  This paper presents a novel non-stationary dynamic pricing algorithm design,
where pricing agents face incomplete demand information and market environment
shifts. The agents run price experiments to learn about each product's demand
curve and the profit-maximizing price, while being aware of market environment
shifts to avoid high opportunity costs from offering sub-optimal prices. The
proposed ACIDP extends information-directed sampling (IDS) algorithms from
statistical machine learning to include microeconomic choice theory, with a
novel pricing strategy auditing procedure to escape sub-optimal pricing after
market environment shift. The proposed ACIDP outperforms competing bandit
algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in
a series of market environment shifts.
