Noisy heteroclinic networks and sequential decision making
Yuri Bakhtin

I will talk about sequential decision making models based on diffusion
along heteroclinic networks of dynamical systems, i.e., multiple
saddle-type equilibrium points connected by heteroclinic orbits. The
goal is to give a precise description of the asymptotic behavior in
the limit of vanishing noise. In particular, I will interpret exit
times for stochastic dynamics as decision making times and give a
result on their asymptotic behavior. I will report on extensive data
on decision making in no a priori bias setting obtained in a
psychology experiment (joint with Joshua Correll, University of
Chicago), and compare the data with the theoretical results. I will
also show that the same behavior of exit times appears in
nonequilibrium models of statistical mechanics.