Based on a cognitive notion of neo-additive capacities reflecting likelihood insensitivity with respect to survival chances, we construct a Choquet Bayesian learning model over the life-cycle that generates a motivational notion of neo-additive survival beliefs expressing ambiguity attitudes. We...

This paper studies a game of strategic experimentation with two-armed bandits whose risky arm might yield a payoff only after some exponentially distributed random time. Because of free-riding, there is an inefficiently low level of experimentation in any equilibrium where the players use...

This paper studies a game of strategic experimentation with two-armed bandits whose risky arm might yield a payoff only after some exponentially distributed random time. Because of free-riding, there is an inefficiently low level of experimentation in any equilibrium where the players use...

This paper studies a game of strategic experimentation with two-armed bandits whose risky arm might yield a payoff only after some exponentially distributed random time. Because of free-riding, there is an inefficiently low level of experimentation in any equilibrium where the players use...

This paper studies a game of strategic experimentation in which the players have access to two-armed bandits where the risky arm distributes lumpsum payoffs according to a Poisson process with unknown intensity. Because of free-riding, there is an inefficiently low level of experimentation in...

Based on a cognitive notion of neo-additive capacities reflecting likelihood insensitivity with respect to survival chances, we construct a Choquet Bayesian learning model over the life-cycle that generates a motivational notion of neo-additive survival beliefs expressing ambiguity attitudes. We...

This paper studies a game of strategic experimentation with two-armed bandits whose risky arm might yield a payoff only after some exponentially distributed random time. Because of free-riding, there is an inefficiently low level of experimentation in any equilibrium where the players use...