This paper investigates a two-agent mechanism design problem without transfers, where the principal must decide one action for each agent. In our framework, agents only care about their own adaptation, and any deterministic dominant incentive compatible decision rule is equivalent to contingent delegation: the delegation set offered to one agent depends on the other's report. By contrast, the principal cares about both adaptation and coordination. We provide sufficient conditions under which contingent interval delegation is optimal and solve the optimal contingent interval delegation under fairly general conditions. Remarkably, the optimal interval delegation is completely determined by combining and modifying the solutions to a class of simple single-agent problems, where the other agent is assumed to report truthfully and choose his most preferred action.
A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer's data are predictive of others' behavior—generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.
A data intermediary pays consumers for information about their preferences, and sells the information so-acquired to firms that use it to tailor their product offers and prices. The social dimension of the individual data - whereby an individual’s data is predictive of the behavior of others - generates a data externality that reduces the intermediary’s cost of acquiring information. We derive the data intermediary’s optimal information policy, and show that it preserves privacy over the identity of the consumers, but provides precise information about market demand to the firms.
We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market in which firms and consumers can tailor their choices to the demand data. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that can reduce the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.
A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that reduces the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and show that it preserves the privacy of the consumers’ identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows large.
We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers can tailor their choices to the demand data. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that can reduce the intermediary’s cost of acquiring the information. We derive the intermediary’s optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.