By Mikhail Anufriev, Valentyn Panchenko (auth.), Professor Charlotte Bruun (eds.)
Perceiving the economic system as a fancy dynamic approach, generates a necessity for brand spanking new instruments for its research. As a positive simulation process, Agent-Based Computational Economics (ACE) has lately confirmed its power and wide applicability. Fields of analysis are extensively unfold inside of economics, with a cluster round monetary markets. This publication is predicated on communications given at AE’2006 (Aalborg, Denmark) – the second one symposium on man made Economics, and covers either general questions of economics, just like the life of marketplace potency, in addition to new questions raised by way of the hot instruments, for instance questions concerning networks of social interplay.
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Additional resources for Advances in Artificial Economics: The Economy as a Complex Dynamic System
In every trading session, each agent selects on which side of the market he attempts to place a trade: he can switch roles across trading sessions, but he cannot place simultaneous orders 20 Marco LiCalzi and Paolo Pellizzari for buying and selling within the same session. The books are completely cleared at the end of each trading session. Prices are quoted using a minimum tick; in other words, they are discretized. Moreover, prices must be nonnegative: if a trader places a bid lower than zero, this is ignored; if a trader places an ask lower than zero, this is automatically converted to the lowest strictly positive price compatible with the existing tick.
2 and reports the merged datapoints for time to convergence. 311) The (average) eﬀect of H, C and B on increasing the time to convergence is statistically signiﬁcant. Remarkably, while side intelligence contributes to this reduction, the coeﬃcient for price intelligence denotes a (weak) contrary eﬀect — when trading is aggressive, time to convergence lengthens. 3 Price Dispersion Our third and ﬁnal performance criterion attempts to quantify the dispersion of prices by measuring the standard deviation of the time series of the prices 27 400 300 200 100 0 Time to convergence (trimmed) 500 2 Allocative Eﬀectiveness and Intelligent Trading B C D H Market Fig.
M aCini ) for the corresponding units. Here M aCi1 is the marginal cost to seller I of the ﬁrst unit, M aCi2 is the cost of the second unit, and so on. Buyer j has nj units to trade and he has a vector of reserve prices (RPj1 , RPj2 , . . , RPjmj ) for the corresponding units. Here RPj1 is the reserve price to seller I of the ﬁrst unit, RPj2 is the reserve price of the second unit, and so on. These valuations are private. Our model does not have environmental restrictions and it allows us to simulate any environment in terms of the number of traders, their units and the valuations of each trader.