality of decisions: Bayesian Decision Theory (BDT). BDT postulates that posterior estimation relies on two categories of information: knowledge gained over time (i.e., prior information) and current sensory input (i.e., likelihood), with greater weight given to the category associated with less uncertainty. Here, I test the possibility that while decisions are typically Bayesian in a qualitative sense, they often deviate from BDT quantitatively. Three empirical studies explored the Bayesian optimality of decision-making behavior in visual search (Chapter 2), across sensorimotor and visual search tasks (Chapter 3), and in patients with Borderline Personality Disorder (Chapter 4). Chapter 2 created a novel hybrid search-decision task, in which participants made a target present/absent response on a display of items with partial occlusion. I found that while participants considered both the target's prevalence ("prior") and the degree of occlusion ("likelihood"), they gave disproportionate weights to visible information, showing a mixture of Bayesian inference and under-matching. Chapter 3 tested behavior in the visual search task and a sensorimotor "coin-catching" task within the same set of individuals across two time points, assessing the degree to which they relied on prior vs. likelihood. I found consistent individual differences within a task, with some measures of both tasks displaying good test-retest reliability, but not between tasks, arguing against a domain-general Bayesian weight. Chapter 4 showed that while patients with BPD performed like controls in the coin-catching task in a qualitatively Bayesian manner, both fell short of quantitative BDT predictions. Overall, these findings demonstrate that BDT is a powerful framework for understanding a range of decision-making behaviors including sensorimotor and attentional decisions, across patients and typical groups. However, they also show that Bayesian weights may not be domain-general, and factors other than prior and likelihood may influence decisions.Manavalan, Mathi. (2025). The Bayesian-optimality of decision-making behavior: across tasks, time, and psychopathology.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/275905
An Oral History Interview with Daniel J. Solove
No full textOral History Interview with Daniel J. Solove Conducted by Gerardo Con Diaz, University of California, DavisThis oral history interview is sponsored by NSF 2202484, “Mining a Usable Past: Perspectives, Paradoxes, and Possibilities with Security and Privacy,” at the Charles Babbage Institute, University of Minnesota. The interview is with Daniel J. Solove, Eugene L. and Barbara Bernard Professor of Intellectual Property and Technology Law at the George Washington University Law School. Solove reflects on his early life in Pennsylvania, his education at Washington University in St. Louis and Yale Law School, and his career trajectory from judicial clerkship to legal academia. He discusses the origins and evolution of his scholarship on privacy, including his taxonomy of privacy harms, his work on data protection, and his interest in bridging legal theory and practical policy. The interview covers his role in shaping privacy law as a discipline, his efforts to influence public and institutional understanding through writing, teaching, and consulting, and his perspectives on regulatory frameworks in the U.S. and Europe. He concludes with reflections on academic impact, interdisciplinary engagement, and the future of privacy scholarship.National Science FoundationSolove, Daniel J.. (2025). An Oral History Interview with Daniel J. Solove. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/274367
Farm legal series: Contracts, Notes, and Guaranties, 2025
No full textThis publication may not reflect current laws, scientific knowledge or recommendations. Current information may be available from the University of Minnesota Extension at www.extension.umn.edu.Peterson, Jeffrey A.; Boothe, Austyn K.. (2025). Farm legal series: Contracts, Notes, and Guaranties, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/273714
Revealing the Hidden Curriculum: Navigating the Unwritten Rules of the College Experience
No full textThe following guide was developed in partnership with the Digital Education and Innovation Team within the College of Education and Human Development.Jehangir, Rashne; Roman, Richard; DeLorme, Lyn; Vang, Zer. (2025). Revealing the Hidden Curriculum: Navigating the Unwritten Rules of the College Experience. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/275436
Implementation of machine learning to improve implantable cardioverter-defibrillator detection algorithms
No full textUniversity of Minnesota M.S. thesis. May 2025. Major: Biomedical Engineering. Advisor: Alena Talkachova. 1 computer file (PDF); v, 20 pages.Background: Sudden cardiac death is a leading cause of death in the US and globally. Implantable cardioverter-defibrillators (ICD) prevent this through electrical therapy, but inappropriate therapy for non-life threatening heart rhythms remains pervasive. Objective: The goal of this study was to improve upon current ICD discrimination algorithms by using supervised machine learning techniques on an annotated database of ICD electrograms (EGM) preceding therapy to discriminate between appropriate (App) or inappropriate (InApp) therapies. Methods: A total of 54 EGMs of therapy events adjudicated by cardiologists were digitized from 49 cases. The signals were analyzed within either a single long window, or four short overlapping windows preceding therapy. The discrimination between App and InApp therapies was done using EGMs recorded over specific windows by separately calculating RR-based and nonlinear dynamic (NLD) based metrics, and creating RR- and NLD-scores, respectively. Linear and quadratic discriminant analysis (LDA and QDA) were then used on the obtained RR- and NLD-scores to predict the App or InApp therapy. These results were then compared to the App and InApp designation by cardiologists. Error rates based on incorrect classifications were used to evaluate the performance of both techniques. Results: We demonstrated that the optimal windows for LDA and QDA can both greatly improve upon modern error rates, with our QDA error going as low as nearly 2% when using an optimal window, as compared to the errors up to 25% found in recent studies. Despite QDA having a lower overall error across all windows, the QDA error came mostly from more dangerous false negatives, which made up 100% of misclassified points in the longest temporal window, and in the temporal window closest to therapy, whereas the majority of LDA error came from less dangerous false positives, which made up 100% of misclassified points in the same two windows. Conclusions: This novel strategy shows promise for use in retrospective discrimination of inappropriate and appropriate ICD therapy events and could improve real time decision making algorithms in ICDs. Additional studies should be completed to assess its utility in real time decision making and case adjudicated.Callaway, Trenton. (2025). Implementation of machine learning to improve implantable cardioverter-defibrillator detection algorithms. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/275830
Thomas J. Farrell's Jungian Profile of Himself and of Donald Trump, and the Thought of C. G. Jung and Walter J. Ong
No full textSee the above abstract.In the wide-ranging and, at times, deeply personal 10,042-word review essay "Thomas J. Farrell's Jungian Profile of Himself and of Donald Trump, and the Thought of C. G. Jung and Walter J. Ong," I succinctly highlight (1) Pete Walker's new 2024 book Holistically Treating Complex PTSD: A Six-Dimensional Approach: Guidance for Therapists, Coaches, and Other Helpers to Repair the Damage and Arrested Development Suffered by Childhood Trauma Survivors (An Azure Coyote Book), (2) the relevant thought of the Swiss psychiatrist and psychological theorist C. G. Jung (1875-1961), and (3) the relevant thought of the American Jesuit Renaissance specialist, cultural historian, and pioneering media ecology theorist Walter J. Ong (1912-2003; Ph.D. in English, Harvard University, 1955) of Saint Louis University, the Jesuit university in the City of St. Louis, Missouri (USA), where, over the years, I took five courses from him.N/AFarrell, Thomas. (2025). Thomas J. Farrell's Jungian Profile of Himself and of Donald Trump, and the Thought of C. G. Jung and Walter J. Ong. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276417
Brain region activation across acute and protracted alcohol withdrawal in male mice
No full textUniversity of Minnesota M.S. thesis. May 2025. Major: Pharmacology. Advisor: Anna Lee. 1 computer file (PDF); iv, 38 pages.Alcohol use disorder (AUD) remains a significant public health issue, with high relapse rates complicated by its poorly understood neurochemical mechanisms. Both acute and protracted withdrawal symptoms contribute to relapse vulnerability, yet the brain regions involved in the development of these symptoms have not been extensively studied in this context. This study aims to bridge this gap of knowledge by investigating changes in the Ventral Tegmental Area (VTA), Habenula (Hb), and Mesopontine Tegmentum (MPT), areas that literature suggests may have plausible involvement, during acute and protracted alcohol withdrawal in male mice. Mice underwent 9 days of passive alcohol administration (2.5 g/kg, i.p. with 4-Methylpyrazole) and brain tissue was collected at either 24 hours (acute), or 7 days (protracted) after cessation. Brain region activity was assessed using immunohistological labeling of cFos as a proxy for neuron activation. During acute withdrawal, we observed a significant decrease in the activity of cholinergic neurons in the Pedunculopontine Tegmental Nucleus (PTg) but found no substantial changes in the other regions listed above. Activity in the Lateral Habenula (LHb), and specifically, cholinergic activity in the MPT, increased during protracted withdrawal. No changes were observed in the VTA or Medial Habenula at 7-day withdrawal. These findings suggest that the LHb and MPT may play a role in modulating neurochemical changes resulting from alcohol withdrawal. Further investigation into these regions may provide insight on the neural basis of relapse vulnerability and lead to more efficacious treatments for AUD.Lanz, Mariana. (2025). Brain region activation across acute and protracted alcohol withdrawal in male mice. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/275827
Farm legal series: Security Interests in Personal Property, 2025
No full textThis publication may not reflect current laws, scientific knowledge or recommendations. Current information may be available from the University of Minnesota Extension at www.extension.umn.edu.Peterson, Jeffrey A.; Boothe, Austyn K.. (2025). Farm legal series: Security Interests in Personal Property, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/273729
K-Move dances to more than just the music
No full textRuntime 20:45BTS and K-pop gave this University of Minnesota dance group a space to be themselves, and a community that feels like homeHeinen, Ceci. (2025). K-Move dances to more than just the music. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276461
Simple policies in dynamic matching markets
No full textUniversity of Minnesota Ph.D. dissertation. May 2025. Major: Industrial and Systems Engineering. Advisor: Nick Arnosti. 1 computer file (PDF); viii, 114 pages.This thesis containts two self contained essays.The first essay studies a foundational model of dynamic matching market with abandonment. This model has been studied by Collina et al. (2020) and Aouad and Saritac(2022), and many other papers have considered special cases. The performance of greedy policies – which identify a set of “acceptable” matches up front, and perform these matches as soon as possible – is compared to that of an omniscient benchmark which knows the full arrival and departure sequence. A novel family of linear programs (PLP) is introduced to identify which greedy policy to follow. We show that the value of PLP is a lower bound on the value of the greedy policy that it identifies in two settings of interest:
• The case where the everyone has the same departure rate.
• The bipartite case where everyone on the same side of the market has the same
departure rate. The proofs of these results use a new result (Lemma 1), which relates the probability that at least one agent from a set of types is present in the system to the expected number of such agents. We show that the value of PLP is at least 1/2 of the reward rate earned by the omniscient policy (Proposition 4). Therefore, for both settings above, the identified greedy policy provably earns at least half of the omniscient reward rate. This improves upon the bound of 1/8 from Collina et al. (2020). In both settings the competitive ratio of 1/2 is the best possible: no online policy can provide a better guarantee (Theorem 2). The second essay models the problem facing a policymaker who must allocate rapid rehousing support to people experiencing homelessness and wishes to minimize the steady-state size of the homeless population. Typically, support is given to the most vulnerable applicants, or to applicants most likely to remain housed. Although these approaches can be effective in some cases, in general they may result in a homeless population that is arbitrarily larger than what could be achieved by an optimal policy. We propose an alternative priority queue that is approximately optimal. We then study a family of policies where the policymaker does not differentiate between agents based on their characteristics. Within this family, FIFO queues best target the most vulnerable. If the most vulnerable households benefit most from housing assistance, then a FIFO queue minimizes the expected unhoused population. Conversely, a LIFO queue is optimal if the least vulnerable households benefit most from housing assistance. Finally, we expand our model to allow households to choose among several allocation systems. We show that even in this larger family of policies, if the most vulnerable households are also the ones that most benefit from housing assistance, a FIFO queue minimizes the expected unhoused population.Simon, Felipe. (2025). Simple policies in dynamic matching markets. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/275924