Instance‐based learning in dynamic decision making

TLDR

The IBLT is a general theory of decision making that applies beyond the specific dynamic task used in the cognitive model. This paper introduces instance-based learning theory (IBLT) as a learning framework for dynamic decision making. IBLT includes five mechanisms—instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates—that allow decision makers to accumulate, recognize, adapt, and refine instances of situation, action, and utility via similarity matching and feedback. The mechanisms were implemented in an ACT‑R model, and experiments demonstrated that IBLT closely approximates human performance trends.

Abstract

Abstract 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.