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α: calibrated so average coauthorship-adjusted count equals average raw count
We consider the problem of valuing inputs in a production environment in which input supply is uncertain. Inputs can be workers in a firm, risk factors for a disease, securities in a financial market, or nodes in a networked economy. Each input takes its values from a finite set and uncertainty is modeled as a probability distribution over this set. First, we provide an axiomatic solution to this problem, uniquely characterizing a valuation scheme called the a priori Shapley value. Second, we solve the problem of valuing inputs a posteriori – that is, after observing output –, obtaining the Bayesian Shapley value. Third, we address the question of rationalizing uncertainty in labor supply in a non-cooperative production game where payoffs are given by the Shapley wage function. We also provide an intuitive condition for the existence of a pure strategy Nash equilibrium. Illustrations of the theory include an application to fidelity networks.