June 30, 2014

Thought (Memetic) Soup, June edition

Happy middle of summer (in the Northern hemisphere, anyways)! Here are some humorous and puzzling items from my leisure time, cross-posted to Tumbld Thoughts. Also an update on Orthogonal Research, which is turning into quite a productive endeavor. This also marks the return of the Thought Soup series. Bemusement and incredulity abound. 

I. Technological Bemusement (for better and for worse)

Miguel Nicolelis (Neuroscientist of BMI fame) is demoing an EEG-controlled exoskeleton at the World Cup [1]. The exoskeleton is able to engage in soccer-related movements, but is controlled by a human brain. Read the Science News interview for more. And here is the outcome [2], courtesy of Neurogadget.

Contrary to the popular trough of disillusionment, Google Glass is a huge development in the world of Augmented Reality. Soon we will all be wearing glass-mounted displays, even if they are not made by Google. Google is apparently very bad at marketing, but that may not be the whole story [3]. Just know that violent responses to so-called "glassholes" is not entirely new.

25 years ago this month: Star Trek V opens. See William Shatner direct a film. Then see William Shatner rock-climb (poorly) and question God. If God is at the center of our galaxy, then are there gods at the center of all of the other galaxies? And if a god created the Big Bang (as some people claim), then is this God merely a middle manager? These are the types of questions audiences should have been asking, but it was 1989 and we were all fascinated with Spock's levitation boots.

II. Speaking Fee Incredulity

Speaking fee incredulity, courtesy of CREMA [4]. The graph is a sampling of economists on the lecture circuit: the x-axis is their relative internet ubiquity, and the y-axis is their minimum speaker's fee. Notice the red arrow and how it points to a cohort that includes Myron Scholes, Dan Kahneman, and Ben Stein. Funny how the world works sometimes.

III. Spam and Pointless Political Resistance Incredulity

I quit, I give up, Nothing's good enough for anybody else, It seems  -- Circle, Edie Brickell.
The chorus of this song [5] seems to summarize the Democratic Party's 2014 grass-roots fundraising strategy against the conservative Super-PAC fundraising [6]. Do you approve of this message? There are some people (progressive-minded bloggers, no less) who do not [7]. I don't think the Queen (a tory who does not have to worry about being elected) is amused, either. 

Of course, it's only a matter of time before democracy-as-market-economics [8] implodes. Perhaps we are witnessing that implosion right now. In the meantime, enjoy this picture of a jihadist stroking his cat. No, it's not a dirty limerick -- it's some form of PoMo resistance. Is this the height of absurdity, or Dr. Evil, martyrdom edition? Big money and bad religion, it's all highly-offensive performance art to me.

IV. Lack-of-funding Incredulity

My slouch towards intangible forms of enterprise continues. The Orthogonal Research activity report for the second quarter (Q2) of the calendar year (not financial) is now available. Busy quarter, but still without funding (although parties interested in changing that can contact me).

Is placing a value on research necessary but not sufficient? Here's one humorous take. COURTESY: PhD Comics.

[1] Servick, K.   Kickoff looms for demo of brain-controlled machine. Science News, 344(6188), 1069-1070 (2014).

[2] Paraplegic Man In Mind-Controlled Robotic Suit Kicks Off World Cup 2014. Neurogadget, June 13 (2014) AND Atkins, H.   Human In Robotic Exoskeleton To Kick Off The World Cup. Popular Science, June 6 (2014).

[3] Edwards, J.   Google glass is going to be huge, and its critics are wrong. Business Insider, June 9 (2014).

[5] Circle, Edie Brickell and the New Bohemians. YouTube video (1988).

[6] Dear Democrats, please stop spamming me for donations. Weasel Zippers blog, February 28 (2013).

[7] Atrios.   From Bean to Cup, You Fuck Up. Eschaton blog, May 27 (2014) AND Myers, P.Z. Democrats: you suck. Pharyngula blog, May 30 (2014).

[8] Avalon, J. and Keller, M.   The Super PAC Economy. Daily Beast, September 18 (2012) AND Aronsen, G.   Are Super PACs Overhyped? Mother Jones, September 28 (2012).

June 27, 2014

Historical Contingencies at the Birthday Party

Historical contingencies are perhaps the most interesting outcomes of the evolutionary process. Stephen J. Gould spent a lot of time and energy making this idea popular, but evidence comes from both paleontology [1], extant populations [2], and experimental evolution [3]. However, the ubiquity of the contingency concept does not resolve its phylogenetic consequences. Is historical contingency highly specific (a hard constraint resulting in unique paths), or is it a softer constraint? And how can we understand the role of convergent evolution within this framework? We will approach this from a mathematical perspective, and answer the riddle of what evolution and birthdays have in common.

Definition of generative science (Wikipedia) and historical science (RationalWiki). 

Evolutionary Histories and Their Accidents
Like human history, evolutionary history is a product of many forces and causes. We often think of these factors as a series of chance events (sometimes unique) that lead to a given outcome [5]. Observers sometimes use this point to argue that history is not systematic and thus cannot be separated from context (and thus comparative history would be quite impossible) [5]. But this also assumes that the factors that make a given evolutionary history unique (its branching events) are "hard". Not only are they irreversible, but also should not have significant similarities. The outcomes of the evolutionary process (genotypes and phenotypes) are locked in to a specific trajectory. By itself, this constraint should favor some changes over others and disallow changes that resemble even closely-related lineages.

If historical contingency is a hard constraint, then this leads us to an evolutionary hypothesis: historical contingency creates irreversible paths to highly-unique phenotypes. While a bit simplistic, this nonetheless serves to understand the consequences of contingency. Recall that the evolutionary process occurs through branching, and results in a series of evolutionary outcomes (Figure 1). While these outcomes are individually different, their degree of uniqueness relies on the "hardness" of the branch that separates one outcome from another. Figure 1 not only shows the results of branching, but also assumes "hard" constraints. The contingencies generated by this model involve hard constraints that results in a unique, lineage-specific partition of the search space.

Figure 1. An example of a phylogeny with unique, non-recurrent evolutionary outcomes. The evolutionary changes act as hard constraints, and each terminal taxon occupies a distinct 1-dimensional subspace. 

In Figure 1, a conventional phylogenetic model demonstrates how a search space can be partitioned through evolutionary branching processes. However, when the constraints are softer, each branching event results in less distinction between the resulting alternative forms and increases the chances that traits or forms that resemble those of a related lineage (even distantly so) will emerge. Figure 2 demonstrates this difference using the analogy of the Plinko game [6]. In this case, the combination of the process and outcome of contingency creates an overlapping search space over time for a given lineage (see the distribution of Plinko balls at the bottom of Figure 2). 

Contingency also rests on the assumption that evolutionary randomness results in unique combinations of traits. One feature of historical contingency involves building upon previously-acquired traits. As complexity is built in this way, the total number of possibilities decreases. But while the stochastic nature of evolution is a matter of conditioned chance, branching is an assumption of theoretical intuition. Therefore, evolutionary outcomes can converge even when their forms nominally exist in different lineages. But given these constraints, shouldn't convergent evolution be impossible? Before we answer the question (and the answer is no) we must take an intellectual detour by way of birthday parties.

Figure 2. What it means to have an overlapping space of evolutionary outcomes enabled by soft historical constraints. COURTESY: Plinko Probability, version 2.02. PhET Interactive Simulations.

How are birthday parties at all relevant here? The birthday party paradox, a statistical curiosity, might help us establish a link between contingency and recurrence. But first, let us revisit our evolutionary process-as-hierarchical tree model. In this model, all possible combinations of are classified using a tree-like structure. Given that the search space is much larger than the number of objects being classified, do they also end up in unique categories? Perhaps. But, as we will learn, it may not matter as much as does the size and complexity of the evolutionary landscape itself.

What is the Birthday Party paradox [7]? Amazingly, this did not make a Quora list of the most counterintuitive mathematical results [8]. But perhaps this result is not so intuitive after all. Say you were to survey a room of n people. Given that every day of the year has an equal chance of being a birthday, how many people will you need to sample in order to find at least two people with the same birthday? The answer you might give depends on your intuitions about randomness. With 365 days in a typical year, one might assume that you would need a lecture hall of at least 300 people. But in fact, once you reach a sample size of 47, the probability (95%) becomes asymptotic to 100%. See Figure 2 for a graphical representation.

Figure 2. Number of people surveyed (x-axis) vs. probability of at least two people having the same birthday (y-axis).

Evolutionary Histories and Their Coincidents
This outcome results from a mathematical principle called recurrence. This principle suggests that motifs and themes can recur at an unknown frequency -- it explains why you get runs of heads or tails in a series of coin flips. This recurrence has nothing to do with the outcomes being related to one another. They are merely conincidences inherent in a generative process. In the evolutionary outcome space example, this suggest that overlap can occur in the form of deep similarities. Can this be applied to the probability that n lineages will exhibit convergence?

Not exactly what we are talking about here, but an evolutionary birthday nonetheless.

Phylogenetic birthday (or contingency) paradox:
In the next few tables, I have shown how the mathematics and problem formulation of the standard birthday paradox can be used to understand a generative set of evolutionary configuration and the probability of a parallel evolutionary outcome. 

What the data should look like (standard Birthday Party paradox):

 What the data should look like (proposed evolutionary paradox):

In the case of the evolutionary paradox, an exceedingly small sample size of 60 possible configurations was used for demonstration purposes. It is of note that this model is scalable to very large numbers of distinct evolutionary configurations. However, it is clear that the probability of convergent evolution is nearly 100% well before a given lineage is locked in to a single point in the configuration space. As the number of changes increases, the number of possible configurations changes is correspondingly reduced. 

But....but.....there are assumptions!
This model makes a few general assumptions. One, while each change is assumed to be countable, there is no accounting of how hard or soft the constraint actually is. This could be resolved through using a soft classifier to characterize each change, although would not remove the effects of geographically-localized specialization. An example of this is in the supplemental Excel dataset (see Notes section below). Another is that all evolutionary configurations are countable in the same way (e.g. no modularity). Again, this can be resolved by generating a matrix for each component of an organism (e.g. phenotypic module). 

Despite these assumptions (for better or for worse), the general principle of recurrence should give us a somewhat useful model for estimating how plausible or implausible convergent evolution is for a given set of evolutionary relationships. Recurrence is a useful tool that is largely ignored in conventional discussions about evolutionary constraints and parallel evolution. Once again, recurrence (by way of Henri Poincare in Figure 3) allows us to use principles of complexity theory to better understand evolutionary phenomena [9].

Figure 3. An example of Poincare recurrence. In this example, an image of Henri Poincare has been permutated, with reconstruction of the original image (or a reasonable approximation) is reached well before the maximum number of possible combinations is reached.

UPDATE (6/30/2014):
It was pointed out to me by a reader that birthdays have a distribution of their own throughout the course of a year. For example, birthdates in the Summer months (June, July) are more common than those in the winter months. This is of course due to human mating preferences and seasonality (and so birthdays are actually a quasi-stochastic process). Hence, there is a clustering of more common (as opposed to less common) birthdates on the calendar (Figure 4).

Figure 4. Visualization of birthdate frequency (in heatmap form) distributed across the calendar year. COURTESY: VizWiz blog and NYTimes.

I imagine this type of probability density is also somewhat true for evolutionary data across the diversity of a genus, order, or domain. But this type of clustering is also an outcome of stochastic processes (and one reason why recurrence is possible). When sampled at a given point in time, the outcome of a stochastic process is often not uniformly distributed -- in fact, it reveals clusters which must be distinguished from clusters that result from non-random processes. The question would be whether or not birthdates (or confounding evolutionary processes) cluster so significantly as to override clusters that result from randomness. The birthday paradox equations don't explicitly take that into account, but that likely does not invalidate the larger pattern.

UPDATE (8/4/2014):
Here is a good recent article from Nautil.us Magazine on evolutionary contingency. Puts a lot of the contemporary support for the idea in perspective.

Zorich, Z.   If the World Began Again, Would Life as We Know It Exist? Nautil.us, June 19 (2014).

Mathematical notation courtesy Wolfram MathWorld (http://mathworld.wolfram.com). Implemented in Excel courtesy of eXcel eXchange (http://excelexchange.com). Excel workbook (computed using pseudo-data) located on Github (https://github.com/balicea/evo-birthdays).

[1] Vermeij, G.J.   Historical contingency and the purported uniqueness of evolutionary innovations. PNAS, 103(6), 1804-1809 (2006).

[2] Taylor, E.B. and McPhail, J.D.   Historical contingency and ecological determinism interact to prime speciation in sticklebacks, Gasterosteus. Proceedings of The Royal Society of London B, 267, 2375-2384 (2000).

[3] Blount, Z.D., Borland, C.Z., and Lenski, R.E.   Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. PNAS, 105(23), 7899-7906 (2008).

[4] Travisano, M., Mongold, J.A., Bennett, A.F., and Lenski, R.E.   Experimental Tests of the Roles of Adaptation, Chance, and History in Evolution. Science, 267, 87-90 (1995).

[5] Fales, E.   Uniqueness and Historical Laws. Philosophy of Science, 47(2), 260-276 (1980).

[6] The Plinko analogy has also been used to describe the epigenetic landscapes of Waddington: Gordon, R. Introduction to differentiation waves Part 2. The evo-devo of epigenetic landscapes as differentiation trees. Embryogenesis Explained course (2013).

[7] Fletcher, J.   The Birthday Paradox at the World Cup. BBC News Magazine, June 15 (2014).

[8] Mathematics: what are some of the most counterintuitive mathematical results? Quora, March 27 (2014).

[9] Crutchfield, J., Farmer, J.D., Packard, N.H., and Shaw, R.S.   Chaos. Scientific American, December (1986).

June 21, 2014

Fireside Science: The Representation of Representations

This content is being cross-posted to Fireside Science, and is the third in a three-part series on the "science of science".

This is the final in a series of posts on the science of science and analysis. In past posts, we have covered theory and analysis. However, there is a third component of scientific inquiry: representation. So this post is about the representation of representations, and how representations shape science in more ways than the casual observer might believe.

The three-pronged model of science (theory, experiment, simulation). Image is adapted from Fermi Lab Today Newsletter, April 27 (2012).

For the uninitiated, science is mostly analysis and data collection with theory being a supplement at best and necessary evil at worst. Ideally, modern science rests on three pillars: experiment, theory, and simulation. For these same uninitiated, the representation of scientific problems is a mystery. But in fact, it has been the most important motivation for much of the scientific results we celebrate today. Interestingly, the field of computer science relies heavily on representation, but this concern generally does not carry over into the empirical sciences.

Ideagram (e.g. representation) of complex problem solving. Embedded are a series of Hypotheses and the processes that link them together. COURTESY: Diagram from [1].

Problem Representation
So exactly what is scientific problem representation? In short, it is the basis for designing experiments and conceiving of models. It is the sieve through which scientific inquiry flows, restricting the typical "question to be asked" to the most plausible or fruitful avenues. It is often the basis of consensus and assumptions. On the other hand, representation is quite a bit more subjective than people typically would like their scientific inquiry to be. Yet this subjectivity need not lead to an endless debate about the validity of one point of view versus another. There are heuristics one can use to ensure that problems are represented in a consistent and non-leading way.

3-D Chess: a high-dimensional representation of warfare and strategy.

Models that Converge
Convergent models speaks to something I alluded to in "Structure and Theory of Theories" when I discussed the theoretical landscape of different academic fields. The first way is whether or not allied sciences or models point in the same direction. To do this, I will use a semi-hypothetical example. The hypothetical case is to consider three models (A, B, and C) of the same phenomenon. Each of these models make different assumptions and includes different factors, but should at least be consistent with each other. One real-world example of this is the use of gene trees (phylogenies) and species trees (phylogenies) to understand evolution in a lineage [2]. In this case, each model uses the same taxa (evolutionary scenario), but includes incongruent data. While there are a host of empirical reasons why these two models can exhibit incongruence [3], models that are as representationally complete as possible might resolve these issues.

Orientation of Causality
The second way is to ensure that the one's representation gets the source of causality right. For problems that are not well-posed or poorly characterized, this can be an issue. Let's take Type III errors [4] as an example of this. In hypothesis testing, type III errors involve using the wrong explanation for a significant result. In layman's terms, this is getting the right answer for the wrong reasons. Even more than in the  case of type I and II errors, focusing on the correct problem representation plays a critical role in resolving potential type III errors.

Yet problem representation does not always help resolve these types of errors. Skeptical interpretation of the data can also be useful [5]. To demonstrate this, let us turn to the over-hyped area of epigenetics and its larger place in evolutionary theory. Clearly, epigenetics plays some role in the evolution of life, but is not deeply established in terms of models and theory. Because of this representational ambiguity, some interpretations play a trick. In a conceptual representation that embodies this trick, scarcely-understood high-level phenomena such as epigenetics will usurp the role of related phenomena such as genetic diversity and population processes. When the thing in your representation is not well-defined or quite popular (e.g. epigenetics), it can take on a causal life of its own. Posing the problem in this way allows us to obscure known dependencies between genes, genetic regulation, and the environment without proving exceptions to these established relationships.

Popularity is Not Sufficiency
The third way is to understand that popular conceptions do not translate into representational sufficiency. In logical deduction, it is often pointed out that necessity does not equal sufficiency. But as with the epigenetics example, it also holds that popularity cannot make something sufficient in and of itself. In my opinion, this is one of the problems with using narrative structures in the communication of science: sometimes an appealing narrative does more to obscure scientific findings than it does in making things accessible to lay people.

Fortunately, this can be shown by looking at media coverage of any big news story. The CNN plane coverage [6] shows this quite clearly: coverage of rampant speculation and conspiracy theory was a way to emphasize an increasingly popular story. In such cases, speculation is the order of the day, while thoughtful analysis gets pushed aside. But is this simply a sin of the uninitiated, or can we see parallels of this in science? Most certainly, there is a problem with recognizing the difference between "popular" science and worthwhile science [7]. There is also precedence from the way in which certain studies or areas of study are hyped. Some in the scientific community [8] have argued that Nature's hype of the ENCODE project [9] results fell into this category.

One example of a mesofact: ratings for the TV show The Simpsons over the course of several hundred episodes. COURTESY: Statistical analysis in [10].

Related to these points is the explicit relationship between data and problem representation. In some ways, this brings us back to a computational view of science, where data do not make sense unless it is viewed in the context of a data structure. But sometimes the factual aspect of data varies over time in a way that obscures our mental models, and in turn obscures problem representation.

To make this explicit, Sam Arbesman has coined the term "mesofact" [11]. A mesofact is knowledge that changes slowly over time given new data. Populations of specific places (e.g. Minneapolis, Bolivia, Africa) has changed in both absolute and relative terms over the past 50 years. But when problems and experimental designs are formulated assuming that facts related to these data (e.g. rank of cities by population) do not change over time, we can get the analysis fundamentally wrong.

This may seem like a trivial example. However, mesofacts have relevance to a host of problems in science, from experimental replication to inferring the proper order of causation. The problem comes down to an interaction between data's natural variance (variables) and the constructs used to represent our variables (facts). When the data exhibit variance against an unchanging mean, it is much easier to use this variable as a stand-in for facts. But when this is not true, scientifically-rigorous facts are much harder to come by. Instead of getting into an endless discussion about the nature of facts, we can instead look to how facts and problem representation might help us tease out the more metaphysical aspects of experimentation.

Applying Problem Representation to Experimental Manipulation
When we do experiments, how do we know what our experimental manipulations really mean? The question itself seems self-evident, but perhaps it is worth exploring. Suppose that you wanted to explore the causes of mental illness, but did not have the benefits of modern brain science as a guide. In defining mental illness itself, you might work from a behavioral diagnosis. But the mechanisms would still be a mystery. Is it a supernatural mechanism (e.g. demons) [12], an ultimate form of causation (reductionism), or a global but hard-to-see mechanism (e.g. quantum something) [13]? An experiment done the same way but assuming three different architectures could conceivably yield statistical significance for all of them.

In this case, a critical assessment of problem representation might be able to resolve this ambiguity. This is something that as modelers and approximators, computational scientists deal with all of the time. Yet it is also an implicit (and perhaps even more fundamental) component of experimental science. For most of the scientific method's history, we have gotten around this fundamental concern by relying on reductionism. But in doing so, this restricts us to doing highly-focused science without appealing to the big picture. In a sense, we are blinded by science by doing science.

Focusing on problem representation allows us a way out of this. Not only does it allow us to break free from the straightjacket of reductionism, but also allows us to address the problem of experimental replication more directly. As has been discussed in many other venues [14], the lack of an ability to replicate experiments has plagued both Psychological and Medical research. But it is in these areas which representation is most important, primarily because it is hard to get right. Even in cases where the causal mechanism is known, the underlying components and the amount of variance they explain can vary substantially from experiment to experiment.

Theoretical Shorthand as Representation
Problem representation also allows us to make theoretical statements using mathematical shorthand. In this case, we face the same problem as the empiricist: are we focusing on the right variables? More to the point, are these variables fundamental or superficial? To flesh this out, I will discuss two examples of theoretical shorthand, and whether or not they might be concentrating on the deepest (and most generalizable) constructs possible.

The first example comes from Hamilton's rule, derived by the behavioral ecologist W.D. Hamilton [15]. Hamilton's rule describes altruistic behavior in terms of kin selection. The rule is a simple linear equation that assumes adaptive outcomes will be optimal ones. In terms of a representation, these properties provide a sort of elegance that makes it very popular.

In this short representation, an individual's relatedness to a conspecific contributes more to their behavioral motivation to help that individual than a typical trade-off between costs and benefits. Thus, a closely-related conspecific (e.g. a brother) will invest more into a social relationship with their kin than with non-kin. In general, they will take more personal risks in doing so. While more math is used to support the logic of this statement [15], this inequality is often treated as a widely applicable theoretical statement. However, some observers [16] have found the parsimony of this representation to be both too incomplete and intellectually unsatisfying. And indeed, sometimes an over-simplistic model does not deal with exceptions well.

The second example comes from Thomas Piketty's work. Piketty, economist and author of "Capital in the 21rst Century" [17], has proposed something he calls the "First Law" which explains how income inequality relates to economic growth. The formulation, also a simple inequality, characterizes the relationship between economic growth, inherited wealth, and income inequality within a society.

In this equally short representation, inequality is driven by the relative dominance of two factors: inherited wealth and economic growth. When growth is very low, and inherited wealth exists at a nominal level, inequality persists and dampens economic mobility. In Piketty's book, other equations and a good amount of empirical investigation is used to support this statement. Yet, despite its simplicity, it has held up (so far) to the scrutiny of peer review [18]. In this case, representation through variables that generalize greatly but do not handle exceptional behavior well produce a highly-predictive model. On the other hand, this form of representation also makes it hard to distinguish between a highly unequal post-industrial society and a feudal, agrarian one.

Final Thoughts
I hope to have shown you that representation is an underappreciated component of doing and understanding science. While the scientific method is our best strategy for discovering new knowledge about the natural world, it is not without its burden of conceptual complexity. In the theory of theories, we learned that formal theories are based on both deep reasoning and are (by necessity) often incomplete. In the analysis of analyses, we learned that the data are not absolute. Much reflection and analytical detail must be taken to ensure that an analysis represents meaningful facets of reality. And in this post, these loose ends were tied together in the form of problem representation. While an underappreciated aspect of practicing science, representing problems in the right way is essential for separating out science from pseudoscience, reality from myth, and proper inference from hopeful inference.

[1] Eldrett, G.   The art of complex problem-solving. MediaExplored blog, July 10 (2010).

[2] Nichols, R.   Gene trees and species trees are not the same. Trends in Ecology and Evolution, 16(7), 358-364 (2001).

[3] Gene trees and species trees can be incongruent for many reasons. Nature Knowledge Project (2012).

[4] Schwartz, S. and Carpenter, K.M.   The right answer for the wrong question: consequences of type III error for public health research. American Journal of Public Health, 89(8), 1175–1180 (1999).

[5] It is important here to distinguish between careful skepticism and contrarian skepticism. In addition, skeptical analysis is not always compatible with the scientific method.

For more, please see: Myers, P.Z.   The difference between skeptical thinking and scientific thinking. Pharyngula blog, June 18 (2014) AND Hugin   The difference between "skepticism" and "critical thinking"? RationalSkepticism.org, May 19 (2010).

[6] Abbruzzese, J.   Why CNN is obsessed with Flight 370: "The Audience has Spoken". Mashable, May 9 (2014).

[7] Biba, E.   Why the government should fund unpopular science. Popular Science, October 4 (2013).

[8] Here are just a few examples of the pushback against the ENCODE hype:

a) Mount, S.   ENCODE: Data, Junk and Hype. On Genetics blog, September 8 (2012).

b) Boyle, R.   The Drama Over Project Encode, And Why Big Science And Small Science Are Different. Popular Science, February 25 (2013).

c) Moran, L.A.   How does Nature deal with the ENCODE publicity hype that it created? Sandwalk blog, May 9 (2014).

[9] For an example of the nature of this hype, please see: The Story of You: ENCODE and the human genome. Nature Video, YouTube, September 10 (2012).

[10] Fernihough, A.   Kalkalash! Pinpointing the Moments “The Simpsons” became less Cromulent. DiffusePrior blog, April 30 (2013).

[11] Arbesman, S.   Warning: your reality is out of date. Boston Globe, February 28 (2010). Also see the following website: http://www.mesofacts.org/

[12] Surprisingly, this is a contemporary phenomenon: Irmak, M.K.   Schizophrenia or Possession? Journal of Religion and Health, 53, 773-777 (2014). For a thorough critique, please see: Coyne, J.   Academic journal suggests that schizophrenia may be caused by demons. Why Evolution is True blog, June 10 (2014).

[13] This is an approach favored by Deepak Chopra. He borrows the rather obscure idea of "nonlocality" (yes, basically a wormhole in spacetime) to explain higher levels of conscious awareness with states of brain activity.

[14] Three (divergent) takes on this:

a) Unreliable Research: trouble at the lab. Economist, October 19 (2013).

b) Ioannidis, J.P.A.   Why Most Published Research Findings Are False. PLoS Med 2(8): e124 (2005).

c) Alicea, B.   The Inefficiency (and Information Content) of Scientific Discovery. Synthetic Daisies blog, November 19 (2013).

[15] Hamilton, W. D.   The Genetical Evolution of Social Behavior. Journal of Theoretical Biology, 7(1), 1–16 (1964). See also: Brembs, B.   Hamilton's Theory. Encyclopedia of Genetics.

[16] Goodnight, C.   Why I Don’t like Kin Selection. Evolution in Structured Populations blog, April 23 (2014).

[17] Piketty, T.   Capital in the 21st Century. Belknap Press (2014). See also: Galbraith, J.K.   Unpacking the First Fundamental Law. Economist's View blog, May 25 (2014).

[18] DeLong, B.   Trying, yet again, to communicate the arithmetic scaffolding of Piketty's "capital in the Twenty-First Century". Washington Center for Equitable Growth blog, June 5 (2014).

June 9, 2014

The Final Phase of Starstuff

Everything must go (to starstuff)! Below are the final two sets (XII and XIII) of supplements for the Cosmos reboot, cross-posted as always to Tumbld Thoughts. Enjoy!

XII. Changing the World, One Carbon Sink at a Time.

Here are the supplemental readings for the twelfth episode of the Cosmos reboot. This episode, called "The World Set Free", is a point-by-point refutation of climate change denial. Also features a trip to Venus (below are an easter egg and a fictitious Venus-Earth mashup). Readings are organized by theme.

Venus and the case of the the Runaway Greenhouse:
Carl Sagan and the Quest for Life in the Universe, Cosmic Horizons, American Museum of Natural History.

Billings, L.   Fact or Fiction? We can push the planet into a runaway greenhouse apocalypse. Scientific American, July 31 (2013).

Kunzig, R.   Will Earth's Ocean Boil Away? National Geographic, July 29 (2013).

Sir, Don't Forget Your (climate) Change!

Carbon Budget, Learn Bazaar.

A limit on CO2 Drawdown. Nature, July 2 (2009).

Glikson, A.   No alternative to atmospheric CO2 drawdown. Skeptical Science blog, February 14 (2013).

Vidal, J.   Geoengineering side effects could be potentially disastrous, research shows. The Guardian, February 25 (2014).

Weather, Climate, and Models:
What's the Difference Between Weather and Climate? NASA Mission Pages, February 1 (2005).

A Breathing Earth. UX Blog, July 29 (2013).

Downscaling Climate Data. Climate-Decisions.org.

Our Boundless Energy Future:
Naam, R.   Smaller, Cheaper, Faster: does Moore's Law apply to solar cells? Scientific American guest blog, March 16 (2011).

Auguste Mouchot and his solar engine. Land Art Generator Initiative.

Alicea, B.   Solar is at a cost-per-unit threshold! Tumbld Thoughts blog, April 23 (2014).

Frank Shuman. Encyclopedia of Earth.

History of Solar Energy. SolarEnergy.com

Teller, A.   Google X Head on Moonshots: 10X Is Easier Than 10 Percent. Wired, February 11 (2013).


XIII. Goodbye and Goodnight, even when there is no sun to set.

Here is the thirteenth (and final) installment of the supplemental readings for the Cosmos reboot ("Unafraid of the Dark"). Readings are (as always) organized by theme. 

Information, Information Everywhere
Chesser, P.   The Burning of the Library of Alexandria. eHistory Archive, June 1 (2002).

Size of the Internet. Wolfram|Alpha.

Moskvitch, K.   Paradox Solved? How Information Can Escape from a Black Hole. Space.com, March 4 (2014).

Expanding your Worldview
Erdapfel and Early World Maps, Wikipedia.

Alicea, B.  Plausibility and de Navitus Models of Complex Systems. Synthetic Daisies blog, March 21 (2013).

The Galactic Circus
Neutron Stars and Pulsars. NASA Goddard Space Flight Center. Picture courtesy Universe Today.

Dark Energy, Dark Matter. NASA Science: Astrophysics. 

Astrobio   New Information about ‘Snowball Earth’ Period. Astrobio.net, March 3 (2013).

Dell'Amore, C.   "Snowball Earth" Confirmed: Ice Covered Equator. National Geographic, March 4 (2010).

Alicea, B.   On Bet Hedging and Evolutionary Futures. Synthetic Daisies blog, January 24 (2014).

A Pale Blue Dot. The Planetary Society.

Voyager has left the building (Solar System):
Voyager I. xkcd blog, #1189, March 22 (2013). Explain xkcd wiki

Witze, A.   First hints of waves on Titan's seas. Nature News and Comment, March 17 (2014).

The Heliosphere. Cosmicopia, NASA.

Benningfield, D.   Manganese Nodules. Science and the Sea, October 25 (2009).