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 Stopping Rules [Decision Making] Posted on March 9, 2017 @ 08:37:00 AM by Paul Meagher 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. Permalink

 Ridgedale Permaculture [Permaculture] Posted on March 6, 2017 @ 03:32:00 AM by Paul Meagher 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. Permalink

 Startup Hypothesis Testing [Bayesian Inference] Posted on March 2, 2017 @ 06:44:00 AM by Paul Meagher 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 Permalink

 Seed Investment [Agriculture] Posted on February 24, 2017 @ 06:39:00 AM by Paul Meagher This weekend I will be going to a Seedy Saturday event where I will invest in some veggie seeds to plant out this year. It is my plan to try to get an earlier start on the growing season this year by transplanting veggies into a cold frame I started building. I got to this stage before winter fully set in. The cold frame sits on the site of a previous failed attempt to build a cold frame using hay bales in an 8 foot by 4 foot layout. I decided to build a more traditional cold frame this time. I dumped alot of plum seeds here after I processed them to make 5 gallons of plum wine and 5 gallons of plum port (see the reddish dots in the soil). One option would be to see if I can get plum seedlings to start growing in the cold frame and, if any of them take, plant them out to the farm this summer and give some away. I'm quite impressed with the productivity I got from 1 plum tree (10 gallons of drinkable wine) and the natural health and vigor of the tree (left to grow on its own) so I am interested in planting out plum seeds that come from this plum mother tree. I also started a more formal experiment on the farm where I planted 30 plum tree seeds harvested late season from under the plum mother tree. I hilled two rows of soil, made a trench in the middle with my hand, planted the seeds roughly equidistant from each other, then put soil back over the seed. When you are growing trees from seeds in cold-temperate climates, your tree seed planting ideally takes place in the late fall so the seeds cold stratify properly. On the topic of planting seeds, Urban Market Gardener, Curtis Stone, has a new video on using the Jang Seeder to plant out a bed of radishes. That seeder looks pretty impressive as is Curtis' technique in seeding out a bed. Seed investors might want to look into a new type of grain seed called Kernza that could be coming to a town near you soon. Check out the Land Institute Vision for perennial agriculture. The Kernza seed is in the initial stages of commercialization. Maybe not what you were expecting under title of seed investment but the financial use of the term "seed" is a metaphor for the functions and roles of actual seeds. Permalink

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