Abstract
This paper suggests a methodological appraisal of the main improvements witnessed by the methodology based on the interplay between Experimental Economics (EE) and Agent-based Computational Economics (ACE) in the last 5–6 years. EE and ACE proved to be “natural allies” in that they complement each other: EE helps ACE in dealing with its “degree of freedom” problem and ACE helps EE in controlling and providing benchmarks for experimental subjects’ behavior. The paper discusses the role Evolutionary Computation plays in this bidirectional relationship.
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Notes
- 1.
It is worth recalling the counterintuitive nature of these two definitions. Multi-population algorithm refers to those EA in which researchers calibrate individual agents. Single-population algorithms refers to the social level. See, among others, Holland and Miller (1991).
- 2.
As pointed out by LeBaron (2000), any modeler should take into account some basic issues about agents’ design in that results are inevitably influenced by the learning methods agents have been endowed with. Moreover, he argued that the problem of the bounded memory perspective on past information (i.e., the time horizon) and that the quantity of data, i.e., information, agents should use to take their decisions are crucial in financial models.
- 3.
More details on those results and the learning to forecast experimental literature can be found also in Bao et al. (2013).
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Giulioni, G., D’Orazio, P., Bucciarelli, E., Silvestri, M. (2015). Building Artificial Economies: From Aggregate Data to Experimental Microstructure. A Methodological Survey. In: Amblard, F., Miguel, F., Blanchet, A., Gaudou, B. (eds) Advances in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-09578-3_6
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