Add to ORKGThis paper explores small decision problems experimentally. Conducted is the current experiment in which agent’s payoff distribution is limited to either high (favourable) distri-bution (“Good News”) or low (unfavourable) distribution (“Bad News”). We conduct cali-bration of numerical optimal solution to search behaviour by Bayesian updating and agents’ tendency in the laboratory experiment in small feedback-based problems. One assumption on an rational agent is that an agent is to behave to maximise his expected payoff. Results of the current experiment, however, show subjects ’ seemingly puzzled tendency inconsistent with the assumption above. The law of small numbers is observed in the experiment. The law of small numbers tells us that a...
In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, s...
Sequential decision-making is an iterative process between a learning agent and an environment. We s...
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (a...
This study explores small feedback-based decision problems experimentally. Conducted were the experi...
We examine decision-making under risk and uncertainty in a laboratory experiment. The heart of our d...
In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The secon...
Bayesian probability tracking We used a previously published Bayesian model to generate optimal esti...
This thesis aims at pursuing an extensive investigation into decision making in small decision-makin...
We study learning in a bandit task in which the outcome probabilities of six arms switch (“jump”) ov...
International audienceIn our laboratory experiment, subjects, in sequence, have to predict the value...
Economists and psychologists have recently been developing new theories of decision making under unc...
In Bayesian decision theory, the performance of an action is measured by its pos- terior expected lo...
We conduct a laboratory experiment to shed light on the cognitive limitations that may affect the wa...
We consider an experimental setting where agents receive one stylized piece of information at a time...
Updating behavior in cascade experiments is usually investigated on the basis of urn prediction. But...
In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, s...
Sequential decision-making is an iterative process between a learning agent and an environment. We s...
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (a...
This study explores small feedback-based decision problems experimentally. Conducted were the experi...
We examine decision-making under risk and uncertainty in a laboratory experiment. The heart of our d...
In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The secon...
Bayesian probability tracking We used a previously published Bayesian model to generate optimal esti...
This thesis aims at pursuing an extensive investigation into decision making in small decision-makin...
We study learning in a bandit task in which the outcome probabilities of six arms switch (“jump”) ov...
International audienceIn our laboratory experiment, subjects, in sequence, have to predict the value...
Economists and psychologists have recently been developing new theories of decision making under unc...
In Bayesian decision theory, the performance of an action is measured by its pos- terior expected lo...
We conduct a laboratory experiment to shed light on the cognitive limitations that may affect the wa...
We consider an experimental setting where agents receive one stylized piece of information at a time...
Updating behavior in cascade experiments is usually investigated on the basis of urn prediction. But...
In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, s...
Sequential decision-making is an iterative process between a learning agent and an environment. We s...
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (a...