My holiday reading consisted of the book Sapiens: A Brief History of Humankind (2014) by Yuval Noah Harari.

This book was on various best sellers lists for a long time when it was released and deservedly so as it offers many useful insights for understanding the progress of humans (Homo sapiens) from our hunter gatherer days to the present. Without an historical perspective we can take for granted how money, credit, and capitalism helped to solve major problems that temporarily held Sapiens back.

Before the invention of money, for example, it was difficult to trade with neighboring humans. In the absence of money we are required to barter with our neighbors to obtain goods that are difficult or impossible to get through our own labor. It can be difficult to know how many units of what you have is required to obtain a certain number of units of what another person might have. Perhaps you have nothing that the other person wants. Then what? While bartering is arguably a good way to build community, if we were limited to that method of exchange then our communities would be smaller, less complex, and less dynamic than communities that use money for exchange. There is a reason we don't barter much anymore and it isn't simply because we don't live in tight-knit communities. It is because there are limitations to bartering that money solves.

While there are technical mechanisms regulating money in an economy, the most important mechanism regulating money is mutual trust. According to Yuval:

Trust is the raw material from which all types of money are minted. When a wealthy farmer sold his possessions for a sack of cowry shells and travelled with them to another province, he trusted that upon reaching his destination other people would be willing to sell him rice, houses and fields in exchange for the shells. Money is accordingly a system of mutual trust, and not just any system of mutual trust: money is the most universal and most efficient system of mutual trust ever devised.
~ p. 180

Another financial invention that was important in shaping modern economies is credit. Prior to easy-to-obtain credit it was difficult for any government or business to get any project off the ground. Governments could raise taxes, raid, and plunder to raise capital but that was not as efficient as getting a loan to finance your plans. Yuval documents how the ascendancy of England and the Netherlands in defending themselves and expanding their territories was in large part due to the development of institutions capable of offering credit on good terms to the governments and businesses in those regions. Countries without easy access to reasonable credit found it hard to assemble armies and finance new business. Without access to credit it is difficult to change your station in life as obtaining credit depends more strictly upon being born into wealth.

It took awhile after money and credit were invented for capitalism to be invented. Some link it to around the time of Adam Smith and his formalization of the idea that economic growth can keep going indefinately if money earned as profit from production is reinvested into more production.

All this depends upon the rich using their profits to open new factories and hire new employees, rather than wasting them on non-productive activities. Smith therefore repeated like a mantra the maxim that 'When profits increase, the landlord or the weaver will employ more assistants' and not 'When profits increase, Scrooge will hoard his money in a chest and take it out only to count his coins.' A crucial part of the modern capitalist economy was the emergence of a new ethic, according to which profits ought to be reinvested in production. This brings about more profits, which are again reinvested in production, which brings more profits, et cetera ad infinitum. Investments that can be made in many ways: enlarging the factory, conducting scientific research, developing new products. Yet all these investments must somehow increase production and translate into larger profits. In the new capitalist creed, the first and most sacred commandment is: 'The profits of production must be reinvested into increasing production'.

That's why capitalism is called 'capitalism'. Capitalism distinguishes 'capital' from mere 'wealth'. Capital consists of money, goods and resources that are invested into production. Wealth, on the other had, is buried in the ground or wasted on unproductive activities. A pharaoh who pours resources into a non-productive pyramid is not a capitalist. A pirate who loots a Spanish treasure fleet and buries a chest full of glittering coin on the beach of some Caribbean island is not a capitalist. But a hard-working factory hand who reinvests part of his income in the stock market is.
~ p. 312

Money, credit and capitalism are often viewed as the root of all evil. What I learned from Yuval is to view these inventions in historical context and the evolutionary problems these inventions helped to solve. I don't aim to be a cheerleader for money, credit and capitalism but I do think that when we criticize one or more of these ideas, we may take for granted the socio-economic problems they helped to solve (i.e., exchange of goods, project financing, economic growth).

Part of my motivation for finishing this book now is because Yuval recently came out with another book Homo Deus: A Brief History of Tomorrow (2016). It also has positive reviews and I hope to start reading this one when it arrives in the mail next week.

If you asked me to invest $100,00 in your business in exchange for $25,000 paid out each year for the next 5 years, you might think that was a good offer. I would be making an extra $25,000 at the end of 5 years in return for the capital I invested today.

There is a potential problem with this offer that has to do with the fact that you have to compare that $25,000 earned after 5 years with what I might make if I invested my $100,000 in some other investment vehicle that returned a certain percentage of my money as profit each year. We can think of this "certain percentage" as an interest rate on my money.

If my initial investment of $100,000 has an interest rate of 8% applied each year over the next 5 years, and the interest-derived total is greater than the overall amount that would be returned by investing in your business, then it is not rational for me invest in your business. I would be losing money relative to the interest rate I'm looking for.

The comparison of a set of investment related cashflows to a discounted interest rate is captured simply and elegantly in the Net Present Value (NPV) formula that looks like this:

You might find it easier to appreciate how this formula works if you see how the NPV calculation can be implemented in the popular PHP web scripting language. In the PHP script below, after we implement the npv function we call the npv function with an interest rate and cash flow series that evaluates the investment offer described above.

<?php
/**
* Calculates total present value of a time series of cash flows.
*/
function npv($rate, $cashflows) {
  $total = 0.0;
  foreach ($cashflows AS $time=>$cashflow) 
    $total += $cashflow / pow(1 + $rate, $time);
  return sprintf("%01.2f", $total);
}

// interest rate 
$rate      = 0.08; 
// cashflow series 
$cashflows = array(-100000, 25000, 25000, 25000, 25000, 25000);
// calculate npv
$npv       = npv($rate, $cashflows);
// print npv result
echo "NPV is $ $npv<br />";
?>

The output of this script is: NPV is $ -182.25. This tells us that we would be losing $182.25 if we made this investment, relative to an interest rate of 8 percent. If we used a more achievable "interest rate" of 4 percent instead, then the NPV is $ 11295.56 which is a positive return that we might be inclined to invest in (would depend on what the NPV of our other potential investments are). The NPV of our investment is a more realistic assessment of what the investment return would be after 5 years that the undiscounted amount of $25,000.

Personally I find some of the terminology used to describe NPV a bit confusing. It may be easier to understand what NPV is all about if you play around with different interest rates and cashflow series using excel or a program like the one above. Note that in the program above the first value in your cashflow series is the proposed investment amount and is always negative. After that are the cashflows you expect for each year of the investment. You can learn more about the Net Present Value formula in How to Evaluate a Business Investment Proposal (where I got the formula displayed above). I encourage you to plug the interest rate and cash flow series in their example into the script above to convince yourself that it can be used to calculate NPV.

If someone asks you what the NPV of your investment offer is, you could say that it is X assuming an interest rate/discount rate of Y percent. That would tell an investor more about your investment offer than simply giving them the non-discounted amount that they might make after Z years. It tells them more because it is compared against an interest rate that might otherwise be earned on an invested amount.

In Christopher Alexander's seminal book, A Pattern Language (1977), he and his co-authors identified 253 significant design patterns that could be used for "Towns, Buildings, Construction" (this phrase is the subtitle of the book). I discussed this book previously in blog titled Learning a Pattern Language.

One design pattern that intrigued me at the time was pattern 163, the "Outdoor Room", because I though it was a pattern that would help me to enjoy working in my vineyard more. Take a break from weeding, pruning, or trellising by walking to a nearby outdoor room that would have some of the comforts of a room in your home. I never did get to build that "Outdoor Room" but it is on my todo list.

Today I realized that I actually built an outdoor room without even knowing it. When I blogged about my Garage Mini-Winery project I mentioned that one of the major issues I ran into was that I used my garage to store alot of junk and that the mini-winery displaced a significant area of the garage that was devoted to storing junk (e.g., barbecue, tires, car ramps, bikes, etc...). I am still dealing with the fallout of not having enough room in my garage for other uses so last weekend I worked on solving that problem by installing a roof underneath my deck. My wife suggested this as an alternative to building a lean to off my garage. So I purchased some 1 in x 6 in x 14 foot long pressure-treated wood, 7 Tuftex polycarbonate panels (10 foot long by 26 inches wide), 500 metal siding screws, and 4 in, 3 in and 2 inch deck screws for the project. I used a table saw to cut the pressure-treated wood evenly into two 1 in x 3 in x 14 foot strips and used these to install the strapping that the panels would attach to. I used 24 inch centers between the strapping and started with 4 layers of strapping for the outermost row of strapping, then 3 layers for the next row of strapping (24 inches apart), and so on. This pattern of strapping created the gradient for water to flow away from the house. Once the strapping was in place, I got my wife and son to hold the panel in place while I screwed in the panel. After the first panel, I installed the next panel by overlapping one ridge of the existing panel with the new panel. I then installed the screws along this overlap at each row of strapping, then I went back to the part of the panel closest the house and systematically installed 3 other screws per row of strapping until I hit the row of strapping furthest away from the roof. My wife pulled gently on the panels so they would install without any buckling. Afterwards I ran a chalk line down the middle of each row of strapping and I put screws at every ridge point of the panel (water runs in the valleys) along the length of the strapping. This is what the final product looked like.

I have since moved stuff into the area under the deck. Today I moved some cheap plastic Adirondack chairs under the deck and with that addition I realized that it was a nice place to sit and watch the wet snow coming down in my suburban back yard.

This for me is a bit of a lesson in how a pattern language works. Find some patterns in the book that appeal to you and you might be surprised how a particular pattern will manifest itself if you are aware of and receptive to the pattern. My "outdoor room" looks nothing like I initially imagined it would but it is sheltered from the elements and I can sit in a relaxing chair while I'm there. I'm not planning on spending large amounts of time in this outdoor room, but it is now an option and it gives me some joy to be able to do so. Note that the "joy" a pattern produces is one of the defining attributes of the design patterns that Christoper Alexander attempted to identify in his book. Any future changes to this space might be done with a view to it being an outdoor room and not simply a storage space. A small propane heater and a glass of wine might be a nice addition the next time I relax in my outdoor room.

I forget where I heard or read it, but someone off-handedly mentioned a "degree in reasoning" and the idea has since resonated with me. As far as I can tell there is only one university, the University of Kent, that offers a post-graduate degree in reasoning. It does not appear to be offered as an undergraduate degree anywhere.

What interests me about the "degree in reasoning" idea is whether it would be a practical degree to have, who might employ such a person, how adaptable and relevant it might be, what the curriculum might consist of, and so on. I have studied reasoning in its various forms for many years starting in my undergraduate years where I took courses in logic, marked argument evaluation assignments in basic logic, studied cognitive science, probability theory, and learned how to program. More recently I am studying Probabilistic Graphical Models, Bayesian Networks, and Causal Inference as newer approaches to reasoning. The problem is that all this learning is happening across different disciplines and departments with no overarching focus. Perhaps if there was a Degree in Reasoning I would have been able to claim a focus and could be judged according to how knowledgeable I was in that discipline. How cool would it be to be take a masters degree in reasoning so you could call yourself a Master of Reasoning :-)

Perhaps I should have taken some tests of reasoning ability in my undergraduate years and then continued to take them through the years to determine if I have become better at reasoning. One would expect that a degree in reasoning would yield improvements in tests of reasoning. One approach to figuring out what a degree in reasoning should consist of would be to consider what types of tests we might use to measure it and design a curriculum that would foster improvement in those test scores.

Reasoning can be viewed as a type of meta-cognitive skill that improves your rate of learning new skills and helps you to correctly figure out consequences of actions before engaging in those actions. Someone who is good at reasoning, for example, should be able to pick up new programming languages more quickly than someone who is not as good. They might also be able to design a program in more detail before engaging in coding than someone with less reasoning skill. They might avoid making as many mistakes because they figure things out in their heads before deciding to act. A good reasoner might be aware of the many biases and fallacies human reasoning is potentially subject to and use that awareness to avoid some of its short-comings. Finally, I don't think reasoning should be confined to the type of thinking that takes place in an ivory tower or in front of computer. There is alot of excellent reasoning that takes place when building a home, fixing a car, planting a garden, or making a sale and we need to make sure these practical forms of reasoning are appreciated and incorporated into the curriculum (e.g., diagnose an automotive problem, build some stairs, etc..).

These are just a few preliminary thoughts on what a degree in reasoning might consist of. As to why we need it, I would argue that because it is so difficult to predict the future and what skills it might require, we might be better off not studying a specific discipline but rather studying how to reason in and across disciplines, be it law, computer science, medicine, politics, business, or the trades.

It is possible that probability theory provides a unifying framework for a large part of what we call "reasoning" so graduates might be expected to have a good grasp of probability theory and be able to solve a variety of probability problems better than those without a reasoning degree. The curriculum also needs to be driven by industry who might spell out what they expect from a degree in reasoning should they decide to hire someone with a degree in reasoning. It might, for example, be the ability to adapt to new job descriptions over time, to learn new things quickly, to make well-argued decisions, etc...

The popularity of books on reasoning such as Daniel Kahneman's Thinking, Fast and Slow (2011) speaks to the desire of many people to improve their reasoning ability. I think, however, that there needs to be more than just some books dedicated to reasoning, some institution needs to step forward and put together a degree in reasoning and see if that is the type of degree needed to address the uncertainty that undergraduates face when trying to decide what to study, and that employers face when trying to decide what type of employee they are looking for. It might also be the case that a degree in reasoning would be excellent preparation for a career as an entrepreneur or investor.

An entrepreneur must wear many hats to survive and thrive. The job description of an entrepreneur includes many different roles and interactions which can be appreciated by examining a diagram of a real estate developer's roles and interactions.

Source: Fig 1-2 from Real Estate Development (2015, 5th Edition) by M. Miles, L. Netherton & A. Schmitz.

The difficulty of successful entrepreneurship might be explained by the challenge of successfully navigating all the roles and interactions that are required. A decision needs to be made as to whether the founder(s) alone can manage all the required roles and interactions or whether they need to put in place a team to manage the complexity. The time required to negotiate all these roles and interactions sometimes means that projects don't unfold as quickly as you might want, but it might be as fast as should be expected given all the hats that need to be juggled.

A diagram like this helps to remind us that being an entrepreneur isn't a simple job description, it stands for a host of challenging roles and interactions.

This weekend I was exploring the competitions section of the Kaggle.com website. One competition that interested me was the competition to predict house prices based on a dataset from Ames, Iowa that includes 79 explanatory variables. The dataset page gives a brief explanation of these 79 explanatory variables but you have to register to get some more detail on these variables, which consists mostly of the values that each variable can take on. Anyone involved in residential real estate might find the list of potential explanatory variables quite interesting to consider as factors that might affect the price of a house (i.e., housing price indicators).

One aspect of the dataset that I find interesting is that all the variables can be considered "intrinsic" to the house that is being predicted. There aren't any socioeconomic variables like net migration, economic growth or wage levels included. Perhaps this makes sense when we consider that the dataset is only for Ames, Iowa so the same socioeconomic variables arguably apply to all the houses in that area. The validation of the predictive models is limited to predicting Ames house prices using a subset of test data that is "held back" from the training data. A predictive model is good to the extent that it can capture most of the variance in the test data based on the model learned from the training data. There is no further validation that says the same predictive model also works in Houston, Orlando or Toronto.

In a previous blog called Residential Housing Valuation I proposed adding a causal model to our real estate valuation model so that we might incorporate some of these socioeconomic factors that determine housing prices. If your interest in residential valuation is confined to one neighborhood then including these socioeconomic factors might not be that useful as all houses are subject to the same factors; however, if you want to predict residential house prices across diverse neighborhoods then including socioeconomic factors is probably required.

I am impressed with the vast array of mathematical techniques that competitors applied to the problem of predicting house prices. These techniques appear to be based on the assumption that house prices are determined by features intrinsic to the house; however, when I step back I have to wonder whether the predictive models that work for Ames, Iowa is going to apply to an area that is experiencing economic growth, in-migration, and higher wage levels. Will we need to incorporate socioeconomic factors into a more general model for predicting house prices?

We also select realtors based on the perception that they will get as a better price for our property than another realtor and perhaps that is another factor that determines house prices. Does it? If so, how much of an effect might it have?

This blog is an attempt to make sense of what a predictive model of house prices might include. I think the generality of the models in this competition is questionable because they don't include socioeconomic factors that are likely to moderate housing prices from one neighborhood to another. I can also think of non-intrinsic attributes like the realtor used, the listing method used, and the hotness of the market that might have a bearing on predicting house prices at closing. Perhaps they don't matter, but my future research will be looking into seeing if they do matter and to what extent.

Update: Today I came across a relevant study on the accuracy of Zillow's housing price predictions (which they call a zestimate). Zillow does not profess to be that accurate (within 20% of the sale price) and in fact is even less accurate in certain markets like New York. A local realtor claims this is because the market is "hot" but what constellation of features leads to a real estate market being considered "hot"? Interest rates and government policies can "cool" these markets so should they also be included in the definition of a "hot" real estate market?

Bill Mollison, the father of Permaculture, died on Sept 24th, 2016 and his dying wish was that people plant a tree in his memory. I had to wait until until a few weeks ago to collect some black walnut seeds and last weekend I had the chance to plant out a black walnut grove in a forest clearing on our farm property.

Here is me planting the first black walnut seed in Bill's memory.

After I finished planting around 25 black walnut seeds, I documented some of it.

I am experimenting with a tree guard design that I fashioned from 4 inch Big-O drain pipe and some metal barbecue skewers that I used for anchors. The idea is that the seedling will burst through the center of the tree guard because this is the path of least resistance (the soil here is crumbled with my hands when I'm putting the soil cores back into the hole). The tree guard will help with identifying where I planted the trees. It also helps with maintenance. I can run a string trimmer against it (tested) to keep the area around it cleared.

I have had success for the last few years planting black walnut seeds in the fall in my garden and having them emerge in early summer. When I moved them to their final spot there was some transplant shock because the root system gets damaged and can't deliver enough water/nutrients to the leaves until it gets re-established. During this time it might look like the seedling is on the way out because leaves are dropping off and turning black but eventually all the seedlings recovered and did well a couple months afterwards. A year previous all my black walnut seedlings died because it stayed dry for a few weeks after I transplanted them. They couldn't overcome the transplant shock.

I will transplant shock by directly planting the seeds where I want them, however, planting in the woods has some different challenges - like knowing where you planted your seeds and a system for maintaining them over time. That is why I added the tree guards. I also planted the trees more densely than I will ultimately want them in the expectation that the woods environment will be less forgiving with rabbits and deer wanting a black walnut snack.

I came into the woods about a month and a half earlier with my tractor and a bush hog attachment to clear out some of the weeds and make paths. I planted the seeds where I thought they would get the most sunlight (towards the center of the cleared out area) and in such a way that I would be relatively easy to harvest if it comes to that. The black walnut tree I harvested the seed from had a broken limb from exposure to wind so I'm concerned about how fragile the tree might be in this area that does experience some high winds. Planting them in a woods clearing gives them more wind protection than in the middle of a field or at the edge of a field where I planted black walnuts this summer. They are doing ok in these field locations so far but will experience more direct winds down the road.

Will have to wait until next summer to know how this forest planting experiment turns out.

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