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1. A decision-space model explains context-specific decision-making

   Beck, Dirk W; Heaton, Cory N; Davila, Luis D; Rakocevic, Lara I; Drammis, Sabrina M; Tyulmankov, Danil; Giri, Atanu; Beck, Shreeya Umashankar; Zhang, Qingyang; Pokojovy, Michael; et al (August 2025, Nature communications)

   Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neu- ropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context- dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presenta- tion. The model provides evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making. 

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2. Decision-Focused Summarization

   https://doi.org/10.18653/v1/2021.emnlp-main.10  

   Hsu, Chao-Chun; Tan, Chenhao (January 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)
3. Constructive decision theory

   https://doi.org/10.1016/j.jet.2021.105306  

   Blume, Lawrence; Easley, David; Halpern, Joseph Y. (September 2021, Journal of Economic Theory)
4. Reconstructing decision trees

   Blanc, G.; Lange, J.; Tan, L. (January 2022, 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022))

   We give the first reconstruction algorithm for decision trees: given queries to a function f that is opt-close to a size-s decision tree, our algorithm provides query access to a decision tree T where: - T has size S := s^O((log s)²/ε³); - dist(f,T) ≤ O(opt)+ε; - Every query to T is answered with poly((log s)/ε)⋅ log n queries to f and in poly((log s)/ε)⋅ n log n time. This yields a tolerant tester that distinguishes functions that are close to size-s decision trees from those that are far from size-S decision trees. The polylogarithmic dependence on s in the efficiency of our tester is exponentially smaller than that of existing testers. Since decision tree complexity is well known to be related to numerous other boolean function properties, our results also provide a new algorithm for reconstructing and testing these properties. 

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5. Improving Decision Sparsity

   Sun, Yiyang; Wang, Tong; Rudin, Cynthia (December 2024, Advances in Neural Information Processing Systems)

   Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value(SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models. 

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