Tripartite Evolutionary Game Model for Agri-food Supply Chain Resilience

A 65.9 KB Excel file presents a tripartite evolutionary game model analyzing neural network adoption in agri-food supply chains. Authored by hansheng wu and last updated in May 2026, the model explores strategic interactions among supply chain members, service platforms, and consumers under bounded rationality. It simulates the effects of technical costs, cooperation risks, and government incentives on system stability.

Use Cases

• Simulating strategic interactions among supply chain actors based on the described tripartite evolutionary game framework.
• Analyzing the impact of government subsidies on consumer support for neural network adoption as mentioned in the description.
• Modeling the sensitivity of service platform cooperation to cost-benefit trade-offs described in the study.
• Investigating how reducing technical costs influences adoption willingness across actors in high-risk supply chains.
• Examining the role of risk mitigation efficiency in promoting collaborative innovation for sustainable agri-food systems.

Strengths

• Model structure and key parameters are explicitly defined in the description, including actors (supply chain members, platforms, consumers) and variables (technical costs, cooperation risks, government incentives).
• The 65.9 KB file size suggests a focused, non-extensive dataset suitable for targeted analysis.
• The model is grounded in specific theoretical frameworks mentioned: bounded rationality and replicator dynamics.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale empirical validation.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small (65.9 KB), indicating limited scope, likely containing model parameters and simulation results rather than extensive empirical data.

Provenance

Source

figshare

Collection Method

Likely contains parameters and results from a developed tripartite evolutionary game model.

Freshness

Last updated 2026-05-10 14:57:04; freshness should be verified.