Controlled Sequential Information Fusion with Social Sensors

TL;DR

This paper develops a framework for incentivizing social sensors in a sequential information fusion setting, providing optimal policies, cost bounds, and conditions for consistent and efficient state estimation.

Contribution

It introduces a threshold-based optimal incentive policy, analyzes its properties, and demonstrates how to achieve consistent and cost-effective information fusion with social sensors.

Findings

Optimal incentive policy has a threshold structure.

Optimal incentives tend to increase over time.

Consistent state estimation is achievable with sub-optimal policies.

Abstract

A sequence of social sensors estimate an unknown parameter (modeled as a state of nature) by performing Bayesian Social Learning, and myopically optimize individual reward functions. The decisions of the social sensors contain quantized information about the underlying state. How should a fusion center dynamically incentivize the social sensors for acquiring information about the underlying state? This paper presents five results. First, sufficient conditions on the model parameters are provided under which the optimal policy for the fusion center has a threshold structure. The optimal policy is determined in closed form, and is such that it switches between two exactly specified incentive policies at the threshold. Second, it is shown that the optimal incentive sequence is a sub-martingale, i.e, the optimal incentives increase on average over time. Third, it is shown that it is possible for the fusion center to learn the true state asymptotically by employing a sub-optimal policy; in other words, controlled information fusion with social sensors can be consistent. Fourth, uniform bounds on the average additional cost incurred by the fusion center for employing a sub-optimal policy are provided. This characterizes the trade-off between the cost of information acquisition and consistency for the fusion center. Finally, when it is sufficient to estimate the state with a degree of confidence, uniform bounds on the budget saved by employing policies that guarantee state estimation in finite time are provided.