For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term losses, leading them to invest less in riskier assets and miss out on higher long-term returns. Recently, advances in uncertainty visualizations have shown improvements in decision-making under uncertainty in a variety of tasks. In this paper, we conduct a controlled and incentivized crowdsourced experiment replicating Benartzi and Thaler (1999) and extending it to measure the effect of different uncertainty representations on myopic loss aversion. Consistent with the original study, we find evidence of myopic loss aversion with bar charts and find that participants make better investment decisions with longer evaluation periods. We also find that common uncertainty representations such as interval plots and bar charts achieve the highest mean expected returns while other uncertainty visualizations lead to poorer long-term performance and strong effects on the equity premium. Qualitative feedback further suggests that different uncertainty representations lead to visual reasoning heuristics that can either mitigate or encourage a focus on potential short-term losses. We discuss implications of our results on using uncertainty visualizations for retirement decisions in practice and possible extensions for future work.
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http://dx.doi.org/10.1109/TVCG.2021.3114692 | DOI Listing |
Alzheimers Dement
December 2024
Oregon Health & Science University, Portland, OR, USA.
Background: Persons with cognitive impairment may experience difficulties with language and cognition that interfere with their ability to make and communicate decisions. We developed an online visual tool to facilitate conversations about their preferences concerning supportive care.
Methods: We conducted Zoom interviews with persons with mild cognitive impairment (MCI) and mild to moderate dementia, using storytelling and a virtual tool designed to facilitate discussion.
Alzheimers Dement
December 2024
Oregon Health & Science University, Portland, OR, USA.
Background: Persons with cognitive impairment may experience difficulties with language and cognition that interfere with their ability to make and communicate decisions. We developed an online visual tool to facilitate conversations about their preferences concerning supportive care.
Methods: We conducted Zoom interviews with persons with mild cognitive impairment (MCI) and mild to moderate dementia, using storytelling and a virtual tool designed to facilitate discussion.
Sci Rep
January 2025
Faculty of Life and Allied Health Sciences, MS Ramiah University of Applied Sciences (RUAS), MSR Nagar, New BEL Road, Bangalore, 560054, India.
Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany.
Florbetaben (FBB) is a radiopharmaceutical approved by the FDA and EMA in 2014 for the positron emission tomography (PET) imaging of brain amyloid deposition in patients with cognitive impairment who are being evaluated for Alzheimer's disease (AD) or other causes of cognitive decline. Initially, the clinical adoption of FBB PET faced significant barriers, including reimbursement challenges and uncertainties regarding its integration into diagnostic clinical practice. This review examines the progress made in overcoming these obstacles and describes the concurrent evolution of the diagnostic landscape.
View Article and Find Full Text PDFBrain Topogr
January 2025
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features.
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