Truthful Learning Mechanisms for Multi-Slot Sponsored Search Auctions with Externalities

Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and to assure both the advertisers and the auctioneer a non-negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard truthful mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs at the same time as implementing a truthful mechanism with a revenue loss as small as possible compared to an optimal mechanism designed with the true CTRs. Previous work showed that, when dominant-strategy truthfulness is adopted, in single-slot auctions the problem can be solved using suitable exploration-exploitation mechanisms able to achieve a per-step regret (over the auctioneer's revenue) of order $O(T^{-1/3})$ (where T is the number of times the auction is repeated). It is also known that, when truthfulness in expectation is adopted, a per-step regret (over the social welfare) of order $O(T^{-1/2})$ can be obtained. In this paper we extend the results known in the literature to the case of multi-slot auctions. In this case, a model of the user is needed to characterize how the advertisers' valuations change over the slots. We adopt the cascade model that is the most famous model in the literature for sponsored search auctions. We prove a number of novel upper bounds and lower bounds both on the auctioneer's revenue loss and social welfare w.r.t. to the VCG auction and we report numerical simulations investigating the accuracy of the bounds in predicting the dependency of the regret on the auction parameters

Efficient Metadeliberation Auctions

Imagine a resource allocation scenario in which the interested parties can, at a cost, individually research ways of using the resource to be allocated, potentially increasing the value they would achieve from obtaining it. Each agent has a private model of its research process and obtains a private realization of its improvement in value, if any. From a social perspective it is optimal to coordinate research in a way that strikes the right tradeoff between value and cost, ultimately allocating the resource to one party- thus this is a problem of multi-agent metadeliberation. We provide a reduction of computing the optimal deliberation-allocation policy to computing Gittins indices in multi-anned bandit worlds, and apply a modification of the dynamic-VCG mechanism to yield truthful participation in an ex post equilibrium. Our mechanism achieves equilibrium implementation ofthe optimal policy even when agents have the capacity to deliberate about other agents' valuations, and thus addresses the problem of strategic deliberation.Engineering and Applied Science

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Auction Design without Commitment

We study auction design when parties cannot commit themselves to the mechanism. The seller may change the rules of the game and the buyers choose their outside option at all stages. We assume that the seller has a leading role in equilibrium selection at any stage of the game. Stationary equilibria are characterized in the language of vonNeumann-Morgenstern stable sets. This simplifies the analysis remarkably. In the one buyer case, we obtain the Coase conjecture: the buyer obtains all the surplus and efficiency is reached. However, in the multiple buyer case the seller can achieve more: she is able to commit to the English auction. Typically the converse also holds, the English auction is the only stable auction mechanism.Auction theory, commitment, stable sets

Analysis and Evaluation of Incentive Compatible Dynamic Mechanisms for Carrier Collaboration

This paper introduces a framework for carrier dynamic collaboration. In particular it proposes and analyzes dynamic collaborative mechanisms that are incentive compatible. The dynamic collaborative environment is characterized by a set of carriers that have a proprietary set of customers that generate a stream of random demands over time. The proposed collaborative mechanism is such that upon each demand arrival, each carrier has the incentive to submit the arrived shipment or service request to the collaborative mechanism. Intuition about the efficiency and workings of the collaborative mechanism is developed. A general framework to formulate and study collaborative frameworks among transportation carriers is proposed. A truckload pickup-and-delivery collaborative environment is simulated and results are analyzed

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Analysis and Evaluation of Incentive Compatible Dynamic Mechanisms for Carrier Collaboration

This paper introduces a framework for carrier dynamic collaboration. In particular it proposes and analyzes dynamic collaborative mechanisms that are incentive compatible. The dynamic collaborative environment is characterized by a set of carriers that have a proprietary set of customers that generate a stream of random demands over time. The proposed collaborative mechanism is such that upon each demand arrival, each carrier has the incentive to submit the arrived shipment or service request to the collaborative mechanism. Intuition about the efficiency and workings of the collaborative mechanism is developed. A general framework to formulate and study collaborative frameworks among transportation carriers is proposed. A truckload pickup-and-delivery collaborative environment is simulated and results are analyzed

Cross Compliance: what about compliance?

We reviewed some moral hazard (MH) models applied to agri-environmental policies and identified the main methodological aspects of the literature on this topics. Imperfect vs incomplete monitoring , static vs dynamic and single vs multiple agents models are the main lines along which the literature has been organised analysing each component of a MH model. Most papers point out the role of farmers' risk aversion in mitigating MH. Others highlight that the observed high rate of compliance is still somewhat paradoxical given current enforcement strategies with low fines and monitoring levels. Cross compliance confirm these findings and urges further studies on dynamic models and farmers' non profit maximising behaviour.Cross-compliance, Moral Hazard, Enforcement, Agri-environmental schemes, Agricultural and Food Policy, Q15, Q58, D82,

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches