Structure Learning in Human Sequential Decision-Making

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. ...

Learning and Sequential Decision Making

Convergence in a sequential two stages decision making process

We analyze a sequential decision making process, in which at each stepthe decision is made in two stages. In the rst stage a partially optimalaction is chosen, which allows the decision maker to learn how to improveit under the new environment. We show how inertia (cost of changing)may lead the process to converge to a routine where no further changesare made. We illustrate our scheme with some...

Possibilistic sequential decision making

Article history: Received 21 March 2013 Received in revised form 14 November 2013 Accepted 15 November 2013 Available online 19 December 2013

Human learning in non-Markovian decision making

Humans can learn under a wide variety of feedback conditions. Particularly important types of learning fall under the category of reinforcement learning (RL) where a series of decisions must be made and a sparse feedback signal is obtained. Computational and behavioral studies of RL have focused mainly on Markovian decision processes (MDPs), where the next state and reward depends only on the c...