While artificial intelligence (AI) and machine learning (ML) have been increasingly used for decision-making, issues related to discrimination in AI/ML have become prominent. While several fair algorithms are proposed to alleviate these discrimination issues, most of them provide fairness by imposing constraints to eliminate disparity in prediction results. However, the use of these fair algorithms may change the behavior of prediction subjects. As such, even though the disparity in prediction results might be removed by fair algorithms, behavioral responses to the use of fair algorithms can still create disparity in behavior which may persist across different groups of prediction subjects. To study this issue, we define a notion called "strategic best-response fairness" (SBR-fair). It is defined in a context that includes different groups of prediction subjects who are ex-ante identical in terms of abilities and conditional payoffs. We utilize a game-theoretic model to investigate whether different types of fair algorithms lead to identical equilibrium behaviors among different groups of prediction subjects. If yes, such an algorithm is considered SBR-fair. We then demonstrate that many existing fair algorithms are not SBR-fair. As a result, implementing these algorithms may impose fairness on prediction results but actually induce disparity between privileged and unprivileged individuals in the long run.

To address network congestion issues, network providers are seeking to shape consumer's usage behavior through economic incentives such as a tiered pricing scheme and/or restrictions on unlimited data plans. Current pricing schemes, however, do not ad- dress the congestion problem directly: the prices differentiation is based on the data volume consumed by the user in a month whereas the congestion is a consequence of the availability of limited bandwidth resources (consumption speed). Furthermore, users subscribing to a costlier plan receive no real- time (higher bandwidth) priority. This paper pro- poses an alternate pricing plan for wireless networks based on Generalized Second Price (GSP) auctions with the aim of improving welfare. Our simulation- based analysis reveals that such a pricing scheme allows users with higher willingness to pay to secure higher throughput of service, without significantly sacrificing the overall system throughput or fairness.

This paper explores requirements for efficient pre-seeding of video-on-demand (VoD) movie data onto numerous customer set-top boxes in a cable ISP environment. The pre-seeded content will then be distributed to other set-top boxes in the same cable community using a peer-to-peer (P2P) network protocol such as BitTorrent. The challenges and solutions required for P2P VoD provided by a fixed provider such as a cable company are fundamentally different from those seen in traditional P2P networks or client-server VoD solutions.

The challenges and solutions required for peer-to-peer video-on-demand (P2P VoD) provided by a fixed provider such as a cable company are fundamentally different from those seen in traditional P2P networks or client-server VoD solutions. Unlike traditional P2P networks, the end nodes (set top boxes with DVR capabilities) are largely under control of the system provider. Consequently, issues like churn and free-loading are less substantial. Unlike client-server solutions, there is always a readily-available resource of peer nodes able to contribute even if they are not using the VoD service! This paper explores requirements for efficient preloading of VoD movie data onto numerous customer set-top boxes. This research is currently exploring mathematical programming algorithms that minimize uplink traffic, given a popularity model for various pieces of content and information about storage and bandwidth capacity constraints at the customer nodes. Given the complex non-linear nature of P2P interactions, these mathematical programs require non-linear optimization approaches or heuristic solutions. However, even heuristic solutions would likely provide substantial advantages over simple dynamic allocation.

In recent years, the rapid growth of peer-to-peer (P2P) networks has provided a new paradigm for content distribution. To improve the efficiency of a P2P system, it is important to provide incentives for the peers to participate and contribute their resources. Various attempts have been made to reward/penalize peers by providing service differentiation based on a requesting peer's history or reputation. However, in a truly distributed, non-cooperative environment, maintaining and preventing the untruthful revealing of such information within the community impose larger computation and communication overheads to the system. These problems are further magnified when large-volume contents are being distributed because of the length distribution processes and the update of history or reputation has to keep up with the distribution process. In this paper, we address the incentive provisioning problem for distribution of large-volume content in P2P networks, and present a "seeing-is-believing" incentive-compatible mechanism (protocol) in which a peer will decide how much resources will be assigned to which neighbors based on what it has experienced. The protocol applies a utility-based resource-trading concept where peers will maximize their contributions for a fair or better return, and we show that by adopting this protocol, the system will achieve Cournot Equilibrium. Furthermore, our protocol is light-weight, completely decentralized, and cheat-proof. Experimental results illustrate significant improvements on the distribution efficiency of our protocol over other adopted alternatives.

In recent years, the rapid growth of peer-to-peer (P2P) networks has provided a new paradigm for content distribution. To improve the efficiency of a P2P system, it is important to provide incentives for the peers to participate and contribute their resources. In this work, we address the incentive provisioning problem for distribution of large-volume content in P2P networks, and present a "seeing-is-believing" incentive-compatible mechanism in which a peer will decide how much resources will be assigned to which neighbors based on what it has experienced. The protocol applies a utility-based resource-trading concept where peers will maximize their contributions for a fair or better return, and we show that by adopting this protocol, the system will achieve Cournot Equlibrium. Our protocol is light-weight, completely decentralized, and cheat-proof.

BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions