Sorry, I Don’t Speak Complexity

I was reading about an interesting study from Cornell this week. Dr. Morton Christianson, Co-Director of Cornell’s Cognitive Science Program, and his colleagues explored an interesting linguistic paradox – languages that a lot of people speak – like English and Mandarin – have large vocabularies but relatively simple grammar. Languages that are smaller and more localized have fewer words but more complex grammatical rules.

The reason, Christensen found, has to do with the ease of learning. It doesn’t take much to learn a new word. A couple of exposures and you’ve assimilated it. Because of this, new words become memes that tend to propagate quickly through the population. But the foundations of grammar are much more difficult to understand and learn. It takes repeated exposures and an application of effort to learn them.

Language is a shared cultural component that depends on the structure of a network. We get an inside view of network dynamics from investigating the spread of language. Let’s look at the complexity of a syntactic rule, for example. These are the rules that govern sentence structure, word order and punctuation. In terms of learnability, syntax offers much more complexity than simply understanding the definition of a word. In order to learn syntax, you need repeated exposures to it. And this is where the structure and scope of a network comes in. As Dr. Christensen explains,

“If you have to have multiple exposures to, say, a complex syntactic rule, in smaller communities it’s easier for it to spread and be maintained in the population.”

This research seems to indicate that cultural complexity is first spawned in heavily interlinked and relatively intimate network nodes. For these memes – whether they be language, art, philosophies or ideologies – to bridge to and spread through the greater network, they are often simplified so they’re easier to assimilate.

If this is true, then we have to consider what might happen as our world becomes more interconnected. Will there be a collective “dumbing down” of culture? If current events are any indication, that certainly seems to be the case. The memes with the highest potential to spread are absurdly simple. No effort on the part of the receiver is required to understand them.

But there is a counterpoint to this that does hold out some hope. As Christensen reminds us, “People can self-organize into smaller communities to counteract that drive toward simplification.” From this emerges an interesting yin and yang of cultural content creation. You have more highly connected nodes independent of geography that are producing some truly complex content. But, because of the high threshold of assimilation required, the complexity becomes trapped in that node. The only things that escape are fragments of that content that can be simplified to the point where they can go viral through the greater network. But to do so, they have to be stripped of their context.

This is exactly what caused the language paradox that the team explored. If you have a wide network – or a large population of speakers – there are a greater number of nodes producing new content. In this instance, the words are the fragments, which can be assimilated, and the grammar is the context that gets left behind.

There is another aspect of this to consider. Because of these dynamics unique to a large and highly connected network, the simple and trivial naturally rises to the top. Complexity gets trapped beneath the surface, imprisoned in isolated nodes within the network. But this doesn’t mean complexity goes away – it just fragments and becomes more specific to the node in which it originated. The network loses a common understanding and definition of that complexity. We lose our shared ideological touchstones, which are by necessity more complex.

If we speculate on where this might go in the future, it’s not unreasonable to expect to see an increase in tribalism in matters related to any type of complexity – like religion or politics – and a continuing expansion of simple cultural memes.

The only time we may truly come together as a society is to share a video of a cat playing basketball.

 

 

Why Reality is in Deep Trouble

If 2017 was the year of Fake News, 2018 could well be the year of Fake Reality.

You Can’t Believe Your Eyes

I just saw Star Wars: The Last Jedi. When Carrie Fisher came on screen, I had to ask myself: Is this really her or is that CGI? I couldn’t remember if she had the chance to do all her scenes before her tragic passing last year. When I had a chance to check, I found that it was actually her. But the very fact that I had to ask the question is telling. After all, Star Wars Rogue One did resurrect Peter Cushing via CGI and he passed away 14 years ago.

CGI is not quite to the point where you can’t tell the difference between reality and computer generation, but it’s only a hair’s breadth away. It’s definitely to the point where you can no longer trust your eyes. And that has some interesting implications.

You Can Now Put Words in Anyone’s Mouth

The Rogue One Visual Effects head, John Knoll, had to fend off some pointed questions about the ethics of bringing a dead actor back to life. He defended the move by saying “We didn’t do anything Peter Cushing would have objected to. Whether you agree or not, the bigger question here is that they could have. They could have made the Cushing digital doppelganger do anything – and say anything – they wanted.

But It’s Not just Hollywood That Can Warp Reality

If fake reality comes out of Hollywood, we are prepared to cut it some slack. There is a long and slippery ethical slope that defines the entertainment landscape. In Rogue One’s case, it wasn’t using CGI, or even using CGI to represent a human. That includes a huge slice of today’s entertainment. It was using CGI to resurrect a dead actor and literally putting words in his mouth. That seemed to cross some ethical line in our perception of what’s real. But at the end of the day, this questionable warping of reality was still embedded in a fictional context.

But what if we could put words in the manufactured mouth of a sitting US president? That’s exactly what a team at Washington University did with Barack Obama, using Stanford’s Face2Face technology. They used a neural network to essentially create a lip sync video of Obama, with the computer manipulating images of his face to lip sync it to a sample of audio from another speech.

Being academics, they kept everything squeaky clean on the ethical front. All the words were Obama’s – it’s just that they were said at two different times. But those less scrupulous could easily synthesize Obama’s voice – or anyone’s – and sync it to video of them talking that would be indistinguishable from reality.

Why We Usually Believe Our Eyes

When it comes to a transmitted representation of reality, we accept video as the gold standard. Our brains believe what we see to be real. Of all our five senses, we trust sight the most to interpret what is real and what is fake. Photos used to be accepted as incontrovertible proof of reality, until Photoshop messed that up. Now, it’s video’s turn. Technology has handed us the tools that enable us to manufacture any reality we wish and distribute it in the form of video. And because it’s in that form, most everyone will believe it to be true.

Reality, Inc.

The concept of a universally understood and verifiable reality is important. It creates some type of provable common ground. We have always had our own ways of interpreting reality, but at the end of the day, the was typically some one and some way to empirically determine what was real, if we just bothered to look for it.

But we now run the risk of accepting manufactured reality as “good enough” for our purposes. In the past few years, we’ve discovered just how dangerous filtered reality can be. Whether we like it or not, Facebook, Google, YouTube and other mega-platforms are now responsible for how most of us interpret our world. These are for-profit organizations that really have no ethical obligation to attempt to provide a reasonable facsimile of reality. They have already outstripped the restraints of legislation and any type of ethical oversight. Now, these same platforms can be used to distribute media that are specifically designed to falsify reality. Of course, I should also mention that in return for access to all this, we give up a startling amount of information about ourselves. And that, according to UBC professor Taylor Owen, is deeply troubling:

“It means thinking very differently about the bargain that platforms are offering us. For a decade the deal has been that users get free services, and platforms get virtually unlimited collection of data about all aspects of our life and the ability to shape of the information we consume. The answer isn’t to disengage, as these tools are embedded in our society, but instead to think critically about this bargain.

“For example, is it worth having Facebook on your mobile phone in exchange for the immense tracking data about your digital and offline behaviour? Or is the free children’s content available on YouTube worth the data profile that is being built about your toddler, the horrific content that gets algorithmically placed into your child’s feed, and the ways in which A.I. are creating content for them and shaping what they view? Is the Amazon smart speaker in your living room worth providing Amazon access to everything you say in your home? For me, the answer is a resounding ‘no’.”

2018 could be an interesting year…

Attention: Divided

I’d like you to give me your undivided attention. I’d like you to – but you can’t. First, I’m probably not interesting enough. Secondly, you no longer live in a world where that’s possible. And third, even if you could, I’m not sure I could handle it. I’m out of practice.

The fact is, our attention is almost never undivided anymore. Let’s take talking for example. You know; old-fashioned, face-to-face, sharing the same physical space communication. It’s the one channel that most demands undivided attention. But when is the last time you had a conversation where you were giving it 100 percent of your attention? I actually had one this past week, and I have to tell you, it unnerved me. I was meeting with a museum curator and she immediately locked eyes on me and gave me the full breadth of her attention span. I faltered. I couldn’t hold her gaze. As I talked I scanned the room we were in. It’s probably been years since someone did that to me. And nary a smart phone was in sight.

If this is true when we’re physically present, imagine the challenge in other channels. Take television, for instance. We don’t watch TV like we used to. When I was growing up, I would be verging on catatonia as I watched the sparks fly between Batman and Catwoman (the Julie Newmar version – with all due respect to Eartha Kitt and Lee Meriwether.) My dad used to call it the “idiot box.” At the time, I thought it was a comment on the quality of programming, but I now know realize he was referring to my mental state. You could have dropped a live badger in my lap and not an eye would have been batted.

But that’s definitely not how we watch TV now. A recent study indicates that 177 million Americans have at least one other screen going – usually a smartphone – while they watch TV. According to Nielsen, there are only 120 million TV households. That means that 1.48 adults per household are definitely dividing their attention amongst at least two devices while watching Game of Thrones. My daughters and wife are squarely in that camp. Ironically, I now get frustrated because they don’t watch TV the same way I do – catatonically.

Now, I’m sure watching TV does not represent the pinnacle of focused mindfulness. But this could be a canary in a coalmine. We simply don’t allocate undivided attention to anything anymore. We think we’re multi-tasking, but that’s a myth. We don’t multi-task – we mentally fidget. We have the average attention span of a gnat.

So, what is the price we’re paying for living in this attention deficit world? Well, first, there’s a price to be paid when we do decided to communicate. I’ve already stated how unnerving it was for me when I did have someone’s laser focused attention. But the opposite is also true. It’s tough to communicate with someone who is obviously paying little attention to you. Try presenting to a group that is more interested in chatting to each other. Research studies show that our ability to communicate effectively erodes quickly when we’re not getting feedback that the person or people we’re talking to are actually paying attention to us. Effective communication required an adequate allocation of attention on both ends; otherwise it spins into a downward spiral.

But it’s not just communication that suffers. It’s our ability to focus on anything. It’s just too damned tempting to pick up our smartphone and check it. We’re paying a price for our mythical multitasking – Boise State professor Nancy Napier suggests a simple test to prove this. Draw two lines on a piece of paper. While having someone time you, write “I am a great multi-tasker” on one, then write down the numbers from 1 to 20 on the other. Next, repeat this same exercise, but this time, alternate between the two: write “I” on the first line, then “1” on the second, then go back and write “a” on the first, “2” on the second and so on. What’s your time? It will probably be double what it was the first time.

Every time we try to mentally juggle, we’re more likely to drop a ball. Attention is important. But we keep allocating thinner and thinner slices of it. And a big part of the reason is the smart phone that is probably within arm’s reach of you right now. Why? Because of something called intermittent variable rewards. Slot machines use it. And that’s probably why slot machines make more money in the US than baseball, moves and theme parks combined. Tristan Harris, who is taking technology to task for hijacking our brains, explains the concept: “If you want to maximize addictiveness, all tech designers need to do is link a user’s action (like pulling a lever) with a variable reward. You pull a lever and immediately receive either an enticing reward (a match, a prize!) or nothing. Addictiveness is maximized when the rate of reward is most variable.”

Your smartphone is no different. In this case, the reward is a new email, Facebook post, Instagram photo or Tinder match. Intermittent variable rewards – together with the fear of missing out – makes your smartphone as addictive as a slot machine.

I’m sorry, but I’m no match for all of that.

Will We Ever Let Robots Shop for Us?

Several years ago, my family and I visited Astoria, Oregon. You’ll find it at the mouth of the Columbia River, where it empties into the Pacific. We happened to take a tour of Astoria and our guide pointed out a warehouse. He told us it was filled with canned salmon, waiting to be labeled and shipped. I asked what brand they were. His answer was “All of them. They all come from the same warehouse. The only thing different is the label.”

Ahh… the power of branding…

Labels can make a huge difference. If you need proof, look no further than the experimental introduction of generic brands in grocery stores. Well, they were generic to begin with, anyway. But over time, the generic “yellow label” was replaced with a plethora of store brands. The quality of what’s inside the box hasn’t changed much, but the packaging has. We do love our brands.

But there’s often no rational reason to do so. Take the aforementioned canned salmon for example. Same fish, no matter what label you may stick on it. Brands are a trick our brain plays on us. We may swear our favorite brand tastes better than it’s competitors, but it’s usually just our brain short circuiting our senses and our sensibility. Neuroscientist Read Montague found this out when he redid the classic Pepsi taste test using a fMRI scanner. The result? When Coke drinkers didn’t know what they were drinking, the majority preferred Pepsi. But the minute the brand was revealed, they again sweared allegiance to Coke. The taste hadn’t changed, but their brains had. As soon as the brain was aware of the brand, some parts of it suddenly started lighting up like a pinball machine.

In previous research we did, we found that the brain instantly responded to favored brains the same way it did to a picture of a friend or a smiling face. Our brains have an instantaneous and subconscious response to brands. And because of that, our brains shouldn’t be trusted with buying decisions. We’d be better off letting a robot do it for us.

And I’m not saying that facetiously.

A recent post on Bloomberg.com looked forward 20 years and predicted how automation would gradually take over ever step of the consumer product supply chain, from manufacturing to shipping to delivery to our door. The post predicts that the factory floor, the warehouse, ocean liners, trucks and delivery drones will all be powered by Artificial intelligence and robotic labor. The first set of human hands that might touch a product would be those of the buyer. But maybe we’re automating the wrong side of the consumer transaction. The thing human hands shouldn’t be touching is the buy button. We suck at it.

We have taken some steps in the right direction. Itamar Simonson and Emanuel Rosen predicted a death of branding in their book Absolute Value:

“In the past the marketing function “protected” the organization in some cases. When things like positioning, branding, or persuasion worked effectively, a mediocre company with a good marketing arm (and deep pockets for advertising) could get by. Now, as consumers are becoming less influenced by quality proxies, and as more consumers base their decisions on their likely experience with a product, this is changing.”

But our brand love dies hard. If our brain can literally rewire the evidence from our own senses – how can we possibly make rational buying decisions? True, as Simonson and Rosen point out, we do tend to favor objective information when it’s available, but at the end of the day, our buying decisions still rely on an instrument that has proven itself unreliable in making optimal decisions under the influence of brand messaging.

If we’re prepared to let robots steer ships, drive trucks and run factories, why won’t we let them shop for us? Existing shopping bots stop well short of actually making the purchase. We’ll put our lives in the hands of A.I. in a myriad of ways, but we won’t hand our credit card over. Why is that?

It seems ironic to me. If there were any area where machines can beat humans, it would be in making purchases. They’re much better at filtering based on objective criteria, they can stay on top of all prices everywhere and they can instantly aggregate data from all similar types of purchases. Most importantly, machines can’t be tricked by branding or marketing. They can complete the Absolute Value loop Simonson and Rosen talk about in their book.

Of course, there’s just one little problem with all that. It essentially ends the entire marketing and advertising industry.

Ooops.

Bias, Bug or Feature?

When we talk about artificial intelligence, I think of a real time Venn diagram in motion. One side is the sphere of all human activity. This circle is huge. The other side is the sphere of artificial intelligent activity. It’s growing exponentially. And the overlap area between the two is also expanding at the same rate. It’s this intersection between the two spheres that fascinates me. What are the rules that govern interplay between humans and machines?

Those rules necessarily depend on what the nature of the interplay is. For the sake of this column, let’s focus on those researchers and developers that are trying to make machines act more like humans. Take Jibo, for example. Jibo is “the first social robot for the home.” Jibo tells jokes, answers questions, understands nuanced language and recognizes your face. It’s just one more example of artificial intelligence that’s intended to be a human companion. And as we’re building machines that are more human, we’re finding is that many of the things we thought were human foibles are actually features that have developed for reasons that were at one time perfectly valid.

Trevor Paglin is a winner of the MacArthur Genius Grant. His latest project is to see what AI sees when it’s looking at us: “What are artificial intelligence systems actually seeing when they see the world?” What is interesting about this is that when machines see the world, they use machine-like reasoning to make sense of it. For example, Paglin fed hundreds of images of fellow artist Hito Steyerl into a face-analyzing algorithm. In one instance, she was evaluated as “74% female”.

This highlights a fundamental difference in how machines and humans see the world. Machines calculate probabilities. So do we, but that happens behind the scenes and it’s only part of how we understand the world. Operating a level higher than that we use meta-signatures; categorization for example – to quickly compartmentalize and understand the world. We would know immediately that Hito was a woman. We wouldn’t have to crunch the probabilities. By the way, we do the same thing with race.

But is this a feature or a bug? Paglin has his opinion, “I would argue that racism, for example, is a feature of machine learning—it’s not a bug,” he says. “That’s what you’re trying to do: you’re trying to differentiate between people based on metadata signatures and race is like the biggest metadata signature around. You’re not going to get that out of the system.”

Whether we like it or not, our inherent racism was a useful feature many thousands of years ago. It made us naturally wary of other tribes competing for the same natural resources. As much as it’s abhorrent to most of us now, it’s still a feature that we can’t “get out of the system.”

This highlights a danger in this overlap area between humans and machines. If we want machines to think as we do, we’re going to have to equip them with some of our biases. As I’ve mentioned before, there are some things that humans do well, or, at least; that we do better than machines. And there are things machines do infinitely better than we. Perhaps we shouldn’t to try to merge these two. If we’re trying to get machines to do what humans do, are we prepared to program racism, misogyny, intolerance, bias and greed into the operating system? All these things are part of being human, whether we like to admit it or not.

But there are other areas that are rapidly falling into the overlap zone of my imaginary Venn diagram. Take business strategy, for example. A recent study from CapGemini showed that 79% of organizations implementing AI feel it’s bringing new insights and better data analysis, 74% that it makes their organizations more creative and 71% feel it’s helping make better management decisions. A friend of mine recently brought this to my attention along with what was for him an uncharacteristic rant: “I really would’ve hoped senior executives might’ve thought creativity and better management decisions were THEIR GODDAMN JOB and not be so excited about being able to offload those dreary functions to AI’s which are guaranteed to be imbued with the biases of their creators or, even worse, unintended biases resulting from bad data or any of the untold messy parts of life that can’t be cleanly digitized.”

My friend hit the proverbial nail on the proverbial head – those “untold messy parts of life” are the things we have evolved to deal with, and the way we deal with them are not always admirable. But in the adaptive landscape we all came from, they were proven to work. We still carry that baggage with us. But is it right to transfer that baggage to algorithms in order to make them more human? Or should we be aiming for a blank slate?

Trust No One, Trust Nothing

In just one day last week looking at the headlines on MediaPost – two different articles mentioned a lack of trust – a lack of trust in contextual ad placement and a lack of trust in audience measurement data. But our industries trust issues go far deeper than just those two instances. Article after article cite an erosion of trust and the spreading of relational fault lines in every aspect of the industry.

The question of the day is “Where did the trust go? The follow up question then becomes, “What do we mean by trust?”

That is a difficult question. Trust is a word with many, many meanings. Over 20 years ago, University of Minnesota business professors D. Harrison McKnight and Norman L. Chervany wrote an extensive review to answer just that question. In it, across the many constructs of trust, they identified four dimensions: benevolence, integrity, competence and predictability. But not all these dimensions are required in all applications of trust.

First of all, there are two broad categories of trust: structural trust – trust in a system – and interpersonal trust – trust in a person. In their analysis, McKnight and Chervany looked at six constructs of trust that can apply in different situations. For the sake of this discussion, let’s focus on two of these:

“System trust: the extent to which one believes that proper impersonal structures are in place to enable one to anticipate a successful future endeavor.”

And…

“Situational Trust – meaning that one has decided to trust without regard to the specific persons involved, because the benefits of trusting in this situation outweigh the possible negative outcomes of trusting.”

What trust that did exist in marketing what an outcome of these two constructs. Both tend to apply to the structure of marketing, not the people in marketing. The headlines I cited earlier both pointed to a breakdown of trust on the system level, not the personal level. Now, let’s look at those four dimensions as they apply to structural trust in marketing. No one has ever accused marketers of being overly benevolent, so let’s set that one aside. Also, I would argue – strenuously – that marketers today – including those at agencies – are more competent than ever before. They have been mostly successful at turning marketing from an arcane guessing game that paraded as art to an empirically backed science. So a lack of competency can’t be blamed for this trust breakdown. That leaves integrity and predictability. I suspect there’s a compound relationship between these two things.

The reason we’re losing structural trust is that marketing is no longer predictable. And this lack of predictability is triggering a suspicion that there has been a corresponding lack of integrity. But the unpredictability of marketing is no one’s fault.

Marketing today is analogous to physics at the turn of the last century. For 200 years the universe had been neatly ruled by Newton’s Laws. Then physicists started discovering things that couldn’t be so neatly explained and the Universe became a place of Uncertainty Principles, Schrödinger’s Cat and Strange Attractors. Everything we thought was predictable in all situations suddenly become part of a much bigger – and more complex – mystery.

Similarly, mass marketing could run by Newton-like laws because we were dealing with mass and weren’t looking too closely. Apply enough force to enough people with enough frequency and you could move the needle in what seemed like a predictable fashion. But today marketing is a vastly different beast. We market one-to-one and those “ones” are all inter-connected, which creates all types of feedback loops and network effects. This creates complexity – so predictability is as dead at the afore-mentioned Schrödinger’s Cat (or is it?)

I don’t think this comes as news to anyone reading this column. We all know we’re being disrupted. I think we’re all beginning to understand the challenges of complexity. So why don’t we just accept it as the new normal and continue to work together? Why are clients feeling personally betrayed by their agencies, market research firms and ad delivery platforms? It’s because our brains aren’t very nuanced when it comes to identifying trust and betrayal. Brains operate by the “when you’re a hammer – everything looks like a nail” principle.

Rationally, we understand the different between interpersonal trust and situational trust, but we have to remember that our rationality is reinforced by emotional rewards and cautions. When we’re in a trusting relationship – or system – our ventrial striatum, medial prefrontal cortex and caudate nucleus all perk happily along, priming our brains with oxytocin and pushing all the right reward buttons. But whether it’s a person or a situation that betrays our trust, the same neural mechanisms fire – the insula and amygdala – creating feelings of frustration, fear, anger and resentment.

Now, none of this is the fault of anyone in marketing. But humans work on cause and effect. If our marketing is not working, it’s easier to assign a human cause. And it’s much easier to feel betrayed by a human than by a system.

 

Together We Lie

Humans are social animals. We’ve become this way because – evolutionarily speaking – we do better as a group than individually. But there’s a caveat here. If you get a group of usually honest people together, they’re more likely to lie. Why is this?

Martin Kocher and his colleagues from LMU in Munich set up a study where participants had to watch a video of a single roll of a die and then report on the number that came up. Depending on what they reported, there was a payoff. Researchers asked both individuals and small groups who had the opportunity to chat anonymously with each other before reporting. The result,

“Our findings are unequivocal: People are less likely to lie if they decide on their own.”

Even individuals who answered honestly independently started lying when they got in a group.

The researchers called this a “dishonesty shift.” They blame it on a shifting weight placed on the norm of honesty. Norms are those patterns we have that guide us in our behaviors and beliefs. But those norms may be different individually than they are when we’re part of a group.

“Feedback is the decisive factor. Group-based decision-making involves an exchange of views that may alter the relative weight assigned to the relevant norm.”

Let’s look at how this may play out. Individually, we may default to honesty. We do so because we’re unsure of the consequences of not being honest. But when we get in a group, we start talking to others and it’s easier to rationalize not being honest – “Well, if everyone’s going to lie, I might as well too.”

Why is this important? Because marketing is done in groups, by groups, to groups. The dynamics of group-based ethics are important for us to understand. It could help to explain the most egregious breaches of ethics we see becoming more and more commonplace, either in corporations or in governments.

Four of the seminal studies in psychology and sociology shed further light on why groups tend to shift towards dishonesty. Let’s look at them individually.

In 1955, Solomon Asch showed that even if individually we believe something to be incorrect, if enough people around us have a different option, we’ll go with the group consensus rather than risk being the odd person out. In his famous study, he would surround a subject with “plants” who, when shown cards with three black lines of obviously differing lengths on it, would insist that three lines were equal. The subjects were then asked their opinion. In 75% of the cases, they’d go with the group rather than risk disagreement. As Asch said in his paper – quoting sociologist Gabriel Tarde – “Social man in a somnambulist.” We have about as much independent will as your average sleepwalker.

Now, let’s continue with Stanley Milgram’s Obedience to Authority study, perhaps the most controversial and frightening of the group. When confronted with an authoritative demeanor, a white coat and a clipboard, 63% of the subjects meekly followed directions and delivered what were supposed to be lethal levels of electrical shock to a hapless individual. The results were so disheartening that we’ve been trying to debunk them ever since. But a follow up study by Stanford psychology professor Philip Zimbardo – where subjects were arbitrarily assigned roles as guards and inmates in a mock prison scenario – was even more shocking. We’re more likely to become monsters and abandon our personal ethics when we’re in a group than when we act alone. Whether it’s obedience to authority – as Milgram was trying to prove – or whether it’s social conformity taken to the extreme, we tend to do very bad things when we’re in bad company.

But how do we slip so far so quickly from our own personal ethical baseline? Here’s where the last study I’ll cite can shed a little light. Sociologist Mark Granovetter – famous for his Strength of Weak Ties study – also looked at the viral spreading of behaviors in groups. I’ve talked about this in a previous column, but here’s the short version: If we have the choice between two options, with accompanying social consequences, which option we choose may be driven by social conformity. If we see enough other people around us picking the more disruptive option (i.e. starting a riot) we may follow suit. Even if we all have different thresholds – which we do – the nature of a crowd is such that those with the lowest threshold will pick the disruption option, setting into effect a bandwagon effect that eventually tips the entire group over the threshold.

These were all studied in isolation, because that’s how science works. We study variables in isolation. But it’s when factors combine that we get the complexity that typifies the real world – and the real marketplace. And there’s where predictability goes out the window. The group dynamics in play can create behavioral patterns that make no sense to the average person with the average degree of morality. But it’s happened before, it’s happening now, and it’s sure to happen again.