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This paper introduces an evolutionary recurrent decision model for analyzing the development of adaptive and maladaptive behaviors, with applications in AI and cognitive systems.
This paper introduces WitnessSim, a deposition simulator using controllable legal personas, and an evaluation framework for assessing behavioral realism in legal simulations.
This paper introduces a behavioral induction framework that fine-tunes language models on structured decision-making tasks to induce stable, context-general shifts in generative distributions, modeling pathology-like behavioral patterns such as depression and paranoia.
This paper identifies the problem of missing observations in inverse reinforcement learning (IRL) that can make expert actions appear suboptimal, and develops a practical algorithm to quantify the minimal perturbations needed for expert actions to appear optimal, validated on synthetic tasks, cancer treatment simulation, and ICU data.
This academic paper identifies and characterizes Simpson's paradox in behavioral curve modeling, demonstrating how aggregation systematically distorts parametric estimates of user dynamics due to survival bias. The authors validate this distortion across datasets like Goodreads and Amazon Electronics and propose hierarchical peak estimation methods to mitigate the issue.