Do Shopbots and Lower Search Costs improve the Efficiency of Electronic Markets? An Agent-based Approach
Eric Darmon ()
Modeling, Computing, and Mastering Complexity 2003 from Society for Computational Economics
Buyers' search behaviors on electronic markets are characterized by two distinctive features: i) diminishing search costs and ii) the use of informational intermediaries such as price comparison agents (shopbots). We build a simple agent-based model that captures these two features, and we use this agent-based modeling to explore how the co-evolution of buyers and sellers (i.e. the joint dynamics of learning) may affect market's efficiency. First, we show the ambiguous role of shopbots on the efficiency of such markets: although the use of shopbots is frequently assumed to induce positive effects - by enlarging buyers' information space and so enhance price comparisons - they have a destabilizing role on buyers' and sellers' learning co-evolution. This may result either in the emergence of a dispersed distribution of prices (commonly noticed by empirical studies) or in some market' crashes. Second, we study the impact of a decrease in search costs. We also point to the existence of a non deterministic relationship between market's efficiency and search costs. This suggests that the use of a priori more efficient matching technologies do not necessarily lead to more efficient outcomes.
Keywords: Agent-based Computational Economics; electronic market; e-commerce; Internet; search behavior; Shopbot (Shopping robots). (search for similar items in EconPapers)
JEL-codes: D43 D83 L11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-net
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Persistent link: https://EconPapers.repec.org/RePEc:sce:cplx03:04
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