Preferred Model Research Articles

A short-term “behavioural support” service for autistic adults without an intellectual disability: a pilot evaluation

Purpose A region’s transforming care partnership identified that autistic adults without an intellectual disability (ID) may be falling through gaps in services when presenting with a significant emotional and/or behavioural need in the absence of a mental health diagnosis. The region’s intensive support teams (ISTs) for adults with ID therefore piloted a short-term “behavioural support service” for this population. The purpose of this paper is to evaluate this pilot. Design/methodology/approach This study represents a mixed-methods service evaluation over a four year pilot period. The quantitative component examined referral rates and demographic data of accepted and declined referrals; and length of referral episodes and Health of The Nation Outcomes Scores (HoNOS) for accepted referrals. The qualitative component used thematic analysis to identify key themes relating to reasons for referral, clinical/therapeutic needs, and the models of support that most informed assessments and interventions at individual and systems levels. Findings The ISTs accepted 30 referrals and declined 53. Most accepted referrals were male (83%), and under 24 years old (57%). Average HoNOS scores were above the thresholds generally associated with hospital admission. Key qualitative themes were: transitional support; sexual risks/vulnerabilities; physical aggression; domestic violence; and attachment, trauma and personality difficulties. Support mostly followed psychotherapeutic modalities couched in trauma, attachment and second- and third-wave cognitive behavioural therapies. Positive Behaviour Support (PBS) did not emerge as a model of preference for service users or professionals. Originality/value This project represents one of the first of this type for autistic adults without an ID in the UK. It provides recommendations for future service development and research, with implications for Transforming Care policy and guidance.

Harnessing microwave energy to transform Nephelium lappaceum L. peel into activated carbon for chloramphenicol eradication in aqueous solutions

Emerging contaminants (EC) like chloramphenicol in wastewater are highly related to human, livestock, and aquaculture therapeutic activities. High levels of chloramphenicol in water may cause toxic effects on an aquatic environment, humans, and animals. The removal of chloramphenicol in wastewater was the main objective of this study. This study explores the use of microwave irradiation in the production of Rambutan peel-based activated carbon (RPAC) at a heating power of 700 W for 15 min under carbon dioxide, CO2 gas flow. RPAC had a BET surface area of 583.44 m2/g, mesopores surface area of 469.87 m2/g, total pore volume of 0.27 cm3/g, and average pore diameter of 2.31 nm. The equilibrium study shows that the concentration of Cp adsorbed by RPAC increased as the initial concentration increased from 4.93 mg/g to 25.10 mg/g. Isotherm adsorption determined that Langmuir was the preferred model since it has the lowest RSME and highest R2 of 3.34 and 0.9994, respectively. It was observed from kinetic studies that the adsorption system relies on the pseudo-second order. According to thermodynamic studies, the enthalpy change, △H° and Gibbs free energy, △G° obtained indicating that the adsorption process occurs in an exothermic and spontaneous state.

Imaging Left-Lateral and Reverse Near-Surface Slip of the 2020 Mw 5.1 Sparta, North Carolina, Earthquake

ABSTRACT Coseismic deformation of the 9 August 2020 Mw 5.1 Sparta, North Carolina, earthquake was captured by an Advanced Land Observing Satellite 2 interferogram, a Sentinel 1A/B interferogram, and differential light detection and ranging (lidar). Owing to the moderate surface deformation compared to the inherent uncertainties of interferometric data, the interferograms suffered from low signal-to-noise ratios, exacerbated by spurious signals arising from the discrepancy in resolution between the interferograms and digital elevation model used during processing. Nevertheless, coseismic slip can be imaged using graph-guided fused LASSO, where LASSO is the least-absolute shrinkage and selection operator—an inverse method that combines sparsity constraints and smoothness regularization—to infer signal from noisy and/or incomplete data. We develop five coseismic slip models, one determined only from the Sentinel 1A/B interferogram, one determined from both interferograms, one determined from all the Interferometric Synthetic Aperture Radar (InSAR) data masking potentially spurious data corresponding to industrial parks, one from lidar 3D displacements only, and one constrained by the masked InSAR and the lidar data. All five models are coseismic slip on a single fault plane, trending northwest–southeast with dips ranging from 41° to 60° to the southwest. Coseismic slip is imaged to be shallow, with predominantly oblique reverse slip extending to about 2 km depth. We find that masking the interferometric data corresponding to the industrial parks yields a coseismic slip model in better concordance with ancillary information about the Sparta earthquake and that the lidar data yield additional constraints on near-surface coseismic slip. We show that our preferred coseismic slip model is consistent with a stress regime that does not depart from an Andersonian thrust regime to a large degree, constraining that the background stress is larger than 20–30 MPa.

An all-inclusive model for predicting invasive bacterial infection in febrile infants age 7-60 days.

Invasive bacterial infections (IBIs) in febrile infants are rare but potentially devastating. We aimed to derive and validate a predictive model for IBI among febrile infants age 7-60 days. Data were abstracted retrospectively from electronic records of 37 emergency departments (EDs) for infants with a measured temperature >=100.4 F who underwent an ED evaluation with blood and urine cultures. Models to predict IBI were developed and validated respectively using a random 80/20 dataset split, including 10-fold cross-validation. We used precision recall curves as the classification metric. Of 4411 eligible infants with a mean age of 37 days, 29% had characteristics that would likely have excluded them from existing risk stratification protocols. There were 196 patients with IBI (4.4%), including 43 (1.0%) with bacterial meningitis. Analytic approaches varied in performance characteristics (precision recall range 0.04-0.29, area under the curve range 0.5-0.84), with the XGBoost model demonstrating the best performance (0.29, 0.84). The five most important variables were serum white blood count, maximum temperature, absolute neutrophil count, absolute band count, and age in days. A machine learning model (XGBoost) demonstrated the best performance in predicting a rare outcome among febrile infants, including those excluded from existing algorithms. Several models for the risk stratification of febrile infants have been developed. There is a need for a preferred comprehensive model free from limitations and algorithm exclusions that accurately predicts IBIs. This is the first study to derive an all-inclusive predictive model for febrile infants aged 7-60 days in a community ED sample with IBI as a primary outcome. This machine learning model demonstrates potential for clinical utility in predicting IBI.

Sequent Calculi for Choice Logics

Choice logics constitute a family of propositional logics and are used for the representation of preferences, with especially qualitative choice logic (QCL) being an established formalism with numerous applications in artificial intelligence. While computational properties and applications of choice logics have been studied in the literature, only few results are known about the proof-theoretic aspects of their use. We propose a sound and complete sequent calculus for preferred model entailment in QCL, where a formula F is entailed by a QCL-theory T if F is true in all preferred models of T. The calculus is based on labeled sequent and refutation calculi, and can be easily adapted for different purposes. For instance, using the calculus as a cornerstone, calculi for other choice logics such as conjunctive choice logic (CCL) and lexicographic choice logic (LCL) can be obtained in a straightforward way.

A permittivity-conductivity joint model for hydrate saturation quantification in clayey sediments based on measurements of time domain reflectometry

Hydrate saturation (Sh) is one of the key parameters for resource assessment of hydrate reservoirs and production optimization of natural gas. There are still significant challenges in determining the Sh in clayey formations. Both dielectric and resistivity logging tools have been used for identifying and evaluating hydrate-bearing formations; however, there is little work on a joint analysis and modelling of the permittivity and resistivity for quantifying the Sh. To bridge the knowledge gap, we have proposed a novel permittivity-conductivity (P–C) joint approach based on TDR (time domain reflectometry)-derived parameters (i.e., apparent permittivity Ka and bulk conductivity σdc) in this work. The proposed P–C joint approach can provide a theoretical basis for the joint interpretation of dielectric and resistivity geophysical measurements on hydrate-bearing formations in the field. First, the basic theory for deriving the Ka and σdc from the TDR responses of hydrate-bearing sediments was formulated based on the dielectric polarization and electrical conduction mechanisms. Second, an experimental campaign was carried out including the development of experimental system, calibration of TDR probe and design of experimental scheme. Third, the influences of hydrate saturation, clay mineralogy and clay content on the TDR responses of unconsolidated sediments were examined. Then the Ka and σdc were related to Sh respectively, and finally a novel P–C joint model for the quantification of Sh in clayey sediments was established and verified. It has been demonstrated that: (1) the Ka of the clayey samples with hydrates decreases almost linearly with an increasing clay content up to 20 %, while the σdc of the smectite-bearing samples decreases nonlinearly in contrast to the linear trend for illite; (2) the power-law mixing formula incorporating an empirical exponent is a preferable permittivity model for hydrate-bearing clayey sediments due to its merits of empirical and theoretical nature, while the Simandoux equation is effective to account for the clay effects on the conductivity of hydrate-bearing sediments with smectite and illite; (3) the P–C joint model can be established by utilizing the porosity of hydrate-bearing sediments as a bridge parameter between Ka and σdc. The variation behavior of Ka and σdc with different types and contents of clay minerals can be explained by the difference of the amount of bound water and swelling effects between the illite-bearing and smectite-bearing samples. The proposed P–C joint model outperforms the standalone permittivity-based and conductivity-based models especially for the clayey cases. The root-mean-squared errors of the P–C joint models are 7.339, 2.930 and 2.065 % for the clean-sand samples, clayey samples with illite and smectite, respectively.

Gamma-ray detection of newly discovered Ancora supernova remnant: G288.8–6.3

Context. The supernova remnant (SNR) G288.8–6.3 was recently discovered as a faint radio shell at high Galactic latitude using observations with the Australian Square Kilometre Array Pathfinder (ASKAP) in the Evolutionary Map of the Universe (EMU) survey. Aims. We performed the first detailed investigation of the γ-ray emission from the G288.8–6.3 region, aiming to characterise the high-energy emission in the GeV regime from the newly discovered SNR, dubbed Ancora. Methods. Fifteen years of Fermi-Large Area Telescope (LAT) data were analysed at energies between 400 MeV and 1 TeV, and the excess seen in the region was modelled using different spatial and spectral models. Results. We detect spatially extended γ-ray emission coinciding with the radio SNR, with detection significance up to 8.8 σ. A radial disk spatial model in combination with a power-law spectral model with an energy flux of (4.80 ± 0.91) × 10−6 MeV cm−2 s−1, with the spectrum extending up to around 5 GeV was found to be the preferred model. Morphologically, hotspots seen above 1 GeV are well correlated with the bright western part of the radio shell. The emission is more likely to be of leptonic origin, given the estimated gas density in the region and the estimated distance and age of the SNR, but a hadronic scenario cannot be ruled out. Conclusions. Ancora is the seventh confirmed SNR detected at high Galactic latitude with Fermi-LAT. The study of this new population of remnants can provide insights into the evolutionary aspects of SNRs and their properties, and further advance efforts of constraining the physics of particle diffusion and escape from SNRs into the Galaxy.

VHL mutation drives human clear cell renal cell carcinoma progression through PI3K/AKT-dependent cholesteryl ester accumulation

Cholesteryl ester (CE) accumulation in intracellular lipid droplets (LDs) is an essential signature of clear cell renal cell carcinoma (ccRCC), but its molecular mechanism and pathological significance remain elusive. Enabled by the label-free Raman spectromicroscopy, which integrated stimulated Raman scattering microscopy with confocal Raman spectroscopy on the same platform, we quantitatively analyzed LD distribution and composition at the single cell level in intact ccRCC cell and tissue specimens in situ without any processing or exogenous labeling. Since we found that commonly used ccRCC cell lines actually did not show the CE-rich signature, primary cancer cells were isolated from human tissues to retain the lipid signature of ccRCC with CE level as high as the original tissue, which offers a preferable cell model for the study of cholesterol metabolism in ccRCC. Moreover, we established a patient-derived xenograft (PDX) mouse model that retained the CE-rich phenotype of human ccRCC. Surprisingly, our results revealed that CE accumulation was induced by tumor suppressor VHL mutation, the most common mutation of ccRCC. Moreover, VHL mutation was found to promote CE accumulation by upregulating HIFα and subsequent PI3K/AKT/mTOR/SREBPs pathway. Inspiringly, inhibition of cholesterol esterification remarkably suppressed ccRCC aggressiveness invitro and invivo with negligible toxicity, through the reduced membrane cholesterol-mediated downregulations of integrin and MAPK signaling pathways. Collectively, our study improves current understanding of the role of CE accumulation in ccRCC and opens up new opportunities for treatment. This work was supported by National Natural Science Foundation of China (No. U23B2046 and No. 62027824), National Key R&D Program of China (No. 2023YFC2415500), Fundamental Research Funds for the Central Universities (No. YWF-22-L-547), PKU-Baidu Fund (No. 2020BD033), Peking University First Hospital Scientific and Technological Achievement Transformation Incubation Guidance Fund (No. 2022CX02), and Beijing Municipal Health Commission (No. 2020-2Z-40713).

294 Strategies for engaging patients in building a model for patient partner engagement to accelerate translational science

OBJECTIVES/GOALS: To describe the strategies our newly created Patient Partners Program has planned for engaging diverse patients in the co-creation of our planned Patient Partner Academy. We will elicit feedback from other CTSAs about their strategies for promoting individual patient partnerships in research. METHODS/STUDY POPULATION: Our program to promote authentic patient-researcher partnerships to advance clinical and translational research is grounded in participatory approaches to maximize meaningful engagement. The process of creating our Patient Partners Academy is co-led by a patient partner and national leader in re-envisioning the role of patients in research, and a University of Michigan faculty member who advocates for patient partnerships in research. Listening sessions and community engagement studios will involve the developers of patient partner training programs, patient partners who have received research capacity training (as well as researchers and patient partners interested in collaborative research. Insights from these sessions will inform the development of learning models and curriculum content. RESULTS/ANTICIPATED RESULTS: Through individual listening sessions and community engagement studios we will collect data on the barriers and facilitators to patient partner engagement in research and the experiences and preferred learning models in patient partner training programs. Both the listening sessions and community engagement studios will be recorded, transcribed, and analyzed for common themes. We aim to answer three questions: What do patient partners need to be meaningfully engaged as equal partners across the research continuum? How can we best engage people who experience serious illness or chronic conditions and may be underserved by healthcare systems? What do researchers, faculty, and clinicians need to create trusting, collaborative relationships with patient partners? DISCUSSION/SIGNIFICANCE: Integrating patient partners into study teams accelerates innovation and translational science, increases the relevance of research findings, improves health outcomes and patient empowerment, and elevates the value of the patient perspective allowing researchers to gain a new point of view from an individual with lived experience.

Validation of the IDDSI funnel for liquid flow testing.

In 2017, the International Dysphagia Diet Standardisation Initiative (IDDSI) introduced the IDDSI flow test which enables patients, clinicians, caregivers, food service professionals and researchers to classify liquid thickness into five levels based on the volume of liquid remaining in a standard 10 mL slip tip syringe after 10 s of flow under gravity. Within a few months of publishing the IDDSI flow test instructions, several barriers emerged: (1) the preferred model of syringe (BD 303134) was not equally accessible around the world, causing some users to perform flow tests with alternate models of syringe; (2) differences in syringe geometry across models led to variations in IDDSI flow test results; and (3) the need to use a second syringe for sample loading added complexity and cost to end users. To address these barriers, IDDSI designed the IDDSI funnel, a novel device, which combines the geometry of the BD 303134 syringe with a kitchen funnel to facilitate easy loading of liquid samples without need for a second syringe. In this report, we compare the IDDSI flow test results across two devices: syringe BD 303134 and IDDSI funnel. IDDSI level classifications were in complete agreement with the syringe reference test results in 67/73 (92%) of the test fluids and temperature conditions with mean difference of residual liquid across devices of 0.2 (2% full scale). These results demonstrate excellent correspondence between the two devices.

Continuous-Time User Preference Modelling for Temporal Sets Prediction

Continuous-Time User Preference Modelling for Temporal Sets Prediction

Impaired extinction of cocaine seeking in HIV-infected mice is accompanied by peripheral and central immune dysregulation

Substance use disorders (SUDs) are highly comorbid with HIV infection, necessitating an understanding of the interactive effects of drug exposure and HIV. The relationship between HIV infection and cocaine use disorder is likely bidirectional, with cocaine use directly impacting immune function while HIV infection alters addiction-related behavior. To better characterize the neurobehavioral and immune consequences of HIV infection and cocaine exposure, this study utilizes a humanized mouse model to investigate the outcomes of HIV-1 infection on cocaine-related behaviors in a conditioned place preference (CPP) model, and the interactive effects of cocaine and HIV infection on peripheral and central nervous system inflammation. HIV infection selectively impairs cocaine CPP extinction without effecting reinstatement or cocaine seeking under conflict. Behavioral alterations are accompanied by immune changes in HIV infected mice, including increased prefrontal cortex astrocyte immunoreactivity and brain-region specific effects on microglia number and reactivity. Peripheral immune system changes are observed in human cytokines, including HIV-induced reductions in human TNFα, and cocaine and HIV interactions on GM-CSF levels. Together these data provide new insights into the unique neurobehavioral outcomes of HIV infection and cocaine exposure and how they interact to effect immune responses.

Aggregating midpoint-indicator results from Environmental Product Declarations for comprehensive evaluations of products' profiles, through the Dominance-based Rough Set Approach: An application in the Durum-Wheat Pasta Sector

To-date there is an unsolved methodological problem for the type-III labelling scheme, namely the Environmental Product Declaration (EPD), that essentially derives from the midpoint approach used for the Life Cycle Assessment (LCA) which the EPD itself is based upon. As a matter of fact, in the current EPD system, it is impossible to perform univocal aggregated evaluations of the product's profile, due to the different equivalent unit-of-measures which the midpoint impacts are expressed with, and to the absence of a scientifically-based, objective scheme for aggregation and weighing.The authors wanted to undertake this challenge with the aim of enhancing the effectiveness and utility of EPDs to guide managers, consumers and other stakeholders in the promotion, distribution, and consumption of sustainable products, in the context of green public procurement. To achieve this goal for the case of durum-wheat pasta, the EPD was complemented by the authors with the Dominance-based Rough Set Approach (DRSA). In result, an aggregate preference model considering all impact categories was obtained, thereby leading to comprehensive comparisons of complete environmental profile of products. The model has the form of a set of minimal decision rules, that can be used by producers for understanding the environmental performance level of their pasta products, and subsequently making strategic decisions for its improvement.Seven of those rules were determined from DRSA application, that regarded grain production and milling, package manufacturing, and a comprehensive downstream phase that included the disposal of the packaging system, and the pasta cooking with water waste disposal. In addition to this, those rules primarily considered the following midpoint categories within the ensemble of those provided by the EPD system for pasta products: acidification, eutrophication, global warming, and mineral element depletion.All of them were certain rules: three of the “at least” type (two at least good, and one at least medium); and four of the “at most” type (one at most bad, and three at most medium).By referring the given PCR-based LCA results to the threshold values of performance on criteria present in the premise of these rules, producers can know in advance the quality class (bad, medium, or good) which their pasta products belong to.In case of a negative evaluation, they can try to improve the value of some crucial indicators before applying for obtaining the EPD, and so enhance the environmental profile of their products.The authors believe that their study advances knowledge and enriches the literature concerning the application of multiple criteria decision analysis (MCDA), wherein DRSA stands out as an outstanding example. This contribution is particularly significant in enhancing and valorising sustainability assessment tools and certification schemes.Under this perspective, the study documented that leveraging DRSA to supplement the EPD system, for the particular case of pasta products, can contribute directly and indirectly to achieving Sustainable Development Goals (SDGs) and, so, to advancing environmental sustainability within the food sector.

Electrocardiographic predictors of response to sacubitril/valsartan therapy in heart failure with reduced ejection fraction

BackgroundSacubitril/valsartan (SV) is currently recommended as a first-line therapy in patients with heart failure and reduced ejection fraction (HFrEF) due to its significant clinical and prognostic benefit; however, not all patients respond to therapy and predictors of clinical response to SV remain under-studied. AimsTo identify electrocardiographic (ECG) predictors of response to SV therapy in HFrEF patients. MethodsA retrospective analysis of a hospital heart failure registry was undertaken. Consecutive HFrEF patients (New York Heart Association class II–III) on maximal-dose SV were studied. Response to SV was defined as ≥10% relative improvement in left ventricular ejection fraction (LVEF) at 3-months post-maximal-dose therapy. Pre-therapy ECGs were retrospectively analyzed for axes and standard wave and interval durations. Logistic regression was used to estimate odds ratios and 95% confidence intervals for associations between predictors and therapeutic response. Backward stepwise regression was employed to develop a parsimonious model. ResultsP-wave duration (PWD) 100–120 ms, PWD >120 ms, and QTc >460 ms were associated with response to SV on univariate analysis: OR 18.00 (4.45–122.90), 5.00 (1.47–20.42), and 3.10 (1.18–9.22), respectively. The preferred model that included the former two predictors in combination with pre-therapy creatinine, mineralocorticoid receptor antagonist use, and LVEF was highly selective (area under the ROC curve = 0.868). ConclusionsProlongation of both PWD and QTc interval on baseline ECG in HFrEF patients is predictive of therapeutic response to maximal-dose SV therapy and may indicate early cardiac remodeling that is highly amenable to reversal.

Learning Optimal Advantage from Preferences and Mistaking It for Reward

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF). Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return. Recent work casts doubt on the validity of this assumption, proposing an alternative preference model based upon regret. We investigate the consequences of assuming preferences are based upon partial return when they actually arise from regret. We argue that the learned function is an approximation of the optimal advantage function, not a reward function. We find that if a specific pitfall is addressed, this incorrect assumption is not particularly harmful, resulting in a highly shaped reward function. Nonetheless, this incorrect usage of the approximation of the optimal advantage function is less desirable than the appropriate and simpler approach of greedy maximization of it. From the perspective of the regret preference model, we also provide a clearer interpretation of fine tuning contemporary large language models with RLHF. This paper overall provides insight regarding why learning under the partial return preference model tends to work so well in practice, despite it conforming poorly to how humans give preferences.

Acceptability and anticipated effectiveness of a safe supply of opioids, among people who inject opioids in King County, WA

BackgroundOpioid overdose mortality in the US has exceeded one million deaths over the last two decades. A regulated opioid supply may help prevent future overdose deaths by reducing exposure to the unregulated opioid supply. We examined the acceptability, delivery model preference, and anticipated effectiveness of different regulated opioid models among people in the Seattle area who inject opioids. MethodsWe enrolled people who inject drugs in the 2022 Seattle-area National HIV Behavior Surveillance (NHBS) survey. Participants were recruited between July and December 2022 using respondent-driven sampling. Participants who reported injecting opioids (N = 453) were asked whether regulated opioids would be acceptable, their preferred model of receiving regulated opioids, and the anticipated change in individual overdose risk from accessing a regulated opioid supply. ResultsIn total, 369 (81 %) participants who injected opioids reported that a regulated opioid supply would be acceptable to them. Of the 369 who found a regulated opioid supply to be acceptable, the plurality preferred a take-home model where drugs are prescribed (35 %), followed closely by a dispensary model that required no prescription (28 %), and a prescribed model where drugs need to be consumed on site (13 %), a model where no prescription is required and drugs can be accessed in a community setting with a one-time upfront payment was the least preferred model (5 %). Most participants (69 %) indicated that receiving a regulated opioid supply would be “a lot less risky” than their current supply, 20 % said, “a little less risky”, 10 % said no difference, and 1 % said a little or a lot more risky. ConclusionA regulated opioid supply would be acceptable to most participants, and participants reported it would greatly reduce their risk of overdose. As overdose deaths continue to increase in Washington state pragmatic and effective solutions that reduce exposure to unregulated drugs are needed.

Proportional Aggregation of Preferences for Sequential Decision Making

We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. Instead of going with the most popular choice in each round, we aim for proportional representation, using axioms inspired by the multi-winner voting literature. The axioms require that every group of α% of the voters, if it agrees in every round (i.e., approves a common alternative), then those voters must approve at least α% of the decisions. A stronger version of the axioms requires that every group of α% of the voters that agrees in a β fraction of rounds must approve β⋅α% of the decisions. We show that three attractive voting rules satisfy axioms of this style. One of them (Sequential Phragmén) makes its decisions online, and the other two satisfy strengthened versions of the axioms but make decisions semi-online (Method of Equal Shares) or fully offline (Proportional Approval Voting). We present empirical results for these rules based on synthetic data and U.S. political elections. We also run experiments using the moral machine dataset about ethical dilemmas. We train preference models on user responses from different countries and let the models cast votes. We find that aggregating these votes using our rules leads to a more equal utility distribution across demographics than making decisions using a single global preference model.

The Moderating Effect of Instant Runoff Voting

Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence focused on case studies and simulations. In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes.

Multi-Objective Bayesian Optimization with Active Preference Learning

There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive search cost, while in many practical scenarios, the decision maker (DM) only needs a specific solution among the set of the Pareto optimal solutions. We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in the MOO with expensive objective functions, in which a Bayesian preference model of the DM is adaptively estimated by an interactive manner based on the two types of supervisions called the pairwise preference and improvement request. To explore the most preferred solution, we define an acquisition function in which the uncertainty both in the objective function and the DM preference is incorporated. Further, to minimize the interaction cost with the DM, we also propose an active learning strategy for the preference estimation. We empirically demonstrate the effectiveness of our proposed method through the benchmark function optimization and the hyper-parameter optimization problems for machine learning models.

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Modern recommender systems lie at the heart of complex recommender ecosystems that couple the behavior of users, content providers, vendors, advertisers, and other actors. Despite this, the focus of much recommender systems research and deployment is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommender systems generate for their users. We argue that modeling the incentives and behaviors of these actors, and the interactions among them induced by the recommender systems, is needed to maximize value and improve overall ecosystem health. Moreover, we propose the use of economic mechanism design, an area largely overlooked in recommender systems research, as a framework for developing such models. That said, one cannot apply “vanilla” mechanism design to recommender ecosystem modeling optimization out of the box—the use of mechanism design raises a number of subtle and interesting research challenges. We outline a number of these in this talk (and paper), emphasizing the need to develop nonstandard approaches to mechanism design that intersect with numerous areas of research, including preference modeling, reinforcement learning and exploration, behavioral economics, and generative AI, among others.