scholarly journals Not so Fast! (And not so Frugal): Rethinking the Recognition Heuristic

2019 ◽  
pp. 1027-1027
Author(s):  
Daniel M. Oppenheimer
Keyword(s):  
Recognition Heuristic

Recognizing Users of the Recognition Heuristic

2008 ◽  
Vol 55 (6) ◽  
pp. 394-401 ◽  
Author(s):  
Benjamin E. Hilbig ◽  
Rüdiger F. Pohl
Keyword(s):  
Decision Makers ◽  
Recognition Heuristic ◽  
Inference Strategy

The recognition heuristic is hypothesized to be a frugal inference strategy assuming that inferences are based on the recognition cue alone. This assumption, however, has been questioned by existing research. At the same time most studies rely on the proportion of choices consistent with the heuristic as a measure of its use which may not be fully appropriate. In this study, we propose an index to identify true users of the heuristic contrasting them to decision makers who incorporate further knowledge beyond recognition. The properties and the applicability of the proposed index are investigated in the reanalyses of four published experiments and corroborated by a new study drawn up to rectify the shortcomings of the reanalyzed experiments. Applying the proposed index to explore the influence of knowledge we found that participants who were more knowledgeable made use of the information available to them and achieved the highest proportion of correct inferences.

Criterion knowledge: A moderator of using the recognition heuristic?

2009 ◽  
Vol 22 (5) ◽  
pp. 510-522 ◽  
Author(s):  
Benjamin E. Hilbig ◽  
Rüdiger F. Pohl ◽  
Arndt Bröder
Keyword(s):  
Recognition Heuristic

scholarly journals On cognitive preferences and the plausibility of rule-based models

2019 ◽  
Vol 109 (4) ◽  
pp. 853-898 ◽  
Author(s):  
Johannes Fürnkranz ◽  
Tomáš Kliegr ◽  
Heiko Paulheim
Keyword(s):  
Cognitive Biases ◽  
User Acceptance ◽  
Conjunction Fallacy ◽  
Recognition Heuristic ◽  
Rule Based ◽  
Discriminative Models ◽  
Weak Preference ◽  
Simple Rules ◽  
Rule Sets ◽  
Cognitive Preferences

AbstractIt is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that—all other things being equal—longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowdsourcing study based on about 3000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recognition heuristic, and investigate their relation to rule length and plausibility.

scholarly journals The use of the recognition heuristic as an investment strategy in European stock markets

2017 ◽  
Vol 22 (43) ◽  
pp. 207-223 ◽  
Author(s):  
Júlio Lobão ◽  
Luís Pacheco ◽  
Carlos Pereira
Keyword(s):  
Financial Markets ◽  
Abnormal Returns ◽  
Investment Strategy ◽  
Google Trends ◽  
Recognition Heuristic ◽  
Monthly Basis ◽  
Content Type ◽  
Additional Information ◽  
Market Portfolio ◽  
Heuristic Strategies

Purpose People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe. Design/methodology/approach In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe. Findings The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month. Research limitations/implications The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies. Practical implications Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic. Originality/value The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.

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