Instance-based learning in dynamic decision making

By Cleotilde González, Javier Lerch, Christian Lebiere. Cognitive Sciences, 2003

649 citations recorded by the source corpus.

Abstract This paper presents a learning theory pertinent to dynamic decision making (DDM) called instance-based 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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/3a7904771114a2b7162197aa51bbdc1e198459f1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

The Relationships between Cognitive Ability and Dynamic Decision Making. (2005, methodology): “DDM and much of real-world decision making are dynamically complex because of the nonlinear relationships that exist among variables, multiple loops, and feedback delays (Sterman, 2000).”
The family-relatedness of work decisions: A framework and agenda for theory and research☆ (2012, background): “Decision makers learn from their experiences over time to recognize decision-making situations based on their similarity to past situations, develop their skills at decision making, and use their skills and knowledge more effectively in new situations (Gonzalez et al., 2003).”
Learning, risk attitude and hot stoves in restless bandit problems (2009, background): “The first change involves the abstraction of instance-based or casebased reasoning (see related concepts in Gilboa and Schmeidler (1995), Gonzalez, Lerch, and Lebiere (2003), Hochman and Erev (2007) and Logan (1988)).”
A Bayesian Recognitional Decision Model (2009, background): “…reliability); (c) the notion of evaluating an option on its own merits, rather than requiring the comparison between alternatives; and (d) the use of knowledge prototypes to make decisions rather than the use of just exemplar-based or casebased reasoning (cf. Gonzalez et al., 2003; Hintzman, 1988).”
Learning and the Economics of Small Decisions (2013, background): “, 2011) quantifies the similar assumptions in a refinement of the instance based learning model (Gonzalez et al., 2003).”
Why don't well-educated adults understand accumulation? A challenge to researchers, educators, and citizens (2009, methodology): “Other methods may be needed to the extent learning in dynamic tasks is implicit so that individuals are unable to verbalize the ways in which they make decisions (Gonzalez, 2005b; Gonzalez, Lerch, & Lebiere, 2003 ).”