We model the ultimate price paid by users of a decentralized ledger as resulting from a two-stage game where Miners (/Proposers/etc.) first purchase blockspace via a Tullock contest, and then price that space to users. When analyzing our distributed ledger model, we find: - A characterization of all possible pure equilibria (although pure equilibria are not guaranteed to exist). - A natural sufficient condition, implied by Regularity (à la [Myerson, 1981]), for existence of a "market-clearing" pure equilibrium where Miners choose to sell all space allocated by the Distributed Ledger Protocol, and that this equilibrium is unique. - The market share of the largest miner is the relevant "measure of decentralization" to determine whether a market-clearing pure equilibrium exists. - Block rewards do not impact users' prices at equilibrium, when pure equilibria exist. But, higher block rewards can cause pure equilibria to exist. We also discuss aspects of our model and how they relate to blockchains deployed in practice. For example, only "patient" users (who are happy for their transactions to enter the blockchain under any miner) would enjoy the conclusions highlighted by our model, whereas "impatient" users (who are interested only for their transaction to be included in the very next block) still face monopoly pricing.

In the domain of high reliability applications, Burn-In testing (BI) is always present since it is one of the prime countermeasures against the infant mortality phenomenon. Traditional static BI testing proves to be inefficient for modern circuit designs. As the devices’ feature size scales down and their structural and architectural complexity increases, so does the complexity and cost of the BI test. Different BI methods are employed by the industry where stimuli are also applied to the devices under test (DUTs) in order to effectively stress and stimulate all nets of the design. One known industry practice resorts to Design for Testability (DfT) infrastructures (e.g., scan) and is based on the application of test vectors at low frequency to excite the DUT as much as possible with the goal of switching each net of the design at least once. In this paper we consider the case where the layout of the circuit is known and propose two novel methods able to automatically produce functional stimuli to switch pairs of neighboring nodes (i.e., nodes that are placed within a specified distance in the DUT) in short periods of time. This solution has been shown to be able to trigger some latent defects in a circuit better than other methods. As a case study, we target functional units within a RISC-V processor (RI5CY). We show that the functional stimuli generated by the exact method described in the paper are able to achieve optimal results (i.e., the maximum functional switching of neighboring pairs), thus maximizing the chance that their at-speed application can activate weak points in the circuit.

Most algorithmic studies on multi-agent information design have focused on the restricted situation of optimal public signaling with no inter-agent externalities; only a few exceptions investigated special game classes such as zero-sum games and second-price auctions. This paper initiates the algorithmic information design of both public and private signaling in a fundamental class of games with negative externalities, i.e., atomic singleton congestion games, with a wide range of applications in scheduling, routing, and network design, etc.

This paper studies the problem of information design in a general security game setting in which multiple independent self-interested defenders attempt to provide protection simultaneously on the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type) whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. We focus on the algorithmic study of information design for private signaling in this game setting. In particular, we develop a polynomial-time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex-ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness on the targets.