Research On Incentive Mechanism Of Blockchain Knowledge Community Based On Hidden Markov Model

Relying on the rapid development of information network technology,the leader of knowledge production has shifted from the enterprise to the public,and the"online knowledge community"composed of"online social network"and"knowledge sharing"has emerged as the times require.The incentive mechanism is an important means for the knowledge community to encourage users to participate in community activities.At present,traditional knowledge communities usually use community points,grades,honor values,etc.as incentives to attract users to continue to participate.However,such virtual spiritual incentives are not enough to complete the long-term incentives for the user’s life cycle^[1].The unique decentralized autonomy advantages of blockchain technology and the incentive mechanism based on tokens have brought new possibilities for the development of online communities.Therefore,the issue of how the blockchain community can better design the incentive mechanism deserves attention.Starting from the needs of users,based on the theory of self-determination and the need for achievement,this research proposes an innovative analysis framework for the incentive mechanism of the blockchain knowledge communities.In addition,this research adopts a dynamic perspective to analyze user contribution behavior,and quantifies the impact of different incentive mechanisms in the blockchain knowledge community through the dynamic structure metrology Hidden Markov Model（HMM）.The observation variable of the Hidden Markov Model is the user’s knowledge contribution level.The research believes that the transfer of the user’s hidden motivation state in the knowledge community is affected by the community incentive mechanism,and the multivariate ordered Logit model is used to model the state transfer process.Considering the negative binomial distribution of user contributions in the knowledge community,this research constructs a negative binomial regression model for the observed state probability to control the influence of personal characteristics and related characteristics of digital currency on user knowledge contribution behavior.This research selects user data from the Steemit community and uses Markov Monte Carlo multi-layer Bayesian process,Gibbs sampling and Hamiltonian Monte Carlo algorithm for model regression.This research uses R language to write RStan program,and iteratively optimizes the estimation of parameters to quantify the impact of different incentive mechanisms.The research validates the heterogeneity of the effects of different incentive mechanisms.Under the action of the achievement incentive mechanism,users in various motivational states are more inclined to maintain the existing motivational state or transfer to a higher motivational state.The power incentive mechanism will make users in the middle and high motivation states more inclined to maintain the existing motivation state or move to a higher motivation state,but will not promote the transition of low motivation state users to a higher motivation state.Affinity incentives only have a significant positive effect on users in low-motivation states,helping them to move to higher states,while they have no significant effect on users in medium-high-motivation states.The economic incentive mechanism has a positive and significant effect on users in low motivation state,and has a negative and significant effect on users in medium motivation state and high motivation state.This research provides new ideas for the mutual influence and integration of blockchain technology and online knowledge communities,and has certain guiding significance for the design of the incentive mechanism of blockchain knowledge communities.,at the same time provides some inspiration for how the traditional knowledge community managers can better"On-chain"and how to deploy various mechanisms after"On-chain".