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Saturday, March 17, 2018

Technological Civilisations

Technological Civilisations


What percentage of the lifetime of a planet is marked by a technical civilisation? In our case, the Earth has harbored a technical civilisation characterized by radio astronomy for only a few decades out of a lifetime of a few million years. So far, then, for our planet the lifespan is less than 1/108, a millionth of a percent! And unfortunately for us, it is hardly out of the question that we might destroy ourselves tomorrow or the day after tomorrow.

Suppose this were to be a typical case, and the destruction so complete that no other civilisation (technical or biological, of the human or any other species) were able to emerge in the billions of years remaining before our Sun dies. Then a simple mathematical equation would prove that at any given time there would be only a handful, a tiny smattering, a pitiful few civilisations which achieved technical phase in the Galaxy, the steady state number maintained as emerging societies replace those recently self-immolated.

Grosso modo, if civilisations tend to destroy themselves soon after reaching a technological phase, there might be no one for us to talk with but ourselves.

(Extract from The Rain, the famous SF novel by Elena and George B.)

Dundas Street, Toronto, Canada. Photo by Elena

Captain’s Log

Captain’s Log


If the Voyager missions were manned, the captain would keep a ship’s log, and the log, a combination of the events of Voyagers 1 and 2, might read something like this:

Day 1: After much concerns about provisions and instruments, which seemed to be malfunctioning, we successfully lifted off from Cap Canaveral on our long journey to the planets and the stars.

Day 2: A problem in the deployment of the boom that supports the science scan platform. If the problem is not solved, we will lose most of our pictures and other scientific data.

Day 13: We have looked back and taken the first photograph ever obtained of the Earth and Moon as worlds together in space. A pretty fair.

Day 150: Engines fired nominally for a mid course trajectory correction.

Day 170: Routine housekeeping functions. An uneventful few months.

Day 185: Successful calibration images taken of Jupiter.

Day 207: Boom problem solved, but failure of main radio transmitter. We have moved to back-up transmitter. If it fails, no one on Earth will ever hear from us again.

Day 215: We cross the orbit of Mars. That planet itself is on the other side of the Sun.

Day 295: We enter the asteroids belt. There are many large, tumbling boulders here, the shoals and reefs of space. Most of them are uncharted. Lookouts posted. We hope to avoid a collision.

Day 475: We safely emerge from the moon asteroid belt, happy to have survived.


Fantasy World. The Real Voyager's Mission is Much Interesting Than the Journey of a Fictious Ship. Because we will never know the end of story. Image:Alternate Reality, Fantasy World, Megan Jorgensen (Elena)

Day 570: Jupiter is becoming prominent in the sky. We can now make out finer detail on it than the largest telescopes on Earth have ever obtained.

Day 615: The colossal weather systems and changing clouds of Jupiter, spinning in space before us, have us hypnotized. The planet is immense. It is more than twice more massive as all the other planets put together. There are no mountains, valleys, volcanoes, rivers; no boundaries between land and air; just a vast ocean of dense gas and floating clouds – a world without a surface. Everything we can see on Jupiter is floating in the sky.

Day 630: The weather on Jupiter continues to be spectacular. This ponderous world spins on its axis in less than ten hours. Its atmospheric motions are driven by the rapid rotation, by sunlight and by the heat bubbling and welling up from its interior.

Day 640: The cloud patterns are distinctive and gorgeous. They remind us a little of Van Gogh’s Starry Night, or works by William Blake or Edvard Munch. But only a little. No artist ever painted like this because none of them ever left our planet. No painter trapped on Earth ever imagined a world so strange and lovely.

We observe the multicolored belts and bands of Jupiter close up. The white bands are thought to be high clouds, probably ammonia crystals; the brownish-colored belts, deeper and hotter places where the atmosphere is sinking. The blue places are apparently deep holes in the overlying clouds through which we see clear sky.

We do not know the reason for the reddish-brown color of Jupiter. Perhaps it is due to the chemistry of phosphorus or sulfur. Perhaps it is due to complex brightly colored organic molecules produces when ultraviolet light from the Sun breaks down the methane, ammonia, and water in the Jovian atmosphere and the molecular fragments recombine. In that case, the colors of Jupiter speak to us of chemical events that four billion years ago back on Earth led to the origin of life.

Day 647: The Great Red Spot. A great column of gas reaching high above the adjacent clouds. So large that it could hold half a dozen Earths. Perhaps it is red because it is carrying up to view the complex molecules produced or concentrated at great depth. It may be a great storm system a million years old.

Day 650: Encounter. A day of wonders. We successfully negotiate the treacherous radiation belts of Jupiter with only one instrument, the photopolarimeter, damaged. We accomplish the ring plane crossing and suffer no collisions with the particles and boulders of the newly discovered rings of Jupiter. And wonderful images of Amalthea, a tiny, red, oblong world that lives in the heart of the radiation belt; of multicolored Io; of the linear markings on Europa; the cobwebby features of Ganymede; the great multi-ringed basin on Callisto. We round Callisto and pass the orbit of Jupiter 13, the outermost of the planet’s known moons. We are outward bound.

Day 662: Our particle and field detectors indicate that we have left the Jovian radiation belts. The planet’s gravity has boosted our speed. We are free of Jupiter at last and sail again the sea of space.

Day 874: A loss of the ship’s lock on the star Canopus – in the lore of constellations the rudder of a sailing vessel. It is our rudder too, essential for the ship’s orientation in the dark of space, to find our way through this unexplored part of the cosmic ocean. Canopus lock reacquired. The optical sensors seem to have mistaken Alpha and Beta Centauri for Canopus. Next port of call, two years hence: The Saturn system.

Humber River

The Humber River


The Humber River is a river in Southern Ontario which is a tributary of Lake Ontario and is one of two major rivers on either side of the city of Toronto, the other being the Don River. TheRiver begins at Humber Springs Ponds on the Niagara Escarpment in Mono, Dufferin County and reaches its mouth at Humber Bay on Lake Ontario in the city of Toronto. The Humber was designated a Canadian Heritage River on September 24, 1999.

The Humber collects from about 750 creeks and tributaries. It encompasses portions of Dufferin County, the Regional Municipality of Peel, Simcoe County, and the Regional Municipality of York. The main branch runs for about 100 kilometres from the Niagara Escarpment in the northwest, while another other major branch, known as the East Humber River, starts at Lake St. George in the Oak Ridges Moraine near Aurora to the northeast. Both rivers join north of Toronto and then flow in a southeasterly direction into Lake Ontario, The river mouth is flanked by Sir Casimir Gzowski Park and Humber Bay Park East.

The Humber has a long history of human settlement along its banks. The first settlers were the Palaeo-Indians who lived in the area from 10,000 to 7000 BC. The second wave, people of the Archaic period, settled the area between 7000 and 1000 BC and began to adopt seasonal migration patterns to take advantage of available plants, fish, and game. The third wave of native settlement was the Woodland period, which saw the introduction of the bow and arrow and the growing of crops which allowed for larger, more permanent villages.

The Anishinaabe refer to the river as Cobechenonk. During the 1600s and 1700s, the river was known by several names before it was given the official name of Humber. Popple’s map of 1733 shows a prominent river beside the native settlement Tejajagon assumed to be the Humber. Its name is given as the Tanaovate River. The river was also known as the Toronto River. Lieutenant-Governor John Graves Simcoe gave the river the name of Humber, likely after the Humber estuary in England.

All the pictures have been taken by Elena.

The Humber has a long history of human settlement along its banks.
A stunning view of the Earth.

Today the majority of the Toronto portion of the Humber is parkland, with paved trails running from the lake shore all the way to the northern border of the city some 30 km away.



Trails following the various branches of the river form some 50 km of bicycling trails, much of which are in decent condition.
The Humber remained relatively free from industrialization as Toronto grew.

No permanent European settlement occurred until the arrival of Jean-Baptiste Rousseau in the late 18th century.

The main branch of the Humber River runs for about 100 kilometres (60 mi)from the Niagara Escarpment to Ontario Lake.
Animal world on the Humber River
The Humber River crosses the districts of Dufferin County, Regional Municipality of Peel, Simcoe County, Regional Municipality of York.

The Humber River is a tributary of Lake Ontario and is one of two major rivers on either side of the city of Toronto, the other being the Don River to the east.

Bicycle trails and birds...

Business Cycles

Business Cycles


The term ‘business cycles’ refers to the perpetual interchanging of economic growth (expansion) with stagnation (recession). Consistently, with this idea, roughly every 5 to 10 years the cycle repeats itself. Cyclical business behaviour, along with investment and productivity shocks, is of interest to economists. However, just as with ceteris paribus, the points made often rest on important assumptions.

Because of the repercussions of globalization, business cycles may have become synchronized. Such synchronization would have occurred between European and Anglophone countries in 1950-1973, and then intensified. However, after analyzing yearly Gross Domestic Product (GDP) for 25 economies over 125 years, the researchers concluded that shocks within, or particular to, the country, are more important in affecting business cycle processes than all other factors. Considerable academic disagreement surrounds the issue. A proposition has been advanced that despite the Euro replacing national currencies in 1999, a European simultaneous cycling has been documented as far back as the 1980s. Yet, others maintain conviction that the exact reverse actually happened. In exploring the impact of globalization on economic cycles, the author defines the first wave of the phenomenon as taking place between 1880-1913. The period from 1973 onwards would be the fourth such wave, featuring financial market integration and selected floating exchange rates.

Internal factors, such as harvests, have considerable influence over business cycle dynamics. As an example let’s quote Davis et al. (2011) who reviews the cotton industry activities in 19th century America and compares the antebellum and postbellum periods.

Un stade. I hate weekends because there is no stock market (Rene Rivkin). Photo: Megan Jorgensen (Elena)

Because volume shifts were unique to that particular crop in the precise lapse of time, the illustration is a classical illustration for the Keynesian model under the gold standard. The gold standard is a monetary system where the unit of measurement is a predetermined amount of gold. Cotton, wheat and corn are the three principal crops of the U.S. agricultural and industrial production markets.

Together, they account for a fundamental part of the country’s GDP. In that moment in time, corn was the least exported product of the trio, and almost ¾ of wheat produce came from the following states: Ohio, Michigan, Illinois, Wisconsin, Minnesota, Indiana, Missouri, the Dakotas, Kansas and Nebraska. Crop production ascribed coalitions of states names such as Southern Wheat Belt and Midwestern Corn Belt (reminiscent of, and overlapping with, the Bible Belt).

The Keynesian economic business cycle model states that fiscal policy ought to be countercyclical. Fiscal policy refers to government and taxes, while monetary policy has to do with the central bank, and thus interest rates and money supply stabilization tools.

References:

Artis, M., Chouliarakis, G. & Harischandra, P. K. G. (2011). Busyness cycle synchronisation since 1880. The Manchester School, 79 (2): 173-207.

Chari V. V., Christiano, L. J. & Kehoe, P. J. (1994). Optimal fiscal policy in a business cycle model. The Journal of Political Economy, 102 (4): 617-652.

Davis, J. H., Hanes, C. & Rhode, P. W. (2011). Harvests and business cycles in nineteenth century America. The Quarterly Journal of Economics, 124 (4): 1675-1727.

Bayesian Financial Methods

Bayesian Financial Methods


Thomas Bayes was an English mathematician and Fellow of the Royal Society of London. He lived in the 18th century and laid the foundations to probability theory. For a discussion of Bayesian test of portfolio efficiency and likelihood approach to low default portfolios see Kandel et al. (1995). Bayes’ Theorem (also called Bayes’ Law or Bayes’ Rule) of conditional probabilities, is widespread in finance:

P(A|B) = P(B|A)P(A)/P(B)

Geweke & Zhou (1996) discuss providing Bayesian background for experimenting with the Arbitrage Pricing Theory (APT), used to estimate the return on an investment. The postulate is tested with traditional factor analysis among other procedures.

Statistics can be inferential and descriptive, and statistical inference is designed to predict population parameters from sample statistics. Bayesian inference Using Gibbs Sampling (BUGS software package) can be used with Markov Chain Monte Carlo (MCMC) methods. Gibbs Sampler is a statistical method that refers to deriving a number using the probability distribution. Advances in computer science have solved the computational problem, since a solid knowledge of integrals is required.

Literature on BUGS and the Metropolis-Hastings algorithm is abundant (e.g. Arminger & Muthen, 1998; Billera & Diaconis, 2001; Chib & Jeliazkov, 2001). Surprisingly many free e-books are available for download on the net.

Everyone has the brainpower to follow the stock market. If you made it through fifth-grade math, you can do it. (Peter Lynch). Illustration: Megan Jorgensen (Elena)

Bayesian econometrics is used in finance to determine option price, forecasting, market, credit and operational risk management, as well as portfolio allocation. The Bayesian framework is hospitable to return distribution, expected utility computation and Sharpe ratios.

Definition, condition and parameters are crucial in econometric modeling. For instance, the disparity between the money market (short-term borrowing and lending only) and the bond market (long-term investment and financing) could affect a computational outcome (Raible, 1998).

Wylie et al. (2006) sheds light on positivism, interpretivism and constructivism. Bayesian modeling quantifies how human information processing and decision-making work. An alternative construct is the frequentist methodology used in business and management studies. The meaning each method assigns to probability is dissimilar. Bayesianism places more emphasis on plausibility and uncertainty.

Because the condition is the new information, or a prior, these have been classified into three categories: informative, noninformative and hierarchical.

Bayesian Belief Networks (BBNs) are models to measure operational risk as depicted by Alexander (2000). Bayesian analysis can be applied to human risk. Human risk (losses due to staffing deficiencies) is part of operational risk management.

Bayesian learning is the method of selecting the best hypothesis (ex. naïve Bayes learner). Geweke & Tanizaki (2001) narrate such topics as Bayes estimator, non-Gaussian and/or Bayesian state space models and rejection sampling as well as their usefulness in finance for market and portfolio analysis.

References:

    Alexander, C. (2000). Bayesian methods for measuring operational risk. Discussion Papers in Finance. Social Science Research Network (SSRN).
    Arminger, G. & Muthen, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs Sampler and the Metropolis-Hastings algorithm. Psychometrika, 63 (3): 271-300.
    Billera, L. J. & Diaconis, P. (2001). A geometric interpretation of the Metropolis-Hasting Algorithm. Statistical Science, 16 (4): 335-339.
    Chib, S. & Jeliazkov, I. (2001). Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 96 (453): 270-281.
    Geweke, J. & Tanizaki, H. (2001). Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling. Computational Statistics & Data Analysis, 37 (2): 151-170.
    Kandel, S., McCulloch, R. & Stambaugh, R.F. (1995). Bayesian inference and portfolio efficiency. Review of Financial Studies, 8 (1): 1-53.
    Wylie, J., Muegge, S. &Thomas, R.D. (2006). Bayesian methods in management research: An application to logistic regression. Administrative Sciences Association of Canada (ASAC), 1-17.