Instance-based learning in dynamic decision making

Instance-based learning in dynamic decision making

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 refin...

Instance-Based Decision Making in Dynamic Environments: Modeling the Learning Process

This paper presents a cognitive model of how people learn to make better decisions in dynamic environments. I argue that, in dynamic environments, the main learning structure is an “instance” representing the knowledge of the situation (decision context), the decision (one among the possible actions), and an evaluation of the decision results (utility). Decision making behavior in dynamic envir...

Instance-Based Cognitive Models of Decision-Making

IRDDS: Instance reduction based on Distance-based decision surface

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

A general instance-based learning framework for studying intuitive decision-making in a cognitive architecture

Cognitive architectures (e.g., ACT-R) have not traditionally been used to understand intuitive decisionmaking; instead, models tend to be designed with the intuitions of their modelers already hardcoded in the decision process. This is due in part to a fuzzy boundary between automatic and deliberative processes within the architecture. We argue that instance-based learning satisfies the conditi...