Tim Brown is the CEO of the design consultancy IDEO. This is a highly respected design consultancy that does design work for the biggest for-profit and non-profit organizations in the world. You have likely encountered the results of their work in your everyday life.

In 2009, Tim published a book called Change By Design: How Design Thinking Transforms Organizations and Inspires Innovation that provided some insight into how they go about their work. Central to that work is what Tim refers to as Design Thinking and that term has become associated with this book. I acquired Tim's book when I started reading Lean UX: Designing Great Products with Agile Teams (2016, 2nd Ed.) and they gave this book significant credit for their approach. Also, Design Thinking is offered as a way innovative organizations might structure their work with many leading companies adopting some of his suggestions.

One of the shortcomings of some business writing on design is that is it not done by leading designers and can only offer limited insight and vocabulary to talk about design. That is not an issue here. The book offers a perspective on design that is worth reading about. It limits design thinking to a unit called The Project rather than, say, a research program in a university that does not have such clear boundaries. It usually starts with a Design Brief that spells out what the constraints of the design problem are in such as way that it is not too detailed (thereby closing down design options) or too vague (providing too little direction). A good design brief kicks of a good design process. Design thinking involves three overlapping stages or spaces referred to as inspiration, ideation, and implementation. Design thinking recognizes a tradeoff between efficiency and innovation. You have to explore ideas that sometimes go nowhere to find ideas worth keeping. Design thinking acknowledges that the constraints of the design problem may be contradictory and that design thinking is needed to find the proper balance among the constraints. Design thinking involves the participant in the design process, not just the officially appointed designer.

These are some of the useful ideas about Design Thinking that I have culled from the book so far into my reading of it (1/3 of the way through this quick-to-read book). I've read enough, however, to recommend the book as one worth reading for anyone with an interest in design. The book also preceded Eric Reis' book The Lean Startup (2011) and provides context for understanding where some of the major ideas in that book came from.

You can follow Tim Brown's thinking on his Design Thinking blog and on various YouTube videos such as this Ted Talk.

There is no shortage of good news stories about startups and businesses that are "crushing it". This can lead to the mistaken belief that making it is easier than it is, that it is just a matter of will and determination. There are many examples of businesses that had lots of will and determination that nevertheless failed. While will and determination might be necessary for business success it is by no means sufficient.

You can't learn much by only studying positive examples, you also have to study the failure modes. You can certainly learn some things by studying how Elon Musk, Bill Gates or Warren Buffet achieved the success they have, but I would argue that you will probably learn more by talking with entrepreneurs who have experienced failure so that you can appreciate how many things can go wrong and how difficult it can be to be successful in business.

It is for this reason I would suggest that entrepreneurs need to cultivate a repository of failed business stories in addition to a repository of successful business stories. You probably don't have to go very far to find entrepreneurs who have started a business that ultimately failed. You might regard this exercise as a dismal undertaking but my experience is that it can be intellectually stimulating and enlightening to find out the many ways in which the world confounds our expectations. So instead of just seeking out mentors, perhaps we should also be seeking out anti-mentors, people whose business has gone down the tubes and who are willing to share their knowledge of what went wrong.

I'll leave you with a quote from the book Smarter, Faster, Better: The Secrets of Being Productive in Life and Business (2016) where author Charles Duhigg points out that those who are best at forecasting the future actively seek out both positive and negative examples:

Making good choices relies on forecasting the future. Accurate forecasting requires exposing ourselves to as many successes and disappointments as possible. We need to sit in crowded and empty theaters to know how movies will perform; we need to spend time around both babies and old people to accurately gauge life spans; we need to talk to thriving and failing colleagues to develop good business instincts.

That is hard, because success is easier to stare at. People tend to avoid asking friends who were just fired rude questions; we're hesitant to interrogate divorced colleagues about what precisely went wrong. But calibrating your base rate requires learning from both the accomplished and humbled. ~p. 196

I recently came into contact with the phrase Profit Resilience from reading Zach Loeks new book The Permaculture Market Garden: A VISUAL Guide to a Profitable Whole-systems FARM BUSINESS (2017). In the farming industry there is the problem that profits often don't come in steadily throughout the year and this can obviously be challenging for the farm business. To maintain income the farmer may have to take out loans to cover costs until the next production and sales period happens. Or, the farmer might create another line-of-business designed to generate income during these slow seasons so that a constant level of profits is achieved throughout the year. The latter option gets you closer to profit resilience.

Farmers can achieve more profit resilience if they don't rely upon one line-of-business to sustain them all through the year. Profit resilience is achieved by introducing specific amounts and specific types of diversity into their income stream. I say specific because too much or too little diversity can be detrimental. If we try to manage too many diverse types of business, then the complexity of managing it all can outweight the marginal benefit of adding a new line-of-business. One solution might be to make the businesses less diverse so that similar skills can be used to manage each line-of-business (e.g., CSA, specialty crop, Garlic seeds). Unfortunately, the lack of industry diversity means that income may all track the same seasonal pattern we are trying to avoid. To achieve greater profit resilience the diversity of the lines-of-business have to be greater (similar to portfolio investing).

Profit resilience depends upon having the proper amount and types of businesses in your enterprise portfolio. The proper amount is 3 lines-of-business and the proper types are lines-of business whose profits are not correlated in time. Why 3 lines-of-business? This is just to give you a feasible number of businesses to consider in your planning. Ultimately, the number should be chosen based upon looking at all the small enterprises out there that are demonstrating profit resilience and tallying the number of lines-of-business they are engaged in. Ideally we would also examine small enterprises that failed and examine the number of lines of business they were engaged in. If 3 lines-of-business was identified as ideal then this could be a normative suggestion. Why do the lines-of-business we select have to be uncorrelated? This is ultimately an empirical question but, in the case of farming, it is clear that there can be problems with seasonality of income and if you select all your lines-of-business without ensuring the profits from them are uncorrelated or anti-correlated then you may not achieve profit resilience. The Portfolio Theory of investing might also be used to justify the need for a higher level of diversity among the businesses you invest in. The portfolio theory is arguably about achieving profit resilience, and not just profits.

Is the goal of your business to be profitable or to have profit resilience? You can be profitable without having profit resilience. It happens in lots of businesses. The automotive shop I frequented went under in early February because they hit a low revenue part of their season and were carrying too many overhead expenses (they moved to a nicer shop with higher rents and more staff). Perhaps having more than 1 line-of-business would have helped but maintaining profit levels is made more difficult when you have higher expenses and encounter rough patches.

Everyone has to be concerned about profit resilience even if you are a senior on a fixed income. When you move from the daily grind into retirement it would be nice if you could achieve some profit resilience in the transition. Your income may be lower but if you also lower your expenses significantly you might be able to achieve some profit resilience during this transition. Likewise, if a business is going through a patch of low sales, then cutting out some expenses might help to achieve some profit resilience (difference between revenues and expenses remains relatively constant). You may also voluntarily decide to restructure your business to have lower income but also lower costs. If profit resilience is your goal rather than simply increasing gross revenue then this makes sense to do.

Profit resilience is a temporal concept in the sense that our time to rebound back to a previous level of profitability is measured by some time interval. If your income nosedives and you rebound back to your previous profit level a year later, is that still an example of profit resilience? Probably not. In the business world we tend to measure things in business quarters and that is probably as good an interval as any to use for measuring profit resilience, although monthly accounting is also a very popular accounting time frame.

Profit resilience can be achieved if we plant things today (e.g., apple trees) that might bear fruit in the future. We can't invest too heavily in the future, however, if we want to retain profit resilience because the future is not generating revenue today. When selecting another line-of-business to add to the enterprises we should have a significant bias towards adding lines-of-business that will generate profits in the near term. One useful example comes from Mark Shepard who purchased more nut tree seedlings that he needed and used the proceeds from selling the extra seedlings to cover his purchase cost and then some. Even though something is being done for the long term it is nice when you can obtain a yield from it in the short term as well.

The term Resilience is popular these days so when I heard the phrase Profit Resilience I thought it was worth spending some time reflecting on various aspects of that phrase might mean. The last observation that I want make about the term Resilience is that it has been contrasted with two other terms - Fragile and Antifragile - by Nicholas Taleb in his important book Antifragile: Things That Gain from Disorder (2012). Profit resilience might be contrasted with profit fragility and profit antifragility, the latter being another concept worth exploring in a future blog.

This is a notice to inform all users of the Dealflow Investment Network that we will be honoring our Irish Heritage and celebrating St. Patrick's Day on Friday, March 17th 2017.

Any entrepreneur investment proposals submitted overnight on the 16th or during the early morning of the 17th will be released, but after that, there will be no proposals released until the mid-morning of Saturday, March 18th 2017. Emails and phone calls will also not be responded to during this period.

We apologize for any inconvenience this may cause and we wish you all a very merry St. Paddy's Day.

In the lean startup literature there is alot of talk about validating the startup vision through Minimal Viable Products (MVP) and customer feedback. In today's blog I want to examine the phrase "startup vision" and why we use might choose to use this phrase.

I would argue that one good reason why we use the term "vision" is because a good startup vision can be visualized, as in represented graphically for others to inspect, share, and discuss. If it cannot be visualized, then another term should probably be used - the startup idea, the startup concept, the startup proposition, for example. This would make usage of the phrase "startup vision" more precise.

A large part of the human brain is dedicated to processing visual information. It is a highly evolved system for navigating and interacting with the world. It is arguably our most important sense. There is also lots of evidence that when we imagine things we engage many of the same brain areas that are used to process visual information. The hypothesis of shared brain areas helps to explain why we have the ability to vividly imagine and visualize things that don't exist in front of our eyes. It can also be used to explain why imagination is a powerful ability - it taps into our highly evolved visual thinking ability.

A virtuous loop can happen when we visualize the startup vision. Being able to see the vision expressed on paper or on screen can get our visual systems engaged in further problem solving and add more detail and specificity to the vision. Our ability to hold ideas in working memory is limited but our ability to visually process many items at a time is much greater. When our working memory cuts out our visual system can cut in and help us with longer chains of reasoning.

If I asked you to tell me what is involved in selling carrots to customers you might imagine someone planting a carrot seed, growing it, harvesting, washing it, and selling it to a customer. If we visualize the full cycle, however, we might stand a better chance of seeing that there are many additional steps involved that our conceptual appreciation omitted. Zach Loeks, in his new book The Permaculture Market Garden: A Visual Guilde to a Profitable Whole-systems Farm Business (2017), created this visualization of the carrot selling process:

Zach's book contains many such visualizations to help readers understand a host of ideas and processes. This particular visualization helps us to appreciate all the steps involved in the carrot sales process and allows us to think in detail about each step in the production process and, perhaps, how we might optimize or improve them. What I also like is that the visualization doesn't require excellent drawing skills to express the steps involved. This sketch looks like something many of us could create with some colored pencils.

Another aspect of Zach's book that is interesting is the use of color in all his visualizations. Often when we think of the startups vision we don't think of it as involving color, but why not? Bill Mollison, in his magnum opus, the Permaculture Designers Manual (1988), also had abundant visualizations but they were all grayscale line drawings. It is hard to make certain distinctions "pop" when you don't use color to code the differences you want to draw attention to. Color also contributes aesthetics to your vision which can also be important.

The purpose of this blog is to draw your attention to the odd use the use of the term "vision" in the phrase "startup vision". Often when discussing the "startup vision" words alone are used to describe what the startup wants to accomplish, however, that does not explain why the term "vision" is used ("startup intention" might be better). When the startup vision is strong, the founder can see what the future should look like as if it is already in front of them and can also visualize it with diagrams, figures and line drawings, ideally with some color included as well. Perhaps we should reserve the term startup vision for cases in which actual visualizations accompany the startups intent to shape the future.

A stopping rule is used to determine when one should stop searching for things like a spouse, parking spaces, investment deals, a new home, a new secretary, etc.... The "look then leap" stopping rule suggest that we should just look for awhile so that we increase the likelihood of encountering the optimal spouse, the optimal parking spot, the optimal investment deal, the optimal house, the optimal secretary, etc... The question is how long we should keep looking before deciding to leap?

A considerable amount of research has been done to find an optimal strategy for determining when we should stop looking. It turns out that we should stop looking at secretary applicants after we have interviewed 37% percent of them. After that we should jump at the next secretary that is better than the previous secretaries we interviewed. If we are looking for a marriage partner, then we should figure out how long we are prepared to look for that partner and once we have used up 37% of that time, we should consider proposing to the next marriage partner that we regard as better than the ones we have been with to date. The 37% rule applies to either the number of items to be searched or the amount of time we have to search.

If you use this optimal strategy then 37% of the time you will pick the optimal item you are looking for. There is no optimal stopping rule that gives you certainty that you will pick the optimal item. The best investment deal may have been in the 37% of deals you reviewed to date and didn't make an offer on or perhaps if you waited until you reviewed 60% of the deals you would have found the optimal deal. If you set your optimal stopping rule at some number other than 37%, however, your chance of finding the optimal item will be less than 37%. That is all that optimal means in this context.

This form of the optimal stopping rule makes alot of assumptions so whether it is applicable or not depends on your particular situation. For example, if you are allowed to go back and pick the best secretary of the 37% you have interviewed, or if the secretary is allowed to refuse your offer, then the math behind the stopping rule changes and we would have a different optimal strategy for that situation.

The "look then leap" stopping rule also assumes that we are ranking items relative to each other (ordinal scale) rather than relative to some absolute scale (cardinal ranking). If we have some absolute criteria we can use to evaluate candidates then we can pick a candidate if they exceed some threshold we have set for selecting them. Using a "threshold rule" to determine when to stop is another stopping rule stategy we can use.

A "threshold rule" allows us to potentially finish our search faster than using the "look then leap" strategy. Instead of looking for who you might "love" the most by comparing each to the last, you instead set some criteria that your potential marriage partner must meet and as soon as the person meets those criteria you propose.

Stopping rules are important to determining when we should walk away from an investment. Those who lost everything during the 1929 Wall Street crash did not stop in time. Gerald Loeb pulled out before the crash and credited his stopping rule for his success in doing so: "If an investment loses 10 percent of its initial value, sell it".

There is also a rule when climbing Mount Everest that if you are not on the top by 2 o'clock then you should turn around. It does not end well for those who ignore this rule.

In my next blog on The Lean Startup book, I'll be dealing with the chapter titled Pivot and we'll see that this is very much concerned with knowing when to stop in your present course and when to persevere.

Stopping rules can be informed by mathematics and probability theory but can also involve general rules of thumb that have proved useful in the past. This discussion of stopping rules was inspired by Algorithms to Live By: The Computer Science of Human Decisions (2016) which focused on the more formal approaches to stopping rules, and Simple Rules: How to Thrive in a Complex World (2015) which focused on the rules of thumb that are used to guide our stopping decisions.

I recently became aware of farmer Richard Perkins. He is an innovative farmer that runs Ridgedale Permaculture in Sweden, at a location that offers six months of winter and six frost-free months. He recently published a book called Making Small Farms Work that has received very positive reviews.

It is impressive how quickly Richard and his wife have converted a 25 acre farm into a productive, profitable and sustainable farm (took over the farm on Mar, 2014). He calls farming a "land-based business" and he is offering permaculturists a vision of how to productively and profitably occupy larger amounts of land ("small farm" acerages). Richard appears to be publishing videos more frequently on his YouTube channel where he shares his ideas.

His video introducing people to Ridgedale Permaculture is a good place to start to learn their systems:

I became aware of Richard when I checked out Permaculture News and came across the video below. Those with little interest in farming might nevertheless learn something that might apply to their situation as well.

In my last lean startup blog on measurement, I talked about using a Minimal Viable Product (MVP) to test hypothesis derived from leap of faith assumptions contained in the startup vision.

In the case of Joe's lemonade stand (see previous blog), the first leap of faith was that the customer would buy the lemonade. Customers purchased the lemonade but not in the amounts he was looking for (10 customers). Joe then tested the price people would pay for it starting off at a premium price of $1.50 a glass. He measured sales volume at that price and another price ($1.00 a cup) and concluded that it was better to sell at the lower price because the volume more than compensated for the lower price. Joe is making progress towards operating a successful lemonade stand.

In this blog I want to look at the process of hypothesis testing in more detail and see how it maps onto some of the terms we have been using.

Let H be a class of hypothesis and h be a specific hypothesis.

Let M be a class of measurement outcomes and m be a specific measurement outcome.

We can use Modus Ponens (latin for "the affirming mode") to draw conclusions about whether our hypothesis is true:

If H=h Then M=m
M=m
-------------
H=h

We can also use Modus Tollens (latin for "the denying mode") to draw conclusions about whether our hypothesis is true:

If H=h Then M=m 
Not M=m 
--------------
Not H=h

So far we are in the realm of formal logic and these two forms of inference are foundational in guiding automated forms of inference.

We can cross over into the realm of informal logic by using the P( ) operator around all our assertions, where P stands for "the probability of".

So Modus Ponens now looks like this:

P(If H=h Then M=m)
P(M=m)
------------------
P(H=h)

And Modus Tollens now looks like this:

P(If H=h Then M=m)
P(Not M=m)
------------------
P(Not H=h)

It is this form of Modus Ponens and Modus Tollens that we are dealing with when we test our startup assumptions. The application of scientific methods to startup hypothesis does not necessarily yield clear cut answers, but answers where one hypothesis might seem be better supported by the evidence than another hypothesis, without being able to completely rule out an alternative hypothesis.

In the case of the learning platform company Grockit (see previous blog), they were adding new peer-learning features to their learning platform and not seeing any effects on their metrics. They concluded that the learner only wanted peer-learning up to a point, then the learner wanted to engage in solo mode learning. A logical possibility was also that Grockit didn't zone in on the proper peer-learning approach yet. The alternative hypothesis is not completely ruled out by testing and measurement, but made sufficiently implausible that a pivot was deemed necessary.

We will be getting into the topic of pivoting in the next blog, but it is important to note here that deciding when to pivot or not is made difficult by the fact that the original and alternative hypothesis may each have merit making it difficult to decide what to do.

Recognizing that probabilities are involved can be helpful in deciding what decision making framework you want to use in your startup hypothesis testing. If P(H=h) is .6 perhaps that is enough certainty to go by in situations of irreducible uncertainty (you don't have the time or resources to achieve greater certainty).

You could examine formula-laden articles on sequential A/B testing and bayesian A/B testing to try to figure out when to stop collecting data and what to conclude (which I recommend reading), but I'm also interested in a more practical approach based on using informal logic to evaluate the probability of the premises P and the probability of the inference (i.e., P(if P Then C)) to arrive at a probability of the conclusion C of an argument.

P(If P Then C)
P(P)
---------------
P(C)

The evaluation of the premises and the inferences is based upon informal logic techniques appropriate to criticizing scientific arguments, combined with common sense, to assign probabilities to each premise. The evaluation of the premises and the inferences of the argument determines the evaluation you assign to the conclusion. Bayesian forms of informal logic may also involve assigning a prior probability to the conclusion so that the posterior probability of the conclusion can be evaluated.

P(If P Then C)
P(P)
P(C)
---------------
P(C)

Whether these probabilities are to be combined additively or multiplicatively to yield the posterior conclusion is worth thinking about, although multiplicative combination tends to used more often and to work better. Informal logic nowadays often involves creating a graphical representation of the argument. Below is how we might graphically express this Bayesian approach to evaluating arguments (where hypothesis testing is just one type of argument). The premises (e.g., the measurements and other assumptions) appear at the top with lines connecting them to the conclusion. The lines are your inferences (if P1 then C, if P2 then C). The prior probability of the conclusion C (based on previous knowledge) appears next to the premises as a separate contribution to the posterior conclusion probability C. The posterior probability of the conclusion at the bottom is what you get when you combine your prior probability of C and a likelihood estimate (the left side of the argument below).

The purpose of this blog was to dig a bit deeper into what startup hypothesis testing might involve from an formal and informal logic perspective. I am not a practicing logician and this is not a peer reviewed discussion so you may or may not find this a useful framework to use when approaching the problem of testing the leaps of faith that your startup vision implies.

Inspiration for this blog and the argument evaluation diagramming comes form my undergraduate mentor Wayne Grennan and his book Informal Logic (1997).

Ian Flemming in his excellent book Lean Logic (2016) has this to say about the relationship between informal and formal logic.

It sounds banal, but the syllogisms of formal logic are the building blocks of reasoning, which - in combination with a series of conditions, affirmed or denied in sequence and in parallel - can develop into a problem-solving capacity of great complexity, used as the logical structure on which artificial intelligence is based.

Informal logic is, of course, the junior partner in all this, since it depends on the reasoning of formal logic, and its mixing up of logic and content is exactly what you cannot do with formal logic. On the other hand, without content, logic has no purpose. Formal logic is the road, informal logic is the journey. ~ p. 165

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