Despite the growing interest in Internet-based learning technologies (IBLT) and the application of advanced Internet technologies in education, research investigating continued usage of these tools has been very scarce. The objective of this study is to gain a better understanding of factors influencing students’ continued usage of these learning technologies. Building upon prior literature, sa...

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...

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or lose money. In the repeated-choice paradigm, par...

Traditional economic theory often describes real-world social dilemmas as abstract games where an individual’s goal is to maximize economic benefit by cooperating or competing with others. Despite extensive empirical work, descriptive models of human behavior in social dilemmas are lacking in both cognitive realism and predictive power. This article addresses a central challenge arising from th...

Developing effective Multi-Agent Systems (MAS) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of Deep Reinforcement Learning (MADRL) in cooperative MAS, one major challenges that remain simultaneous learning interaction independent agents dynamic environments presence stochastic rewards. State-of-the-art MADRL models struggle to...

We devise a novel method of interactive set reconciliation for efficient block distribution. Our approach, called Graphene, couples a Bloom filter with an IBLT. We evaluate performance analytically and show that Graphene blocks are always smaller. For example, while a 17.5 KB Xtreme Thinblock can be encoded in 10 KB with Compact Blocks, the same information can be encoded in 2.6 KB with Graphen...

Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from descript...

An ACT-R model of sensemaking in a geospatial intelligence task was developed based on Instance-Based Learning Theory (IBLT). The model (a) maintains hypotheses about the probability of attacks by insurgent groups, (b) seeks new information based on those hypotheses, and (c) updates hypotheses based on new evidence. The model provides a functional account of how these sensemaking processes are ...

Instance-based learning theory (IBLT) has explained human decision-making in several decision tasks. IBLT works by retrieving past experiences (i.e., instances) using a subset of cognitive mechanisms from a popular cognitive architecture, ACT-R. Until recently, most IBLT models were built within the ACT-R architecture. However, due to an integrated view of cognition and ACT-R’s complexity, it i...