Time-Constrained Sequential Neural Decision Procedures in Multialternative Choice Problems
Fabio Maccheroni, Bocconi University
We introduce an algorithmic decision process for multialternative choice, the Neural Metropolis Algorithm, that combines binary comparisons and Markovian exploration. We show that a stochastic version of transitivity, a basic tenet of rationality, makes this algorithm value-based. In so doing, we extend to decision procedures some classic stochastic choice notions.