Adversarial learning for revenue-maximizing auctions

We introduce a new numerical framework to learn optimal bidding strategies in repeated auctions when the seller uses past bids to optimize her mechanism. Crucially, we do not assume that the bidders know what optimization mechanism is used by the seller. We recover essentially all state-of-the-art analytical results for the single-item framework derived previously in the setup where the bidder knows the optimization mechanism used by the seller and extend our approach to multi-item settings, in which no optimal shading strategies were previously known. Our approach yields substantial increases in bidder utility in all settings. Our approach also has a strong potential for practical usage since it provides a simple way to optimize bidding strategies on modern marketplaces where buyers face unknown data-driven mechanisms

Spectrum sharing models in cognitive radio networks

Spectrum scarcity demands thinking new ways to manage the distribution of radio frequency bands so that its use is more effective. The emerging technology that can enable this paradigm shift is the cognitive radio. Different models for organizing and managing cognitive radios have emerged, all with specific strategic purposes. In this article we review the allocation spectrum patterns of cognitive radio networks and analyse which are the common basis of each model.We expose the vulnerabilities and open challenges that still threaten the adoption and exploitation of cognitive radios for open civil networks.L'escassetat de demandes d'espectre fan pensar en noves formes de gestionar la distribució de les bandes de freqüència de ràdio perquè el seu ús sigui més efectiu. La tecnologia emergent que pot permetre aquest canvi de paradigma és la ràdio cognitiva. Han sorgit diferents models d'organització i gestió de les ràdios cognitives, tots amb determinats fins estratègics. En aquest article es revisen els patrons d'assignació de l'espectre de les xarxes de ràdio cognitiva i s'analitzen quals són la base comuna de cada model. S'exposen les vulnerabilitats i els desafiaments oberts que segueixen amenaçant l'adopció i l'explotació de les ràdios cognitives per obrir les xarxes civils.La escasez de demandas de espectro hacen pensar en nuevas formas de gestionar la distribución de las bandas de frecuencia de radio para que su uso sea más efectivo. La tecnología emergente que puede permitir este cambio de paradigma es la radio cognitiva. Han surgido diferentes modelos de organización y gestión de las radios cognitivas, todos con determinados fines estratégicos. En este artículo se revisan los patrones de asignación del espectro de las redes de radio cognitiva y se analizan cuales son la base común de cada modelo. Se exponen las vulnerabilidades y los desafíos abiertos que siguen amenazando la adopción y la explotación de las radios cognitivas para abrir las redes civiles

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

Commitment in First-Price Auctions

We study a variation of the single-item sealed-bid first-price auction where one bidder (the leader) is given the option to publicly pre-commit to a distribution from which her bid will be drawn

Stylised Facts and the Contribution of Simulation to the Economic Analysis of Budgeting

The application of computer simulation as a research method raises two important questions: (1) Does simulation really offer added value over established methods? (2) How can the danger of arbitrariness caused by the extended modelling possibilities be minimised? We present the concept of stylised facts as a methodological basis for approaching these questions systematically. In particular, stylised facts provide a point of reference for a comparative analysis of models intended to explain an observable phenomenon. This is shown with reference to a recent discussion in the "economic analysis of accounting" literature where established methods, i.e. game theory, as well as computer simulations are used: the susceptibility of the "Groves mechanism" to collusion. Initially, we identify six stylised facts on the stability of collusion in empirical studies. These facts serve as a basis for the subsequent comparison of four theoretical models with reference to the above questions: (1) We find that the simulation models of Krapp and Deliano offer added value in comparison to the game theoretical models. They can be related to more stylised facts, achieve a better reproduction and exhibit far greater potential for incorporating yet unaddressed stylised facts. (2) Considered in the light of the stylised facts to which the models can be related, Deliano's simulation model exhibits considerable arbitrariness in model design and lacks information on its robustness. In contrast, Krapp demonstrates that this problem is not inherent to the method. His simulation model methodically extends its game theoretical predecessors, leaving little room for arbitrary model design or questionable parameter calibration. All in all, the stylisedfactsconcept proved to be very useful in dealing with the questions simulation researchers are confronted with. Moreover, a "research landscape" emerges from the derived stylised facts pinpointing issues yet to be addressed.Computer Simulation, Stylised Facts, Methodology, Groves Mechanism, Collusion, Game Theory

Modesty Pays: Sometimes!

Standard non-cooperative game theoretical models of international environmental agreements (IEAs) draw a pessimistic picture of the prospective of successful cooperation: only small coalitions are stable that achieve only little. However, there also exist IEAs with higher participation and more success. In order to explain this phenomenon, this paper departs from the standard assumption of joint welfare maximization of coalition members, implying ambitious abatement targets and strong free-riding. Instead, it considers that countries agree on modest emission reduction targets. This may sufficiently raise participation so that the success of treaties improves in terms of global emission reduction and global welfare. Thus, modesty may pay, though the first best optimum cannot be achieved.International environmental agreements, Internal&external stability, Modest emission reduction

Contextual Search in the Presence of Irrational Agents

We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard game-theoretic formulations of this problem assume that agents act in accordance with a specific behavioral model. In practice, however, some agents may not prescribe to the dominant behavioral model or may act in ways that are seemingly arbitrarily irrational. Existing algorithms heavily depend on the behavioral model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents. We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying behavioral model. In particular, we provide two algorithms, one built on robustifying multidimensional binary search methods and one on translating the setting to a proxy setting appropriate for gradient descent. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis.Comment: Compared to the first version titled "Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents", this version provides a broader scope of behavioral models of irrationality, specifies how the results apply to different loss functions, and discusses the power and limitations of additional algorithmic approache

Cartel Sustainability and Cartel Stability

The paper studies how does the size of a cartel affect the possibility that its members can sustain a collusive agreement. I obtain that collusion is easier to sustain the larger the cartel is. Then, I explore the implications of this result on the incentives of firms to participate in a cartel. Firms will be more willing to participate because otherwise, they risk that collusion completely collapses, as remaining cartel members are unable to sustain collusion.Collusion, Partial cartels, Trigger strategies, Optimal punishment