Sociologists of education increasingly highlight the role of opportunity hoarding in the formation of Black-White educational inequalities. Informed by this literature, this article unpacks the necessary and sufficient conditions under which the hoarding of educational resources emerges within schools. It develops a qualitatively informed agent-based model which captures Black and White students' competition for a valuable school resource: advanced coursework. In contrast to traditional accounts -- which explain the emergence of hoarding through the actions of Whites that keep valuable resources within White communities -- simulations, perhaps surprisingly, show hoarding to arise even when Whites do not play the role of hoarders of resources. Behind this result is the fact that a structural inequality (i.e., racial differences in social class) -- and not action-driven hoarding -- is the necessary condition for hoarding to emerge. Findings, therefore, illustrate that common action-driven understandings of opportunity hoarding can overlook the structural foundations behind this important phenomenon. Policy implications are discussed.

This article investigates mechanism-based explanations for a well-known empirical pattern in sociology of education, namely, that Black-White unequal access to school resources-- defined as advanced coursework--is the highest in racially diverse and majority-White schools. Through an empirically calibrated and validated agent-based model, this study explores the dynamics of two qualitatively informed mechanisms, showing (1) that we have reason to believe that the presence of White students in school can influence the emergence of Black-White advanced enrollment disparities and (2) that such influence can represent another possible explanation for the macro-level pattern of interest. Results contribute to current scholarly accounts of within-school inequalities, shedding light into policy strategies to improve the educational experiences of Black students in racially integrated settings. Keywords: Black-White inequalities; agent-based modeling; advanced course-taking; school organization; racial composition.

Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning techniques have been proposed in the literature, there is one particular setting that has not been explored yet: multi agent systems where agent identities remain anonymous. For instance, in financial markets labeled data that identifies market participant strategies is typically proprietary, and only the anonymous state-action pairs that result from the interaction of multiple market participants are publicly available. As a result, sequences of agent actions are not observable, restricting the applicability of existing work. In this paper, we propose a Policy Clustering algorithm, called K-SHAP, that learns to group anonymous state-action pairs according to the agent policies. We frame the problem as an Imitation Learning (IL) task, and we learn a world-policy able to mimic all the agent behaviors upon different environmental states. We leverage the world-policy to explain each anonymous observation through an additive feature attribution method called SHAP (SHapley Additive exPlanations). Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly. We evaluate our approach on simulated synthetic market data and a real-world financial dataset. We show that our proposal significantly and consistently outperforms the existing methods, identifying different agent strategies.

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk and scene graph learning method for trajectory forecasting of heterogeneous road agents, which consists of a Heterogeneous Risk Graph (HRG) and a Hierarchical Scene Graph (HSG) from the aspects of agent category and their movable semantic regions. HRG groups each kind of road agent and calculates their interaction adjacency matrix based on an effective collision risk metric. HSG of the driving scene is modeled by inferring the relationship between road agents and road semantic layout aligned by the road scene grammar. Based on this formulation, we can obtain effective trajectory forecasting in driving situations, and superior performance to other state-of-the-art approaches is demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and Argoverse datasets.

Adaptive Loop Closure Detection: Spurious loop closures are frequent in environments with repetitive appearances, such as long streets. On the one hand, false positive place I. SYSTEM OVERVIEW retrievals may easily break the global optimization system, As shown in Figure 1, AutoMerge provides an automatic and ideally 100% accuracy can avoid these optimization map merging system for the large-scale single-/multi-agent failures for large-scale mapping. On the other hand, low recalls mapping tasks. Each agent is equipped with a LiDAR mapping can provide partial data association, which will affect global module to enable the self-maintained sub-map generation and optimization performance. Hybrid loop closure detection takes odometry estimation. The AutoMerge system consists of three advantage of sequence matching to provide continuous true modules: 1) fusion-enhanced place descriptor extraction, 2) an positive retrievals over long overlaps, and RANSAC-based adaptive data-association mechanism to provide high accuracy single frame detection for local overlaps. By analyzing the and recall for segment-wise place retrievals, and 3) a partially feature correlation between segments, we can balance the place decentralized system to provide centralized map merging and retrievals from sequence-/single-frame matching to provide single agent self-localization in the world frame.

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.

We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses Bayesian optimization and a ``bandit-over-bandit'' framework to dynamically adjust strategy parameters in response to market conditions. We use Bristol Stock Exchange (BSE) to simulate financial markets containing heterogeneous populations of automated trading agents and compare PRBO with PRSH, a reference trading strategy that adapts strategy parameters through stochastic hill-climbing. Results show that PRBO generates significantly more profit than PRSH, despite having fewer hyperparameters to tune. The code for PRBO and performing experiments is available online open-source (https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation).

Empirical studies suggest a deep intertwining between opinion formation and decision-making processes, but these have been treated as separate problems in the study of dynamical models for social networks. In this paper, we bridge the gap in the literature by proposing a novel coevolutionary model, in which each individual selects an action from a binary set and has an opinion on which action they prefer. Actions and opinions coevolve on a two-layer network. For homogeneous parameters, undirected networks, and under reasonable assumptions on the asynchronous updating mechanics, we prove that the coevolutionary dynamics is an ordinal potential game, enabling analysis via potential game theory. Specifically, we establish global convergence to the Nash equilibria of the game, proving that actions converge in a finite number of time steps, while opinions converge asymptotically. Next, we provide sufficient conditions for the existence of, and convergence to, polarized equilibria, whereby the population splits into two communities, each selecting and supporting one of the actions. Finally, we use simulations to examine the social psychological phenomenon of pluralistic ignorance.

This paper develops a fully distributed differentially-private learning algorithm to solve nonsmooth optimization problems. We distribute the Alternating Direction Method of Multipliers (ADMM) to comply with the distributed setting and employ an approximation of the augmented Lagrangian to handle nonsmooth objective functions. Furthermore, we ensure zero-concentrated differential privacy (zCDP) by perturbing the outcome of the computation at each agent with a variance-decreasing Gaussian noise. This privacy-preserving method allows for better accuracy than the conventional $(\epsilon, \delta)$-DP and stronger guarantees than the more recent R\'enyi-DP. The developed fully distributed algorithm has a competitive privacy accuracy trade-off and handles nonsmooth and non-necessarily strongly convex problems. We provide complete theoretical proof for the privacy guarantees and the convergence of the algorithm to the exact solution. We also prove under additional assumptions that the algorithm converges in linear time. Finally, we observe in simulations that the developed algorithm outperforms all of the existing methods.

This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the limited availability of guide animals that are accessible to only two to three percent of the potential blind or visually impaired (BVI) users. To build a successful guide robot, our paper explores three key topics: (1) formalizing the navigation mechanism of a guide dog and a human, (2) developing a data-driven model of their interaction, and (3) improving user safety. First, we formalize the wayfinding task of the human-guide robot team using Markov Decision Processes based on the literature and interviews. Then we collect real human-robot interaction data from three visually impaired and six sighted people and develop an interaction model called the ``Delayed Harness'' to effectively simulate the navigation behaviors of the team. Additionally, we introduce an action shielding mechanism to enhance user safety by predicting and filtering out dangerous actions. We evaluate the developed interaction model and the safety mechanism in simulation, which greatly reduce the prediction errors and the number of collisions, respectively. We also demonstrate the integrated system on a quadrupedal robot with a rigid harness, by guiding users over $100+$~m trajectories.