We present an overview of hybrid Casper the Friendly Finality Gadget (FFG): a Proof-of-Stake checkpointing protocol overlaid onto Ethereum's Proof-of-Work blockchain. We describe its core functionalities and reward scheme, and explore its properties. Our findings indicate that Casper's implemented incentives mechanism ensures liveness, while providing safety guarantees that improve over standard Proof-of-Work protocols. Based on a minimal-impact implementation of the protocol as a smart contract on the blockchain, we discuss additional issues related to parametrisation, funding, throughput and network overhead and detect potential limitations.
The rapid evolution of the blockchain community has brought together stakeholders from fundamentally different backgrounds: cryptographers, protocol designers, software developers, startup entrepreneurs, corporate executives and investors, academics of various disciplines, and end users. The result is a diverse ecosystem, presently exemplified by the development of a wide range of different blockchain protocols. This raises questions for policy and decision makers: How do different protocols compare? What are their trade-offs? Existing efforts to survey the area reveal a fragmented terminology, and the lack of a unified framework to make the different desirable properties of blockchain protocols explicit.In this paper, we work towards bridging this gap. We evaluate protocols within a five-dimensional design space with the following axes. Optimality: does the protocol achieve its main goals? Stability: are the incentives of its participating agents well-aligned? Efficiency: is its output maximal relative to its use of resources? Robustness: can it cope when its operational assumptions are invalid or perturbed? Persistence: can it recover from catastrophic events? Based on the relevant literature, we organize the properties of existing protocols in subcategories of increasing granularity. The result is a dynamic scheme -termed the PREStO framework. Its scope is to aid the communication between stakeholders of different backgrounds, including managers and investors, and to identify research challenges and opportunities for blockchain protocols in a systematic way. We illustrate this via use cases and make a first step to understand the blockchain ecosystem through a more comprehensive lens.
Exploration-exploitation is a powerful and practical tool in multi-agent learning (MAL), however, its effects are far from understood. To make progress in this direction, we study a smooth analogue of Q-learning. We start by showing that our learning model has strong theoretical justification as an optimal model for studying exploration-exploitation. Specifically, we prove that smooth Q-learning has bounded regret in arbitrary games for a cost model that explicitly captures the balance between game and exploration costs and that it always converges to the set of quantal-response equilibria (QRE), the standard solution concept for games under bounded rationality, in weighted potential games with heterogeneous learning agents. In our main task, we then turn to measure the effect of exploration in collective system performance. We characterize the geometry of the QRE surface in low-dimensional MAL systems and link our findings with catastrophe (bifurcation) theory. In particular, as the exploration hyperparameter evolves over-time, the system undergoes phase transitions where the number and stability of equilibria can change radically given an infinitesimal change to the exploration parameter. Based on this, we provide a formal theoretical treatment of how tuning the exploration parameter can provably lead to equilibrium selection with both positive as well as negative (and potentially unbounded) effects to system performance.
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