CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Internet Data Quota Assistance for Students Using Reference Point MOORA Decision Analysis

Decisions are made by those who are authorized and responsible decision makings for an institution or organization. Many decisions involve stakeholders who are individuals and organizations who can be affected by the future consequences of these decisions, including decision-making on Internet data quota assistance distributed by the Ministry of Education and Culture to support students' online learning from home (LFH). This Assistance Policy is very appropriate and well provided during the Covid-19 Pandemic. However, in an effort to optimize and objectify distribution so that it is right on target, this research is proposed solutions for applying the analysis decision-making mode using Multi-criteria Decision-making (MCDM). The criteria used include the amount of internet data consumption, Academic credits, courses, and student's economic capacity. The criteria weighting method uses Rank-order Centroid (ROC) and reference point MOORA for determining the best alternative.  The results showed that the implementation of the MCDM ROC-Reference MOORA method affected the preference value and alternative ranking of internet data quota assistance. This shows that the criteria weight value and the analysis method are critical aspects in decision-making. Differences in weight, even the slightest change in weight, can drastically change the final decisio

Kinerja Metode Rank Sum, Rank Reciprocal dan Rank Order Centroid Menggunakan Referensi Poin Moora (Studi Kasus: Bantuan Kuota Data Internet untuk Mahasiswa)

Penentuan bobot kriteria merupakan masalah yang sering muncul di banyak metode MCDM dan merupakan aspek kritis dalam pengambilan keputusan. Perbedaan bobot, bahkan perubahan bobot sekecil apa pun, dapat mengubah keputusan akhir secara drastis. Tujuan utama dari penelitian ini adalah menawarkan metode pengambilan keputusan multi-kriteria dalam penyaluran bantuan kuota data internet untuk pembelajaran online mahasiswa dari rumah (learn from home) agar tepat sasaran berdasarkan kebutuhan. Tujuan khususnya adalah untuk menggambarkan kinerja metode perankingan pembobotan kriteria dan bagaimana hasil akhir dari model keputusan multi-kriteria bergantung pada penggunaan metode pembobotan yang berbeda. Kriteria pengambilan keputusan bantuan kuota data internet berdasarkan kebutuhan pembelajaran online dan kemampuan biaya ekonomi mahasiswa. Metode perankingan bobot menggunakan teknik pembobotan Rank Sum (RS), Rank Reciprocal (RR) dan Rank Order Centroid (RoC) dan  metode analisis keputusan preferensi menggunakan referensi poin dan optimasi Moora. Hasil penerapan pemilihan 5 sampel alternatif terbaik dari setiap metode menunjukkan terdapat perbedaan dalam urutan pemeringkatan (ranking) ke-4 dan ke-5, sedangkan urutan ranking ke-1, ke-2 dan ke-3 memilih alternatif yang sama, hal ini menunjukkan bahwa ketiga alternatif terbaik yang sama dari hasil preferensi metode RS-Moora, RR-Moora dan ROC-Moora merupakan alternatif yang direkomendasikan untuk mendapatkan bantuan kuota data internet bagi mahasiswa yang memiliki kebutuhan beban pembelajaran online yang tinggi, namun kemampuan biaya ekonomi rendah. AbstractDetermination of criterion weights is a problem that occurs often in many MCDM methods and is a critical aspect of decision making. Differences in criterion weights, even the slightest change in weight, can drastically change the final decision. The main objective of this research is to offer an implementation multi-criteria decision-making (MCDM) method in distributing internet data quota assistance for students' learning online from home to be right on target based on the needs. Its special purpose is to describe the performance of the criteria-weighted ranking method and how the final outcome of the MCDM model depends on the use of different weighting methods. The decision-making criteria for internet data quota assistance are based on the needs of online learning and the ability of the student's economic costs. The weight ranking method uses the Rank Sum (RS) weighting technique, the Reciprocal Rank (RR) and the Rank Order Centroid (RoC), with the decision analysis method uses the reference point Moora. Results the selection of the 5 best alternative samples from each method shows that there are differences in the 4th and 5th rankings, while the 1st, 2nd and 3rd ranks choose the same alternative, this shows that the three best alternatives are the same From the results of the preference of the RS-Moora method, RR-Moora and ROC-Moora are the recommended alternatives to obtain internet data quota assistance for students with a high online learning load, but low economic ability.

Generative Pretraining for Black-Box Optimization

Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often have access to a fixed, offline dataset for pretraining. Prior approaches seek to utilize the offline data to approximate the function or its inverse but are not sufficiently accurate far from the data distribution. We propose BONET, a generative framework for pretraining a novel black-box optimizer using offline datasets. In BONET, we train an autoregressive model on fixed-length trajectories derived from an offline dataset. We design a sampling strategy to synthesize trajectories from offline data using a simple heuristic of rolling out monotonic transitions from low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using a causally masked Transformer and evaluate it on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.Comment: International Conference for Machine Learning 2023 NeurIPS Workshop for Foundational Models for Decision Making (Oral) 202

IST Austria Thesis

Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the sake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range of training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the data they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting

Value-Added Scores Show Limited Stability over Time in Primary School

Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students’ achievement. If VA scores lack stability over time and vary across outcome domains (e.g., mathematics and language learning), their use for high-stakes decision making is in question and could have detrimental real-life implications: teachers could lose their jobs, or a school might receive less funding. However, school-level stability over time and variation across domains have rarely been studied together. In the present study, we examined the stability of VA scores over time for mathematics and language learning, drawing on representative, large-scale, and longitudinal data from two cohorts of standardized achievement tests in Luxembourg (N = 7,016 students in 151 schools). We found that only 34-38% of the schools showed stable VA scores over time with moderate rank correlations of VA scores from 2017 to 2019 of r = .34 for mathematics and r = .37 for language learning. Although they showed insufficient stability over time for high- stakes decision making, school VA scores could be employed to identify teaching or school practices that are genuinely effective—especially in heterogeneous student populations.

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed