Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Simulating organizations: computational models of institutions and groups
Simulating organizations: computational models of institutions and groups
Agent-based computational economics: modeling economies as complex adaptive systems
Information Sciences—Informatics and Computer Science: An International Journal
Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity)
To innovate or not to innovate?
IEEE Transactions on Evolutionary Computation
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Agent-based computational economics (ACE) is used to study on the social science fields. It can powerfully advance a distinctive approach to social science. In this paper, by applying the ACE idea, a group behaviour evolution model is built in order to directly study the emergence of organisation behaviour from individual interactions. This model shows the effect of the interactions among behaviour agents on the size and maximum benefits of the firm. Firstly, through analysing the organisation behaviour, the based-agent group behaviour evolution model is provided, and the modelling method and the internal architecture are described in detail. Then, the local details and implementations of the model are shown. Finally, the simulation results explain how the micro-individual interactions affect the macro-organisation behaviours, and the analysis of the simulation results show that infinite expansion of the firm size could lead to profit shrinkage. In the summary, the paper briefly summarises the research contents and significance, and further illustrates that paying attention to the micro level of a general behaviour, the complex phenomena may emerge from a set of given rules in a simulation. Moreover, the direction of further research is brought forward.