Information aggregation and computational intelligence

Chen, Shu-Heng and Venkatachalam, Ragupathy. 2017. Information aggregation and computational intelligence. Evolutionary and Institutional Economics Review, 14(1), pp. 231-252. ISSN 1349-4961 [Article]

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Abstract or Description

This study examines the possibility that the computational intelligence (CI) inspired tools can effectively aggregate the rich information generated from the Web 2.0 economy and, thereby, enhance the quality of decision-making. Despite many advancements and commendable applications of CI in recent years, this issue has not been well addressed. We argue that this question is intimately related to the central issue of the socialist calculation debate since the time of Friedrich Hayek. In terms of information aggregation, we examine whether there is a better engineering than the market mechanism. More precisely, we focus on whether the CI-driven sentiment analysis can generate signals like prices and whether CI can process unstructured text data better than the market. We argue that Web 2.0 economy may not be able to set us free from information overload problems that have long coexisted with the presence of markets. We attribute this to the tacitness and subjectivity of knowledge and the recursive (feedback) characteristic of the sentiments. In this sense, Hayek’s fundamental assertion that the effectiveness of the market mechanism may not be so much conditioned on the information and communication technology still applies.

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The S.-H. Chen and the R. Venkatachalam are grateful for the research support in the form of Ministry of Science and Technology (MOST) grants, MOST 103-2410-H-004-009-MY3 and MOST 104-2811-H-004-003, respectively.


Web 2.0, Computational intelligence, Socialist calculation debate, Text mining, Sentiment analysis, Tacit knowledge, Evolutionary computation

Departments, Centres and Research Units:

Institute of Management Studies
Institute of Management Studies > Structural Economic Analysis


1 May 2016Submitted
4 August 2016Accepted
11 August 2016Published Online
June 2017Published

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Date Deposited:

17 Jan 2017 12:16

Last Modified:

26 Feb 2024 13:14

Peer Reviewed:

Yes, this version has been peer-reviewed.


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