We use the theory of complex networks in order to quantitatively characterize the formation of communities in a particular financial market. The system is composed by different banks exchanging on a daily basis loans and debts of liquidity. Through topological analysis and by means of a model of network growth we can determine the formation of different group of banks characterized by different business strategy. The model based on Pareto's law makes no use of growth or preferential attachment and it reproduces correctly all the various statistical properties of the system. We believe that this network modeling of the market could be an efficient way to evaluate the impact of different policies in the market of liquidity. Coevolution and interaction between different agents is known to be one of the ingredients of the so-called complex systems. Several examples can be found in social ͓1,2͔, biological ͓3-6͔, economical ͓7͔, and technological systems ͓8͔. Any of these systems is composed by a set of agents competing and sometimes receiving reciprocal advantage interacting each other. In the above situation both coalition and competition are at the basis of the process of co-evolution and self-organization of the system. While this class of problems has been traditionally studied in game theory, more recently it has been introduced an approach based on graph theory ͓9,10͔ By using networks ͓11,12͔, we can characterize quantitatively the interaction between agents by means of a series of topological quantities. The case of study presented here is composed by banks operating in the Italian market ͓13͔. Banks try to maximize their returns given some constraints from the European Central Bank. This complex interaction results in a differentiation of the strategies that is well described by means of graph cliques. More specifically banks of the same size tend to form a cluster and to adopt a similar business strategy.
An analysis of the Japanese credit market in 2004 between banks and quoted firms is done in this paper using the tools of the networks theory. It can be pointed out that: (i) a backbone of the credit channel emerges, where some links play a crucial role; (ii) big banks privilege long-term contracts; the "minimal spanning trees" (iii) disclose a highly hierarchical backbone, where the central positions are occupied by the largest banks, and emphasize (iv) a strong geographical characterization, while (v) the clusters of firms do not have specific common properties. Moreover, (vi) while larger firms have multiple lending in large, (vii) the demand for credit (long vs. short term debt and multi-credit lines) of firms with similar sizes is very heterogeneous. JEL: E51, E52, G21
Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options (n = 2). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents' density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation. Keywords Dynamic environments • Collective decision making • Best-of-n • Swarm robotics • Complex adaptive systems B Eliseo Ferrante
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