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1. The Sharing Economy for the Electricity Storage

   https://doi.org/10.1109/TSG.2017.2748519  

   Kalathil, Dileep; Wu, Chenye; Poolla, Kameshwar; Varaiya, Pravin (April 2017, IEEE Transactions on Smart Grid)

   The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s Smart Grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas. 

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2. Buying and Selling: Exploring Residential Landlords’ Acquisition and Disposition Decisions

   https://doi.org/10.1080/15214842.2025.2462343  

   Kuhlmann, Daniel; Rongerude, Jane; Mueller, Elizabeth (March 2025, Journal of Real Estate Practice and Education)

   We examine rental landlords’ decisions to buy and sell investment properties. We use the results of a new survey of owners of rental properties in nine major US cities, focusing on a subset of rental investors who own properties themselves, where we ask questions about their demographic and economic backgrounds, rental portfolios, and business management practices, and questions about their interest in acquiring new investments and plans to sell properties currently in their portfolio. We use these data to specify a series of regressions examining the factors that shape owners’ decisions to grow or shrink their businesses. First, we examine whether financial factors affect acquisition and disposition decisions. In this category, we include a variety of measures, including rents, external shocks, the owner’s reliance on rental income, debt, and portfolio characteristics. Second, we examine the impact of the owner’s personal characteristics—including age, gender, race, and ownership length—on investment behavior. Finally, we examine the influence of operating experience on future investment decisions, including interactions, vacancies, evictions, property investment, and business impacts from COVID-19 and other external events. Our analysis contributes to a growing body of research on the businesses of small landlords and their impact on the housing system. 

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3. Project Success Prediction in Crowdfunding Environments

   https://doi.org/10.1145/2835776.2835791  

   Li, Yan; Rakesh, Vineeth; Reddy, Chandan K. (January 2016, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining)

   Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40%. Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance. 

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4. Information Retention in the Multi-Platform Sharing of Science

   Sohyeon Hwang, Emoke-Agnes Horvat (June 2023, THE 17TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA)

   The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article’s abstract. We evaluate our measure using ground truth data labeled by within-field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin on social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time—posing a critical concern for researchers, policymakers, and citizens alike—but suggest that multi-platform discussions may improve information retention overall. 

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5. Building a Pillowfort: Political Tensions in Platform Design and Policy

   https://doi.org/10.1145/3492835  

   Dym, Brianna; Pasupuleti, Namita; Fiesler, Casey (January 2022, Proceedings of the ACM on Human-Computer Interaction)

   Social media platforms make trade-offs in their design and policy decisions to attract users and stand out from other platforms. These decisions are influenced by a number of considerations, e.g. what kinds of content moderation to deploy or what kinds of resources a platform has access to. Their choices play into broader political tensions; social media platforms are situated within a social context that frames their impact, and they can have politics through their design that enforce power structures and serve existing authorities. We turn to Pillowfort, a small social media platform, to examine these political tensions as a case study. Using a discourse analysis, we examine public discussion posts between staff and users as they negotiate the site's development over a period of two years. Our findings illustrate the tensions in navigating the politics that users bring with them from previous platforms, the difficulty of building a site's unique identity and encouraging commitment, and examples of how design decisions can both foster and break trust with users. Drawing from these findings, we discuss how the success and failure of new social media platforms are impacted by political influences on design and policy decisions. 

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