Rational Behavior in Committee-Based Blockchains

We study the rational behaviors of participants in committee-based blockchains. Committee-based blockchains rely on specific blockchain consensus that must be guaranteed in presence of rational participants. We consider a simplified blockchain consensus algorithm based on existing or proposed committee-based blockchains that encapsulates the main actions of the participants: voting for a block, and checking its validity. Knowing that those actions have costs, and achieving the consensus gives rewards to committee members, we study using game theory how strategic players behave while trying to maximizing their gains. We consider different reward schemes, and found that in each setting, there exist equilibria where blockchain consensus is guaranteed; in some settings however, there can be coordination failures hindering consensus. Moreover, we study equilibria with trembling participants, which is a novelty in the context of committee-based blockchains. Trembling participants are rational that can do unintended actions with a low probability. We found that in presence of trembling participants, there exist equilibria where blockchain consensus is guaranteed; however, when only voters are rewarded, there also exist equilibria where validity can be violated

On Blockchain We Cooperate: An Evolutionary Game Perspective

Cooperation is fundamental for human prosperity. Blockchain, as a trust machine, is a cooperative institution in cyberspace that supports cooperation through distributed trust with consensus protocols. While studies in computer science focus on fault tolerance problems with consensus algorithms, economic research utilizes incentive designs to analyze agent behaviors. To achieve cooperation on blockchains, emerging interdisciplinary research introduces rationality and game-theoretical solution concepts to study the equilibrium outcomes of various consensus protocols. However, existing studies do not consider the possibility for agents to learn from historical observations. Therefore, we abstract a general consensus protocol as a dynamic game environment, apply a solution concept of bounded rationality to model agent behavior, and resolve the initial conditions for three different stable equilibria. In our game, agents imitatively learn the global history in an evolutionary process toward equilibria, for which we evaluate the outcomes from both computing and economic perspectives in terms of safety, liveness, validity, and social welfare. Our research contributes to the literature across disciplines, including distributed consensus in computer science, game theory in economics on blockchain consensus, evolutionary game theory at the intersection of biology and economics, bounded rationality at the interplay between psychology and economics, and cooperative AI with joint insights into computing and social science. Finally, we discuss that future protocol design can better achieve the most desired outcomes of our honest stable equilibria by increasing the reward-punishment ratio and lowering both the cost-punishment ratio and the pivotality rate