Fairness is an important consideration for dynamic resource allocation in multi-agent systems. Many existing methods treat fairness as a one-shot problem without considering temporal dynamics, which misses the nuances of accumulating inequalities over time. Recent approaches overcome this limitation by tracking allocations over time, assuming perfect recall of all past utilities. While the former neglects long-term equity, the latter introduces a critical challenge: the augmented state space required to track cumulative utilities grows unboundedly with time, hindering the scalability and convergence of learning algorithms. Motivated by behavioral insights that human fairness judgments discount distant events, we introduce a framework for temporal fairness that incorporates past-discounting into the learning problem. This approach offers a principled interpolation between instantaneous and perfect-recall fairness. Our central contribution is a past-discounted framework for memory tracking and a theoretical analysis of fairness memories, showing past-discounting guarantees a bounded, horizon-independent state space, a property that we prove perfect-recall methods lack. This result unlocks the ability to learn fair policies tractably over arbitrarily long horizons. We formalize this framework, demonstrate its necessity with experiments showing that perfect recall fails where past-discounting succeeds, and provide a clear path toward building scalable and equitable resource allocation systems.

There is growing interest in algorithms that match passengers with drivers in ride-sharing problems and their fairness for the different parties involved (passengers, drivers, and ride-sharing companies). Researchers have proposed various fairness metrics for matching algorithms, but it is often unclear how one should balance the various parties' fairness, given that they are often in conflict. We present FairVizARD, a visualization-based system that aids users in evaluating the fairness of ride-sharing matching algorithms. FairVizARD presents the algorithms' results by visualizing relevant spatio-temporal information using animation and aggregated information in charts. FairVizARD also employs efficient techniques for visualizing a large amount of information in a user friendly manner, which makes it suitable for real-world settings. We conduct our experiments on a real-world large-scale taxi dataset and, through user studies and an expert interview, we show how users can use FairVizARD not only to evaluate the fairness of matching algorithms but also to expand on their notions of fairness.

We introduce the General Incentives-based Framework for Fairness (GIFF), a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions. In resource-constrained settings, agents optimizing for efficiency often create inequitable outcomes. Our approach leverages the action-value (Q-)function to balance efficiency and fairness without requiring additional training. Specifically, our method computes a local fairness gain for each action and introduces a counterfactual advantage correction term to discourage over-allocation to already well-off agents. This approach is formalized within a centralized control setting, where an arbitrator uses the GIFF-modified Q-values to solve an allocation problem. Empirical evaluations across diverse domains, including dynamic ridesharing, homelessness prevention, and a complex job allocation task-demonstrate that our framework consistently outperforms strong baselines and can discover far-sighted, equitable policies. The framework's effectiveness is supported by a theoretical foundation; we prove its fairness surrogate is a principled lower bound on the true fairness improvement and that its trade-off parameter offers monotonic tuning. Our findings establish GIFF as a robust and principled framework for leveraging standard reinforcement learning components to achieve more equitable outcomes in complex multi-agent systems.

Next location prediction underpins a growing number of mobility, retail, and public-health applications, yet its societal impacts remain largely unexplored. In this paper, we audit state-of-the-art mobility prediction models trained on a large-scale dataset, highlighting hidden disparities based on user demographics. Drawing from aggregate census data, we compute the difference in predictive performance on racial and ethnic user groups and show a systematic disparity resulting from the underlying dataset, resulting in large differences in accuracy based on location and user groups. To address this, we propose Fairness-Guided Incremental Sampling (FGIS), a group-aware sampling strategy designed for incremental data collection settings. Because individual-level demographic labels are unavailable, we introduce Size-Aware K-Means (SAKM), a clustering method that partitions users in latent mobility space while enforcing census-derived group proportions. This yields proxy racial labels for the four largest groups in the state: Asian, Black, Hispanic, and White. Built on these labels, our sampling algorithm prioritizes users based on expected performance gains and current group representation. This method incrementally constructs training datasets that reduce demographic performance gaps while preserving overall accuracy. Our method reduces total disparity between groups by up to 40\% with minimal accuracy trade-offs, as evaluated on a state-of-art MetaPath2Vec model and a transformer-encoder model. Improvements are most significant in early sampling stages, highlighting the potential for fairness-aware strategies to deliver meaningful gains even in low-resource settings. Our findings expose structural inequities in mobility prediction pipelines and demonstrate how lightweight, data-centric interventions can improve fairness with little added complexity, especially for low-data applications.

A wide variety of resource allocation problems operate under resource constraints that are managed by a central arbitrator, with agents who evaluate and communicate preferences over these resources. We formulate this broad class of problems as Distributed Evaluation, Centralized Allocation (DECA) problems and propose methods to learn fair and efficient policies in centralized resource allocation. Our methods are applied to learning long-term fairness in a novel and general framework for fairness in multi-agent systems. Our methods outperform existing fair MARL approaches on multiple resource allocation domains, even when evaluated using diverse fairness functions, and allow for flexible online trade-offs between utility and fairness.

Memory allocation is a fairly mature field of computer science. However, we challenge a prevailing assumption in the literature over the last 50 years which, if reconsidered, necessitates a fundamental reevaluation of many classical memory management algorithms. We pose a model where the allocation algorithm runs on host memory but allocates device memory and so incur the following constraint: the allocator can't read the memory it is allocating. This means we are unable to use boundary tags, which is a concept that has been ubiquitous in nearly every allocation algorithm. In this paper, we propose alternate algorithms to work around this constraint, and discuss in general the implications of this system model.

BACKGROUND AND PURPOSE:Conclusions from prior literature regarding the impact of sex, age, and height on spinal cord (SC) MRI morphometrics are conflicting, while the effect of body weight on SC morphometrics has been found to be nonsignificant. The purpose of this case-control study is to assess the associations between cervical SC MRI morphometric parameters and age, sex, height, and weight to establish their potential role as confounding variables in a clinical study of people with multiple sclerosis (MS) compared to a cohort of healthy volunteers. METHODS:Sixty-nine healthy volunteers and 31 people with MS underwent cervical SC MRI at 3 Tesla field strength. Images were centered at the C3/C4 intervertebral disc and processed using Spinal Cord Toolbox v.4.0.2. Mixed-effects linear regression models were used to evaluate the effects of biological variables and disease status on morphometric parameters. RESULTS:Sex, age, and height had significant effects on cord and gray matter (GM) cross-sectional area (CSA) as well as the GM:cord CSA ratio. There were no significant effects of body weight on morphometric parameters. The effect of MS disease duration on cord CSA in the C4 level was significant when controlling for all other variables. CONCLUSIONS:Studies of disease-related changes in SC morphometry should control for sex, age, and height to account for physiological variation.

This paper develops - and applies - a micro-macroeconomic modeling approach for assessing major welfare system reforms. With a growing interest in the value of bold welfare reforms in the light of persistent and widening inequalities, we demonstrate the value of a comprehensive analysis of both the (micro) impact upon the distribution of household incomes and wider (macro) impacts upon national income, unemployment and government spending. By combining microsimulation with CGE modeling, we argue that our findings demonstrate the importance of any major social welfare or broad fiscal reform being the subject of a micro-macro modeling approach. We illustrate this through an application to the introduction of a universal basic income in Scotland.

We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user), where the goal is for the explainee to understand the explainer's decision. We formally describe the operational semantics of our proposed framework, providing theoretical guarantees. We then evaluate the framework's efficacy ``in the wild'' via computational and human-subject experiments. Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.

We present DR-HAI -- a novel argumentation-based framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting an argumentation-based dialogue paradigm, DR-HAI enables interactive reconciliation to address knowledge discrepancies between an explainer and an explainee. We formally describe the operational semantics of DR-HAI, provide theoretical guarantees, and empirically evaluate its efficacy. Our findings suggest that DR-HAI offers a promising direction for fostering effective human-AI interactions.

State-of-the-art order dispatching algorithms for ridesharing batch passenger requests and allocate them to a fleet of vehicles in a centralized manner, optimizing over the estimated values of each passenger-vehicle matching using integer linear programming (ILP). Using good estimates of future values, such ILP-based approaches are able to significantly increase the service rates (percentage of requests served) for a fixed fleet of vehicles. However, such approaches that focus solely on maximizing efficiency can lead to disparities for both drivers (e.g., income inequality) and passengers (e.g., inequality of service for different groups). Existing approaches that consider fairness only do it for naive assignment policies, require extensive training, or look at only single-sided fairness. We propose a simple incentive-based fairness scheme that can be implemented online as a part of this ILP formulation that allows us to improve fairness over a variety of fairness metrics. Deriving from a lens of variance minimization, we describe how these fairness incentives can be formulated for two distinct use cases for passenger groups and driver fairness. We show that under mild conditions, our approach can guarantee an improvement in the chosen metric for the worst-off individual. We also show empirically that our Simple Incentives approach significantly outperforms prior art, despite requiring no retraining; indeed, it often leads to a large improvement over the state-of-the-art fairness-aware approach in both overall service rate and fairness.

There has been a significant body of research on improving social welfare in resource allocation, but much of it has focused on single-shot allocation scenarios, where a given pool of resources must be divided equitably. In contrast, my research aims to address the unique challenges posed by temporal resource allocation problems that involve many repeated allocations, with both resources and beneficiaries able to re-enter the market at different points in time. Automated algorithms are often employed to guide resource allocation in these scenarios by estimating and comparing utilities of different allocations, making algorithmic fairness a concern as well. In this work, I aim to improve long-term social welfare in addition to maximizing the utility of such systems through the lens of pre-, in-, and post-processing fairness. I propose a simple incentive-based approach for post-processing fairness with black-box value functions, outperforming existing baselines in a ridesharing application. I discuss two other research thrusts using fairness-aware dataset balancing for pre-processing fairness and learning non-myopic fairness policies for in-processing fairness. Combining all of these approaches, my goal is to present a holistic view of improving social welfare in temporal resource allocation through the lens of algorithmic fairness.

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.

Pediatric spinal cord morphometry has been relatively understudied because of non-optimal image quality due to the difficulty of spine imaging, rarity of post-mortem analysis, motion artifacts, and pediatric MR imaging research focus on understanding spinal injury or pathology. The pediatric brain has been comparatively well-studied with white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) differences observed with age and gender. Therefore, a greater understanding of pediatric cervical and thoracic spinal cord morphometry would be beneficial for developing clinically relevant cord growth models. We focused on retrospectively characterizing cervical and thoracic spinal cord growth and morphometry changes in a healthy pediatric population. High resolution multi-echo gradient echo (mFFE) images were acquired from pediatric spinal cord scans from 63 patients (mean: 9.24 years, range: 0.83-17.67 years). The mFFE scans were then registered to the template space for uniform viewing and analysis by using a customized semi-automatic processing pipeline involving Spinal Cord Toolbox (SCT). Jacobian control determinants were calculated, and subsequent WM, GM, dorsal column, lateral funiculi, and ventral funiculi scalar averaging was conducted. Random effects models were used to model age-related Jacobian scalar differences. Observing the growth of cord matter by patient age and vertebral level suggests that the upper cervical spinal cord, specifically C2-C3, and mid- thoracic spinal cord, T3-T8, grow faster than other cervical levels and thoracic levels, respectively. This knowledge will facilitate clinical decision making when considering spine interventions and conducting radiological analysis in children with cervical and thoracic spine abnormalities.

Regional development now encompasses inclusive growth so that welfare spending becomes a potentially important policy for regions with devolved powers. Universal Basic Income (UBI), an unconditional payment to all citizens, has been gaining traction, including internationally. We provide a system-wide analysis of a region-specific UBI for Scotland on the level and distribution of regional activity. Using both micro- and macroeconomic models, we find that although UBI has a beneficial effect on equity among households, it may adversely impact the level of economic activity unless there is a social contract in place and/or there is a substantial stimulus to productivity.

Order dispatching algorithms, which match passenger requests with vehicles (agents) in ridesharing systems, are able to achieve high service rates (percentage of requests served) using deep reinforcement learning techniques to estimate the relative values of the different combinations of passenger-vehicle matches. While the goal of such algorithms is to maximize the service rate, this may lead to unintended fairness issues (e.g., high disparity between the service rates of geographic zones in a city). To remedy this limitation, researchers have recently proposed deep reinforcement learning based techniques that incorporates fairness components in the value function approximated. However, this approach suffers from the need to retrain should one wish to tune the degree of fairness or optimize for a different fairness function, which can be computationally expensive. Towards this end, we propose a simpler online approach that uses state-of-art deep reinforcement learning techniques and augments their value functions with fairness components during the matching optimization step. As no additional training is needed, this approach can be adapted to use any existing value function approximator and benefits from improved flexibility in evaluating different fairness objectives efficiently. In this paper, we describe several fairness functions that can be used by this approach and evaluate them against existing state-of-the-art deep RL based fairness techniques on standard ridesharing benchmarks. Our experiments show that our fairness functions outperform existing fairness techniques (i.e., it finds matching solutions that result in higher service rates and lower service rate disparity across zones), demonstrating the practical promise of this approach.

In 2017 the Scottish Government passed the Child Poverty (Scotland) Act with the commitment to significantly reduce the relative child poverty rate from the current prevailing level of around 25% to 10% by 2030/31. In response, the government introduced the Scottish Child Payment (SCP) that provides a direct transfer to households at a fixed rate per eligible child – currently £25 per week. In this paper we explore, using a micro to macro modelling approach, the effectiveness of using the SCP to achieve the Scottish child poverty targets. While we find that the ambitious child poverty targets can technically be met solely using the SCP, the necessary payment of £165 per week amounting to a total government cost of £3 billion per year, makes the political and economy-wide barriers significant. A key issue with only using the SCP is the non-linearity in the response to the payment; as the payment increases, the marginal gain in the reduction of child poverty decreases – this is particularly evident after payments of £80 per week. A ‘policy-mix’ option combining the SCP, targeted cash transfers and other policy levels (such as childcare provision) seems the most promising approach to reaching the child poverty targets.

•A randomized, placebo-controlled trial of adjunct homeopathy with standard of care for COVID-19 was conducted on 300 patients.•Primary outcome on total symptom score measured over 10 days decreased significantly favoring Standard of care + Homeopathy.•Recovery was earlier in Standard of care + Homeopathy group by 02 days showing the added benefits of homeopathy.•Resolution of fever was 20 h earlier in Standard of care + Homeopathy.•Arsenicum album, Bryonia alba and Phosphorus were the most frequently prescribed medicines.

Advancements in explanation generation for automated planning algorithms have moved us a step closer towards realizing the full potential of human-AI collaboration in real-world planning applications. Within this context, a framework called model reconciliation has gained a lot of traction, mostly due to its deep connection with a popular theory in human psychology, known as the theory of mind. Existing literature in this setting, however, has mostly been constrained to algorithmic contributions for generating explanations. To the best of our knowledge, there has been very little work on how to effectively convey such explanations to human users, a critical component in human-AI collaboration systems. In this paper, we set out to explore to what extent visualizations are an effective candidate for conveying explanations in a way that can be easily understood. Particularly, by drawing inspiration from work done in visualization systems for classical planning, we propose a visualization framework for visualizing explanations generated from model reconciliation algorithms. We demonstrate the efficacy of our proposed system in a comprehensive user study, where we compare our framework against a text-based baseline for two types of explanations – domain-based and problem-based explanations. Results from the user study show that users, on average, understood explanations better when they are conveyed via our visualization system compared to when they are conveyed via a text-based baseline.