Alex Uriarte – Page 3

Repetition and Belief

Post author:Alex Uriarte
Post published:January 19, 2021
Post category:Fan

“You can fool all the people some of the time and some of the people all the time, but you cannot fool all the people all the time.” Respond quickly: who is the author of this statement? The most common attribution is Abraham Lincoln, although, as often happens with quotes, there is little evidence to support this attribution. There seems to be some evidence that the statement was actually made by the seventeenth century French protestant, Jacques Abbadie (Quote Investigator, 2013; Parker, 2016). However, we have heard the attribution to Abraham Lincoln so often, that we assume it to be true.

Over the end of the year holidays I read (most of) Daniel Kahneman’s book, “Thinking, Fast and Slow.” Kahneman, a psychologist, won the Nobel Prize for Economics in 2002 (shared with Vernon Smith) for the contributions to economics from his research on human judgement and decision-making under uncertainty. One aspect of human cognition that he describes in his book, is that we are more likely to believe what we find easy to. Various factors can contribute to our “cognitive ease”, including a clear display of the information we are exposed to, having been “primed” by association with a prior piece of information, being in a good mood, and also by repeated exposure to the information, whether that information is true or not (Kahneman, 2011, Chapter 5).

Repetition is also often discussed as an important component in learning (see, for example, the literature on spaced repetition), change management and other aspects of our daily life that depend on our understanding and perception of reality. I once read (and never forgot) a passage in Machiavelli’s “The Prince” where he states that injuries should be inflicted at once, while benefits should be provided piecemeal, overtime, if a ruler is to ensure permanence in power. I always understood this as reflecting how repetition affects perception (Machiavelli, 1998, chapter VIII).

Recent political discussions in the U.S. have referred to the idea of the “Big Lie,” the idea that less plausible lies are often easier to sell to the public than small ones… if sufficiently repeated (RationalWiki contributors, 2021). A recent paper by Fazio et al (2015) argues, based on a couple of experiments, that repetition of false information impacts belief even when those exposed know better, in an effect they call “knowledge neglect,” and that reflects a primacy of processing fluency (cognitive ease) over retrieval of knowledge under certain conditions which include repetition.

What do I take from the above? The more confident I am in new acquired knowledge, the more I will repeat, remind myself, to better internalize, garner the power of repetition. The less confident I am about new knowledge, the more suspicious I will be when seeing it repeated. I guess that is a new years resolution and, yes, those work: I heard it many times.

References

Fazio, L. K; N. M. Brashier; B. K. Payne and E. J. Marsh. 2015. “Knowledge Does Not Protect Against Illusory Truth.” In Journal of Experimental Psychology 144(5): 993-1002. Available: https://www.apa.org/pubs/journals/features/xge-0000098.pdf. Accessed: January 18, 2021

Kahneman, Daniel, 2011. Thinking, Fast and Slow. Farrar, Straus and Giroux.

Machiavelli, Nicolo, 1998. The Prince. The Project Gutenberg eBook. Translated by W.K.Marriott. Available: https://www.gutenberg.org/files/1232/1232-h/1232-h.htm#chap08. Accessed: January 09, 2021.

Parker, David B., 2016. “You Can Fool All the People”: Did Lincoln Say It?. History News Network. Available: https://historynewsnetwork.org/article/161924. Accessed: January 09, 2021

Quote Investigator, 2013. You cannot fool all the people all the time. Available: https://quoteinvestigator.com/2013/12/11/cannot-fool/#return-note-7793-2. Accessed: January 09, 2021.

RationalWiki contributors, 2021. “Big Lie,” In RationalWiki. Available: https://rationalwiki.org/wiki/Big_lie. Accessed: January 09, 2021.

Defining Data

Post author:Alex Uriarte
Post published:November 28, 2020
Post category:Fan

A few weeks ago I watched a few of Crash Course’s Data Literacy elearning videos on YouTube (Arizona State University and Crash Course 2020). It’s first episode defines “data” as “specific information we collect to make decisions.” This is a different definition from others I have heard. It does have some interesting aspects to it. Under this definition:

Data would be a subset of information. That is, all data would be information but not all information data.
It uses collection and decision making to define what information is data and what is not.

Other definitions are very different.

A common distinction between data and information is that found in the so-called DIKW pyramid or similar representations. DIKW stands for Data – Information – Knowledge – Wisdom, and usually suggests a hierarchy where data is the broader concept that is then filtered as information, that is in turn filtered as knowledge and finally as wisdom. This seems to be commonly used in the knowledge management community and is often attributed to an article by Russell Lincoln Ackoff in the Journal of Applied Systems Analysis in 1989 (e.g., see Bernstein 2009)

Under this representation, data are often interpreted as facts, noise or signals. There are many criticisms to this representation, from whether “filtering” is actually a good way of thinking about the connections between these concepts, to proposed changes to the pyramid, to what is actually the broader concept, data or information (for just a few examples of a relatively large literature, see Weinberger 2010; Tuommi 1999; and Dammann 2018).

Yet a third way of thinking about data is the definition contained in US law. US Federal statutes define data as “recorded information, regardless of form or the media on which the data is recorded” (44 U.S. Code § 3502). The definition is less innocuous than what it may seem at first. Recording information is in good part what distinguishes our handling of information from cultures who rely (or relied) on voice of mouth transmission and the potential loss of content associated with such practices: think of the telephone game that kids play, whispering a sentence in another one’s ear, who then whispers to another one, and so on until the last child states out loud what his/her understanding of the sentence is, often to find the sentence arrived at the end of the communication chain completely altered or distorted. Under this definition, however, information is the broader concept.

The table below summarizes the three different definitions of “data.”

	ASU and Crash Course 2020	U.S. Federal Statutes	DIKW pyramid
Definition or understanding	Specific information we collect to make decisions	Recorded information, regardless of form or the media on which the data is recorded	Facts, noise, signals
Highlight	Data has a purpose: decision-making	Data must be recorded	Data as facts, no specific purpose or characteristic
Data relative to information	Information > Data	Information > Data	Data > Information

I do not find the last row – showing the relation between data and information – particularly useful in understanding data: it is a result of how we define not just data but also information, and may be more useful for discussions focused on knowledge. I include it in the table only for the sake of comparison and may explore it in other posts. I do find the “highlight” of each definition useful in thinking about data, how to manage and use them:

Data should reflect facts. Whether it does or not, depends on how it was collected and managed. This is important to keep in mind in discussions about data collection, data curation and trusted repositories.
Data should be recorded. This reinforces the importance of data curation and particularly of metadata in enabling us to understand what “facts” do the data actual capture.
Data may be used for decision making. Hence, it is important to keep this in mind the many considerations around data bias, completeness, presentation and interpretation.

In this blog, I will use the highlight of each of the three definitions to discuss data.

References

44 U.S. Code § 3502. Legal Information Institute. Cornell Law School. Available: https://www.law.cornell.edu/uscode/text/44/3502#:~:text=(A)%20means%20the%20obtaining%2C,or%20format%2C%20calling%20for%20either%E2%80%94. Accessed: November 14, 2020

Arizona State University and Crash Course, 2020. Study Hall: Data Literacy. Available: https://www.youtube.com/watch?v=0H8awA3GBPg&list=PLNrrxHpJhC8m_ifiOWl1hquDmdgvcviOt&index=14. Accessed: November 27, 2020

Bernstein, J. H., 2009. The Data-Information-Knowledge-Wisdom Hierarchy and its Antithesis. In: Proceedings from North American Symposium on Knowledge Organization. Vol. 2. Available: https://journals.lib.washington.edu/index.php/nasko/article/viewFile/12806/11288. Accessed: November 27, 2020.

Dammann, Olaf. 2018. Data, Information, Evidence, and Knowledge: A Proposal for Health Informatics and Data Science. In: Online Journal of Public Health Informatics10(3):e224. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435353/pdf/ojphi-10-e224.pdf. Accessed: November 27, 2020.

Tuommi, Ikka. 1999. Data is more than knowledge: Implications of the reversed knowledge hierarchy for knowledge management and organizational memory. In: Journal of Management Information Systems 16(3):103-117.Available: https://www.researchgate.net/publication/328803142_Data_is_more_than_knowledge_Implications_of_the_reversed_knowledge_hierarchy_for_knowledge_management_and_organizational_memory. Accessed: November 27, 2020

Weinberger, 2010. The Problem with the Data-Information-Knowledge-Wisdom Hierarchy. In: Harvard Business Review. Available: https://hbr.org/2010/02/data-is-to-info-as-info-is-not. Accessed: November 27, 2020.

On Using and Citing Wikipedia

Post author:Alex Uriarte
Post published:November 1, 2020
Post category:Fan

I am a big fan of Wikipedia. Although I expect to never use it as a sole source of information in any of my posts (ahem: with the exception of this article), I do often use it as a starting point when researching any subject, and I often click on some of the references cited in Wikipedia entries to follow-up on whatever I am researching. You will see me referencing Wikipedia articles often.

Wikipedia warns that “nothing found here has necessarily been reviewed by people with the expertise required to provide you with complete, accurate or reliable information” (Wikipedia contributors, 2020a). In addition, they warn that, as a community-built reference, it may contain errors. Yet, I often cite Wikipedia for two reasons:

First, I do typically use it as a starting point and in combination with other sources in my posts. It’s encyclopedic nature – comprehensive, even if not necessarily in-depth – make it exactly the great starting point it is intended to be;
Second, because it is a community-built source and because it has come to occupy such a central role as reference on the internet, it is actually very subject to spot checking and reviews, even if not necessarily by recognized specialists in any given area, and not consistently for all entries. I do use my judgment on what entries I am more likely to trust. I am more likely to trust one on, say, butterflies, than one on a small country’s minor dictator from a hundred years ago, where there may be less people capable of or interested in verifying, as well as less reliable sources. A subject for which there is less information and is of less general interest will not receive the same amount and frequency of authoritative checks.

Some minimum parameters do exist for Wikipedia entries: based on its “About” page: material “must fit within Wikipedia’s policies, including being verifiable against a published reliable source. Editors’ opinions and beliefs and unreviewed research will not remain.” And “many experienced editors are watching to ensure that edits are improvements. […] its contributors work on improving quality, removing or repairing misinformation and other errors” (Wikipedia contributors, 2020b).

As food for thought: compare what you find on Wikipedia to what you would typically find circulating on social media about any specific subject. I know, that is a very low bar but, nonetheless, it illustrates that it is possible to build value collaboratively under certain rules and within defined processes (I am now thinking of open source software, but will leave that for another post).

For those ready to never read one of my posts again, check the article on “Criticism of Wikipedia” on ummm…shhh, Wikipedia: Wikipedia contributors, 2020.

References

Wikipedia contributors, 2020a. “Wikipedia: General Disclaimer,” In Wikipedia, The Free Encyclopedia. Available:https://en.wikipedia.org/wiki/Wikipedia:General_disclaimer. Accessed: October 25, 2020.

Wikipedia contributors, 2020b. “Wikipedia: About,” In Wikipedia, The Free Encyclopedia. Available: https://en.wikipedia.org/wiki/Wikipedia:About. Accessed: October 25, 2020.

Wikipedia contributors, 2020c. “Criticism of Wikipedia,” Wikipedia, The Free Encyclopedia/ Available: https://en.wikipedia.org/w/index.php?title=Criticism_of_Wikipedia&oldid=985922235. Accessed: November 1, 2020.

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