On Beginnings: Part 4

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

Nate Silver’s political forecasting methods employ a permutation of conditional probability known amongst Number Munchers as Bayesian inference.  In the 18th century, Thomas Bayes authored an unpublished essay offering a method for advancing inquiries in the face of undefined variables, using probabilities. Silver’s application of Bayes’ theorem falls under the subjectivist umbrella of its use, in which an experiential hunch is assigned some initial, arbitrary degree of likelihood, called the prior probability.  We could just call the prior a hypothesis – one with specific odds of being borne out by observation.  Observations themselves then “condition” the prior, either supporting or refuting the hypothesis according to the value each bit of data brings to the equation, expressed in probabilities.  These conditional probabilities of data, more often called “evidence,” are calculated somewhat circularly, given some combination of objective and subjective measures of the likelihood of the hypothesis predicting the evidence, and the likelihood of the evidence implying the hypothesis.  These conditional probabilities are then compounded with the prior probability to yield a new likelihood of the hypothesis, accounting for the evidence that may have implied the hypothesis that might have predicted the evidence.  That’s right.  This process can continue in the same helical form until any adequate series of adjusted probabilities (called posteriors) lead one to accept, redefine, or discard the prior.  Bayes’ theorem has many specialized derivatives, but is best known for allowing the user to estimate a range of likelihoods with meager information, because it assigns both hypothesis and conditional data those interdependent, never-100% probabilities, from which the composite probability of the outcome is calculated – the idea being that each trial will either contradict or confirm your prior, allowing you to update and refine its likelihood as your trials progress.  Contrast this to the frequentist approach championed by Ronald Fisher in the 1920’s, in which a theory with no initial value slowly acquires credibility over a series of rigorous trials, the results of which must be filtered through their p-values:  a standard measure of the odds that any deviation from the expected values of those results would be the product of chance alone.  Each method seems to have its advantages – the frequentist one claiming more objectivity and a process of validation that relies more on exhaustive trials and peer review; the Bayesian one allowing the user to pursue a line of inquiry well into the realm of unknowns, using a pliant estimate of likelihoods to alternately spur or yoke a premise.  Bayesian inference seems to offer more flexibility in this regard, as an informal, ad hoc tool for assessing chance; it also has the benefit of smelling like what most of us would consider plain vanilla reason, wherein our existing beliefs and values can be tweaked in light of new circumstances while continuing to inform the decisions we make as we go along.  As a forecasting tool, Bayesian inference would appear to have its limits – most notably, when a prior probability is not available, in the face of unprecedented events or complex circumstances.  I would trust Bayesian priors, for instance, if I were trying to remember where I parked my bike last night.  I would not trust Bayesian inference to predict what will happen if I press this button.

Part 5 coming tomorrow morning…

On Beginnings: Part 3

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

Statistical inference and probability can employ as masochistic a level of computation as the user wishes to pursue, but we can look at some basic principles that will move the conversation along without exceeding the ten-digit limit of nature’s abacus.  Statistics, broadly defined, is a branch of the formal sciences that deals with the collection, organization, and analysis of data.  Data, for our purposes, may be anything we wish to define as objects of our attention.  When you sit on a curb licking a Push Pop and counting blue cars vs. red cars, you are gathering and cataloging statistical data.  We often use this data to infer what we may not directly observe – that is, to use our sample to posit a broader statement:  “I think there are more red cars than blue cars in this town.”  Our conclusion is an example of inductive reasoning, in which bits of information are collected and used to formulate a general proposition.  This is where probability comes into play, to gauge the likelihood of our hypothesis holding up in the face of new information – or, just as often, to turn a profit on a more heuristic, casually calculated gamble:  “I bet you two Skittles there are more red cars than blue cars.”  The confidence you place on such a bet can be assigned a numeric value based on the colors of various cars you have already tallied – kindling, in turn, any spark of compunction in your pals.  Probability, in a textbook sense, is an expression of the chance ascribed to a specific event against all possible events within a fixed set of conditions:  rolling a die, for example, which has a 1 in 6 chance of landing on any given face.  Continue reading

History, Identity, and Memory: The ‘Melting Pot’ is Bubbling Over!

The recent flap over the January 27, 2017, official White House Press Release of President Trump’s Statement on International Holocaust Remembrance Day and its egregious omission of the primary victims of the Nazi genocide—the Jews—instead identifying and honoring “the [unnamed and unreferenced] victims, survivors, and heroes” beggars logic.  Coming as it did on the heels of the “Executive Order: Protecting the Nation from Foreign Terrorists Entry into the United States”—and attempting to temporarily ban legitimate refugees from seven predominantly Muslim-majority countries [Iraq, Syria, Iran, Sudan, Libya, Somalia and Yemen]—only compounds the absurdity of the Statement and reveals the astounding ignorance of those seemingly hard at work at 1600 Pennsylvania Avenue.  More to the point, however, one may also perceive the Statement as part of an overall commitment to whitewashing—yes, the word is pointedly chosen! —religious, ethnic, historical, and racial differences and diversities which remain unique to this experiment we call the United States in favor of a false homogenization whereby we are all alike, even though we are not.  Taken to its absurd extreme, it may yet prove to be but one more example, early on, of the President’s pandering to his own electoral base of primarily disgruntled white males on the economic fringes (both the haves and the have-nots) as a not-so-thinly-disguised attempt to re-paint American society in only one color and only one set of identifying labels (white, male, citizen, hard-working, Protestant, and/or what have you). Continue reading

On Beginnings: Part 2

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

You have probably heard of Nate Silver.  His forecasts set political and social media atwitter in 2008 when he correctly estimated the Electoral College votes of the U.S. presidential election in 49 out of 50 states, as well as the District of Columbia; he also predicted the outcomes of 35 out of 35 senatorial elections that year.  I initially attributed Silver’s success to an extraordinary bit of luck – after all, somewhere in the prognostic clamor surrounding any national election, someone is bound to come within the mark of the final results, the way every eleventh snowball I launch makes contact with my co-worker.  Then, in 2012, Nate Silver correctly forecast the results of the U.S. presidential election in all 50 states and the District of Columbia, and picked the winners of 31 of 33 senatorial elections.  This is how I was suckered into reading his book.  I was hoping to glean some insights on the statistical data analyses that inform Silver’s forecasting techniques – perhaps not enough to re-present in essay form, but at least a dirty trick or two I could take to the races.  The Signal and the Noise, as it turns out, is scant on the specifics of Silver’s particular methods, but he does describe some general premises of statistical inference he applies in making his forecasts, with none of the usual esoteric symbols that lull laypeople like me into a mental parade Religious Studies graduate Jennifer Goodman once succinctly described:  “monkey…banana…bicycle…ball.”  I don’t agree with many of Nate Silver’s presuppositions nor the conclusions he draws from them, but I think the methods he shares for approaching statistical inference are worth exploring, not only to understand his uncanny success, but to confront the growing popular relevance of statistics as we march (onward, forward) into the titillating age of Big Data, amassing ever-larger caches of minutiae to formulate anything from optimized marketing strategies to post-graduate career prospects.  If I can’t use statistics to make my fortune, could I at least use them to elucidate the arc of my “career?”  Or yours?  For an eloquent and thorough rendition of statistical applications, let me refer you to Silver’s book.  Read it and come back, I’ll wait here for you.  If you’re satisfied with the train wreck version, climb aboard.  If you’re admiring a pony in the mental parade, just close your eyes.  I’ll wake you at the end with a rousing musical number.

 In order to talk about statistics we should also talk about probability.  Please stay.  I love you.

Part 3 coming tomorrow morning…

On Beginnings: Part 1

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

What kind of liberation would that be to forsake an absurdity which is logical and coherent and to embrace one which is illogical and incoherent?

I began using that Joyce line, years ago, as a proof of dreams. I work odd jobs to eke out my living from town to town, so most vertiginous mornings start with my catechism: “What are you thinking? Is it logical?” Yes. “Is it coherent?” Yes. “You were dreaming.” The next few moments I spend drawing up the day’s cosmography. It will only last the day. It takes all the effort of imagination now to divide nature from contrivances. Surely the stars are natural. The streetlamp is not. A peach may grow organically around a counterfeit stone. A tree is rustic from this angle, but the tidy row behind traces an arc of artifice as I pass along the unnatural road. Is my path natural? How else to judge but to measure myself against convention? Which I am also obliged to invent? I resist the gauche urge to victimize my friends, since it’s easy enough to fashion lives for people I don’t know. I tear a few from my perforated templates: CEO. Software Engineer. Marketing Consultant. I plop them into plush deck chairs in St. Barts and place lowball glasses into their pale fingers. I dress them in marvelous chi-chi outfits but their faces are all the same. Or I should say, they are a bank of one impassive face repeating — the bland portrait of a turquoise horizon, merging and vanishing in an oblique line of thermoplastic facsimile across a luminous liquid crystal field. Beyond the offing, their faces hum a hot squall of technical, statistics-based formulas for streamlining my online payment or fielding my search query, “what happened to tootie facts of life?” Most of the time, even when I seem lost in thought, I’m not really thinking about anything — more likely at any given moment I am trying to remember the lyrics to “Informer.” If each of these things is like the other, mine must be the face that has wandered into the dark. It is comforting to imagine, at least, that strange illustrious heads are keeping vigil over the cosmic order, over drinks, under a far-off sun.

Once I’ve exhausted their vague brilliance, my fancies mellow into a general wonder of how people choose their careers. Sometimes I am content to wonder at the speculation surrounding how people choose, or ought to choose, or if they choose. Which is the natural thing? Continue reading

Words and Things: What’s in the Black Box?

Shannon Trosper Schorey is a doctoral candidate in the Religious Studies Department at UNC Chapel Hill. Her dissertation “The Internet is Holy” charts the fusion of religion and information technologies in Silicon Valley since the mid-20th century. (The introduction to the series is posted here.)

In our Religious Studies Department at UNC Chapel Hill I teach an undergraduate course called Technology, the Self, and Ethical Problems. The course serves two purposes, the first is to introduce students to the range of work being done at the intersection of religious studies and communication studies. The second is to prepare students to think critically about the relationship between words and things — what kind of social worlds do we build between and out of our shared ideas, languages, and material stuffs? Is it useful, or even possible, to think about these relations as existing between ontologically distinct categories? Continue reading

50th Anniversary Fun Fact #8

Although dating to 1932, in 2016-17 we’re celebrating our 50th anniversary, given how the Department was reinvented in 1966-7 — in keeping with how the study of religion was established then across public universities in the US. No longer confessionally-oriented and staffed by campus ministers, it became a cross-culturally comparative and interdisciplinary field.

So all semester we’ll be posting some weekly fun facts from 1966 — not that long ago for some of us yet ancient history for others. Continue reading

Words and Things: From Critiquing Ancient Religion to Imagining No Religion

Andrew Durdin is a lecturer in the Humanities at the University of Michigan-Dearborn. He will receive his PhD from the University of Chicago Divinity School in the spring of 2017. His research focuses on Roman religion, magic and religion in the Roman Empire, and issues of theory, method, and historiography of religion in the ancient Mediterranean world. (The introduction to the series is posted here.)

A few years ago, I sat on an AAR/SBL panel dedicated to Nongbri’s Before Religion. Since then I’ve continued to reflect on the ideological import of seeing religion in ancient cultures and how it serves to bolster the lingering notion of religion as a universal human experience. My interest was piqued, then, when I looked over the 2016 AAR/SBL program book and saw two SBL/NAASR panels dedicated to interrogating the category of ancient religion. The first explored the continued relevance of Nongbri’s book. The second panel was titled after Carlin A. Barton and Daniel Boyarin’s newly released book, Imagine No Religion. The panels’ juxtaposition on the program implied them as complementary, and, what is more, Barton and Boyarin explicitly situate their book as an expansion of Nongbri’s initial “sketch.” They adopt what I see as an eminently useful form of critical philology in an attempt to situate Latin religio and Greek thrēskeia in their ancient vocabularies without appeal to religion as an interpretive category. Having attended the panels and read both books, I’d like to offer some reflections on moving from Nongbri’s critique of ancient religion to Barton and Boyarin’s practice of imagining no religion. Continue reading