heur-e-ka

Today marks the 2nd annual #BlueMind all-day meeting, held this year at The Romberg Tiburon Center for Environmental Studies, CA. The inaugural meeting was held at The California Academy of Sciences in San Francisco’s Golden Gate Park on early June, 2011.

As a former marine biologist, volunteer marine science and marine mammalogy instructor for The Marine Mammal Center in Sausalito, CA, longtime consulting statistician-applied mathematician, and currently working as founder and Chief Scientist at heur-e-ka, LLC, a small human-decision-making consulting firm and software outfit in San Mateo, CA seeking to take its 20+ year-old shrink-wrapped “semiotic-semantics“ offering to SaaS/PaaS world, I undertook to research, analyze, and ultimately test the veracity of the founding #Bluemind premise, conundrum, kōan;

“Why do we all feel better near the ocean, near water?”

While it’s clear to us all that some fear the water while others cannot splash around in it enough, #BueMind’s founding premise is a subtly complex invitation to multi-disciplinary participation in research into the neuroscience of humanity’s relationship with water. As evolved life-forms we literally owe our ancestry to the marine environment, perhaps most notoriously witnessed in the salinity of our own blood mirrored by the salinity of sea-water.

“Mother Ocean” appears to be a rather common cultural and spiritual notion throughout humanity’s natural history, a theme keenly reflected by the opening lines of “A Pirate Looks At Fotry” by the well-known folk-music star Jimmy Buffett.

Mother, mother ocean, 
I have heard you call,
Wanted to sail upon your waters 
since I was three feet tall. You’ve seen it all, 
You’ve seen it all. 

Here I present the findings form my analysis of sixty, randomly chosen “Ocean Stories“, random meaning the first 50 I could find and download via the Google query “ocean stories“. Obvious biases aside, like the relative frequency of

• adventure stories vs. love poems
• fiction vs. non-fiction
• commercially published vs. self= published authors
• 19th C. vs. 20th C.
• low sample-size (60) relative to the enormous bibliography of “ocean story” literature

…we feel our orignal premise that the emotional portent of “ocean words“ would be both bi-modal and dimensionally-significant, we pressed on. Our methodology was simple and fairly transparent.

• formulate oppositional emotion pairs (9) borrowing heavily from the work of;

1. Richard Lazarus and his “cognitive emotion model” (Lazarus, Bernice N Passion and Reason: Making Sense of Our Emotions, 1994, Passion and reason: Making sense of our emotions New York: Oxofrd University Press).
2. Paul Ekman’s classifications of basic emotions ( Ekman, P., & Friesen, W. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2), 124-129.).
3. Robert Plutchik’s “wheel of emotions” (Plutchik , R. (2002). Nature of emotions. American Scientist,89, 349.) suggesting eight primary bipolar emotions: joy versus sadness; anger versus fear; trust versus disgust; and surprise versus anticipation.

• scrape the text from the top 60 “ocean stories” query to Google.
• analyze that text with Readware, the semiotic-semantics toolkit.
• Cluster each essay’s weighted output against the 9 selected emotion-pairs.
• Graph the frequency of the significant terms found and their aggregated, standardize valence (emotional strength).

• loving-hateful
• brave-cowardly
• humble-prideful
• giving-needy
• happy-sad
• calm-excited
• joyful-angry
• trusting-suspicious
• kind-mean

There is much to see here (the length of the graphs indicate variation in terms of how much “term domination” [sheer vocabulary] of the emotional-dimension there is). The color indicates the overall strength of the emotional valence at work.

While I’ve not labeled the “emotions“, per se’, I’ve allowed their lexical anchors to represent each emotional dimension’s content for some interpretative flexibly (and less theoretical hubris on my part). Note the radial, 180-degree opposition of the 9 emotional-dimensions, done purposely to dramatize the effect.

The strength (valence) is all in the “tails” (color), which bears out the bi-modality of emotion theory I’ve had all along. There is not a lot of “half-way” in the way people speak about emotions in general, and particularly so regarding the ocean.

So, here we see, in the context of terms authors use to describe their relationship with the ocean;

more mean than kind

more needy than giving

 

more loving than hateful

more suspicion than trust

 

more calm than excited

more humility than pride

Interestingly, happy and sad essentially equate!

We are pleased by the results despite understanding a host of changes face us as we work out some daunting sampling issues; ranging from topical bias, utterly inadequate estimates of the population’s multivariate distribution, and the number and subsequent ranges of term-to-emotion variance inherent to “ocean literature“.

But an over-arching and superior chord has been struck and proven; dynamically generated topics and emotions are far more analytically true, inferentially useful, and naturally representative of human cognitive processing than the highly artificial and all too often superficial results that emante from “NLP-centric pairwise keyword classifiers” and their concomitant “static and low-association sentiment synonyms”

Here are the 60 “ocean stories” analyzed;

“Yet another media company proves to be clueless to the existence of User-Sentiments”

Watch the following online slideshow;

One Night At Zombie Burger

First, I’d like to say; “Mmmmm”.

Second, I’d like to say; “C’mon, online advertising, you can’t possibly still be this clueless since dotcom-1.0 went dot.comatose then eventually wound us as dot.compost!”

Imagine a conversation between a vendor and his customer, the one who pays that vendor’s bills as it were. Now imagine that no matter what that customer says about aspects of the treatment they experience in that vendor’s establishment the vendor not only doesn’t listen, but continues to do those very things with impunity every time that customer visits.

What do you think the reaction of most consumers would be? If you think that “just grin and bear it” is the most common answer then it’s been far too long since you’ve been serviced in a way that most consumers have proven across time immemorial is part-and-parcel of what drives their consumer decisions!

MetroMix of Des Moines, Iowa is walking that road as we speak.

How could one not notice the serially irritating, repetitive encroachment by MetroMix’s other clientele’s online-advertising blurbs popping up as that slideshow played?

While they supplied the requisite ⓧ button to allow the user to close each contextually irrelevant ad so one could actually see the well-photographed and cleverly labeled “Walking Ched“, “DD Chicken-Tenders” or “DD Chicken Salad“, they actually forced that exact same explicit user-behavior on every slide. For my part, I actually “signalled” to MetroMix “I’m still not interested” 30-times in a row!

Will the vaunted forces of “free-markets” exert its long-touted power here? Or will the transparent latency of the message to get you to want to walk through their front door for dinner tonight relative to your ostensible irritation at that message get lost in your desire to chow down on a “Walking Ched, Fries, and a Thomas H. Handy Sazerac Rye Whiskey on the rocks” out-weigh all that?

A part of me wants to jump on a plane and visit Zombie Burger + Drink Lab for dinner tonight.

Another part of me wants to start a protest and form a picket line at their front door for patronizing and endorsing the lack of advertising prowess and insensitivity to the consumer’s online experience by their vendor, MetroMix, the online-advertising outfit that featured the slideshow that initially informed me about the fabulous fare to be found there.

They essentially received a valuable finder’s fee for getting me there in the first place. Yet, one day soon, they may well experience a much more costly “loser’s fee” if they don’t wise up soon.

Having recently presented “Topics & Emotions v. Keywords & Sentiments” at “The Sentiments Analysis Symposium Research Day” in San Francisco’s Barclay Reserve Building last November, I can asure you that an entire analytical industry is emerging that may soon be able to quantify the actual cost of making one’s online customers suffer such cluelessly invasive and message-killing treatments.

The notion that consumers can only communicate with its chosen vendors via the out-dated binomial model of “click v. didn’t click” has been rendered dead, or nearly so, by imaginative analytics that can interpret user behavior for its contextual message, its true “fleeting moment of relevance” to those smart enough discover it and wise enough to look for it in the first place .

And what was that clearly struck message here for both MetroMix and Zombie Burger + Drink Lab when I, and likely many other viewers, dutifully clicked that ⓧ button at the outset of every slide?

“For God’s sake, stop making me click on each and every serially-irrelevant ad you serve every time I advance the slideshow that obscures the picture and text telling me about the stuff I’m currently engaged in;

“Zombie Burger + Drink Lab’s” fabulously photographed, editorially well described, and utterly delicious looking fare, not books by unfortunately yet similarly named authors.”

How interested could anyone possibly be about Amazon’s offer of the book “Personality” written by Jerry M. Burger as I watched a slide-show about a hip new burger joint in Des Moines, Iowa?

Did you get that? Amazon just paid Google who paid MetroMix to advertise a book written by a guy named Burger on a site about burgers!

There’s nothing here that’s particularly hard to understand. Or deploy.

All you really need are the analytical the tools to do so and a general awareness not only that your customers can tell you what they think of you “in real time“, but that they really mean it! Zombie Burger + Drink Lab might be able to avoid the blowback since their product is more desirable than the slide-show ads were irritating. From a pure bottom-up-and-top-down business-plan standpoint it is abundantly clear that MetroMix cannot.

And if MetroMix goes out of business for their cluelessness to the sentiments of their online customers’s customers then how soon can it be that such poor business acumen adversely affects Zombie Burger + Drink Lab in turn?

When will they begin wondering why their once booming business and long-lines have shrunk and begin to look around for other vendors to advertise their message about where to go in Des Moines, Iowa to find good food that is uniquely presented and served in a setting of great ambiance?

In closing, check out the truly relevant link supplied below in a recurring theme here at heur-e-ka;

A book written by Jerry M. Burger is a relevant advertising keyword trigger for an online slideshow about a new burger emporium?

Please!

If your organization were utilizing a service like LinkedIn for hiring, would you be concerned that such a study could be used to prove your own participation in age discrimination in the context of EEO law?

In the context of running a leading service to employers seeking qualified talent and professionals seeking opportunity and ascendency in their professions, I’d want my research organization to be designing experiments, proposing ways to help LinkedIn determine if we were unwittingly facilitating illegal trends in hiring.

BACKGROUND

• Age discrimination in simulated employment contexts: An integrative analysis.
• Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity.

DATA

• impute age of job seekers from dates on education, job-history, and other demographic cues (likes on music,
• ditto for endorsements
• impute aggregated age metrics of their linkedIn social.Graph

Collect prospective and ultimately “hired” and “considered” employees from LinkedIn’s proprietary “employers” database – without direct knowledge of this database I can’t describe the exact data elements but know in my “quantitative bones” that i could extract this data from LinkedIn traffic, engagement measures like;

• time spent of candidate’s page
• subsequent contact of candidates’ endorsers
• direct billing transactions
• a host of other click-through behaviors

HYPOTHESES

• Are hiring decisions (hired/considered but not hired) correlated to the age profile of LinkedIn’s employment subscriber organizations?
• Is age a significant predictor in hiring decisions made using LinkedIn’s services?

METHODOLOGY

There are a number of tests;

• Canonical correlation of employer’s “age-profile” and “hired-candidates age-profile” against the full range of independent, meta-data elements available in the ultimate hiring decision (hired/considered but not hired/not considered).
• Logistic regression of likelihood to be hired as a function of age-relatedness of prospective applicants and the hiring organization.
• CART (classification and regression tree) of the data elements that significantly stratify pockets of hired/considered but not-hired/not considered).

Were such proof possible to publish, the potential for class-action lawsuits in this regard is not altogether an exercise in fanciful or embittered pay back.

Such is the logical result of offering services that, regardless of intent, foster and support a service-sector that facilitates dicrminatory practices that violate @EEO law.

Note also that if such a thing could be proven to be true that other EEO dimensions (race, sex, nationality, and religion) loom large as being provable, hence actionable, against any SaaS that claims to facilitate both employers and prospective employees.

If I was employed at an organization like LinkedIn I’d be relating our potential liability to my CEO, Chief Counsel, and even influential Board members as potential problems that could directly and negatively impact our business plan.

This is a story about the nature of inference, its source, the purity of motivation that belies it, the do’s and dont’s surrounding the discovery process, and perhaps most important of all;

how to ensure you’ll never manage to either generate or leverage it!

It was just after The LikeMinds Personalization Server was purchased by IBM, still alive and kicking inside their WebSphere Portal Server, that I learned this lesson. And, in perfect devotion to its own purity, taught to me by a man that didn’t so much teach it to me as allowing me the opportunity to learn through his own real-life experience in generating it.

As “Analytics Architect – LikeMinds” at Andromedia, them Macromedia I found myself with a new title; “Sr. Software Engineer/Statistician” in IBM’s Software Group. Charged with overseeing the products port from C++ to Java, as soon as that team was in place for what turned out to be a two-year effort I went on a prolonged tour of “product evangelism“, seeking users, customers, and both internal and external partners for LikeMinds ability to find content similarity based on shared engagement activities like explicit ratings, implicit transactions, and straight-up “content-centric” item-similarity.

King Arthur Flour was an IBM customer who began using LikeMinds almost immediately after my arrival there. Small, hungry for market-share, and both technically agile and talented they represented a perfect client for my emerging product evangelist role. We soon found several ways to enhance LikeMinds’ inputs by adding geographical, price-based, and even shipping weight analytical dimensions with the ability to co-vary multiple inputs across all the available dimensions.

That last one, shipping weight, is where my lesson in the nature of inference. I was lucky to meet a man named Thomas Sweet, who in typical small business fashion was in charge of three different departments there;

• the King Arthur.com website, hence all customer emails.

• their eCommerce storefront including all direct mail, email, telemarketing, and online order fulfillment.

• the management of all their external shipping vendors and operations.

You see, although now considered an out-of-date, perhaps even “quaint and old-timey” term, Thomas Sweet was a walking “federated database“. As a function of his familiarity with three different management responsibilities his insight into King Arthur Flour’s business transcended a number of horizons. In this case he was driving home one evening and three different business realities and dynamics merged in his own “inference engine“, his brain, with these three inputs combing to beget one whopper of a winning output;

the inputs;

• the universal retailer “golden fleece“, their constant search for ways to increase the average order value; how can I get my customers to crooss-purchase or up-purchase every time I see them?

• their number one impediment to online sales at the time as it runs out, not altogether untrue even in today’s Web2.0 world) was shipping costs, numerous customer emails and subsequent surveys confirming it.

• a recent report that showed most customers orders tended to minimize, not maximize, their shipping costs, which were priced according to classic FedEx/UPS/Purolator shames regarding distance/priority/and weight-classification.

• Having been long centered in the American Northeast, The King Arthur Flour company’s rapidly expanding customer base was still quite heavily skewed toward what geo-clusters might classify as “The Shrewd and Frugal Yankee“.

the outputs;

• Thomas realized that the latter was the critical variable there, both distance and priority being static with a high level of variability  inherent to the final weight of the order, hence the final shipping cost. His customers were unwittingly leaving money on the  table by shipping orders closer to the minimum in every weight classifications as opposed to the maximum.

• His idea; why not combine LikeMinds “top-scoring item by similarity” mitigated by the items’ weights such that the final order would not change the final shipping cost.

Voilá; true inference made to order;

• average order sizes increased significantly, not to mention immediately.

• customers’ feedback reflected gratefulness for the increase in financial efficiency despite acknowledging their awareness that such savings came with a higher overall “spend” than they had anticipated at the outset of the transaction.

• King Arthur Flour’s shipping vendors were soon able to offer more competitive rates due to an overall increase in their own delivery efficiency metrics from the overall higher consistency of the order weights within their own weigh-classification parameters.

As we discussed then implemented his brain-child with great success his internal computing process, essentially what was referred to back then as “federated data mining“, began to reveal itself to me. In a “life-imitates-art” fashion, he had rather unconsciously “supervised” a higher level predictive algorithm that was working against a set of “unsupervised analytics platforms” that had revealed a seemingly disjount set of results.

One truly insidious false meme about “data-mining/machine-learning” is how it works in “unsupervised  mode“. You hear it a lot; team enough CPU’s with a sufficiently efficient clustering algorithm and eventually “inferential gold will begin to flow“. Nothing could be further from the truth, particularly with what is now being touted as “big-data“. My favorite synonym for “big-data” is “big-noise“.

One of statistical inference’s true “heroes” is Leo Goodman. Formerly of the famous University of Chicago Population Research Center, he is now joint professor of the equally famous Sociology and Statistics Departments at the University of California-Berkeley. The “godfather of the logit“, his pioneering research into the use of the “log-of-the-odds-ratio” breathed new life into what was then considered the rather stagnant and low-inference field of “discrete data analysis“.

He referred to what I now see as today’s “false big-data meme” as “ransacking the dataset seeking pockets of significance“. In his view it was the fastest and most naivé way of generating spurious and easily misinterpreted “inference“!

Thomas Sweet managed to use his own brain as the inference engine to leverage three separate sub-systems, analytical platforms from what I recognized as typical members of any business’ usually vertically aligned, hence disconnected databases; here retail, customer service, and vendor-services!

The myth of unsupervised learning has done a huge disservice to what many look to “big-data” to solve for them. As computing platforms have experienced a dramatic increase in the performance of scaled data-parsing, one of Google’s more conspicuous contributions to the world known as map-reduce, many applications continue to dream that this “ransacking” can and will generate the sort of inference that Thomas Sweet generated with his own personal “neural-network“ by surrounding it with a number of “supervised analytics efforts“.

The moral of the story? It’s a three-pronged lesson best characterized by several time-worn maxims;

the lessons;

• whatever appears at first blush to lend itself toward easy automation almost never does.

• unknown and unexpected patterns of data-relatedness hidden inside datasets in ways that classic domain experience cannot explain or anticipate are almost always a myth.

• new technology doesn’t change the way people think and behave in the context of their current lives. Such only changes the manner in which we must measure their old habits across new platforms of their experience.

 

the maxims;

• Inference is only where you find it when you look in those places it belongs in ways that are most likely to reflect their discovery.

• What appears new is not, and yet what was always been seen as “tried and true” must find a path to novelty.

• Human nature is rather maddeningly static in a world that changes wrappers so often it appears to change as quickly as technology.

• With a natural resistance to the synthetic, true inference won’t materialize as a function of computational fury as much as appearing when we seek it with a purity of objectivity in tune with well-regulated sensibility.

TV

It seems simple at first blush; what one person finds too incredible to accept another person has scads of proof to support.  As autonomous beings we should get to believe what we want. Yet hidden inside this convenient mantra to sell-imposed denial of physical reality is how much more than one mere notion must be dismissed by doing so.

Inside any profession of fringe disbelief lurks a score of intellectual and historically accepted underpinnings one must also deny at the same time. The entire compilation of human knowledge is rightfully impossible to comprehend, then master in a single lifetime. As easy as it is for most modern humans to build a fire today archeological evidence reveals that huge tracts of time passed before human beings discovered, then ultimately learned to control and even exploit fire in their lives.

Such findings remind us that the compendium of skills we take for granted today came at a dear price to our ancestors. Even today the number of people who could build a functioning battery with materials routinely found in nature (an acid, an oxidizing metal,  an oppositely charged conductor) is astonishingly low.

Perhaps the very best place to build one’s post-apocalyptic campsite, were some huge conflagration to set humanity back to the dark-ages overnight, would be the local library. How else could such knowledge and experience bereft people, not to mention their subsequent generations, be able to recall the many technological lessons humanity has massed across the ages? From the somewhat simple mechanics of constructing a building that won’t fall down in the wind to a far more complex nuclear power plant, beginning anew  would be a daunting task for those cast into such an horrific new reality.

Our current body of knowledge was hard-won, but doesn’t now reside within the technological grasp of any single human being on earth today. Such is the down-stream social reality of humanity’s evolution from totally autonomous beings, one against the world, to families, then tribes, culminating in entire societies comprising many different specialized individuals, each contributing something to the common good.

(1)”I just don’t find the concept of evolution to be credible.”

(2) “The Earth has been warming an cooling since forever; I can’t believe it’s man-made this time.”

(3) “Those Twin-Towers in New York City came straight down just like with demolitions, so it must have been planned.”

Inherent to each of these one-sentence denials is a concomitant dismissal of much that the denier themselves would readily admit they don’t deny by simple virtue of their face validity. Yet they do so all the time with a seemingly glib unconsciousness as they hang on to one “belief” that simply cannot exist side-by-side with a host of their other duly unchallenged “beliefs“. The simple act of denying some reality or another might appear to be taking the easy way out for those who practice it by those of us who don’t. But just under the surface of this seemingly simple act is a complicated series of lies and self-deception those who choose to do so must also embrace.

(1) To deny evolution is to deny the reality that destructive bacteria like pneumococcus and crops-destroying pests like the boll-weevil become immune in rather short-order to antibiotics and pesticides.

(2) To deny that anthropogenic climate-change is real is to deny what we have come to collectively accept as true; the same hydrodynamic actions at work in deep-ocean currents we see in every river or stream we walk beside with our leashed pets on their daily walk, classic thermodynamics witnessed in our own coffee cups every morning, and the manner in which a wedding reception’s celebratory dancing eventually drives us all outside  for a breath of fresh air.

(3) To willfully accuse the government of an inexplicable act of terrorism against its own people by planting hidden thermite charges in the World Trade Center’s steel-girders years before is not just an indictment of a faceless government, but an accusation of treason and serial murder against the legions of their fellow citizens who would have been required to pull off an operation of the scope of 9/11.

They rarely do accept the mutual disjoints, however, thereby exposing a key element for counter-acting the practice of willful ignorance.

The real shame in all this is not with any single act of denial, but in the social orchestration of that denial. Look around at each of these seemingly independent “campaigns of denial” to see who its sponsors really are. In each case organization, effort, and most of all money are required to conceive of, devise, and maintain the requisite amount of disinformation to succeed. Even then the end result never convinces nor carries the majority.

Rather, such a process depends upon having to captivate and capture a mere noisy minority whose only real social goal is to make it easy for our facile political systems to stymie progress we might have been be able to make in the absence of such pockets of denial among our fellow citizens.

At the bottom of every seemingly simple denial lies the ugliest of exposed truth; an organized campaign of disinformation built around a special interest whose only concern is their own precious, institutional self-preservation.

Denial might appear to be a simple act of conscious negation to both those who practice it as well as those who work to counter-act it.

In the beginning and end of it all, deniers are mere fodder for practiced lies over hard-won truth. Yet an honest self-evaluation would reveal that within every ostensible non-denier beats the heart of someone who has the capacity to deny difficult to accept new realities whose oppositional arguments they have yet to encounter.

Despite the fact that such a change in mindset guarantees one future changes to the “facts“, hence a loss of the seeming peace of unchanging truth, as more correct information surfaces across the span of time such an exercise in critical-thinking allows one to celebrate the mystery of life rather than cloak that mystery in the swaddling cloth of misinformed and disingenuous certainty.

The next time you undertake to change the mind of a denier, don’t attack their beliefs, per sé. Instead, first think of examples from your own difficult capitulations to reality of the past, recalling how you managed to move from a state of peaceful and easy disbelief to a state of inevitably tumultuous acceptance. Only then will you have any chance to help them recognize the chain of established truths likewise undermined by their denial, which they likely never really intended nor perhaps even realized they were going to have to deny as well.

What seemed to them to have been a simple act of exercising their own intellectual autonomy will suddenly occur to them as much more complicated that tha, not so easy for them to deny when considered as a body of knowledge rather than a series of independent notions about the physical world.

You might be glad you did since such an approach seeks agreement, not conflict. If you succeed we’ll all be better off for having one less victim of organized denial actively spreading the maddening confusion and social distance of denial.

Perhaps what is most important to the “human condition” is that both of you will have learned, first hand, the valuable lesson of cooperatively shared education over  isolationist willful ignorance!

Consider the humble sandwich. Then consider its passive-aggressive cousin, the club-sandwich.

The former has the same thing on the outside and something different on the inside. The latter has that same, same thing on the outside that has also become the middle thing, too, while the different thing in the two new middles hasn’t changed a bit yet has no idea it’s no longer in the middle anymore.

Why is this interesting? It’s not, to most people. And certainly isn’t to the people who inevitably bring up “hadoop” and “mahout” as the first and most critical topics we need to discuss in every consulting assignment and job interview in NorCal’s Silicon Valley, San Francisco’s Multimedia Gulch, or New York City’s Silicon Alley.

While we’re big-fans, power-users, and developers of computer-systems as tools, strong proponents of analytically-centric approaches that extract inference from large datasets appearing at first-blush to be utterly bereft of it, we acknowledge them only for what they are; tools. The same sort of tools that investment-bankers, architects, and construction-contractors don’t find any need to spend their precious limited time together discussing, as in claw-hammers vs. pneumatic-nailguns.

Note that both are concocted words. In truth, hadoop is the real made-up word while mahout is its symbolic cousin that originated from some Open-Source hound with more semiotically-semantic insight than they realize. Yet the primal symbolism in this is the cruz of our point.

For those uninitiated in the lexicon of “machine-learning” out there;

Hadoop is;

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Hadoop is the elephant, large and powerful, considered intelligent in its own way without being particularly wise. It does the heavy lifting in big-data.

Mahout is;

Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. The field is closely related to data mining and often uses techniques from statistics, probability theory, pattern recognition, and a host of other areas. Although machine learning is not a new field, it is definitely growing. 

Mahout is the driver who directs that elephantine power toward the realization of a specific goal. For all that heavy lifting prowess, the elephant itself would neither be able to or even be inclined to accomplish any human-directed output if left to its own devices.

So, Hadoop works hard. Mahout derives the value from that work.

And both were named as lexical club sandwiches. And not by accident, we assure you. Look at the contrast between the naming of some older relative to newer utilities that have served the computational needs of power-users all over the world.

Computing sandwiches; Consonant-Vowel-Consonant

emu, yak, nawk, gawk, yawk, sed, ted, yagg

Computing Club-Sandwiches; Consonant-Vowel-Consonant-Vowel-Consonant

hadoop, mahout

What happened between 1995 and 2005? It’s not as if the likes of the W3C, Apache, and Open-Source don’t believe in symbolism. They’ve obviously put a lot of time, effort, and money into cultivating the symbolism around the many software products they’ve offered the world for years. That “cute little elephant toiling away under its wisely benevolent handler” are highly accurate symbols of the very qualities they are imbued with and represent to the world.

Both are exceedingly powerful symbols because of that.

So, only a total dweeb or ivory-tower researcher who simply isn’t interested in making things happen in the real world would obsess over such a minor distinction, right? So why did those people who see themselves as the sole, if not newest, representatives of “getting things done in the online world” choose these specific made-up words for their products?

In an endless continuation of “platform as a product” concept Google created that the rest of the online content delivery world now follows like “The inferential Holy Grail“, comes the endless procession of other made-up acronyms meaning both less and more than their stated meaning, SaaS, PaaS, PaaP, IaaS (all sandwiches, thank you very much).

But what we find most relevant to our own pitch for Venture-Capital or Angel-Investors is the manner by which the industry that has gone to great lengths to stress it’s toolsets as the final arbiters of user-inference. But, as they crow long and loud about this to the public, they are unconsciously practicing semiotics while consciously eschewing the first-principles utility of semiotically-derived content classification as a viable data-source in those same-said platform’s endless computing cycles aimed at leveraging inference of online-users!

Imagine our wretched feeling of cruel irony here. We can’t find investors for the very content-classification tool they ostensibly don’t think can accomplish this while the very architects of the platforms they have invested in name their pet utilities with semiotically-cogent tokens without a clue that they are doing so. Yet these are the “scientists” getting funded?

One or another of the over 100 companies within 50 miles of our offices in San mateo, CA that have managed to get funding for ideas, not products ready to roll like ours, to the tune of the easily over $1,000,000,000. Each one has a bundle invested in them all despite all of them having a business-plan less distinguishable from one another than ears-of-corn in a ready-for-market silo!

We are on-tol-logica. Not Google. Or Yahoo, Bing, Pandora, Ness, Mediaplex, Commission Junction, AdBrite, RocketFuel, MyBuys, BayNote, StumbleUpon, KeepMedia, …

Nor do we want to be. But we do want to be the most relevant, most successful, only actual Cost-per-Action (CPA) aware provider of automated content classification PaaS provider in the world.

No one is doing what we’re doing to enhance the overall role of relevance in content-targeting-and-delivery because they are too busy fighting for space at Google’s trough using keywords that specialize in generating clicks instead of actual sales! Does it strike no other than us as economically moronic and theoretically counter-indicated that the vaunted captains-of-the-big-data-industry simply cannot or will not recognize our first-principles and field-tested-successful-application as a big-data shop of its own?

We’ll literally take all that inanely and overly-clumpy meta-tagged inventories of pre-classified content all their portfolio-companies depend upon for succes and render into a form that would allow all of them to do just that.

And we’ll do it in a way that allows them to fulfill their own ROI-obligations that they have yet to deliver, nor likely ever will, despite their promises of “soon” in those “where’s the revenue?” board-meetings they are forced to attend every month?

While the Cost-per-Click (CPC) online advertising world will soon see, the advertisers of this world want sales, not clicks. They never wanted clicks, despite all the Google-wanna-bes’ endless hunt for more clicks in hopes of building an inventory that might, one day, actually be hoped to perhaps generate its first fulfillment, a Cost-per-Action (CPA) event, instead of a mere click.

An actual sale instead of yet another window-shopper? So, how about on-to-logica all you VC’s and Angels? despite being ahead of the game with our approach to making big-data smaller yet more effective we’re pretty much convinced that we’ll never get hired by those who think they are ahead of the game but are so busy burning through their venture-capital far faster than their current or projected revenue generation, how could we be a bad bet?

• ahead of the game and aware of it, not behind the game without knowing it.
• proven time and again in the field as shrink-wrapped software, not an untested SaaS.
• unchallenged by any viable competitors. For now.
• lacking only a cloud to deploy our PaaS instead of needing capital to design one first.

Do you want to invest in a steel-maker to the entire world or just keep investing in one-channel manufacturers that requires steel to produce their own products that generate profit-margins for them and their investors!

Not only are Hadoop and Mahout made up words, they are of the club-sandwich variety. So is the term “Gollum“. Each is a powerful symbol whose sounds, cognitively encoded by the specific permutational triplet of the consonants and vowels used, literally define their own meaning. Recall that Gollum’s real name was Smeagol, a regular lexical sandwich. Hmmmm. Both have semiotic roots, but neither name symbolizes the same thing. They denote the same living-being but do not represent the same contextual-object in the least!

All of them can be characterized and exploited as more than mere lexical tokens, the approach of the uber-popular-but-still-stumped-by-anything-that-actually-is-natural crowd who insist on wallowing in ironically synthetic solutions to this problem; Natural Language Processing (NLP). These symbols represent not only what their inventors have spent lots of marketing-capital defining in industry advertisements and Open-Source blogosphere, they literally define themselves if you know how to decode 5-dimensional cognitively permutational components inherent to all human-generated lexical content!

Who knew? We did!

the sounds in the names we give to things are cognitively-derived maps that define them

I’ll admit to having presented the advertisers over at Facebook with an inferential dilemma. As a trained biologist, volunteer marine mammalogy instructor, and practicing human-cognition scientist I like to keep track of the scientific nonsense and political poison emanating from the Intelligent Design movement.

But it wasn’t much of a dilemma. The answer was right there waiting to be found via the most cursory clickstream analysis, not even requiring knowledge of what context is or technology that can detect and leverage contextual relevance.

Conceived and deployed by its parent movement, Creation Science, as a sleekly reconfigured set of “scientific theories” they counted on to slide seamlessly into any public school science curriculum, their activities bear watching by those of us who prefer our science “straight-up“, neither shaken nor stirred with pseudo-science or outright acts of organized religion masquerading as science.

So, having “liked” two Facebook Groups, Institute for Creation Research and Creation Science Evangelism, I’d also joined a host of “pro-science efforts” on that same social.Net platform. Far more, an order of magnitude more as it turns out; 21. So, the context of my interest in “intelligent design” is informed by my far deeper and broadly defined interest in science, science education, and multi-disciplinary scientific research.

Contains{Context}

1: the parts of a discourse that surround a word or passage and can throw light on its meaning.

2: the interrelated conditions in which something exists or occurs.

Let’s not mince words here. This is “inside the bus or subway-car” advertising.

Metaphorically speaking, they’ve decided this particular “advertising conveyance” is so heterogenous that it as as likely to be seen by a “solider of fortune” as it is a “hippie“. Why bother tracking all that trafic, collecting all that meta-data, and promoting all that social cross-connectivity when all you intend to do is to target your messages with a “shotgun instead of a sniper rifle?”

Those of us who have put much hard work and deep thought into online recommendation engines, now called ad-networks ostensibly because they want to distance themselves from the sort of recommendations they’ve come to think of as analytically passe from the “olden days” of  dotcom.1.0, simply shake their heads over this online advertising mis-step.

The real problem with keywords turns out to be how easily they can be purchased and deployed. Despite the utter, demented irrelevance of offering Green Bay Packers curios to someone who just purchased a Chicago Bears jersey based on “football” user meta-data and content-tags, the keyword-and-auction factory was literally designed for that kind of lame and ultimately failed advertising.

We understand that this ridiculous advertising tandem was not the direct work of Facebook staffers. But their own policies and platform-architecture have forced their customers, you and me and every Facebook user’s friends/non-friends/un-friends, to have to put up with such nonsense. They either refuse to, or don’t know how to, define topics on an advertising platform monetized by Google’s AdWord/AdSense technology; the advertising keyword auction.

Such a technology would allow them to know ahead of time that “Intelligent Design Movement” and “Good at Biology?” are topically oppositional. A simple boolean filter would have prevented them from being featured together. What might have been estimated to be a relatively good recommendation on it’s own got laughed off my Facebook-Wall due to being featured along with its well-known contextual enemy.

Such is the price of passing oneself off as a “contextual advertisng platform” when you haven’t either the intellectual chops or marketing savvy to grasp what context is and how to leverage it positively. This kind of non-targeted targeting loses money for everyone involved by ignoring context, quite likely to be the true culprit for “user blindness/” to the top, right-side gutter, and bottom of every page on the WWW. This is not a new problem, but at least the dotcom_1.0 bubble bursting from similar inferentially-bereft online advertising efforts, which celebrated its 10-year anniversary last January, didn’t claim to everyone who would listen to be “the only ones” leveraging context.

Do you recall those bad old days? We do.

Remember, the following examples are all online advertising slots filled with remnant inventory placed wherever it could earn a nickel. They were not borne of the sort of analytics-centric online-advertising platforms designed to leverage all the vaunted social.Net traffic and meta-data hawked to VC’s and Angel Investors up and down both US coasts in the last 5 years, all claiming to make such advertising blunders and senseless targeting a thing of the past.

Facebook was born amidst all of that, making those very same claims in its pitches to potential early investors.

Not{Contains(Context)}

Burn, Baby, Burn – Iomega’s ad for a CD-burner served on a newspaper article about a Christmas-time fire that killed one child.

Put a Fembot in Office – Svedka Vodka served this ad in an article about Hillary Clinton crying in Connecticut after losing the NH primary to Barack Obama.

PutYourFeetUp.com – their banner ad showed up side-by-side with the rather gruesome story on CNN.com about severed feet washing up on shore in British Columbia

Grill Like An Expert – Competition Briquets placed this doozy of a loser ad on a newspaper article about a couple jailed for “grilling” a toddler.

BoGo Pulled Pork New Sandwich – White Castle’s new pulled-pork sandwich ad was a big flop when featured on The Jerusalem Post.com.

So, can they do worse, you ask? Oh, yes they can.

Just keep surfing online. Similar moments have already occurred in front of you had you been paying attention to the ads served on your Wall or last search-landing-page. Which you don’t. No one does. Hmmm; could that be the real problem? Perhaps another blog post on this very notion is in order for next week.

Let’s not forget that the LikeMinds Personalization Engine from 1999 on had a more credible and analytically efficient recommendation portfolio, did so with sums-of-squares statistics using mostly cumulative arithmetic in offline processing instead of the current penchant for real-time machine-learning algorithms those architects think need to be run in real-time, and scaled nicely with recommendations returns under 30 mS five years before the scaling utilities known as map-reduce, hadoop, and mahout were even invented.

The saddest thing about that last statement is that the current “state of the art” has decided a priori that every scrap of data from every online transaction ever made is viable “data” for the content predictions they make every day; up to 50MM per day.

When you consider every transaction generated every day in terrabyte-volumes as potentially housing predictive relevance you need to go back to Statistics 101 and Probability 210!  How, you ask, would we reduce the volume issue?

• simple boolean filtering across simple Transact-SQL that eliminate any product never purchased online ever.
•  calculating user’s browsing statistic cumulatively so new transactions can be adjunct calculations, not sitting on top of a stack that goes through the entire vector calculating 90% of what it calculated the week, day, minute, or even second before. Once calculated, such never changes. Why would anyone choose to calculate it over and over and over and over again no matter how many map-reduce computing clusters you have available?
• eliminate all dhtml pages. They are full of embedded content put there by even more analytically feeble approaches than you are using.
• drop any content tags of the “Pareto Principle“ paradigm variety (20% of the tags appear on on 80% of the content). they are predictively useless for their what is most accurately characterized as “clumpiness“.

And that’s just for starters. The real problem is how this “industry” has become dominated by machine-learning experts who know far more about machines than they do learning.

And while they might know something about “learning” they know so little about and have paid so little attention to the lessons of classic consumer-behavior and advertising metrics they have convinced themselves that the same consumer sitting in front of the computer or tapping away on their smart-phone isn’t the very same consumer those industries have been studying for years.

In other words, they fail due to “hubris“ more than anything else. All of this led me to playfully refer to the featured snapshot according to the Rocky-and-Bullwinkle protocol;

“Why I left online-advertising as a profession.”

or

“”Why online-advertising as a profession left me.”

I’m confident that the social.Net has to be about far more than selling unneeded and unsustainable goods and services online under the clever guise of keeping old friends in touch via snapshots and 140-character vignettes of their current lives via freebie online services.

Author and socio-biologist Rebecca Costa first characterized, then called out, the super-memes at work in and among our modern social systems in her seminal work The Watchman’s Rattle: Thinking Our Way Out of Extinction as clear social hurdles to our society’s maintenance of our current way of life. The manner by which these memes  are born and promulgated often boils down to who said it first.

Yet, as this article reports, who said it first research has proven to be arduous, fraught with false stops-and-starts, and prone to incompleteness due mostly to the volume of stuff said in public and the many sources who subsequently publish it, or even more insidiously, just parts of it.

Besides connecting us all via the products we buy and the content we seek out and consume, the social.Net ought to be able to help us elucidate how important the who said it and when they said it since we all depend upon our most trusted social-sources for information on that which we simply cannot fathom on our known.

As smart as many of us are, no one can be an expert on everything. From biology to economics, geology, finance, epidemiology, art, engineering, physics, medicine, literature, environmental toxicity, what-have-you, we all look to what we see as our topical life-lines to help us form our own opinions, which form the basis of much of our ostensible voting record. One can’t vote for the best looking candidate every time, right?

Based on research carried out by our friend, Cai Ziegler, Ph.D., we’ve begun to apply topical-momentum metrics for a variety of uses, like gauging when one effective search-session has ended and another entirely new set of search intentions are begun. On another merging content-generation and consumption front, Dr. Marti Hearst of UC-Berkeley’s School of Information and Computer Science has devised a program called Text-tiles wherein one paragraph can be mapped to other paragraphs in any lexically-based tome as a way to characterize what that page is about.

So, once we can determine who said it first the critical-thinking crowd ought to see that characterizing that is not nearly as critical as determining who said it second. This would, in turn, rather reliably point us all in the direction of whom it is that first took up that notion as a rallying cry. Getting to the bottom of the why’s and how’s inherent to any social conundrum will ultimately be more informed by why anyone would want to rally support around what often originated as a simple statement borne of an earnest desire to shed new inferential light on something about our poorly understood world.

The seed of truth within all memes is not corrupt. Nor is that original truth the meme itself. The meme is actually a downstream result of the process by which an innocent conclusion is transformed into a social crusade. We all have witnessed that dynamic throughout history, many of us coming to recognize that such efforts are inherently prone to corruption without being necessarily beholden or fated to becoming so.

Mark Twain’s prophetic truism…

A lie can travel halfway around the world while the truth is putting on its shoes.

…has never been more on point in modern political discourse than it is today.

One person’s adopted political mantra is another person’s example of the power of misinformation, intentional or otherwise. It is not at all unlikely that those who were part of any political meme’s first few steps in increasing any topic’s socially-realized momentum had a specific, hence identifiable, intention for doing so.

Sadly, when discovering someone’s intentions after the fact in similar efforts in the past it has all too often been found to have been borne out of a clearly self-serving process catering to one special-interest or another’s predetermined position. And that position, as has also been frequently discovered in such cases, wasn’t made at all clear to those who unwittingly took up that banner in the “who said it third” part of this cycle.

Those who internalize that meme, helping to take it into the viral-territory of social-topical-momentum, are usually doing so unintentionally. They merely queried trust-bonds of their own, a process counted upon by the original momentum givers to take help take that meme to the next logical level; to those they can influence in turn via the very same social trust-bond dynamic.

By the time those who said it fourth, fifth, and sixth pass it along they are all part of the exponential parade imminent biologist EO Wilson referred to in his foreward  and endorsement of Rebecca Costa’s message to the world; finding our sense of place and real-selves as individual members within a species apparently hell-bent on a path toward self-destruction before it’s too late for all of us.

These graphs are from the PR-firm Edelman in NYC about how companies get their information, their Edelman Trust Barometer research framework is utterly applicable to how people, hence voters, do likewise. If we are serious about thinking our way out of extinction we are going to have propose and follow through on some mechanisms to do so. How to know what is a lie and what is truth?

There are plenty of people out there listening to their trust-circles right now who would rightfully challenge that very characterization of lies-versus-truth. All our socially-derived definitions of a problem, hence any proposed solutions, become a festival of perceptions largely based on social trust-bonds.

If we can successfully track down who it is that contributed most heavily to the initial moments of momentum of the many memes at work in defining both our social problems and ostensible social solutions, our human skill at discerning someone else’s motives for anything they do will rise to the top. These over-arching, socially formative memes seem always to spring to life on the wings of someone else’s upstream self-serving agenda.

In the end, that is the one thing we humans all excel at; figuring out someone else’s motives, especially those we see working against our own self-interests. Once exposed as such typical evangelical means like leveraging modern mass-marketing techniques or the endless repetition of loud and emotionally evocative lies as appeals to the people’s sensibility, for example, will fail.

But that would not be so with a process that allows each person to recognize for themselves just how sinister that meme is in both formulating and molding their opinions. In that way society might have finally discovered a conduit to meme destruction.

What a fresh concept, given that only the meme construction part of the meme-life-cycle appears to have been the case for millennia. Since memes are born then they can die, too.

Perhaps ought to die is a better way to think of it. To date, memes appear to have been immortal.

Using “who said it second” as a mechanism for finally assigning a natural life-cycle to the memes that are forming our very inability to think critically as a society might well be the only way to both think and decide our way out of extinction!

The network computing to accomplish this will be messy, the inherent social and temporal connections difficult to sequence. And the looming prospect of mistaken assignation of specific motive to these meme-propagators by any or even all of us is as difficult to imagine being free of social-agenda as is the assumption that we are all intellectually prepared to undertake such a task.

But let’s give it a whirl anyway all you vaunted Social.Net crawlers and indexers! Who knows, there might just be a truly seminal, socially-relevant dissertation or thesis waiting for you here? Not to mention your next start-up!

“Following” is a concept introduced to the modern world by Twitter. It’s not a particularly complicated notion, following someone essentially means that their “tweets” are automatically written to your own home-wall. It allows the user access to that user’s posts without having to search for them. This feature is the essence of building increased connectivity between users, the commercial heartbeat of every social.net, from Digg, to Twitter, to FaceBook, to LinkedIn, to Google+.

“Rumor” has it that Facebook is in the process of adding a follow feature, a formal capitulation to a pseudo-move they made in that direction a year or so ago where they allowed one to “ignore” a request to “friend“, thereby connecting those two individuals in their social.Graph with a “half-vertex” instead of a full, bi-directional vertex in classic Graph Theory.

In one clever “adaptation” they furthered the overall connectivity of their user base despite a non-response usually treated as meaning “no interest” by most of the online content-targeting world.

But Facebook’s implementation of “follow” is doomed to failure if they “follow” the rest of the industry in implementing this critical function for connecting people to each other, not to mention the many content “products” that form the basis for added implicit (as opposed to explicit) connectivity.

I call it the “Time-Taxonomy” problem. If I am interested in “following” any one poster, the technology is easy. The same goes for explicit pages or groups. But what if I want to follow all posts on Facebook that are about “time-mangement“?

The application of multiple booleans via keywords might look like “watch | clock | sundial | timepiece” deployed inside a “contains” regular-expression.

So, would a “calendar” qualify? Or would the poorly advertised utility of keywords miss it entirely? How about stop-watches, iPhone “alert” apps, magnetic refrigerator whiteboards, “Family chores” charts, and weekly pill-dispensers?

The point is that unless you can read any content in an automated fashion and determine “what it is“, the social.Net community’s voracious appetite for linking people via the content they sample online in order to offer those same people more of what they seem to want will go largely unfulfilled.

By depending upon status-quo keyword/meta-tag technology they’d be putting all your commercially motivated bets on the explicit specificity of both their users’ “follows” and the efficacy of the meta-tags the authors of that content applied at publishing time.

The long-tail exists everywhere, not insignificantly within the content meta-tagging world. Meta-tags tend to clump into their own Pareto-Distribution where 90% of the content is tagged with 10% of the possible content-tags. They’re not useful if you seek either precision or recall, and disastrous to those who seek both at the same time despite those highly sought after notions’ mutual-exclusivity in real life!

heur-e-ka, LLC and its proposed platform, on-to-logica.com, can both read and classify online content, outputting either taxonomic or multiple-ranked-by-degree tags.

This alone would allow users to “follow” topics, notions, and high-level concepts for the first time, with an added bonus for the advertisers and platform-owners; content recommendations free of the feeble performance yielded by classic keyword matching for the first time ever!

Interested? Help us find investors.

We have the engine, we have the API’s, we have domain-specific cultures (nee; customized topical ontologies), we have a proprietary syntax and interface for building those cultures, we even have a formal training curriculum for building and leveraging customized cultures for unanticipated domains.

With a well-documented track-record of success in this very marketplace as shrink-wrapped enterprise software, all that stands between us and breathing life into the follow function for any social.Net smart enough to use our service is the cloud; the pipes, the web-services access, our own infrastructural Platform-as-a-Service.

Eventually, everyone you know who uses the online world to expand their place and participation in that world will be glad you did!

I recently spoke with a CVS executive and learned how hard they are working to drive their chosen business model.

No longer driven by becoming “Not Walgreen’s“, they now claim to be headed that way for reasons due to maximizing profit, reducing expense via waste, and a corporate Discovery process that saw unsustainability on the commercial future. That is to say, doing so for positive market-driven reasons, not negative or fear-based hubris typical of CEO-driven rationales.

Visit both stores and you’ll see one huge difference.

Walgreen’s, like most drugs-stores, is an inventory monster. A business-development friend of mine calls it the “SKU-niverse“. Their stores are literally stuffed from floor to rafters. When I was building mathematical models predicated upon content-preference events registered on Health Central.com sites triggering product recommendations from Drugstore.com, the scope of their SKU’s was a revelation to me.

Their inventory ranging the full content-preference gamut from XXX-rated sex tools and costumes to baby-food to religious artifacts to the entire family of RonCo kitchen and household aids.

The semantic associations between “chronic pain” and “breast cancer” patients allowed us to arbitrage low-value leads on the latter by transforming them into leads fro the former, chronic-pain sufferers willing to try anything and spend as much as required to suppress their pain in a way that breast-cancer patients are inclined by their own complicated and chemotherapy-centric treatment to avoid.

CVS, on the other had, has apparently realized that selling 29 different sizes, shapes, and packaging sizes of ibuprofen is a losing proposition since only 5 or 6 of them comprise 9)% of their sales in that SKU category.

The Pareto Principle is everywhere in our natural world.  Even in retail, arguably an artifact of humanity, but driven according to our natural behaviors for acquiring that which we ned to live better and more fully informed lives. It appears that they are seeking to move to smaller physical retail footprints, lower inventory  (hence financial carrying costs), less distribution cost, less loss-leading and more profit-driving.

And all of that ostensibly due to an almost socially-responsible eye on the lower-plastic pollution aspect of over-pacckaging rampant in the OTC drug world.

Here in California, CVS recently purchased Long’s Drugstores, the Long family having endowed (among many other philanthropic endeavors) The Long Marine Lab at the University of California at Santa Cruz. I switched from a longtime Walgreen’s customer to Long’s Drugstores for that sole reason. When they were acquired by CVS I began to despair that my inner- environmentalist motivation might well have been mootified!

Apparently not. So, good on ‘ya CVS.

They might well be on the right track still consistent with their own capitalist leanings; actually allowing the free-market dynamics to work for them in a natural manner instead of merely talking about free-markets when testifying at Congressional hearings in typical corporate resistance to any regulation!

Dr. Barry Schwartz, The Dorwin Cartwright Professor of Social Theory and Social Action at Swarthmore College, has published widely about “The Paradox of Choice“.

His ideas resonate quite well inthis regard, his theories about human behavior in complex adaptive systems helping each of us face off the daily trade-off every consumer is confronted with every day; separating what I need from what I’m offered!

I’m keeping my eye on their stores for signs of their adherence to this newfound economic philosophy and business model.  It won’t be hard to do now that I shop there regularly again.