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"We have already had one investor for $25K, and another who is very involved in the food business, who could be a funder on a much larger level. So we are very pleased, and offer our thanks."
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 BLOG >> April 2016

Recommending New Amenities for Neighborhoods [Systems Thinking
Posted on April 28, 2016 @ 09:06:00 AM by Paul Meagher

This morning I started reading a research paper by MIT researchers César A. Hidalgo and Lisa E. Castañer called Do we need another coffee house? The amenity space and the evolution of neighborhoods (2015, PDF Link). The concept of an Amenity Space is a potentially powerful concept as it can be used to recommend what amenities might be missing, or not, in a neighborhood. The concept leads to a potential discovery technique for entrepreneurs and investors to find new businesses that might be viable in a given neighborhood.

One of the authors, Cesar Hidalgo, recently wrote a book called Why Information Grows: The Evolution of Order, from Atoms to Economies (2015) that looked like it might be interesting.

I decided to read the article to get some exposure to Cesar's thinking and because I was exploring the Atlas for Economic Complexity and realized he was also a driving force behind that impressive visualization project.

In this article, the authors used Google Maps to gather data on the amenities available in various neighborhoods. They computed the correlations between the amenities (how strongly the presence of one amenity predicted another) and represented the strength of these correlations by the thickness of the links between the amenity nodes. You can read more about how the visualization was constructed at the bottom of the diagram.

The paper proposes an algorithm that generates recommendations for new amenities for a neighborhood. I recommend you read the paper if you want these details.

The concept of an amenity space might be useful not just for making recommendations for new neighborhood amenities but for explaining why certain destinations are good tourist destinations, why real estate prices are higher in certain neighborhoods, what cities might do to improve their neighborhoods, etc....

Another point the authors make in the paper is that neighborhoods often have a pattern of specialization (e.g., tourism amenities) which might suggest a particular type of amenity that would be a good fit. An example from my own experience is the relationship between world-class golf courses and the need for nearby airport facilities for private jets. One tourist amenity (world-class golf courses) drives the need for a complementary amenity (airport for private jets to land). High-end golfers apparently also like high-quality whiskey and a nearby single-malt whiskey distillery with lounge and lodgings benefited significantly from the arrival of the golf course to the neighborhood. The golf courses are driving a new evolution of amenities in this rural community. While the concept of amenity space was developed based upon business co-location data in larger cities, it might be useful for thinking about expected co-location patterns in rural areas as well.

It also appears that the recommender system might take the form of a lens model as linear regression techniques were used to make the recommendations.


Roadside Garbage Cleanup [Eco-Green
Posted on April 27, 2016 @ 11:10:00 AM by Paul Meagher

One way to express your love of nature is by going for a walk in it or a taking a picture of it. Another way is to pick up garbage that accumulates there. Usually, I'm walking or taking pictures but today I decided it was time to express my love in a different way, by picking up roadside garbage along a riverside route where I often walk and take pictures.

In the early springtime around here you can easily see the garbage in the woods beside the road. It is a good time to pick up garbage. In a few more weeks the roadside will start to get grown in with abundant emerging life.

On the radio the other day a fellow expressed his deep disappointment in the litter along the highway and how disrespectful that is to the earth and to our civic responsibilities. Although I respected his message, his hectoring tone was a turn off to me; as if we were a bunch of kids that didn't know how to clean up after ourselves. He felt righteous because he was part of a corporate group that was picking up roadside garbage and thought by expressing his disappointment in the citizenry the littering would slow down.

You can also pick up garbage for some fairly self-interested reasons. I walk this area frequently and don't like seeing the garbage so if I pick up the garbage I can solve that problem myself. I will probably enjoy my future walks more and feel I did something a bit more substantial than take pictures and go for walks to express my love of nature.

It was a cool and sunny morning. There were no mosquitoes to deal with and it was a good exercise replacement for the time I might have taken for a morning walk. I was not used to the amount of bending I had to do and my back told me it was time to stop (for now). It is fairly rewarding work as it doesn't take long to fill up a normal household garbage bag. I filled 6 of them in an hour and a half.

Picking up garbage tells you something about the world we live it that you might not really appreciate any other way. I encountered lots of Coke and Pepsi containers of various sorts, beer bottles and cans, beer cases, diapers, coffee cups, cigarette packages, plastic bags, chip bags, shoes, fast food wrappers, tires, ashphalt shingles, car parts, and more. Cleaning up roadside garbage is an example of an externality that companies like Pepsi, Coke, Pampers, beer companies, chip companies, and more don't account for in their expense column. We all know what type of garbage is most prevalent in our communities (if not, perhaps it would be good to do some accounting on it) but we generally don't see the companies who are in part responsible paying any money to address the issue. Do they have a duty to fund community cleanups if we encounter a large amount of their garbage in our communities? Of course, the person who decided to litter is responsible, but Coke and Pepsi should arguably include an assumed background rate of littering into their expense column and help take care of the roadside mess they are helping to create.

In truth, I don't really care that much if Coke or Pepsi decide to carry their share of the burden. Just a thought I had when I kept encountering the same types of garbage. What motivates me is keeping an area of manageable size clear of some garbage so that I can enjoy nature there more. I decided I wouldn't be a bystander waiting for the county or someone else to address the issue because it wasn't going to be addressed judging by the age of some of the garbage I picked up.

Picking up roadside garbage can be addictive if you decide to actively express your love for a piece of nature in that manner. It can be an investment into your own future enjoyment and the enjoyment of others.

You might also encounter interesting wildlife in a roadside garbage pickup such as this school of young trout I encountered today. They are living in an offshoot from the river next to the road where the water is calm and they have some protection in the submerged leaves, grasses, and stream bank.


April 2016 Book Order [Books
Posted on April 26, 2016 @ 06:55:00 AM by Paul Meagher

Today I will continue my 1 month old tradition of reporting the books that I purchase each month to add to my home library. I usually purchase around 4 books a month. Because I have purchased them, they would appear to have my endorsement, however, I haven't fully read any of them. Without further ado, here is my April 2016 book order.

1. Antifragile: Things That Gain from Disorder (2012) by Nassim Nicholas Taleb.

I borrowed this book from the local library and decided I would need a copy to finish reading it and as a reference for ideas related to Antifragility. There are many valuable ideas for startups and investor in this book which I hope to blog about at some future date.

2. The Lucky Years: How to Thrive in the Brave New World of Health (2016) by David B. Agus.

I picked this up because of the testimonials from Howard Stern, Walter Issacson, Arianna Huffington, Al Gore, Larry Ellison, Ashton Kutcher, Michael Dell, etc.., and because it looked like a good resource to learn about some of the health advances that are happening. The first few pages on parabiosis sucked me in as well. It now makes me think about vampirism as science fiction rather than fantasy.

3. Surfing Uncertainty: Prediction, Action, and the Embodied Mind (2015) by Andy Clark.

I purchased this book in part because I really liked Philip Tetlock's Superforcasters book and wanted to read more good discussion on uncertainty and prediction. I also purchased this book because I have read some of Andy Clark's previous books. He is an eloquent, clear, and leading thinker on issues at the frontiers of cognitive science and the philosophy of mind. Here is a sample paragraph I read this morning:

Brains like ours, this picture suggests, are predictive engines, constantly trying to guess at the structure and shape of the incoming sensory array. Such brains are incessantly pro-active, restlessly seeking to generate the sensory data for themselves using the incoming signal (in a surprising inversion of much traditional wisdom) mostly as a means of checking and correcting their best top-down guessing. Crucially, however, the shape and flow of all that inner guessing is flexibly modulated by changing estimations of the relative uncertainty of (hence our confidence in) different aspects of the incoming signal. The upshot is a dynamic, self-organizing system in which the inner (and outer) flow of information is constantly reconfigured according to the demands of the task and the changing details of the internal (interoceptively sensed) and external context. (~ p. 3)

The book was brought to my attention by a blog that I recently encountered and quite liked called Judgment and Decision Making: The State of the Art. We are reading some similar research so I took this as a recommendation to read the book.

4. The Oh She Glows Cookbook: Over 100 Vegan Recipes to Glow from the Inside Out (2014) by Angela Liddon.

I purchased this book as a mother's day gift for my wife. My wife is a vegan and likes recipe books so I figured this would be a good match. It is a top rated vegan cookbook on Amazon. The food photos look delicious. Makes me wonder why there are not more vegan restaurants. The fact that vegans cook under the constraint of not including certain food groups does not mean that they don't have alot of tastes and textures still at their disposal to produce delicious meals. It is a different palette of flavors, textures and colors to work from. The Thug Kitchen Cookbook is another popular vegan recipe book with lots of profanity to accompany the recipes.


Applying the Lens Model to Investment Pitching [Lens Model
Posted on April 21, 2016 @ 06:07:00 AM by Paul Meagher

Cognitive scientist, Peter Juslin, used Brunswik's lens model (which I've discussed in previous blogs) to study and depict how emotion is communicated during music performance. In his research article Cue Utilization in Communication of Emotion in Music Performance: Relating Performance to Perception (PDF) he presented this useful variation of Brunswik's lens model diagram.

So you have a piece of music notation and you ask a bunch of musicians to convey certain emotions with that music and, correspondingly, you ask listeners to state what emotion is being conveyed by the performed music piece. What Juslin's research showed is that skilled music performers are pretty good at expressing emotion using various performance cues (tempo, loudness, spectrum, articulation, etc...) that signal the emotion they were asked to convey because music listeners were pretty good at reporting on what the intended emotion was based on the musician's performance cues.

Juslin's lens model of emotional communication in music is interesting for a couple reasons:

  1. It offers a useful example of how the lens model can be used to understand uncertainty associated with performance and not just uncertainty associated with perception and judgment. In the case of music, there can be uncertainty associated with the means you should use to achieve a desired emotional effect. The conveyance of emotion through branding might be conceptualized in a similar manner and whether people pick up on the emotion(s) your brand is trying to convey would have to be tested by whether potential consumers report some of the keywords associated with the emotions your brand is trying to convey. Brunsik's probabilistic functionalism included the idea that there is uncertainty involved the selection of means to achieve goals but there hasn't been nearly as much research on the performance side of the lens model as the perception/judgment side. This diagram offers up an example of how the lens model can be applied to performance situations.
  2. The diagram also shows how the performer and the listener each have their own lens - the performer trying to communicate emotion (output side) and the listener implicitly trying to glean the emotional intent from the performance (input side). In Juslin's study there was success in communicating emotional intent through music performance to listeners, but what happens if you are not so skilled or the listener is taking in the music in a loud bar under the influence of alcohol? There might be little emotional communication between the performer and the listener in such circumstances. Or their might be compensation by the skilled musician to the loud bar context that involves using other cues to achieve the emotional effects they are looking for. Brunswik called the flexible use of alternative cues/means in judgment/performance vicarious functioning and it was a very important idea in his probabilistic functionalism framework.

The lens model in the above form might offer up a framework to understand the relationship between an entrepreneur pitching an investment opportunity and an investor picking up on the cues that suggest that it is a good investment opportunity. There are a variety of semi-reliable indicators of success and character that an entrepreneur tries to communicate to an investor in an investment pitch which an investor may or may not pick up on. Even if the investor picks up on them, they have their own lens model of what constitutes a good investment or character that may not correspond in the first place with what an entrepreneur is trying to communicate in their pitch. The lens model as extended by Juslin provides a framework for both understanding and studying what goes into successful pitching as it emphasizes studying both the performance and the perceptional aspects of cue utilization in pitching investment opportunities. Some of the cues that go into successful face-to-face investment pitching (e.g., novelty, prizing, status, attention control, etc...) have been discussed anecdotally by Oren Klaff in his book Pitch Anything, but could perhaps be studied more rigorously using the lens model framework.


Real Estate Appraisal [Lens Model
Posted on April 19, 2016 @ 08:44:00 AM by Paul Meagher

In my last blog (The Lens of Common Sense) I discussed the Lens Model of judgment in more depth and the idea that one way to implement the lens model is by using multiple linear regression. In today's blog I want to follow up on that idea and show how useful a lens model can be for the purposes of real estate appraisal, and by implication, many other domains that involve judgment under uncertainty.

The data that I want to show you came from an old stats textbook (p. 727) and was provided by a real estate appraisal company who were asked to help an apartment building owner fight a property tax bill. The owner felt that the tax bill was too high and the appraisal company was brought in to formalize the owner's intuition and help argue the owner's case.

The appraisal company randomly selected 25 apartment buildings that were sold in 1990. The data was organized according to 5 indicators of worth along with the apartment building sales price:

The procedure the appraiser used to determine whether the owner was paying too much was to generate a linear model from this data using multiple linear regression. The linear model looked something like this:

Sales Price = X + (Weight1 * Num Apt. Units) + (Weight2 * Age of Structure) + (Weight3 * Lot Size) + (Weight4 * Num Parking Spaces) + (Weight5 * Gross Building Area)

The appraiser then applied the linear model to data from the owner's apartment to arrive at an estimate of it's probable sales price. Any significant discrepancy between the predicted sales price and property tax valuation could be argued to be unfair.

So what we have here is a situation where the owner believed the value of the apartment building was assessed too highly. Why did the owner believe this? Were they able to verbalize all the cues they were using to arrive at that judgment? The real estate appraiser arguably used a formal statistical tool, namely, multiple linear regression, to make the apartment owner's common sense model explicit and probably also improved upon it.

The purpose of today's blog is get a bit more down to earth with the lens model than my last blog and to perhaps convince you that the lens model is a useful "mind tool" for understanding and improving judgment under conditions of uncertainty. One way to interpret and apply the lens model is by using the statistical technique of multiple linear regression which allows you to estimate the weights that should be applied to each indicator in your lens model. There is alot of evidence that if you do this for something you have to make frequent probabilistic judgments about, your lens model will outperform you! Humans lack consistency of judgment but a formalized lens model always outputs the same numbers given the same inputs. Lack of consistency in judgment is one explanation for why a formalized lens model (for judgments under uncertainty) exhibits superior performance to a person's common sense lens model.


The Lens Of Common Sense [Lens Model
Posted on April 18, 2016 @ 08:58:00 AM by Paul Meagher

In 3 recent blogs (1, 2, 3) I've been discussing the Lens Model which was proposed by the psychologist Egon Brunswik (1903-1955) as a way to simultaneously understand how an organism relates to world and how we might go about researching and designing experiments to understand that relationship.

The Lens Model has been used and applied in various domains of psychology (perception, decision making, social judgment, etc...) since Brunswik first proposed it. The person most responsible for promoting the lens model after Brunswik's death in 1955 was professor Kenneth R. Hammond (1917 - 2015) so to explore the lens model in more detail I tracked down Kenneth's most highly cited book, Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice (1996), which includes a discussion of Brunswik's contributions, the lens model, and many other topics. In 1997 this book won the Outstanding Research Publication Award from the American Educational Research Association. It deserves the recognition and I highly recommend it to anyone with an interest in judgment and decision making. Hammond was almost 80 when he wrote the book and offers many insights into the philosophical and scientific basis of judgment and policy. He wrote 2 more books after this one.

In today's blog I want to focus more narrowly on Kenneth's discussion of the Lens Model and how information from cues is organized. I'll begin by displaying Ken's version of the Lens Model which appeared on page 168 of his book:

There are four things I want you to notice regarding Kenneth's version of the lens model:

  1. Kenneth prefers to use the term "indicators" rather than "cues". In this version of the lens model the organism's judgment about some intangible aspect of world is mediated by Multiple Fallible Indicators.
  2. The degree of validity between an indicator (e.g., obesity) and some intangible state of the world (e.g., diabetes) is depicted by the thickness of the line connecting them.
  3. The degree to which an indicator (e.g. obesity) is utilized in making a judgment about the world (e.g., person has diabetes) can also be depicted by thickness of the line connecting them. The ecological validity of an indicator may not be matched by a corresponding degree of utilization of that indicator in making a judgment (i.e., line thickness may change as it passes through the lens).
  4. There is an arc that runs from "Judgment" to the "Intangible State" that is being judged. This functional arc is a measure of the "Accuracy of Judgment". Brunswik labelled the functional arc with the word "Achievement" but Hammond had a particular theoretical axe to grind in this book (correspondence vs coherence theories of truth) and preferred the phrase "Accuracy of Judgment" to stress the importance of correspondence over rational coherence in accounting for "Achievement".

To more fully understand the lens model we need to understand how the information from multiple fallible indicators is combined to yield a judgment. Here is Ken explaining how this happens:

One feature of the lens model is its explicit representation of the cues used in the judgment process. Although such diagrams are useful, they do not show one of the most important aspects of the judgment process - the organizing principles, the cognitive mechanism by which the information from multiple fallible indicators is organized into a judgment. One such principle is simply "add the information". Thus, if the task is selecting a mate, and, on a scale from 1 to 10 cue No. 1 (wealth) is a 5, cue No. 2 (physique) is a 7, and cue No. 3 (chastity) is a 2, the organism simply adds these cue values and reaches a judgment of 14 (where the maximum score is 30). Another principles involves averaging the cue values; another principle requires weighting each cue according to its importance before averaging them. An interesting and highly important discovery, first introduced to judgment and decision making researchers by R.M. Dawes and B. Corrigan, is that organizing principles of this type will be extremely robust in irreducibly uncertain environments. That is, if (1) the environmental task or situation is not perfectly predictable (uncertain), (2) there are several fallible cues, and (3) the cues are redundant (even slightly), then (4) these organizing principles (it doesn't matter which one) will provide the subject with a close approximation to the correct inference about the intangible state of the environment, no matter which organizing principle may actually exist therein - that is, even if the organism organizes the information incorrectly relative to the task conditions!

I cannot overemphasize the importance of the robustness of what the professionals call the linear model. "Robustness" means that this method of organizing information into a judgment is powerful indeed. Any organism that possesses a robust cognitive organizing principle has a very valuable asset in the natural world, or in any information system involving multiple fallible indicators and irreducible uncertainty. Its value is twofold:

1. It allows one to be right for the wrong reason - that is, one can make correct inferences even if the principles used to organize the information is not the correct one (one may exhibit correspondence competence without correct knowledge of the environmental system and without coherence competence).

2. One does not have to learn what the correct principle is in order to make almost correct, useful inferences. This conclusion suggest that learning was not an important cognitive activity in the early days of Homo sapiens. Whether early Homo sapiens learned this robust organizing principle or were endowed with it - that is, their biological make up included it from the very beginning - I cannot say, of course, nor can anyone else. I can say, however, that any organism possessing such a robust principle would have - and I will insist, did have - an evolutionary advantage over any organism that relied on a more analytical - and thus less robust and more fragile - organizing principle.

Because we can arrive at accurate judgments about the world with a lens model that uses one of these simple organizing principles, Ken Hammond, and Egon Brunsik before him, argued that alot of our thinking is "quasirational". It occupies a middle ground between pure intuition and a fully-coherent rational explanation. Hammond argues that this quasirational thinking is what people are referring to when they use the term "common sense". Where many have argued that entrepreneurship and investing are matters of either intuition (system 1) or sophisticated rational models (system 2), Hammond and Brunswik are arguing that many entrepreneurial and investment judgments, because they operate in an environment of extreme uncertainty, occupy a quasirational middle ground which a lens model attempts to capture. Common sense is used to organize information from multiple fallible indicators into a judgment that is often accurate.

A final comment to make on the lens model is to point out that it was developed during an historical period of time when multiple correlation and multiple regression statistical techniques were being pioneered and introduced into academic research. Gerd Gigerenzer has argued (link to PDF article) that statistical tools often get turned into psychological theories (i.e., tools to theories heuristic) so that one might view the lens model as the type of psychological model you get when you generalize the importance of multiple correlation and multiple linear regression techniques and ideas. Multiple correlation and multiple linear regression are often used to create and evaluate lens models. While the lens model can sometimes be usefully equated with multiple linear regression, part of Brunsik's inspiration for the lens model was how our senses combine information from multiple fallible cues to arrive at accurate perceptual judgments. Further development of the lens model might take inspiration from nature (i.e., how vision, hearing, touch, smell, and taste combine multiple fallible cues to yield accurate judgments) to find additional organizing principles.


Prototyping A Tree Guard [Design
Posted on April 14, 2016 @ 05:00:00 PM by Paul Meagher

One itch that I wanted to scratch involves protecting young apple trees and vines on my farm from damage caused by string trimmers. I was also looking for a way to speed up the job of trimming away the grass and weeds that grow around their base, ideally avoiding alot of hand weeding. I have used white plastic collars to protect my apple trees from rodents but I don't trust them to protect against string trimmers as the plastic is too thin, too tight to the tree, and has exposed areas between the coils of plastic. I researched the issue online and a fellow in a forum mentioned that he used Big O drainage pipe to protect his trees from string trimmers. I took this idea and ran.

I purchased 150 feet of 4 inch Big O drainage pipe to use for making my tree guards. I figure if I don't end up using it for my tree guards I can find a use for it it's intended purpose as drainage pipe. I also had a large coil of heavy gauge wire that was given to me that I wanted to use to make some pegs to hold the tree collars in place. This is what the parts of my tree guard design looks like so far.

When I snipped my wire into pieces using bolt cutters, I used an anvil to flatten and shape the wire into L-shaped pegs.

Finally, this is what my tree guard prototype looks like on an apple tree. Note that I made a slit in the Big O pipe so I could wrap it around the tree.

From this exercise, I learned that under certain conditions I need longer pegs to get a better bite into the ground. I'll experiment with creating longer pegs and how they perform under different ground conditions.

Unfortunately, I didn't have any string trimmers that I wanted to start as I've had them in storage for 4 months and didn't have fresh mixed gas readily available to use. That will be the next test of my prototype.

The motivation for prototyping a tree guard came to me when I started to think this might be a good idea and realized that my progress would be stalled unless I could build a prototype to begin testing it. The prototype provided me with some useful feedback about peg sizing, how easy it is to make the plastic collars (handsaw is fast but not precise without a jig) and how easy it is to make the L-shaped pegs (the bottleneck in the process). I still have some prototype testing to do in terms of seeing how the collar handles contact with my Stilhl string trimmer. If that works out, maybe I'll try to figure out a process for making L-shared pegs faster from a heavy gauge coil of wire (weights 250-300 lbs).

My brother suggested that I put something inside the Big O pipe to eliminate the need for hand weeding (e.g., gravel? geotextile?) or to protect the tree or vine from pests such as slugs (diatomaceous earth?). Consider this an opensource farm hack and if you come up with any better ideas for protecting young trees and vines from string trimmers let me know.


Multiple Fallible Indicators [Decision Making
Posted on April 14, 2016 @ 01:35:00 PM by Paul Meagher

In my last 3 blogs (1, 2, 3) I've been discussing the Lens Model which was proposed by the psychologist Egon Brunswick (1903-1955) as a way to simultaneously understand how a person relates to world and how we might go about researching and designing experiments to understand that relationship. Today I want to add a few more details.

If you do a google image search using the term "lens model" you will see lots of variations of Egon's original lens model. Here is a variation from Kenneth R. Hammond's book Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice (1996).

Kenneth Hammond was very influential in promoting Egon's ideas and also expanded upon his ideas in several books. For example, instead of using the term "cues", Kenneth prefers to use the term "indicators". In the lens model above the indicators could be economic indicators such as jobless rate, GDP growth, business sentiment, etc.... and we might be trying to figure out if the economy will grow in the next quarter or not.

One aspect of the lens model that I have not discussed so far is the arc at the top of the diagram labelled "Accuracy". Egon preferred the term "Achievement". The arc is sometimes referred to as the "functional arc". The idea is In my version of the diagram, I might use the term "Adaptation" because the utilization of indicators to make judgements is in the service of adapting to the environment. We do that if our judgements are "accurate" or if the result leads to an "achievement" of some sort. When we speak of judgements being accurate or not, Kenneth argues that Brunswick was putting forth a correspondence theory of truth in contrast to a coherence theory of truth. Most theories of decision making look at how well decisions cohere with some logical or normative ideal and in so doing portray reasoning as fallacious, biased, and error prone and we are left to wonder how we get along in the world. Egon didn't see coherence as being necessary to achieving success in the world and put forth the lens model as a way to explain how our cognitive system can adapts to the world. Note that most popular books on human reasoning dwell on errors in reasoning (using a coherence framework) and as such don't really tell us much about how we get along in the world. Egon offers a different worldview, which he called Probabilistic Functionalism, that is more focused on explaining how we achieve perceptual and cognitive competence in light of the multiple fallible indicators that we must rely upon to make judgements.


Applying the Lens Model [Lens Model
Posted on April 7, 2016 @ 07:19:00 AM by Paul Meagher

I decided it might be useful to apply the lens model (see my lens model introduction and my lens model in action blogs for background) to one aspect of growing grape vines .

I just finished planting some grape cuttings in my pit greenhouse and these are the 1 yr old grape vine cuttings I have planted out so far:

As I was preparing the vine cuttings to be planted in my greenhouse, I began thinking about how to apply the lens model to the problem and came up with the following lens model:

I created the diagram using the free draw.io web application which is a tool I highly recommend for creating diagrams. In my overall scheme for growing 1 yr old grape vines this year, creating the cuttings is one critical part where I have a choice to do it in several different ways so at to achieve a maximum number of viable 1 year old grape vines. The policy I have chosen may lead to the desired goal, however, it is possible that creating longer canes and leaving more buds on the vine would lead to a greater number of viable 1 year old grape vines. I have had some success in the past with my minimalist approach so went with this approach again but decided to formalize the rules a bit more this year. I do not use a ruler when measuring sizes so when I say 8 inches I really mean my subjective perception of the size of the cutting is around 8 inches. This size limitation means I can comfortably fit my cuttings in a common cat litter container I have around. I like to soak the cuttings in water for awhile before planting out and this rectangular container keeps the cuttings oriented in the correct direction when I soak them.

This particular lens model only captures an aspect of what is required to maximize the production of 1 yr old grape vines. In the expanded diagram below I begin to hint at some of the other factors that are important, each one of which would have its own set of simple rules designed to yield a maximum number of 1 yr old grape vines.

It comes as no surprise to me that there might be multiple rules arranged hierarchically that are required to achieve some high level goal. That is usually how higher level goals are accomplished. Often what happens is that if you have been performing some goal oriented activity for awhile alot of these lower level steps become routinized and when we come up with our lens models they refer to higher level requirements for achieving our goal. If you have to teach someone else to grow 1 yr old grape vines, however, you have to begin to break things down like this and in the process you might question whether your approach is really the best one for achieving the goal you want to achieve.

I will ultimately find out if my approach will maximize the production of 1 yr old grape vines if I don't end up leaving the door closed on the greenhouse on a sunny day and baking some plants. Alot of things have to go right in order for me to determine if my approach to preparing cuttings is the best approach to achieving maximum production of 1 yr old grape vines

It is useful to vary the conditions of your experiment to the extent that you are able in order to determine what conditions maximize productivity. I had some soil that had leaf mulch on top that I covered with potting soil. I ran out of potting soil and decided to grow some cuttings in the leaf mulch without any potting soil on top. The leaf mulch might maintain cooler soil conditions than having the soil exposed so I'll be looking for differences in growth that might be attributable to the soil exposed +- factor. Unfortunately I only have one variety of grape vine cutting planted in the leaf mulch. It would be better to be testing growth of the same vine type with and without leaf mulch. The lens model can be paired with some theory about how to conduct representative and generalizable experiments to refine your lens model(s). That was not followed in this case :-)

It is not difficult to come up with an action-oriented lens model and diagram it out. You do, however, have to get clear about what your goal is and the means you will be selecting to try to achieve it. The act of representing goal + means relationships via lens diagrams might be useful. To establish the ecological validity of your preferred means you should consider varying your means to see if they yield an outcome as good or better than your preferred approach. Some of the cutting length/number of buds experimentation occurred in previous years so this year I wanted to push the envelope a bit more in terms of maximizing 1 yr old grape vine yield in my greenhouse.


Lens Model in Action [Lens Model
Posted on April 5, 2016 @ 08:43:00 AM by Paul Meagher

In my last blog I introduced you to the lens model. In today's blog I want to expand upon the lens model by introducing you to another important diagram that the founder of the lens model, Egon Brunswick, also used to explain the lens model.

The reason I find it necessary to expand the lens model is that I was trying to apply the lens model to the problem of preparing grape vine cuttings to achieve the maximum number of propagated vines. This happens to be a task I'm occupied with at the moment.

The problem I ran into was that the pruning policies I followed in preparing the cuttings are not best described as "cues" emitted from the environment, they are rather the "means" I have chosen to achieve a goal. The lens model seems to be more focused on accounting for "perception" than "action".

Further examination of the lens model, however, reveals that the lens model as I presented it yesterday is only the left hand side of a larger model that Egon Brunswick offered to explain the relationship between the organism and the environment. Here is the expanded lens model:

Source: Brunswik’s original lens model (PDF link).

Note the perfect bi-lateral symmetry of the model. Note also that "cues" stand between the organism and the environment on the perception end (input side) and that "means" stand between the organism and the environment on the "action" end (output side). This would seem to imply that everything I said yesterday about "cues" mediating between the world (or distal object) and the observer also applies to the "means" that mediate between some goal object and the observer.

In other words, to achieve some goal object we must select the means to get there. The lens model applies to situations where the means to achieving some goal in the future is not certain so we choose "means" that we think will get us there but which, in reality, might not have a strong relationship to achieving the goal. We have an internal model that we use to explain the relationship between the means selected and the goal object that may not in fact be the best means we could have chosen to achieve that goal object. It is a happy day when the means we have chosen have high ecological validities in achieving the goal state.

So I stand by my assertion that the lens model is a useful framework to use in understanding where simple rules might fit in the overall scheme of things. There can be simple rules for handling decision making related to the input side of things and there can be simple rules for selecting the means to achieve some goal object. We may appear to be in Plato's cave observing shadows on the wall and trying to figure out the objects that they represent, however, Brunswick's model offers the hope that we can get closer to the opening of the cave to behold the object in an ever clearer light. The distal or goal object does not live in a world of ideal forms, but rather is an object that we can focus on more or less clearly depending upon the cues and means we choose and how strongly correlated they are with the distal or goal object.

The lens model has something useful to offer investors who must make investment decisions based upon multiple unreliable cues, and for entrepreneurs seeking a goal state and needing to select from multiple means that are more or less correlated with the goal state. We are searching for best cues and the best means and these can often be formulated as simple rules.


The Lens Model [Lens Model
Posted on April 4, 2016 @ 09:00:00 AM by Paul Meagher

What is the lens model?

The lens model was developed by psychologist Egon Brunswick between 1930 and 1950. He did some research in perceptual psychology and, in particular, did some research on depth perception. A big problem in depth perception is that you have a 3 dimensional world and a 2 dimensional retina that the light from the world impinges upon. How are you able to reconstruct a three dimensional world from this limited two dimensional information?

It turns out that there are are a large number of cues for determining depth that we can glean from 2 dimensional imagery. There are cues such a parallax, stereopsis, occlusion, linear perspective, texture gradients and so on. There are even more cues if we incorporate observer and world motion into the mix.

Egon observed that each cue, under certain circumstances, can provide misleading information about depth (see Ames Room). He also suggested that the importance we assigned to a cue should depend how reliably the cue signals information about depth. The mental leap that Egon took was to say that what is true of perception is true more generally in psychology, namely, that there are often multiple cues that might indicate, for example, what a person's psychiatric diagnosis should be and that we should only put our faith in those cues that have high ecological validities (i.e., are reliably correlated with the criterion we are trying to determine).

Egon proposed the Lens Model as a foundational model that psychology could use for research design and model building. The basic idea is that the real state of the world (the distal stimulus or the criterion to be judged) on the left hand side emits multiple and sometimes redundant cues about the state of world (think depth perception cues). On the right hand side we have the observer who assimilates this cue information to arrive at a decision about the state of the world. The observer never sees the world directly, instead they view the world through a lens. That lens consists of multiple cues that we take to be a proxy for some state of the world (e.g., depth relations among objects).

When we rely upon a cue (e.g., arrival of geese) to inform us about some state of the world (e.g,. whether spring has arrived) we can assign that cue a weight. There are often multiple cues providing us with more or less reliable information about some state of the world and Egon believed that we intuitively assign weights to these various cues, sum the weighted cues, and then infer whether some state of the world is true or not depending on whether some decision threshold is met or not. Our depth perception system would appear to perform such calculations automatically but we can also perform such calculations in other areas in a more controlled way using the lens model.

The final aspect of this model that is worth noting is that on the left hand side we can do research to establish how reliably correlated a given cue (e.g., sleep length) is to some state of the world (e.g., patient has depression) to determine the ecological validity of the cue. On the right had side, we might also use the cue (e.g., sleep length) to arrive at a diagnosis of depression but we might assign it an incorrect weight. We might also be using a cue that is not reliably associated with the criterion (low ecological validity) and arriving at an incorrect assessments as a result. So we need to distinguish between the ecological validities of cues on the left hand side and cue utilization validities on the right hand size (i.e, whether our psychological model is capturing the right cues and assigning them the right weights).

The reason I decided to discuss the lens model is because the Simple Rules book I have been blogging about recently didn't offer up an overall framework for thinking about how Simple Rules relate to the world. In order to use Simple Rules more effectively I would argue that you would benefit from a correspondingly Simple Model of how they relate to the world, why they work, why they don't work, and how they can be improved. I believe the Lens Model provides one such a framework. Simple rules can be understood as weighted cues we use to arrive at particular decisions or actions.

Some interesting research has been done on simple linear models of decision making (which the lens model would be an example of) where you assign a weight to each cue, multiply the measured value of the cue by the weight, sum the terms, and compare the total to some threshold in order to make your decision. For example, the graduate school admission ratings that psychologist Reid Hastie used could be modelled with this equation (from Rational Choice in an Uncertain World, 2nd Ed. 2010, by Reid Hastie & Robin Dawes, p. 49):

Admissibility Rating = 0.012 (Verbal GRE Test Score) + 0.015 (Quantitative GRE Test Score) + 0.25 (Warmth of Recommendations) + 0.410 (College Quality) + Other Factors - 13.280.

Notice that some variables don't have much weight and don't affect the rating much so could be removed. The most heavily weighted cues are "warmth of recommendations" and the "college quality" of the applicants plus, possibly, "other factors" that have non-trivial weights. Using this simple model of graduate admission ratings, Reid Hastie could replace a more complicated screening process with a much simpler screening process and arrive at roughly the same rating. This is one way to arrive at simple rules.

The purpose of today's blog was to talk about the lens model so that I can refer to it in any future blogs I want to. The second reason why I discussed the lens model is because a deficiency of the simple rules book from my perspective is that it didn't offer a simple graphical framework we might use to formally or graphically understand why simple rules work, how the rules can be combined, how they can go wrong, and what can be done to improve them. I believe the lens model provides some general guidance on how to properly think about and use simple rules.

If simple rules work then simple linear models can also be argued to work. Simple linear models have the advantage over more complicated structural models that we can do mental arithmetic with them because they only involve simple additions and multiplications. We can also simplify the weighting scheme so we only use weights that are easy to mentally work with (e.g., -1,0, 1/4, 1/3, 1/2, 2/3, 3/4, 1). If we are bounded in our computational abilities, working memory, and so on then we must find techniques that are sufficiently simple that we stand a chance of using them in the real world. Perhaps the lens model, in its simplest interpretation, is a good starting point.

Here is a lens model handout that you might find useful for exploring the lens model and simple linear modelling further.


Simple Rules Video [Decision Making
Posted on April 1, 2016 @ 11:15:00 AM by Paul Meagher

I just finished reading the book Simple Rules (2015) and will be blogging more about the contents next week. In previous blogs I introduced simple rules and discussed some intellectual context for simple rules.

In today's blog I want to offer up a nice Steve Jobs quote from the book, present a video of one of the authors discussing the book, and finish off with a possible simple rule for startups courtesy of Jessica Livingston, partner at Y-Combinator.

First, a relevant Steve Jobs quote to consider:

You have to work hard to get your thinking clear to make it simple. But it is worth it in the end because once you get there you can move mountains. ~ Steve Jobs, Simple Rules, p. 225.

Here is first author, Donald Skull, discussing the contents of the book:

Co-author Kathleen Eisenhardt has also given talks on simple rules.

Finally, Jessica Livingston wrote an interesting essay that argues for a simple rule for startups:

Why Startups Need to Focus on Sales, Not Marketing.

The simple rule focus on sales, not marketing can be applied to starting a business and serves to improve the chance of a startup succeeding. The reason to focus on sales is not necessarily to make sales but to gather critical early feedback on your product or service that you can only get if you are engaging in sales.




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