Prescriptive Bayesian decision making has reached a high level of maturity, supported by efficient, theoretically well-founded algorithms. While the long-standing problem of describing a sigle decision maker´s bounded rationally in well-known, the similar problem for systems of multiple decision makers with limited cognitive, acting and evaluative abilities/resources has not been considered systematically. The goal of this workshop is to explore such connections between descriptive and prescriptive decision making of multiple decision makers
In a wide range of applications, decisions must be made by combining information from multiple agent...
Thesis (Ph.D.)--University of Washington, 2021Existing computational models of decision making are o...
Neuroeconomics seeks to gain a greater understanding of decision making by combining theo-retical an...
Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported alg...
Bayesian decision theory provides a strong theoretical basis for a single-participant decision makin...
Decision making is a core competence for animals and humans acting and surviving in environments the...
This volume focuses on uncovering the fundamental forces underlying dynamic decision making among mu...
The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is th...
Expert analysis and decisions are highly valued assets in a wide variety of fields, from social serv...
Abstract—Decision making in a semistructured or unstructured problem should consist of a combination...
Expert analysis and decisions are highly valued assets in a wide variety of fields, from social serv...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often ...
Learning and decision making is one of the universal cornerstones of human and animal life. There ar...
Humans regularly perform tasks that require combining infor-mation across several sources of informa...
In a wide range of applications, decisions must be made by combining information from multiple agent...
Thesis (Ph.D.)--University of Washington, 2021Existing computational models of decision making are o...
Neuroeconomics seeks to gain a greater understanding of decision making by combining theo-retical an...
Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported alg...
Bayesian decision theory provides a strong theoretical basis for a single-participant decision makin...
Decision making is a core competence for animals and humans acting and surviving in environments the...
This volume focuses on uncovering the fundamental forces underlying dynamic decision making among mu...
The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is th...
Expert analysis and decisions are highly valued assets in a wide variety of fields, from social serv...
Abstract—Decision making in a semistructured or unstructured problem should consist of a combination...
Expert analysis and decisions are highly valued assets in a wide variety of fields, from social serv...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often ...
Learning and decision making is one of the universal cornerstones of human and animal life. There ar...
Humans regularly perform tasks that require combining infor-mation across several sources of informa...
In a wide range of applications, decisions must be made by combining information from multiple agent...
Thesis (Ph.D.)--University of Washington, 2021Existing computational models of decision making are o...
Neuroeconomics seeks to gain a greater understanding of decision making by combining theo-retical an...