Incentive Mechanism Design for Distributed Autonomous Organizations Based on the Mutual Insurance Scenario

Abstract

The rise of blockchain has led to discussions on new governance models and the cooperation of multiple participants. Due to the cognitive defects of the blockchain protocol in terms of intelligent contracts and decentralized autonomous organizations (DAOs), it is often unclear as to how to make decisions about the evolution of blockchain applications. Many autonomous organizations, with the support of network technologies such as blockchain, blindly absorb members and expand the scale of the capital pool, while ignoring the cost advantage of traditional autonomous organizations based on social relations and mutual supervision to fight information asymmetry. In this context, this study analyzes the evolutionary trend of autonomous organizations and their members’ strategies under different policy environments. To this end, under the digital economy background, based on game theory, the evolutionary dynamics method, and the form of the mutual insurance organization, this study constructs an evolutionary dynamics model of distributed autonomous organizations. The results show that blind expansion without review aggravates the overall risk pool’s moral hazard, in the context of mutual insurance. Organizational strategies, such as risk pool splits, can effectively improve the risk pool’s operating performance and establish a benign competition elimination mechanism. Driven by cooperation efficiency and split supervision based on homogeneous clustering, the comprehensive application of the market elimination mechanism can effectively combat moral hazards, restrain the adverse effects of member flow, expand the living space of small- and medium-sized insurance organizations, curb the emergence of a large-scale monopoly risk pool, and improve market vitality. These conclusions and suggestions also apply to autonomous organizations based on social relations and mutual supervision. The results offer specific decision-making guidance and suggestions for the government, insurance companies, and risk management.

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“…Another fundamental theory in this study is mutual information theory (MIT) [16][17][18][19][20][21][22][23][24][25][26]. In 1948, Shannon proposed that "information is something used to remove random uncertainty."…”