Increasing customer apprehensions regarding the security and nutritional value of agricultural goods are compelling governments and industries to implement traceable, transparent, and reputable logistics management systems. Blockchain-based agricultural logistics management systems guarantee the permanence of data once it is uploaded but cannot cope with the risk of data being falsified before uploading to the blockchain. In this work, we developed a collaborative game model between government bodies and agricultural enterprises based on the evolutionary game theory and explored the influencing factors of enterprises following the rules to share the real traceability information through numerical simulation using MATLAB. The findings show that government incentives and penalties promote positive behavior, and consumer and media supervision contribute to supply chain transparency, but firms tend to share truthful information only when it benefits them. This study builds upon existing research on the impact of social variables on both members' decision-making behavior. It highlights the positive roles of consumers and the media in the supervision of agricultural product traceability, which can help to raise public awareness of social responsibility and thus promote positive interaction in the market.

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.