We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...
Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...
Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivat ...
Metal-based drugs and imaging agents are extensively used in the clinic for the treatment and diagnosis of cancers and a wide range of other diseases. The current clinical arsenal of compounds operate via a limited number of mechanisms, whereas new putativ ...
The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...
The brain has been theorized to employ inferential processes to overcome the problem of uncertainty. Inference is thought to underlie neural processes, including in disparate domains such as value-based decision-making and perception. Value-based decision- ...
We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to e ...
International Foundation for Autonomous Agents and Multiagent Systems2019
