This paper investigates pricing in laboratory markets when human players interact with an algorithm. We compare the degree of competition when exclusively humans interact to the case of one firm delegating its decisions to an algorithm, an n-player generalization of tit-for-tat. We further vary...

Although algorithms make more accurate forecasts than humans in many applications, decision-makers often refuse to resort to their use. In an economic experiment, we examine whether the extent of this phenomenon known as algorithm aversion can be reduced by granting decision-makers the...

While many companies use algorithms to optimize their pricing, additional human oversight and price interventions are widespread. Human intervention can correct algorithmic flaws and introduce private information into the pricing process, but it may also be based on less sophisticated pricing...