What is the Ludic Fallacy, what does it have to do with Citizen Sociolinguistics, and why does it matter?
According to Finance Philosopher Nassim Nicholas Taleb, who coined the term in his best seller Black Swan (2007), the Ludic Fallacy is the misapplication of the rules of games to real life situations.
To exemplify the Ludic Fallacy, Taleb contrasts how an imagined Professor of Statistics and an imagined Actual Gambler would act after 99 coin tosses land on “heads”: Asked to predict the 100th toss, the Professor (perpetrator of the Ludic Fallacy) asserts there is still a 50/50 chance that the coin will land on “tails.” The Actual Gambler recognizes a problem with that prediction, concluding (and I paraphrase), “Dude, the coin is loaded, get your head out of your a*s!” Taleb thus suggests that otherwise intelligent finance people ignore statistical aberrations and as a result make mistakes that lose them a lot of money; The Ludic fallacy also helps us avoid some pitfalls of the world of Sociolinguistics by drawing our attention to the knowledge of Actual Language Users, or Citizen Sociolinguists.
To illustrate, let’s bring in an Old School Linguist for the role of Professor from Taleb’s example and an Actual Speaker of Language for the Gambler: The Linguist may have noticed some statistical regularities about language and the distribution of its variations, but the Actual Speaker, who has no knowledge of those regularities, goes about their day by assessing each situation more holistically. The Old School Linguist may predict that an Irish immigrant in New York will speak one way, for example, that he would express, feeling weary, a plan to head to bed “airrly” on “Tursday”. But, more unpredictably, that same Irishman, having lived in New York for 30 years may say “Early Thursday” one moment and “airrly Tursday” another—whether he’s talking to a friend, a customer, the news media, whether he is a bit tipsy, or sleepy, or hungry, or not! Like the Actual Gambler, who may provide the best prediction for the coin toss, based on his experience with gambling, those who know the Irishman well will be able to best predict which pronunciation will emerge in any given condition.
Similarly, an Old School Linguist may predict, based on statistical regularities that have been studied for decades, that an African American child in school will speak a “well-formed” variety of African American English. This may include emblematic features of that variety like the use of double negatives (Ain’t nobody got time for that!), copula deletion (What up?), or characteristic words, like a Philadelphian’s classic use of “Jawn” to refer to a random something (Hand me that jawn over there!). But kids at school in Philadelphia today might be able to predict much more—White kids may also be using these words and turns of phrase, and many Black kids will not! Ain’t nobody got time for the Ludic Fallacy here—instead, people are learning about each other and using language to make their way from classroom to classroom, friend group to friend group, online to offline, home to work, high places and low, and back.
Now throw that Old School Linguist into any given interaction: What up playa? It’s been a minute! How you been? You finna make a couple bucks at that gig? Ask that Old School Linguist, based on some recordings played in his office: Who is talking here? What is their demographic? Age, Gender, Race, Class? Likelihood for success? An approach based on platonic ideals about how our world operates, like those enshrined in statistical models, cannot come anywhere close to modeling the way language works in any real-world encounter or predict how it will work in the future. Speakers gain such understanding through long term experience of language and life.
While Linguists may not be able to have all that experience themselves, they can ask questions and value the experience of those who use language every day, the same way a statistician in Vegas, working out the bottom line, may need to consult with some of the seasoned players in the Casino. If it looks like the dice are loaded, the Actual Gambler will be able to tell you how dice get loaded, who fixes the dice that way, where you get them, and maybe even how you can outsmart that cheater! Similarly, an Actual Language User—aka a Citizen Sociolinguist —can tell you who speaks one way or another, why and when, how and with whom. But they certainly can’t share their insight if nobody includes them in their work or bothers to ask.
Obviously, I’ve overstated the case here—as did Taleb when he exemplified the Ludic fallacy. No Statistician, after witnessing the coin fall the same way 99 times in a row, would be such an idiot as to discount the Gambler’s experience. They might instead start to value that Gambler’s insight; No Sociolinguist would insist that Irish American or African American speech works one way, after hearing new ways of speaking, addressing a range of people, in different contexts, again and again. No sociolinguist would discount that speaker’s own explanation for the speech in their community. Would they?
The Ludic Fallacy illustrates that ignoring those real-world voices leads to a mistaken understanding of the world. In finance, this may mean lost money. In the study of language and society, it can lead to wrong-headed judgements about what people can do.
The Ludic Fallacy also provides a refreshing reminder that an aberration from one perspective, is, from another perspective, an obvious illustration of how the world works. The 99 coin tosses yielding “tails” may be an outlier for a Statistician but may be a normal occurrence of a practice easily recognizable to the real-life Gambler. Similarly, an encounter with always new language in the schools in Philadelphia, may seem like an aberration to an outsider Linguist, but happily ordinary to the students in any given classroom. In any case, the unexpected offers an opportunity to engage more deeply with the language, interactions, and the people who create our world.
Citizen Sociolinguistics 