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Instance‐based learning in dynamic decision making
Author(s) -
Gonzalez Cleotilde,
Lerch Javier F.,
Lebiere Christian
Publication year - 2003
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2704_2
Subject(s) - dynamic decision making , computer science , task (project management) , artificial intelligence , heuristic , context (archaeology) , process (computing) , machine learning , action (physics) , instance based learning , similarity (geometry) , cognition , adaptation (eye) , decision theory , active learning (machine learning) , mathematics , psychology , paleontology , physics , management , quantum mechanics , neuroscience , economics , image (mathematics) , biology , operating system , statistics
This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.