Social learning with heterogeneous agents and sequential decision making

Learning and Sequential Decision Making

Social Influences in Sequential Decision Making

People often make decisions in a social environment. The present work examines social influence on people's decisions in a sequential decision-making situation. In the first experimental study, we implemented an information cascade paradigm, illustrating that people infer information from decisions of others and use this information to make their own decisions. We followed a cognitive modeling ...

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. ...

Quantum decision making by social agents

Decision making of agents who are members of a society is analyzed from the point of view of quantum decision theory. This generalizes the approach, developed earlier by the authors for separate individuals, to decision making under the influence of social interactions. The generalized approach not only avoids paradoxes, typical of classical decision making based on utility theory, but also exp...

Learning Methods for Sequential Decision Making with Imperfect Representations

Sequential decision making from experience, or reinforcement learning (RL), is a paradigm that is well-suited for agents seeking to optimize longterm gain as they carry out sensing, decision, and action in an unknown environment. RL tasks are commonly formulated as Markov Decision Problems (MDPs). Learning in finite MDPs enjoys several desirable properties, such as convergence, sample-efficienc...