Mondal paper

Author: cakiki

lichess.bib

@@ -929,6 +929,21 @@ @phdthesis{mok:2024:measuring-digital-welfare-online-systems
   type          = {Doctoral Thesis},
 }
 
+@article{mondal:2025:adaptive-decision-making-in-the-wild-case-study-chess,
+  title         = {Adaptive decision making in the wild: a case study of chess},
+  author        = {Supratik Mondal and Jakub Traczyk},
+  year          = {2025},
+  journal       = {Thinking \& Reasoning},
+  publisher     = {Routledge},
+  volume        = {0},
+  number        = {0},
+  pages         = {1--21},
+  doi           = {10.1080/13546783.2025.2550306},
+  url           = {https://doi.org/10.1080/13546783.2025.2550306},
+  abstract      = {Chess is a game of strategic thinking and time management, where a player can lose a game on time despite making all the best moves. Finding the best move is a deliberate and energy-intensive process in a game where players are often under time pressure. Therefore, players who can balance this trade-off will have a significant advantage. The current study explores such instances where winning is contingent on how well players balance their accuracy under time pressure. We found that winning players, compared to their opponents, followed a more adaptive decision strategy--they made more theoretical best moves (i.e., accurate moves) in highly critical positions. However, the accuracy difference between the opponents was very similar in less critical positions. We conclude that winning players have a better understanding of when and how to allocate their limited resources efficiently, even when controlling for differences in skill levels, compared to their opponents.},
+  keyword       = {Chess, Adaptive Decision Making, Resource Constraints, Skilled Decision Maker, Evaluation},
+}
+
 @misc{morelbalbi:2025:learning-rank-estimation-partial-sparse-noisy-comparisons,
   title         = {Learning when to rank: Estimation of partial rankings from sparse, noisy comparisons},
   author        = {Sebastian Morel-Balbi and Alec Kirkley},