An evolutionary model with best response and imitative rules
Lorenzo Cerboni Baiardi () and
Ahmad Naimzada ()
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Lorenzo Cerboni Baiardi: University of Milano - Bicocca
Decisions in Economics and Finance, 2018, vol. 41, issue 2, No 12, 313-333
Abstract We formulate an evolutionary oligopoly model where quantity setting players produce following either the static expectation best response or a performance-proportional imitation rule. The choice on how to behave is driven by an evolutionary selection mechanism according to which the rule that brought the highest performance attracts more followers. The model has a stationary state that represents a heterogeneous population where rational and imitative rules coexist and where players produce at the Cournot–Nash level. We find that the intensity of choice, a parameter representing the evolutionary propensity to switch to the most profitable rule, the cost of the best response implementation as well as the number of players have ambiguous roles in determining the stability property of the Cournot–Nash equilibrium. This marks important differences with most of the results from evolutionary models and oligopoly competitions. Such differences should be referred to the particular imitative behavior we consider in the present modeling setup. Moreover, the global analysis of the model reveals that the above-mentioned parameters introduce further elements of complexity, conditioning the convergence toward an inner attractor. In particular, even when the Cournot–Nash equilibrium loses its stability, outputs of players little differ from the Cournot–Nash level and most of the dynamics is due to wide variations of imitators’ relative fraction. This describes dynamic scenarios where shares of players produce more or less at the same level alternating their decision mechanisms.
Keywords: Imitation; Heterogeneity; Evolutionary game; Logit dynamics; Dynamic instability; Dynamic systems (search for similar items in EconPapers)
JEL-codes: C62 C63 C72 C73 (search for similar items in EconPapers)
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