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Nonprofit organizations (NPOs) lack resources, hindering the quality and quantity of service they can deliver. Meanwhile, NPOs at times have underutilized or even spare resources due to the inability to scale expertise in staffing and tangible resources to meet temporally shifting service demands. These observations motivate us to propose a novel resource sharing system, SWAP, which to the best of our knowledge, is the first resource sharing system that facilitates resource exchanges where NPOs can obtain resources by offering their own. SWAP consists of four elements: a collaborative auction-based sharing process, complete with an offering mechanism, a bidding mechanism, and the virtual currency, SWAPcredit, to facilitate liquidity in exchange; a central technology that represents the award determination problem with a multilateral exchange optimization model, generating resource exchange outcomes; an online platform, the SWAP Hub, where NPOs can offer and bid on available resources, and receive exchange results; and human-centric co-design, shaping the understanding and design decisions of a research collective, that includes the authors and NPO professionals. We conduct a series of experiments using both empirical and simulated data to illustrate the benefits and potential of SWAP. Our results demonstrate that SWAP can address temporal resource needs in practice; show that optimal exchange outcomes can be generated even for large-scale SWAP markets; and provide strong evidence in support of guidance to inform the progression for future versions of SWAP. The SWAP system is presently implemented in Howard County, MD, USA, with ongoing enhancements and potential for future expansion. 

To make daily decisions, human agents devise their own "strategies" governing their mobility dynamics (e.g., taxi drivers have preferred working regions and times, and urban commuters have preferred routes and transit modes). Recent research such as generative adversarial imitation learning (GAIL) demonstrates successes in learning human decision-making strategies from their behavior data using deep neural networks (DNNs), which can accurately mimic how humans behave in various scenarios, e.g., playing video games, etc. However, such DNN-based models are "black box" models in nature, making it hard to explain what knowledge the models have learned from human, and how the models make such decisions, which was not addressed in the literature of imitation learning. This paper addresses this research gap by proposing xGAIL, the first explainable generative adversarial imitation learning framework. The proposed xGAIL framework consists of two novel components, including Spatial Activation Maximization (SpatialAM) and Spatial Randomized Input Sampling Explanation (SpatialRISE), to extract both global and local knowledge from a well-trained GAIL model that explains how a human agent makes decisions. Especially, we take taxi drivers' passenger-seeking strategy as an example to validate the effectiveness of the proposed xGAIL framework. Our analysis on a large-scale real-world taxi trajectory data shows promising results from two aspects: i) global explainable knowledge of what nearby traffic condition impels a taxi driver to choose a particular direction to find the next passenger, and ii) local explainable knowledge of what key (sometimes hidden) factors a taxi driver considers when making a particular decision. 

Learning to make optimal decisions is a common yet complicated task. While computer agents can learn to make decisions by running reinforcement learning (RL), it remains unclear how human beings learn. In this paper, we perform the first data-driven case study on taxi drivers to validate whether humans mimic RL to learn. We categorize drivers into three groups based on their performance trends and analyze the correlations between human drivers and agents trained using RL. We discover that drivers that become more efficient at earning over time exhibit similar learning patterns to those of agents, whereas drivers that become less efficient tend to do the opposite. Our study (1) provides evidence that some human drivers do adapt RL when learning, (2) enhances the deep understanding of taxi drivers' learning strategies, (3) offers a guideline for taxi drivers to improve their earnings, and (4) develops a generic analytical framework to study and validate human learning strategies.