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1. Fairness Traps & Checks

   Bennett, J; Marchetti, C; Blackwell, G; Bailey, M; Rivero, I (May 2025, Open Science Framework)

   Fairness traps represent opportunities to fortify fairness by identifying guiding principles, raising awareness about assumptions being made, and inserting fairness checks into the process. 

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2. Nonlinear multistable potential traps

   https://doi.org/10.1103/PhysRevA.111.013514  

   Mirzaei-Ghormish, S.; Smalley, D.; Camacho, R. (January 2025, Physical Review A)
3. Complementary Information and Learning Traps*

   https://doi.org/10.1093/qje/qjz033  

   Liang, Annie; Mu, Xiaosheng (October 2019, The Quarterly Journal of Economics)

   null (Ed.)

   Abstract We develop a model of social learning from complementary information: short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Long-run outcomes fall into one of two cases: (i) efficient information aggregation, where the community eventually learns as fast as possible; (ii) “learning traps,” where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often. 

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4. Pessimism Traps and Algorithmic Interventions

   https://doi.org/10.4230/lipics.forc.2025.5  

   Blum, Avrim; Diana, Emily; Ravichandran, Kavya; Tolbert, Alexander (June 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bun, Mark (Ed.)

   In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative and a public history of others' actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken - therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We extend this to the case of multiple communities, each of which might have a different optimal action, and a government providing subsidies that cannot discriminate between communities and does not know which action is optimal for each. We study this both theoretically and empirically. 

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5. Tunable optical traps over nonreciprocal surfaces

   https://doi.org/10.1364/OE.476269  

   Paul, N_K; Gomez-Diaz, J_S (December 2022, Optics Express)

   We propose engineering optical traps over plasmonic surfaces and precisely controlling the trap position with an external bias by inducing in-plane nonreciprocity on the surface. The platform employs an incident Gaussian beam to polarize targeted nanoparticles, and exploits the interplay between nonreciprocal and spin-orbit lateral recoil forces to construct stable optical traps and manipulate their position within the surface. To model this process, we develop a theoretical framework based on the Lorentz force combined with nonreciprocal Green’s functions and apply it to calculate the trapping potential. Rooted on this formalism, we explore the exciting possibilities offered by graphene to engineer stable optical traps using low-power laser beams in the mid-IR and to manipulate the trap position in a continuous manner by applying a longitudinal drift bias. Nonreciprocal metasurfaces may open new possibilities to trap, assemble and manipulate nanoparticles and overcome many challenges faced by conventional optical tweezers while dealing with nanoscale objects. 

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