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Information aggregation and computational intelligence

Limits to price discovery

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Abstract

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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Notes

  1. The term Web 2.0 was popularized by Tim O’Reilly in 2004. O’Reilly and Battelle (O’Reilly and Battelle 2009) provide a systematic guide to the origin and development of Web 2.0.

  2. Unstructured information refers to the absence of a clearly defined data model, such as relational, hierarchical, network etc, according to which the information is organized.

  3. The following can be regarded as an intuitive definition of sentiment analysis: ‘Sentiment analysis or opinion mining is the computational study of peoples opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of the existing users before they use a service or purchase a product.’ - p. 415, Liu and Zhang (2012).

  4. Pooling and processing often assume that the information or knowledge is iid and not interdependent.

  5. Hayek did use the term ‘symbol’ in his discussion on the marvels of the price system. “In abbreviated form, by a kind of symbol, only the most essential information is passed on, and passed on only to those concerned. (Ibid, p. 527; Italics added.)” A broader interpretation of prices as symbols makes it easier to see the relevance of Hayek’s analysis to the modern Web 2.0 economy, in which the use of the sentiment analysis to the big data available in the social media network, such as FinanceTwitter, normally leads to many linguistic-type indices, rather than numerical prices.

  6. A detailed discussion of this feasibility is beyond the size of a chapter. The interested reader is referred to Chen (2015); specifically, those models involve the use of sunspot variables.

  7. The use of statistical tools to understand patterns in textual data has a long history in linguistics. In the 1930s, the American linguist George Kingsley Zipf studied the distribution of words in natural language (Zipf 1936, 1949) and observed that there was a proportional relationship between the frequency of word occurrence and its rank in the frequency list. This relationship is now popularly known as the Zipf’s law.

  8. This can be taken to further granular or advanced levels such as 5-point scale for classifying the opinions based on more precise moods (supportive, confused, excited, sad, and so on).

  9. However, we do not diminish the achievements made in processing opinions or subjective text over the years; see Liu and Zhang (2012), Sects. 3–6.

  10. For instance, Liu and Zhang (2012) notes that

    Moreover, it is also known that human analysis of textual information is subject to considerable psychological biases, e.g., people often pay greater attention to opinions that are consistent with their own preferences. People also have difficulty, owing to their mental and physical limitations, producing consistent results when the amount of information to be processed is large. Automated opinion mining and summarization systems are thus needed, as subjective biases and mental limitations can be overcome with an objective sentiment analysis system. (Ibid, p.415–416; Emphasis added.)

  11. It should be noted that the possibility of ‘emergence’ is another aspect of these systems that make prediction hard, even in theory. For instance, if the changes or shifts in beliefs held by agents in a market or a society are themselves ‘emergent’, then predicting them based on the information concerning micro-states (agents and their tweets) can be a difficult task.

  12. See p. 828, Tables 1 and 2 in Li et al. (2014) for a useful summary.

  13. See Murthy (2015), who argues that twitter feeds play more of a reactive rather than predictive role in elections.

  14. See Otnes (2011) for a detailed discussion on this issue.

  15. Vogt and De Boer Vogt and De Boer (2010) further distinguish two types of agent-based models of language evolution, namely, agent-based analytical model and agent-based cognitive model. For a list of examples on each, the interested reader is referred to Vogt and De Boer (2010), p. 8.

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Authors and Affiliations

  1. Department of Economics, AI-ECON Research Center, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen & Ragupathy Venkatachalam

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  1. Shu-Heng Chen
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Correspondence to Ragupathy Venkatachalam.

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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. We thank the two anonymous referees for their valuable suggestions that helped to improve the paper. All remaining errors are our own.

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Chen, SH., Venkatachalam, R. Information aggregation and computational intelligence. Evolut Inst Econ Rev 14, 231–252 (2017). https://doi.org/10.1007/s40844-016-0048-z

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