Economics of networks

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Economics of networks is an increasing new field on the border of economics and network sciences. It is concerned with understanding of economic phenomena by using network concepts and the tools of network science. Some main authors in this field are Sanjeev Goyal, Matthew O. Jackson and Rachel Kranton.

This term should not be confused with network economics or network externality, a theory explaining that a product or service has an increasing demand, that is, the more people use it, the more utility it brings.

Models of networked markets[edit]

Using the concept of networks during the analysis of markets can enable us to better understand its functioning. On the border of network science and market theory, several models have emerged explaining different aspects of markets.

Exchange theory[edit]

Exchange theory explains how economic transactions, tradon of favors, communication of information or other goods’ exchanges are affected by the structure of relationship among the involved participants.[1] The main concept is that the act of exchange depends on the agents’ other opportunities and their environment, and thus getting a deeper understanding is possible only by examining these factors. The position of a given agent in the network, for example, can endow her with power over the auctions and deals she make with her partners.[2]

Bilateral trading models[edit]

As part of exchange theory, bilateral trading models consider sellers and buyers and use game theory models of the bargaining in networks in order to predict the behaviour of agents depending on the type of network.[1] The outcome of transactions can be determined by, for example, the number of sellers a buyer is connected to, or vice versa for which Corominas-Bosch[3] built a very simple model. Another case is when the agents agree on the transaction through an auction and their decision making during the auction depends on the link structure. Kranton and Minehart[4] came to the conclusion that if we consider markets as networks it can enable sellers to pool uncertainty in demand. As building links is costly, due to the trade-off not everybody need to be linked to everybody in the network. Sparsity in the network will prove to be efficient.

Informal exchange[edit]

The first networks in economics were discovered prior to the development of network science. Károly Polány, Claude Levi-Strauss or Bronislaw Malinowski studied such tribes where complicated gift exchange mechanisms constructed networks between groups, families or islands. Although modern trade systems differ fundamentally, such systems based on reciprocity can still survive and reciprocity-based or personalised exchange deals persists even when a market would be more efficient. According to Kranton,[5] informal exchange can exist in networks if transactions are more reciprocal than market-based. In this case, market exchange is hard to find and associated with high search costs, therefore yields low utility. Personalised exchange agreements ensure the possibility of long term agreements.

Scale-free property and economics[edit]

Recent studies have tried to examine the deeper connection between socioeconomic factors and phenomena and the scale free property. They found that business networks have scale-free property, and that the merger among companies decreases the average separation between firms and increase cliquishness.[6] In another research,[7] scientists found that payment flows in an online payment system exhibits free-scale property, high clustering coefficient and small world phenomenon, and that after 9/11 attacks the connectivity of the network reduced and average path length increased. These results were found to be useful in order to understand how to overcome a possible contagion of similar disturbances in payment networks.

World trade web[edit]

World trade is generally highlighted as a typical example for huge networks. The interconnectedness of the countries can both have positive and negative externalities. It has been shown that the world trade web exhibits scale free properties, where the main hub is the United States. 18 out of 21 analyzed developed countries showed large synchronization in economic performance and cycles with the US during 1975-2000.[8] The remaining three countries are special cases. Austria’s performance correlates highly with that of Germany, though Germany and Japan took completely different economic paths. It seems, despite their embeddedness into the global economy, that the unusual economic measures following Germany’s unification in 1992 and the Plaza Accord in 1985 (which appreciated the Japanese Yen), drove these two countries off the normal economic track. The importance of regional economic (and political) cooperation also appears in this analysis.

See also[edit]


  1. ^ a b Jackson, Matthew O. (2008). Social and economic networks. Princeton: Princeton University Press. ISBN 9780691134406.
  2. ^ Cook, Karen S.; Emerson, Richard M. (October 1978). "Power, Equity and Commitment in Exchange Networks". American Sociological Review. 43: 721–739. doi:10.2307/2094546. JSTOR 2094546.
  3. ^ Corominas-Bosch, Margarida (2004). "One Two-Sided Network Markets". Journal of Economic Theory. doi:10.1016/s0022-0531(03)00110-8.
  4. ^ Kranton, Rachel E.; Minehart, Deborah F. (June 2001). "A Theory of Buyer-Seller Networks". American Economic Review. 91: 485–508. doi:10.1257/aer.91.3.485.
  5. ^ Kranton, Rachel (1996). "Reciprocal Exchange: A Self-Sustaining System" (PDF). American Economic Review.
  6. ^ Souma, Wataru; Fujiwara, Yoshi; Aoyama, Hideaki (2003). "Complex networks and economics". Physica A. 324: 396–401. doi:10.1016/s0378-4371(02)01858-7.
  7. ^ Soramaki, Kimmo; Bech, Morten L.; Arnold, Jeffrey; Glass, Robert J.; Beyeler, Walter E. (June 2007). "The topology of interbank payment flows". Physica A. 379: 317–333. doi:10.1016/j.physa.2006.11.093.
  8. ^ Li, Xiang; Jin, Yu Ying; Chen, Guanrong (October 2003). "Complexity and synchronization of the World Trade Web". Physica A. 328: 287–296. doi:10.1016/S0378-4371(03)00567-3.


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