Thomas Mbrice

Thomas Mbrice

Prediction markets such as Polymarket aggregate crowd beliefs into real-time probability estimates, and the comments traders post beneath each market contain rich directional stance signals that prices alone cannot capture. This work introduces the first stance detection study applied to prediction market commentary, a domain characterized by extreme brevity, trader- specific vernacular, and severe class imbalance (only 8.7% of comments oppose the market outcome). RoBERTa-base is fine-tuned across a 4 x 3 ablation: four input configurations ({2- class, 3-class} x {with/without market context}) and three augmentation conditions (baseline, 50% synthetic, 100% synthetic). Synthetic minority-class samples are generated via LLM-driven Pro -> Anti counterfactual flips using the Anthropic API. Results show that (1) market context is the single most impactful factor, raising 3-class Anti recall from 0.10 to 0.45; (2) counterfactual augmentation is conditionally effective, improving Anti F1 in weak configurations (0.10 -> 0.24) while degrading strong ones (2-class-ctx macro F1: 0.68 -> 0.50 at full dose); and (3) 50% augmentation is the optimal dose, with 100% consistently hurting performance. Attention-based interpretability analysis provides mechanistic support for all three findings.

Thomas Mbrice, Yuyu Liu

Reinforcement learning (RL) has emerged as a promising paradigm for Internet congestion control, achieving higher link utilization than classical heuristics. However, RL-based controllers trained in single-flow environments are not guaranteed to share bandwidth equitably when deployed in multi-flow networks. This paper investigates the fairness properties of Aurora~\cite{jay2019aurora}, a state-of-the-art deep RL congestion controller, and evaluates three post-hoc fairness strategies that preserve Aurora's RL architecture: \emph{reward shaping} (Strategy~A), \emph{observation augmentation} (Strategy~B), and \emph{loss-sensitivity tuning} (Strategy~C). Using a custom shared-bottleneck simulator and Jain's fairness index as the primary metric, we find that modest reward shaping achieves the best fairness while preserving aggregate throughput. All strategies maintain the total bandwidth budget with fairness being achieved through redistribution, not reduction. Beyond the 2-flow homogeneous setting, an extended evaluation across mixed Aurora--CUBIC competition and dynamic flow entry/exit scenarios shows that Strategy~C's loss-sensitivity emerges as the most TCP-friendly mechanism, while Strategy~B is the most stable through dynamic flow-set changes.