Data-driven mechanisms for network freight platforms: An evolutionary game perspective
Chan He, Qun Xu, Xu Xu, Feng Kong, Feng Shen
TL;DR
This paper explores how data-driven mechanisms can improve cooperation and efficiency in freight platforms using evolutionary game theory.
Contribution
The study introduces an evolutionary game model to analyze the impact of data-driven mechanisms on stakeholder cooperation in freight platforms.
Findings
Consistent data-driven mechanisms promote stable and honest cooperation among stakeholders.
Subsidies, penalties, and incentives significantly influence fair transaction behaviors.
Specific costs act as deterrents to dishonest practices in freight platforms.
Abstract
This paper examines the impact of data-driven mechanisms in network freight platforms. The main objective is to understand how these mechanisms can improve operational efficiency and encourage cooperation among key stakeholders, including a risk-neutral shipper, a loss-averse carrier and network freight platform. The study uses evolutionary game theory to model the interactions between these parties. Numerical simulations are conducted to evaluate the effects of initial conditions and important parameters on cooperation. The results show that consistent implementation of data-driven mechanisms fosters stable and honest cooperation. Key factors, such as subsidies, penalties for dishonest behavior, the likelihood of detecting dishonesty, and incentives for honest actions, motivate carriers and shippers to participate in fair transactions. Additionally, specific costs are identified as…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Experimental Behavioral Economics Studies · Digital Platforms and Economics