By Herbert Dawid, Willi Semmler
This quantity is based round the factor of industry layout and ensuing industry dynamics. the commercial difficulty of 2007-2009 has once more highlighted the significance of a formal layout of marketplace protocols and institutional information for fiscal dynamics and macroeconomics. Papers during this quantity trap institutional info of specific markets, behavioral info of brokers' choice making in addition to spillovers among markets and results to the macroeconomy. Computational tools are used to duplicate and comprehend industry dynamics rising from interplay of heterogeneous brokers, and to increase types that experience predictive energy for advanced industry dynamics. eventually remedies of overlapping generations versions and differential video games with heterogeneous actors are provided.
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Extra resources for Computational Methods in Economic Dynamics
Our conclusion regarding the objective of the paper is that computer agents can replicate the behavior of students in an electricity auction effectively. In fact, the agents were also able to exploit unusual situations by, for example, behaving as free riders when the students in a session speculated aggressively. These results suggest that it is appropriate to do additional sensitivity tests using all computer agents. This is a promising line of research that is essential for developing realistic simulation models of deregulated electricity markets.
Figure 6 shows the best bid and ask under both π -resampling (on the left) and γ -resampling (on the right) with n = 200 agents. 7] to enhance readability. Predictably, as a mere visual inspection confirms, the clear winner is the γ resampling rule that is based on an explicit form of price control. Table 4 validates this conjecture by listing the lowest mean spread obtained under π - and γ resampling for thin, thick, and crowded markets. The difference between the mean values is statistically significant for each choice of n.
In addition, some 34 H. D. Mount Table 1 Experiment settings Categories I II Forward contracts Number of students Number of software agents VIF IPT LS Test 1 No forward contracts 2 2 2 0 Test 2 Contract on first 50 MW at $60/MW (permanent) 2a 2 2 0 Test 3 Contract on first 50 MW, updated every 10 periods (renewable) 2a 2 2 0 Test 1 No forward contracts 0 2 2 2 Test 2 Contract on first 50 MW at $60/MW (permanent) 0 2 2 2a Test 3 Contract on first 50 MW, updated every 10 periods (renewable) 0 2 2 2a Note: Category I—the first set of experiments with 4 agents and 2 students and Category II—the second set of experiment with all agents a Indicates the holder of the forward contract suppliers are represented by computer agents.