Switching behavior in online auctions: Empirical observations and predictive implications

There has been substantial work exploring strategies, both theoretical and empirical, for selling and buying in online auctions. However, much of this work has considered single auctions in isolation, partially because it is hard to examine multiple simultaneous auctions using traditional math modeling approaches. In reality, many auctions occur simultaneously, so there is competition not just among bidders, but also among auctions. In this paper, we use simulation to explore bidders´ switching behavior between auctions for similar products. Using an empirical dataset, we first examine the distribution of switching and associated bidding behavior in real auctions. We use this data to create an agent-based model that reproduces the price process observed in the empirical data. Using this model we then explore the effects of: (1) different switching distributions, (2) the switching rule, i.e., which auction to switch to, and (3) different auction start rates. In the end, we show that in order to maximize the final price and to minimize the price disparity, auction platforms should encourage users to switch to a low-price auction that is ending soon.