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August 30, 2012

Mechanism Design for Eliciting Costly Observations in Next Generation Citizen Sensor Networks

  • May 18, 2024 till May 18, 2024
  • Main Lecture Room

Citizen sensor networks are open information systems in which members of the public act as information providers. The information distributed in such networks ranges from observations of events (e.g. noise measurements or monitoring of environmental parameters) to probabilistic estimates (e.g. projected traffic reports or weather forecasts). However, due to rapid advances in technology it is expected that citizen sensor networks will evolve. Given this projected evolution, one key difference between future citizen sensor networks and conventional present ones is the emergence of self-interested behaviour, which can manifest in two main ways. First, information providers may choose to commit insufficient resources when producing their observations, and second, they may opt to misreport them.
It is these issues that we deal with in this research through the introduction of a series of novel two-stage mechanisms, based on strictly proper scoring rules. By using payments that are based on such scoring rules, our mechanisms effectively address the issue of selfish behaviour by motivating information providers in a citizen sensor network to, first, invest the resources required by the information buyer in the generation of their observations, and second, to report them truthfully.
To begin with, we introduce a mechanism that allows the centre (acting as an information buyer) to select a single agent that can provide a costly observation at a minimum cost. Building on this, we then make two further contributions to the state of the art, with the introduction of two extensions of this mechanism. First, we extend the mechanism so that it can be applied in a citizen sensor network where the information providers do not have the same resources available for the generation of their observations. Second, we consider a setting where the information buyer cannot gain any knowledge of the actual outcome beyond what it receives through the agents’ reports.
For the initial mechanism and each of the two extensions, we prove their economic properties (i.e. incentive compatibility and individual rationality) and then present empirical results comparing a number of specific scoring rules. Finally, we empirically evaluate both extended mechanisms, compare them with existing literature and show that in all the three mechanisms we consider, the total expected payment is the same, while for both our mechanisms the variance in the total payment is significantly lower.

Talk slides in pdf [~1,6MB]….

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