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

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

PLATFORM COOPS NOW!: A team entrepreneurship capacity building program to create platform coops

Traditional labour relationships have been disrupted due to the digital platforms based businesses. This article aims on the one hand to share the consequences the sharing economy has generated for workers, and how MONDRAGON’s principles as one of the best examples of worker owned business group in the world, can be applied within the new digital era. On the other hand, this paper provides a literature review on how digital platforms can operate with fairer principles based on the framework that platform coops consist of. Last but not least, Mondragon University and The New School have set up a capacity building program on team entrepreneurship and an online incubation program that aims to support the creation of platform coops, whose results after two editions and future opportunities for research are shared

Reading "all about" computerization: five common genres of social analysis

This paper examines unstated, but critical, social assumptions which underlie analyses of computerization. It focuses on the popular, professional and scholarly literature which claims to describe the actual nature of computerization, the character of computer use, and the social choices and changes that result from computerization. This literature can be usefully segmented five ideal type genres: utopian, anti-utopian, social realism, social theory, and analytical reduction. Each genre is characterized and illustrated. The strengths and weaknesses of each genre are described. In the 1990s, there will be a large market for social analyses of computerization. Utopian analyses are most likely to domínate the popular and professional discourse. The empirically oriented accounts of social realism, social theory and analytical reduction, are likely to be much less common and also less commonly seen and read by computer professionals and policymakers. These genres are relatively subtle, portray a more ambiguous world, and have less rhetorical power to capture the imagination of readers. Even though they are more scientific, these empirically anchored genres don't seem to appeal to many scientists and engineers. It is ironic that computing -- often portrayed as an instrument of knowledge -- is primarily the subject of discourses whose knowledge claims are most suspect. Conversely, the discourses whose claims as valid knowledge are strongest seems to have much less appeal in the mass media and technological communities

An intelligent tutoring system for the investigation of high performance skill acquisition

The issue of training high performance skills is of increasing concern. These skills include tasks such as driving a car, playing the piano, and flying an aircraft. Traditionally, the training of high performance skills has been accomplished through the use of expensive, high-fidelity, 3-D simulators, and/or on-the-job training using the actual equipment. Such an approach to training is quite expensive. The design, implementation, and deployment of an intelligent tutoring system developed for the purpose of studying the effectiveness of skill acquisition using lower-cost, lower-physical-fidelity, 2-D simulation. Preliminary experimental results are quite encouraging, indicating that intelligent tutoring systems are a cost-effective means of training high performance skills

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii