Many practitioners currently use rules of thumb to price tasks on online labor markets. Incorrect pricing leads to task starvation or inefficient use of capital. Formal pricing policies can address these challenges. In this paper we argue that a pricing policy can be based on the trade-off between price and desired completion time. We show how this duality can lead to a better pricing policy for tasks in online labor markets. This paper makes three contributions. First, we devise an algorithm for job pricing using a survival analysis model. We then show that worker arrivals can be modeled as a non-homogeneous Poisson Process (NHPP). Finally using NHPP for worker arrivals and discrete choice models we present an abstract mathematical model that captures the dynamics of the market when full market information is presented to the task requester. This model can be used to predict completion times and pricing policies for both public and private crowds.
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