Optimal Smart Contracts with Costly Verification

We study optimal smart contract design for monitoring an exchange of an item performed offine. There are two parties, a seller and a buyer. Exchange happens of-chain, but the status update takes place on-chain. The exchange can be verified but with a cost. To guarantee self-enforcement of the smart contract, both parties make a deposit and the deposits must cover payments made in all possible final states. Both parties have an (opportunity) cost of making deposits. We discuss two classes of contract: In the first, the contract only interacts with the seller, while in the second, the contract can also interact with the buyer. In both cases, we derive optimal contracts specifying optimal deposits and verification policies

Welfare theorems for random assignments with priorities

Motivated by the application of designing fair and efficient school choice lotteries, we consider constrained efficiency notions for random assignments under priorities. We provide a constrained (priority respecting) version of the ordinal efficiency welfare theorem for random assignments. Moreover, we show that a constrained version of a cardinal second welfare theorem fails to hold

Multicast Network Design Game on a Ring

In this paper we study quality measures of different solution concepts for the multicast network design game on a ring topology. We recall from the literature a lower bound of 4/3 and prove a matching upper bound for the price of stability, which is the ratio of the social costs of a best Nash equilibrium and of a general optimum. Therefore, we answer an open question posed by Fanelli et al. in [12]. We prove an upper bound of 2 for the ratio of the costs of a potential optimizer and of an optimum, provide a construction of a lower bound, and give a computer-assisted argument that it reaches $2$ for any precision. We then turn our attention to players arriving one by one and playing myopically their best response. We provide matching lower and upper bounds of 2 for the myopic sequential price of anarchy (achieved for a worst-case order of the arrival of the players). We then initiate the study of myopic sequential price of stability and for the multicast game on the ring we construct a lower bound of 4/3, and provide an upper bound of 26/19. To the end, we conjecture and argue that the right answer is 4/3.Comment: 12 pages, 4 figure

Network Creation Games: Think Global - Act Local

We investigate a non-cooperative game-theoretic model for the formation of communication networks by selfish agents. Each agent aims for a central position at minimum cost for creating edges. In particular, the general model (Fabrikant et al., PODC'03) became popular for studying the structure of the Internet or social networks. Despite its significance, locality in this game was first studied only recently (Bil\`o et al., SPAA'14), where a worst case locality model was presented, which came with a high efficiency loss in terms of quality of equilibria. Our main contribution is a new and more optimistic view on locality: agents are limited in their knowledge and actions to their local view ranges, but can probe different strategies and finally choose the best. We study the influence of our locality notion on the hardness of computing best responses, convergence to equilibria, and quality of equilibria. Moreover, we compare the strength of local versus non-local strategy-changes. Our results address the gap between the original model and the worst case locality variant. On the bright side, our efficiency results are in line with observations from the original model, yet we have a non-constant lower bound on the price of anarchy.Comment: An extended abstract of this paper has been accepted for publication in the proceedings of the 40th International Conference on Mathematical Foundations on Computer Scienc

Selfish Network Creation with Non-Uniform Edge Cost

Network creation games investigate complex networks from a game-theoretic point of view. Based on the original model by Fabrikant et al. [PODC'03] many variants have been introduced. However, almost all versions have the drawback that edges are treated uniformly, i.e. every edge has the same cost and that this common parameter heavily influences the outcomes and the analysis of these games. We propose and analyze simple and natural parameter-free network creation games with non-uniform edge cost. Our models are inspired by social networks where the cost of forming a link is proportional to the popularity of the targeted node. Besides results on the complexity of computing a best response and on various properties of the sequential versions, we show that the most general version of our model has constant Price of Anarchy. To the best of our knowledge, this is the first proof of a constant Price of Anarchy for any network creation game.Comment: To appear at SAGT'1

FileBounty: fair data exchange

Digital contents are typically sold online through centralized and custodian marketplaces, which requires the trading partners to trust a central entity. We present FileBounty, a fair protocol which, assuming the cryptographic hash of the file of interest is known to the buyer, is trust-free and lets a buyer purchase data for a previously agreed monetary amount, while guaranteeing the integrity of the contents. To prevent misbehavior, FileBounty guarantees that any deviation from the expected participants' behavior results in a negative financial payoff; i.e. we show that honest behavior corresponds to a subgame perfect Nash equilibrium. Our novel deposit refunding scheme is resistant to extortion attacks under rational adversaries. If buyer and seller behave honestly, FileBounty's execution requires only three on-chain transactions, while the actual data is exchanged off-chain in an efficient and privacypreserving manner. We moreover show how FileBounty enables a flexible peer-to-peer setting where multiple parties fairly sell a file to a buyer

Network Creation Games with Traceroute-Based Strategies

Network creation games model the autonomous formation of an interconnected system of selfish users. In particular, when the network will serve as a digital communication infrastructure, each user is identified by a node of the network, and contributes to the build-up process by strategically balancing between her building cost (i.e., the number of links she personally activates in the network) and her usage cost (i.e., some function of the distance in the sought network to the other players). When the corresponding game is analyzed, the generally adopted assumption is that players have a common and complete information about the evolving network topology, which is quite unrealistic though, due to the massive size this may have in practice. In this paper, we thus relax this assumption, by instead letting the players have only a partial knowledge of the network. To this respect, we make use of three popular traceroute-based knowledge models used in network discovering (i.e., the activity of reconstructing the topology of an unknown network through queries at its nodes), namely: (i) distance vector, (ii) shortest-path tree view, and (iii) layered view. For all these models, we provide exhaustive answers to the canonical algorithmic game theoretic questions: convergence, computational complexity for a player of selecting a best response, and tight bounds to the price of anarchy, all of them computed w.r.t. a suitable (and unifying) equilibrium concept