Incentive-compatible Intelligence Collaboration Analysis Framework Based on Blockchain and Evolutionary Game.

Collaborative intelligence data analysis can effectively dig out valuable information from massive data to support decision-making. Ensuring data credibility in collaboration under the background of big data is a crucial problem to be solved. In addition, the low willingness of analysts to share results in low efficiency of collaborative tasks. To solve the above problems, this paper proposes a blockchain-based intelligence collaborative analysis and sharing framework, ICASChain, which contains a highly trusted intelligence collaboration mechanism and a dynamically adjustable incentive mechanism. Based on Evolutionary Game Theory, this paper analyzes the evolutionary stability strategy of intelligence sharing willingness of analysts by constructing a replication dynamic equation. We use a smart contract incentive algorithm including ICASToken to dynamically regulate them to increase the proportion of users participating in intelligence sharing. Finally, the influencing factors of information sharing and the incentive effect of the incentive model are discussed through simulation experiments. The simulation results show that the proposed ICASChain has essential application value in improving the analysts’ willingness to collaborate and share intelligence and the correctness of the analysis results.