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1. Childhood Skill Development and Adult Political Participation

   https://doi.org/10.1017/S0003055417000119  

   HOLBEIN, JOHN B. (August 2017, American Political Science Review)

   Recent child development research shows that the psychosocial or noncognitive skills that children develop—including the ability to self-regulate and integrate in social settings—are important for success in school and beyond. Are these skills learned in childhood also important for adult political behaviors like voting? In this article, I use a unique school-based 20-year field experiment to explore whether children who develop psychosocial skills early on are more likely to vote in adulthood than those who do not. Matching subjects to voter files, I show that this intervention had a noticeable long-run impact on political participation. These results highlight the need to better understand how childhood experiences shape civic behaviors later in life. During this critical period, children can be taught the not explicitly political, but still vital, skills that set them on a path toward political participation in adulthood. 

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2. Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making

   https://doi.org/10.1145/3544548.3580996  

   Robertson, Samantha; Nguyen, Tonya; Hu, Cathy; Albiston, Catherine; Nikzad, Afshin; Salehi, Niloufar (April 2023, CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making. 

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3. A Truthful Cardinal Mechanism for One-Sided Matching

   https://doi.org/10.1137/1.9781611975994.129  

   Abebe, Rediet; Cole, Richard; Gkatzelis, Vasilis; Hartline, Jason D. (January 2020, ACM-SIAM Symposium on Discrete Algorithms)

   We revisit the well-studied problem of designing mechanisms for one-sided matching markets, where a set of n agents needs to be matched to a set of n heterogeneous items. Each agent i has a value vij for each item j, and these values are private information that the agents may misreport if doing so leads to a preferred outcome. Ensuring that the agents have no incentive to misreport requires a careful design of the matching mechanism, and mechanisms proposed in the literature mitigate this issue by eliciting only the ordinal preferences of the agents, i.e., their ranking of the items from most to least preferred. However, the efficiency guarantees of these mechanisms are based only on weak measures that are oblivious to the underlying values. In this paper we achieve stronger performance guarantees by introducing a mechanism that truthfully elicits the full cardinal preferences of the agents, i.e., all of the vij values. We evaluate the performance of this mechanism using the much more demanding Nash bargaining solution as a benchmark, and we prove that our mechanism significantly outperforms all ordinal mechanisms (even non-truthful ones). To prove our approximation bounds, we also study the population monotonicity of the Nash bargaining solution in the context of matching markets, providing both upper and lower bounds which are of independent interest. 

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4. Keep it Simple: How Visual Complexity and Preferences Impact Search Efficiency on Websites

   Amanda Baughan, Tal August (April 2020, Human factors in computing systems)

   Past research has shown that people prefer different levels of visual complexity in websites: While some prefer simple websites with little text and few images, others prefer highly complex websites with many colors, images, and text. We investigated whether users’ visual preferences reflect which website complexity they can work with most efficiently. We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests. 

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5. Welfare optimization for resource allocation with peer effects

   https://doi.org/10.1093/pnasnexus/pgaf298  

   Qiu, Zirou; Rosenkrantz, Daniel J; Jackson, Matthew O; Levin, Simon A; Ravi, S S; Stearns, Richard E; Marathe, Madhav V (August 2025, PNAS Nexus)

   Gavrilets, Sergey (Ed.)

   Abstract Allocating students to schools or universities, people to teams or groups, people to urban housing, and matching users on social platforms are prominent examples of allocating limited goods, spaces, or positions to optimize social welfare. We study a welfare maximization problem that arises when such resource allocation scenarios involve peer effects, where people have preferences over the others who are nearby (e.g. their classmates, teammates, neighbors, or partners). We first develop a unified mathematical framework for this “position allocation problem,” which assigns people to positions in a given network, with people caring about both their positions and their neighbors’ attributes. We show that welfare maximization for the corresponding position allocation problem is computationally intractable, even when people have preferences that depend only on who is allocated to nearby positions, and those preferences satisfy simple constraints that arise naturally in urban and other real-world systems. In contrast to this computational lower bound, we show that if people can be classified into a fixed number of (demographic) groups and the network satisfies certain realistic spatial conditions, then efficiently computable allocations can be obtained for many natural scenarios. Importantly, the achieved social welfare is either optimal or arbitrarily close to optimal for natural forms of preferences. Our methods provide a foundation for position allocation with peer effects, and guide the design of optimal allocation strategies when people can be classified into a fixed number of groups in which members share similar preferences. 

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