What if assumptions are wrong?   By a little? By a lot? These kind of what ifs can create interesting insights.  A recent example of testing big assumptions in finance may offer an interesting new perspective for marketing.

It’s not greed…
At the risk of simplifying too much,  financial models rely on the widely held assumption that greed drives the market.  But one theory submitted by Eric Falkenstein says Why Envy Dominates Greed.  While he goes on to build a compelling case in his book one of the clearest arguments is based on evolution and cognitive science.  Basically, the social nature of man is built on a capability to monitor relative status, hence envy, rather on the unbounded demands of uncapped needs, or greed.  Cognitively, if greed were dominant, we’d likely get mentally swamped in trying to make decisions allocating our resources and energies.And that little assumption can change a lot.

Falkenstein goes on to generate new financial models based on the changed assumption of envy with empirical testing that seems to show how this may be a real improvement to classical financial theory.

Are we making the same assumption in marketing?
Sales and Marketing have long used the model of the “funnel” to structure their efforts.   This notion of the funnel has a couple attributes which standout:
  • It assumes the customer proceeds through the funnel sequentially in a process of satisfying the need for consumption (a.k.a. greed)
  • The structure is built from the perspective of the organization’s view and the customer is, well, fodder.

If these assumptions are incorrect could it help us rethink how we structure sales and marketing?  Beyond greed or envy is there another way to view our ways of connection with the customer?  Could this help us rethink the funnel? 

More soon!

As the models of how our cortex actually functions, those like HTM, rapidly improve, the follow-on implications are even more exciting.  This deeper understanding will begin to put a much more solid footing under marketing specialties as well.

In Probably Approximately Correct, Leslie Valiant a well known computer scientist offers what boils down to a the common characteristic of “thinking”:
“Much of everyday human decision making appears to be of a similar nature- it is based on a competent ability to predict from past observations without any good articulation of how the prediction is made or any claim of fundamental understanding of the phenomenon in question. They need to be merely useful enough.”
This is the land where marketing lives.  And models like HTM offer perhaps more in explanatory power of behavior and biases than anything we have had to date.  This is just the beginning for real data powered marketing automation underpinned by natural models of behavior.
We have decided to “warm-up” on Anchoring since a lot of people have either heard of it as well as have experienced it themselves.  During decision making anchoring occurs when an initial piece of information inappropriately influences subsequent decisions.  And understanding  Hierarchical Temporal Models (HTM) takes us a long way toward developing a useful model.

Anchoring gives us a sense that in decision making we are bringing a lot of parallel activity to bear to arrive at a conclusion.  We also suspect that encounters of sequential events must have affect our deliberations.

Does HTM work this way too?  Very much so.

Some key aspects of  how an HTM works:
  • Model Hierarchy. Models are learned by building on previous models and over time into a hierarchy of reusable models.  One can even think of memory and model as one in the same.  While the  potential for “memory” exists in the HTM (and likely our cortex), it is only when patterns have been built that we might really consider these real memories.
  • It’s all predictions.  Likewise memories & models are also simultaneously predictions.   A model seeks opportunities for confirmation every chance it gets.
  • Temporal structure. We often think of memories as static and spatial, like images- say of our cat.  And our power of recognition is exemplary (ie. satisfying the prediction “is that my cat?”).  But what is also baked in and less obvious is that models exist in association with many contexts (other models), and significatly those connected directly in time.  Yes, I am good a recognizing my wife’s voice saying “Hi, I’m home”, but she typcially returns around 5:30p and of course I am expecting (predicting) it.
  • Sparse representation. And finally memories, because they can be built from other models, can also be represented what we call very “sparsely”.  Combine pieces to get entirely new models very compactly, which at the same time explains the power,speed, and efficiency of the human cortex.

To simplify then:
  1. memory = model = prediction (and later also = action!),
  2. memory’s are also always wired in a temporal structure, and
  3. memories can be incredibly reliable and efficient because they are based on sparse representations.

Now we can play out some of the hypothetical ways and HTM helps us understand Anchoring.  In most anchoring experiments the two task are related somehow even though they technically have nothing to do with one another.  For example both may  involve numbers.   And of course, the two tasks are linked in time, one right after the other.  With the first task invoking models related to numbers and forming predictions, perhaps it is not surprising these models remain active and influential through the second estimation task.

And one other aspect comes to light.  And that is the value of making a wrong prediction.  As we said these sparse representations can quickly form complex models, in part by allowing less than perfect predictions to proceed.  In typical anchoring experiments the tasks at hand are not critical to survival for example.  It would be interesting to explore the anchoring bias relative to the importance of a decision and see if it breaks down there.  [Put another way,  “It doesn’t really matter what I put out there as an estimate, but it sure will be interesting to see if these two task were really linked in some way when I hear the answer!”]

Considering these aspects of the HTM then might let us predict how anchoring plays out.  It is likely:

  • The sequential nature of the tasks is essential to impacting the observable effect.
  • Finding further model links should strengthen the effect.  For example invoking the anchoring not just with a number but the same units as well (say currencies) should strengthen the effect.
  • We might hypothesize there is a model serving the notion of “estimation of numbers”, and that creates the expectation of a series of related tasks.  Changing estimation domains, such as predicting a non-numeric answer for the second task likely reduce the anchoring bias.

As HTM models can further inform and clarify how our minds engage these kinds of tasks, we expect them to also spur the development of new tools to help deliver information and inform decision making across a range of applications.
We have been exploring the links between emerging work in Hierarchical Temporal Models (HTM) algorithms and the fields of behavioral finance, economics and marketing.  Particularly interesting is the explanatory power for biases, the kinds demonstrated in behavioral studies and such as those popularized in Predictably Irrational.  While we think better models may come out of this, a different perspective on behavior itself also emerges.  A focus on the irrational may not really apply.

Starting more simply, without even going into the details of the algorithmic models yet, if we can assume that evolutionary processes had a role in our development then it is all the kinds of experiences that we normally encounter which played a role in evolving the mind and it’s training.  We rely on experiences coming from a “common source in the world”.  When this becomes less true, that is when we can have observable bias emerge.

The classic experiment demonstrating the Anchoring bias prompts individuals to focus on an initial numeric memory recall task, then subsequently on an unrelated but numerical estimation task, which in turn demonstrates an influential bias of the first task on the second.    We can certainly demonstrate the bias, but keep in mind the experiment itself uses an artificial series of events: it’s unpredictable!  

Perhaps getting the estimation wrong makes sense? If you got the estimation right you might still wonder if the second task followed the first for a reason?  Getting things wrong to speed learning new things is exactly what we should be doing isn’t it? Perfectly rational!

Studying the HTM actually offers much  more explanatory and potentially useful models of biases.  We’ll take a another pass at the anchoring bias reflecting more on the actual HTM soon.

In 2002 Daniel Kahneman and Amos Tversky shared the Nobel Prize in Economics for their work on Prospect Theory, which opened the door to looking at risk from a behavioral perspective.  They showed the diversion from "rationality" is that people simply were more averse to loss than inclined toward gains.  While we look for instances of Prospect Theory in action in our modeling of behavioral finance and marketing, the evidence is all around us- even in pro golf.

Some research reported recently in NYTimes article (link) evidently shows that loss aversion affects the putting behavior of even top golf pro's.  To what extent, well its estimated for the top 20 golfers, to the tune of $1.2 million in prize money per year!  And where does the behavior come from?  To avoid a bogey, golfers put more aggressively to make par, than if they are putting for a birdie, even though from a rational perspective they need to be make the same putting decisions in either case.

While we can't fight human nature, and the desire to avoid "bogeys" is strong, these behavioral biases are all around us and are hard to see.  By identifying those that may stand in the way of larger longer term goals, we might just figure out what really matters and get there a little more easily.


A great story in The (Mis)Behavior of Markets from Benoit Mandlebrot shines a floodlight on how broad the opportunities in Behavioral Arbitrage can be.  Evidently Warren Buffet once poked fun that he would like to fund even more university chairs in the classic Efficient Market Hypothesis, so they could train and send out even more mis-guided decision makers- whose money he could then proceed to take! 

Like any classical behavioral bias, a prevalent theory, particularly when it is a simplification of human behavior as Efficient Markets theory does, yet which becomes entrenched, dramatically shapes large group behaviors.  And in turn, this creates opportunities for those who can detect and design counter positions.

Our work in Behavioral Arbitrage, looking where deepset biases are in play, and developing ways to improve performance around them also applies of course to the financial markets.  Now if we could just capture more of Warren Buffett's intuitions!


Last week while traveling on a long flight home from Ukraine, I struck up a conversation with my neighbor on the plane.  At some point after I had described what our company does, its current projects, and what I thought makes us special, I got the question: "Interesting, what's the name of your company?"  I replied, "Darwin's Grove," and my neighbor immediately asked, "What's that mean?"  I realized that despite having what we think is an effective, focused strategy, exciting capabilities, and some good progress this year, we might not be making the connection clear.

So let me explain.  First, I want to say what our name does not mean.  For us, it's not about applying Darwin's theory where it need not be applied.  For example, it's not about jumping from an explanation for the evolution of biological life (involving natural selection, competition, etc.) straight into explanations for social phenomena or economic systems simply because they too are complex.  This does Darwin's work a disservice.

Rather, we think that Darwin's ideas do serve us in our work in behavioral analytics, marketing, finance, and strategy.  In all of those, behavior, our minds, our consciousness, even our spirit (and even spirituality, which inevitably my neighbor and I on the plane touched on--it was an 11-hour flight!), does run on some substrate--our brains.  And the continued furthering of the fundamental understandings of how we can better understand behavior will likely follow at least in part from simply taking more of a stance like that of Charles Darwin himself--that is, asking, "what could a brain be for from an evolutionary perspective, and how did evolution deliver it?"  For example by understanding how the brains stores models and how it recognizes patterns, we are able to develop software that mimics these natural processes in tools that serve our clients in marketing and business strategy.

Let me share a few references which inspired us to create our services. Daniel Dennett's Consciousness Explained and later Darwin's Dangerous Idea: Evolution and the Meanings of Life together seem to provide some of the clearest, most thoughtful, science-based approaches to the big problem of consciousness.  And more recently, Jeff Hawkins and Sandra Blakeslee's On Intelligence walks readers right through the process of how evolution "treats us" to the powers we have to date to discover models in life.  If by chance you've read these authors and see these connections too, feel free to check in!

We certainly don't go as far as these two (and many other great researchers) on the academic or scientific fronts,  but their work significantly informs ours, and applying their work to the problems we look at continues to produce interesting opportunies for our clients.  Darwin's Grove it is.


One of the things we do is design, instrument, and execute viral marketing programs, and we find in working with our clients that breaking down the nature of viral marketing into its primary components helps get everyone on the same page.

Two separate ideas underlie every viral marketing effort out there. Looking at them separately can help you prioritize your efforts and improve chances of success.

The first requirement of any successful viral campaign is recognizing the need to meet each individual user's purchase value threshold ( or sometimes also thought of as “willingness to pay”). Let's call this the direct user value. In the old days, this used to be called things like "winning customers one at a time" or even more vaguely "know your customer".  While it sounds blatantly obvious, we also see over and over how this is simply overlooked. While you might argue that when you have thousands of customers, the network effects will create fantastic customer value (more on this later), it’s too easy to forget that you need to provide real value, early on, to every single adopter.

Keeping the early focus on those first adopters should make you reconsider each feature of your offering. Is your "AllYogaMats.com" site really going to grow virally from having social networking functionality or would just making it easier to use with a better "Find the Right Mat" search interface make customers buy? Those first eBay adopters (was it Pez collectors?) were well served right out of the gate by the simple-to-understand auction model and easy-to-use site, even though it didn't have a big audience yet.

The second component of viral business models is what most people actually think of first: the network effect. While everyone is familiar with this effect by now, it simply states the value of the network increases with the square of the number of users. The classic example is fax machines. It was not much use to be the first purchaser of a fax machine unless you had a thing for thermal paper rolls. Real viral models must provide some additional user value due to the size of the network- real network effect value.

The problem is we are often optimistic about both the numbers of users our campaigns can reach (at a reasonable cost) and the network effect's actual value a user gets. But it’s also hard to convince users that at a future date when they and lots of others join there will be some value for them. Distinguishing between direct user value and network effect value helps focus where efforts should be applied at each stage of business growth.

direct user value + network effect = viral growth!



Over the past year we have been laying the foundations for Darwin's Grove, and it’s time to start sharing what we are up to these days.  Our approach begins with some basic notions about a better way to do business.
After finishing the MIT Sloan Fellows Program (an intense business program in Cambridge, MA), I realized more than ever that there is a big gap between what many boldly claim and what is really true.  The assumptions that have shaped entire organizations and industries aren't always built on such solid ground.  Sooner or later these assumptions reveal themselves, often when it is too late.  In one sense, Darwin's Grove is a bad-assumption hunter, and we will be sharing our findings as we go.

At Darwin’s Grove we have three aims in mind....

Openness- pushing the envelope organizationally.  Back in 2005 I met Ricardo Semler, a Brazilian entrepreneur who inherited a slow-growth family business and over 20 years made it open and transparent, as well as a truly stable growing enterprise.  At his company, Semco, meetings are optional, salaries are often self-set, and employees decide where investments are made. (His books Maverick! and The Seven-Day Weekend: Changing the Way Work Works describe the democratic workplace he helped develop.)  Darwin's Grove believes openness can strengthen our workplace and help our team make better decisions, and therefore better serve our customers.

Empathy- a little more could help out right now.  We think it matters how deeply you listen to your customers.  I have a practice these days of polling higher-level managers and executives, asking "What percent of your time is spent thinking specifically about your business customers and their needs?" Ask yourselves.  The average answer I hear is about five percent. Is that enough empathy?  Given that it is the most likely source for value-creating ideas, I think we need much more.  At Darwin’s Grove, our efforts are centered on the people we serve.

And finally, curiosity.  Google's CEO Eric Schmidt recently described efforts to make Google "a systematic innovator at scale;" they do it by creating an environment encouraging curiosity.  Curiosity allowed to run free leads to interesting things. Our talented team prefers working on challenging problems that require our curiosity to run a little looser.  

We live in a time where complexity is growing rapidly, and it will be those equipped with the best of these tools that will design the exciting new products and services of the future.  Darwin's Grove focuses on the intersection of behavioral sciences, analytics, and algorithms.  And our curiosity about this area indicates there is much to do here.  Stay tuned.