Abstract
There has been an increasing interest in the geographic aspects of economic development, exemplified by P. Krugman’s logical analysis. We show in this paper that the geographic aspects of economic development can be modeled using multi-agent systems that incorporate multiple underlying factors. The extent of information sharing is assumed to be a driving force that leads to economic geographic heterogeneity across locations without geographic advantages or disadvantages. We propose an agent-based market model that considers a spectrum of different information-sharing mechanisms: no information sharing, information sharing among friends and pheromone-like information sharing. Finally, we build a unified model that accommodates all three of these information-sharing mechanisms based on the number of friends who can share information. We find that the no information-sharing model does not yield large economic zones, and more information sharing can give rise to a power-law distribution of market size that corresponds to the stylized fact of city size and firm size distributions. The simulations show that this model is robust. This paper provides an alternative approach to studying economic geographic development, and this model could be used as a test bed to validate the detailed assumptions that regulate real economic agglomeration.
Highlights
Researchers have become increasingly interested in the geographic aspects of economic development
Economic analysis has shown that centers may emerge as a result of attempts by producers to minimize the costs of production and delivery or because larger cities can support a wider range of activities [1]
There are studies emphasizing that physical geography is highly differentiated and that these differences have a large effect on economic development
Summary
Researchers have become increasingly interested in the geographic aspects of economic development. There are very few works that study the effects of different types of information-sharing mechanisms on the spatial structure of agent-based systems. Disregarding many practical factors, such as moving cost, trade demand diversity, market structure, information dissemination efficiency and the economic environment, we modeled the spatial evolution of the market as a result of changes in the trading positions of agents with different information-sharing mechanisms. Side length of the lattice Total number of agents Random walk probability Sight range Cluster radius Amount of information available after a trade Information evaporation rate Number of friends in each information-sharing mechanism doi:10.1371/journal.pone.0058270.t002. Between the above-mentioned two extremes, we model a typical mid-level information-sharing mechanism in which the agents can share information in a community This mechanism corresponds to the second (decentralized) coordination structure in reference [10], in which decisions are made separately in each market using local knowledge and data. Ct calculates the average number of agents in all neighborhoods at time-step t
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
