We provide a model to investigate the tension between information aggregation and spread of misinformation in large societies (conceptualized as networks of agents communicating with each other). Each individual holds a belief represented by a scalar. Individuals meet pairwise and exchange information, which is modeled as both individuals adopting the average of their pre-meeting beliefs. When all individuals engage in this t3'pe of information exchange, the society will be able to effectively aggregate the initial information held by all individuals. There is also the possibility of misinformation, however, because some of the individuals are "forceful," meaning that they influence the beliefs of (some) of the other individuals they meet, but do not change their own opinion. The paper characterizes how the presence of forceful agents interferes with information aggregation. Under the assumption that even forceful agents obtain some information (however infrequent) from some others (and additional weak regularity conditions), we first show that beliefs in this class of societies converge to a consensus among all individuals. This consensus value is a random variable, however, and we characterize its behavior. Our main results quantify the extent of misinformation in the society by either providing bounds or exact results (in some special cases) on how far the consensus value can be from the benchmark without forceful agents (where there is efficient information aggregation). The worst outcomes obtain when there are several forceful agents and forceful agents themselves update their beliefs only on the basis of information they obtain from individuals most likely to have received their own information previously.

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We provide a model to investigate the tension between information aggregation and spread of misinformation. Individuals meet pairwise and exchange information, which is modeled as both individuals adopting the average of their pre-meeting beliefs. "Forceful" agents influence the beliefs of (some of) the other individuals they meet, but do not change their own opinions. We characterize how the presence of forceful agents interferes with information aggregation. Under the assumption that even forceful agents obtain some information from others, we first show that all beliefs converge to a stochastic consensus. Our main results quantify the extent of misinformation by providing bounds or exact results on the gap between the consensus value and the benchmark without forceful agents (where there is efficient information aggregation). The worst outcomes obtain when there are several forceful agents who update their beliefs only on the basis of information from individuals that have been influenced by them.

The application of congestion control can have a significant detriment to the quality of service experienced at higher layers, especially under high packet loss rates. The effects of throughput loss due to the congestion control misinterpreting packet losses in poor channels is further compounded for applications such as HTTP and video leading to a significant decrease in the user's quality of service. Therefore, we consider the application of congestion control to transport layer packet streams that use error-correction coding in order to recover from packet losses. We introduce a modified AIMD approach, develop an approximate mathematic model suited to performance analysis, and present extensive experimental measurements in both the lab and the "wild" to evaluate performance. Our measurements highlight the potential for remarkable performance gains, in terms of throughput and upper layer quality of service, when using coded transports.

Most medium access control (MAC) mechanisms discard collided packets and consider interference harmful. Recent work on Analog Network Coding (ANC) suggests a different approach, in which multiple interfering transmissions are strategically scheduled. Receiving nodes collect the results of collisions and then use a decoding process, such as ZigZag decoding, to extract the packets involved in the collisions.In this paper, we present an algebraic representation of collisions and describe a general approach to recovering collisions using ANC. To study the effects of using ANC on the performance of MAC layers, we develop an ANC-based MAC algorithm, CMAC , and analyze its performance in terms of probabilistic latency guarantees for local packet delivery. Specifically, we prove that CMAC implements an abstract MAC layer service, as defined in [14,13]. This study shows that ANC can significantly improve the performance of the abstract MAC layer service compared to conventional probabilistic transmission approaches.We illustrate how this improvement in the MAC layer can translate into faster higher-level algorithms, by analyzing the time complexity of a multi-message network-wide broadcast algorithm that uses CMAC .