April 29, 2014

Intra- and Inter-generational Physiological Evolution: three case studies

Evolutionary processes are crucial to driving forward physiological processes. While inter-generational adaptation is considered to be the "gold standard" of evolutionary change, natural selection-driven adaptive processes can also act on intra-generational timescales. Like many epigenetic mechanisms, intra-generational adaptive processes are not known to transmit nor retained over many generations.

In this post, we will discuss three cases of how evolutionary processes (two explicitly intra-generational, one extensively inter-generational) affect the operation of physiological systems. In the case of our inter-generational examples (I and III), physiological processes exhibit their own adaptive dynamics. In the case of our inter-generational example, the macro-evolutionary distribution of gene variants provides a basis for intra-generational adaptation.

I. Role of Intra-generational Selection in tRNA Availability and Translation

Mahlab, S. and Linial, M.   Speed Controls in Translating Secretory Proteins in Eukaryotes: an Evolutionary Perspective. PLoS Computational Biology, 10(1), e1003294 (2014).

This paper deals with transcription and the production of secretory molecules (production of the secretome) as an intra-generational evolutionary process. The secretome involves a vast array of peptides produced via a special ribosomal structure located at the cell membrane (Figure 1). The resulting peptides (manufactured on the outside of this membrane) are then involved in cell-cell communication.

Figure 1. The specialized translational pathway that leads to the production of signaling peptides.

The authors focus on role of tRNA (or transfer RNA) adaptation and the N' terminal of secretory proteins. tRNAs are internal to the translational process, and serve to translate open reading frames of DNA into amino acids. As adaptors, tRNAs are specialized by codon and function. Each type of specialization results in a population (or pool) which has a certain degree of diversity. This results in something called codon usage bias, a concept to which we will return. The N' terminal regions of the signaling peptide are associated with the so-called "fast" tRNAs. Secretory proteins made in this fashion are also found to contain segmental information that allow for various signaling functions. The signaling functionality is in turn a product of adaptation by natural selection within a single human generation (and perhaps even within a single cellular generation). "Fast" tRNAs are just one type of specialized tRNA molecule that exist in different proportions depending on various factors. The consequences of selection on these ratios is to affect the production of some codons (and thus peptides) over others.

Changes in the proportion of tRNA types requires an adaptive mechanism. While the specifics of this mechanism are unknown (but see Figure 2), the amount of diversity is governed by the number of tRNA molecules of a certain specialized type. To illustrate this, I use a conceptual model of translation called the "Hungry, Hungry Hippos" model, named after the popular children's board game (see Figure 3). The game begins with four hippos and a game board full of freely-moving marbles. Then, each hippo eats as many marbles as they can before the marbles are all eaten. This race exemplifies how tRNAs are utilized in the process of translation: mRNA is moved through the ribosome at different speeds, which tRNA molecules compete to bind to the incoming sequence and replicate their information in a new generation of peptides.

Figure 2. The site of translation and the trade-offs inherent in tRNA selection. COURTESY: Figure 1 (a and b) from [2].

Figure 3. The game "Hungry, Hungry Hippos", a model for transcription?

While one might think of this as a stochastic process, tRNA pools adapt to the needs of a given cell, including the speed of translation and amino acid bias. One measure of how these pools evolve is the tRNA adaptation index [1], which is based on the concept of codon usage bias (Figure 4).

Figure 4. the tRNA (codon) adaptation index, a intra-generational natural selection index. CAI is a weighted geometric mean for all categories of codon.

The premise of tRNA adaptation and the potential role of natural selection is that gene expression is correlated with codon bias [3]. Depending on the needs of the cell, the production of proteins can be biased towards certain amino acids through controlling both the speed of translation and the (perhaps more importantly) the composition of tRNA pools. The consequences of this codon bias can be observed when plotted against gene expression (e.g. the production of mRNAs -- see Figure 5). In general, when gene expression (or transcriptional noise) is more active, the greater the bias in codon-specific tRNA activity (in the form of codon frequency).

Figure 5. Changes in codon frequency with respect to gene expression. Figure 2 from [4]. 

II. Inter-generational Selection for Antigens

Forni, D., Cagliani, R., Tresoldi, C., Pozzoli, U., De Gioia, L., Filippi, G., Riva, S., Menozzi, G., Colleoni, M., Biasin, M., Lo Caputo, S., Mazzotta, F., Comi, G.P., Bresolin, N., Clerici, M., and Sironi, M. An Evolutionary Analysis of Antigen Processing and Presentation across Different Timescales Reveals Pervasive Selection. PLoS Genetics, 10(3), e1004189 (2014).

The human immune system is a complex system that consists of recognition and defense mechanisms (Figure 6). These mechanisms operate both intracellularly and extracellularly. In addition (see Figure 7), there is both an innate system (which is evolutionarily conserved) and an adaptive system (which is derived but shared among vertebrates). Given this complexity, it is often hard to find the inter-generational underpinnings of intra-generational adaptation. One form of intra-generational adaptation in the immune system involves antigen processing and presentation. This is determined by both the inter-generational evolutionary history of antigen-specific genes and the role of selection within and between generations.

Figure 6. A quick refresher on the human immune system architecture. COURTESY: [5].

In this study, the authors examined the evolutionary history of 45 antigen-specific genes in Homo sapiens. In doing so, they and looked at both the intra-specific variation and inter-specific diversity of genes related to antigen-related processes. This study also used a comparative genomic approach to better understand the evolutionary history of antigen-specific genes in humans. This was done in two different ways. The first was to use several different statistical tests to identify the target of selection. Then, the targets were characterized using low-coverage, whole-genome Sanger sequencing (e.g. high-throughput analysis using next-gen sequencing). In the end, it was found that 9 genes in the antigen processing and presentation (APP) pathway have undergone adaptation within Homo sapiens. Taken collectively, this study gives us a structural view of diversity in the immune system that may predict variation in immune-related physiological responses.

Figure 7. Evolution of adaptive immunity in the Tree of Life. COURTESY: [6]

III. Intra-generational Selection in Tumor Survival (Cancer Evolution)

Ostrow, S.L., Barshir, R., DeGregori, J., Yeger-Lotem, E., and Hershberg, R.   Cancer Evolution Is Associated with Pervasive Positive Selection on Globally Expressed Genes. PLoS Genetics, 10(3), e1004239 (2014).

Much like Evolutionary Psychology, evolutionary views of cancer has become increasingly popular as conceptual models. Unlike Evolutionary Psychology, however, evolutionary views of cancer are not based on attempts to broadly characterize human behavior. The evolutionary view of cancer is similar to the population dynamics of organismal evolution by natural selection. Except that in this case, population processes are intra-generational and occur within specific tissues. What makes them "evolutionary"? For one, cancer can be characterized as a genealogical (branching) process, with many cancer cells originating from a single deleterious mutant (Figure 8).

Figure 8. Oncogenesis as a branching bush (evolution from common descent). COURTESY: [8].

In fact, one could think of evolutionary models of cancer as an instance of evolutionary dynamics rather than the outcome of reproductive fitness. Nevertheless, the usual suspects still participate in the process. For example, genetic variation in the form of standing variation or somatic mutations is selected upon through the process of tumorigenesis [7]. Mutations that are robust to positive selection contribute to the proliferation and microenvironmental maintenance of tumors (Figure 9). This is distinct from the natural selection that acts on germ line cells. Nevertheless, reproductive fitness is still the criterion for selection.

Figure 9. LEFT: Schematic showing the role of selection on cell populations and their microenvironmental ecosystem. RIGHT: comparison of clonal populations and their evolution with organismal species and their evolution. COURTESY: [9].

One important but often overlooked aspect of treating cancer as an intra-generational evolutionary process is that the constituent cells of a tumor can be viewed as replicators. Figure 10 demonstrates how lineages bud from single mitotically-dividing cells given various environmental and microenvironmental triggers. Yet single- cell replicators are also theoretical units upon which selection acts. In the case of Eukaryotic somatic and stem cells, variants can compete to determine the intensity or metastatic ability or a given type of cancer. These replicators also operate in an environmental context that often acts as a source of selection.

Figure 10. Evolutionary process in a single body (e.g. intra-generational cell population). COURTESY: Figure 2 in [10].

Despite some conceptual difficulties, these three studies give us a window into intra-generational adaptive and evolutionary processes. Far from being a black box, these processes are often distinct from but are influenced by inter-generational evolution. While these studies ignore the role of currently hyped adaptive mechanisms such as epigenetics and the microbiome, there is a lesson for interpreting the true contribution of these types of mechanisms on the long-term evolutionary process.

[1] dos Reis, M., Savva, R., and Wernisch, L.   Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Research, 32(17), 5036–5044 (2004).

[2] Pechmann, S. and Frydman, J.   Evolutionary conservation of codon optimality reveals hidden signatures of co-translational folding. Nature Structural and Molecular Biology, 20, 237–243 (2013).

[3] Neame, E.   Structure vs. Codon Bias. Nature Reviews Microbiology, 7, 406 (2009).

[4] Shah, P. and Gilchrist, M.A.   Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift. PNAS, 108(25), 10231-10236 (2011).

[5] The Human Immune System. The Molecules of HIV website (2006).

[6] Danilova, N.   Evolution of the Immune System. MIT OpenCourseWare, Spring (2005).

[7] Anderson, A.R.A., Weaver, A.M., Cummings, P.T., and Quaranta, V.   Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment. Cell, 127, 905-915 (2006) AND Magiliocco, A.M.   Tumor Heterogeneity in Breast Cancer, Concepts, and Tools. Figshare.

[8] Looi, M-K.   Cancer, genomes, evolution, and personalized medicine - it's complicated. Wellcome Trust blog, March 7 (2012).

[9] Greaves, M. and Maley, C.C.   Clonal Evolution and Cancer. Nature, 481, 306-313 (2012).

[10] Yates, L.R. and Campbell, P.J.   Evolution of the Cancer Genome. Nature Review Genetics, 13, 795-806 (2012).

April 24, 2014

New Directions in Space, Time, and Thought

Here is the latest news from the realm of Tumbld Thoughts. All features are interesting. In this post, we move from the latest episodes of Cosmos to new directions in economic value and the arXiv, to new directions in practicing research.

I. Making Amphibians Out Of Quarks and Other Tales of Scale

Here are the supplementary readings for Episode 6 of the Cosmos reboot, called “Deeper, Deeper Still”. These are organized by theme. I am not responsible for any groans my puns may cause.

(Episode) Origins…..
A sneak peek for this week. Daily Galaxy blog, April 12 (2014).

Ziggy Stardust and the Extra Dimensions (on Mars?):
Berkowitz, J.   The Stardust Revolution. Prometheus Books (2012).

Greene, B.   The Search for Hidden Dimensions. Richard Dawkins Foundation for Reason and Science. YouTube, May 17 (2010).

A human = 10^30 quarks?
Wolchover, N.   A Jewel at the Heart of Quantum Physics. Quanta Magazine, September 17 (2013).

Carroll, S.   Jaroslav Trnka on the Amplituhedron. Preposterous Universe blog, March 31 (2014).

Filmer, J.   New Discovery Simplifies Quantum Physics. From Quarks to Quasars blog, September 19 (2013).

Huang, C.   Scale of the Universe II. Scaleofuniverse.com.

Tardigrages and Angiosperms:
Stromberg, J.   How Does the Tiny Waterbear Survive in Outer Space?Smithsonian.com, September 11 (2012).

Nichols, P.B., Nelson, D.R., and Garey, J.R.   A family-level analysis of tardigrade phylogeny. Hydrobiologia, 558, 53-60 (2006).

Soltis, P., Soltis, D., and Edwards, C.   Angiosperms. Tree of Life (2005).

Plants Move Towards the Light and Make Food:
Wyatt, S.E. and Kiss, J.Z.   Plant tropisms: from Darwin to the International Space Station. American Journal of Botany, 100(1), 1-3 (2013).

Artificial Photosynthesis. Wikipedia, April 13 (2014).

Carbon is Versatile:
Buckminsterfullerene. Wikipedia, April 13 (2014).

Carbon Nanotube. Wikipedia, April 13 (2014).

Wall of Forever:

Tate, K.   How Gravitational Waves Work (Infographic). Space.com, March 17 (2014).

Third from top: Book Cover, You are Stardust. Elin Kelsey and Soyeon Kim.

Fourth from top: Ichetucknee Springs, North Florida, USA.

Bottom Image: Evidence for Cosmic Inflation following the Big Bang, COURTESY:BICEP2 Group.

II. Clean Room Redux

Here are the supplemental readings for the seventh episode of the Cosmos reboot entitled "The Clean Room". A bit of a departure from the previous episodes in that the focus was on the social consequences of scientific findings. As usual, readings are thematic.

Meteors, Sediments, and Early Earth:
Scientists Building Asteroid Threat Early-Warning System. Space.com, February 20 (2013).

Diverging evolution of early Earth and Mars revealed by meteorites. The Daily Galaxy blog, April 17 (2014).

Appenzeller, T.   Early Earth. National Geographic, December (2006).

Clean Rooms and Isotopes:
Radioactive Decay: a sweet simulation of a half-life. AAAS Science NetLinks.

Radioactive Dating Game. PhET Interactive Simulations.

Lewington, R.   A virtual tour of Applied Materials' clean room. Applied Materials blog.

Chemophobia vs. Public Relations and the role of science:

How corporations corrupt science at the public's expense. Union of Concerned Scientists, Center for Science and Democracy.

Washburn, J.   Science's Worst Enemy: corporate funding. Discover Magazine, October (2007).

"Silent Spring" at 50. The credit, and the blame, it deserves. Big Think blog, June 19 (2012).

Lead poisoning and health. World Health Organization, Fact sheet #379. September (2013).

Needleman, H.L.   The removal of lead from gasoline: historical and personal reflections. Environmental Research, 84(1), 20-35 (2000).

III. Pushing the Boundaries of the arXiv

I guess I am literally pushing the boundaries of the arXiv. On Tuesday, March 25, I submitted a paper called "Contextual and Structural Representations of Market-mediated Economic Value". While they normally announce the paper at Midnight (GMT) the following weekday, this paper was not announced until two days later (Friday morning).

Usually, when a paper is delayed, it means there is an issue with classification. Ultimately, the paper was placed in the q-fin.GN category. Then, 12 days later, arXiv introduced two new categories: q-fin.EC (economics) and q-fin.MF (mathematical finance). While this could be a coincidence, I still like to think that my paper broke their system. Hopefully, it ends up breaks new ground and old paradigms as well.

IV. The New, Potentially Paradigm-busting Paper on the arXiv

How do we assign value to economic transactions? In my latest paper, now available at the arXiv, I approach this problem using a computational and evolutionary approach. "Contextual and Structural Representations of Market-mediated Economic Value" is my first paper in the "q-fin" category (1403.7021, q-fin.GN).

Culturally-mediated biological markets are used to model several aspect of object valuation. Contextual Geometric Structures (CGSs) [1] are used to model individual minds in an agent-based simulation. Read the paper to fully appreciate what this means. While it is a purely computational study, it might also be of interest to behavioral economists and evolutionary anthropologists.

Proceedings of Artificial Life, 13, 147-154 (2012).

IV. Orthogonal Research: slouching towards research enterprise

In lieu of a formal academic position, I am now publishing and conducting work under the affiliation "Orthogonal Research". This is (currently) a money-less start-up, focused on research in mathematical modeling and data analysis. Right now, this just involves myself. However, potential collaborators, co-PIs, and funders are welcome to contact me.

Things are a great deal more serious than this.

The Orthogonal Research Q1 activity report is now available. "Q1" refers to the first quarter of the calendar year, not financial.

April 19, 2014

It's Algorithmic Indulgence for the Masses (or the niche market)

Introducing the latest addition to the Synthetic Daisies blog: Popular Algorithmics. Popular Algorithmics (a takeoff on Popular Mechanics) is a collection of posts originally presented as a series on Tumbld Thoughts. Each entry is a take-off on an established algorithmic approach from the scientific literature. Of particular interest are lesser known algorithmic approaches from the standpoint of both theory and application.

April 11, 2014

Starstuff Squared

This post presents the supplemental readings for the fourth (I) and fifth (II) episodes of the Cosmos reboot. These materials are cross-posted to Tumbld Thoughts.

I. Stellar Evolution is Six Points the Natural Law

Here are the supplemental readings for Cosmos, episode IV: "A Sky Full of Ghosts". The readings are organized by theme, and relate to scenes in the episode.

1. The Speed of Light is Not Attainable:
Life's Little Mysteries Staff   Can Matter Travel at Light Speed? LiveScience, September 27 (2012).

Variable Speed of Light: Einstein's theory of General Relativity. Speed of Light in Gravity blog.

2. Relativity Is Not Rocket Science -- it's much harder:
Minute Physics   Relativity Isn't Relative. YouTube, March 28 (2013).

Wolfson, R.   Simply Einstein: Relativity Demystified. W.W. Norton (2003).

3. The Earth is Not a Sphere (and the horizon is an illusion):
Choi, C.Q.   Strange but true: Earth is not a sphere. Scientific American, April 12 (2007).

Tyson, N.D.   On Being Round. Natural History Magazine, March (1997).

4. The Universe is Not Flat:
Archive of images from the Hubble telescope (Hubblesite).

Weiss, M.   What Causes the Hubble Redshift? Original Usenet Physics FAQ (1994).

5. Life in Space is Not Easy to Understand:
Loftin, R.B. and Kenney, P.   Training the Hubble space telescope flight team. IEEE Computer Graphics and Applications, 15(5), 31-37 (1995).

Halpern, P.   How large is the observable universe? The Nature of Reality blog, October 10 (2012).

Ceurstemont, S.   What a trip through a wormhole would look like. New Scientist TV, March 13 (2012).

6. Other Things that are Not Well-known:

History of Cyanotype. COURTESY: Alternative Photography and John Herschel.

II. The Lightness of Starstuff

Here are the supplementary readings for the fifth episode of the Cosmos reboot. The readings are organized by theme and observation. These relate loosely to scene and observation.

This Style of Popularization is Frustrating:
Orzel, C.   Cosmos F*$&ing Loves Science. Uncertain Principles blog, March 31 (2014).

Camera Obscura:

Mo Tse and Questioning Cultural Convention:
Fraser, C.   Mohism. Stanford Encyclopedia of Philosophy (2010).

Chinese Culture, Strategy, and Innovation. Chapter 2 of "Innovative China", T.C.R. van Someren and S. van Someren-Wang. Springer-Verlag (2013).

Al-Hazen's Book of Optics.

Replacing the Extramission Theory of Vision:
Madan, U.   The Beginnings of Sight. The Weekend Historian blog, May 26 (2009).

Graziano, M.   How Consciousness Works. Aeon Magazine, August 23 (2013).

The Strange World of Light:
Dzierba, A.   QCD with a light touch. American Scientist, April (2009). Book Review of "The Lightness of Being" by Frank Wilczek.

The Standard Model, Part 2: QCD. Spontaneous Symmetry blog, June 27 (2009).

Siegel, E.   The Cosmic Speed Limit. Starts with a Bang! blog, April 26 (2013).

April 6, 2014

Fireside Science: The Structure and Theory of Theories

This content is being cross-posted to Fireside Science. This post represents a first-pass approximation (and is perhaps a confounded, naive theory in itself). Hope you find it educational at the very least.

Are all theories equal? In an age where creationism is making its way into the school curriculum (under the guise of intelligent design) and forms of denialism and conspiracy theory are becoming mainstream, this is an important question. While classic philosophy of science and logical positivist approaches simply assume that the best theories evolve through the scientific process, living in an era of postmodernism, multiculturalism, and the democratization of information, demands that we think about this in a new way.

Sense-making as Layers of Information
By taking cues from theoretical artificial intelligence and contemporary examples, we can revise the theory of theories. Indeed, we live in interesting times. But what is a theory --  and why do people like to say it's "just a theory" when they disagree with the prevailing model? One popular view of theory is that of "sense-making" [1]: that is, theories allow us to synthesize empirical observations into a mental model that allows us to generalize without becoming overwhelmed by complexity or starting from scratch every time we need to make a predictive statement.

The process of making sense of the world by building theories. Keep this in mind as we discuss the differences between naive and informed theories. COURTESY: Figure 2 in [1b].

Yet sense-making is not the whole story, particularly when theories compete for acceptance [2]. Are all theories equal, or are some theories more rigorous than others? This question is in much the same vein as the critique of "absolute facts" in postmodern theory. To make sense of this, I propose that there are actually two kinds of theory: naive theories and informed theories. Naive theories rely on common sense, and can often do very well as heuristic guides to the world. However, they tend to fall apart when presented with counter-intuitive phenomena. This is where informed theory becomes important. Informed theories are not synonymous with scientific theories -- in fact, some ancient beliefs and folk theories can fall into this category alongside formal scientific theories. We will see the reasons this nominal equivalence (and non-equivalence of more naive theories) as we go through the next few paragraphs.

Naive and informed theories can be distinguished by their degree of "common sense". Normally, common sense is a value judgement. In this case, however, common sense involves a lack of information. Naive theories tend to be intuitive rather than counterintuitive. Naive theories are constructed only from immediate observations and abductive reasoning between these observations. Naive theoretical synthesis can be thought of as a series of "if-and-then" statements. For example, if A and B are observed, and they can be linked through co-occurrence or some other criterion, then they are judged to be plausible outcomes.

The role of abductive theories in organizations. COURTESY: Free Management Library.

Informed theories, on the other hand, utilize deduction and can be divided into working theories (e.g. heuristics) and deep theories that explain, predict, and control. Working theories tend to utilize inductive logic, whereas deep theories tend to rely upon deductive logic. Since deep theories are inductive, they tend to be multi-layered constructs with mechanisms and premises based on implicit assumptions [3]. As a deductive construct, a deep informed theory can lead to inference. Inference gives us a powerful way to predict outcomes that are not so intuitive. The inference of common ancestors in phylogenetic theory allows us to reconstruct common ancestors to extant species that may look nothing like an "average" or a "cross" between these descendants.

A contingency table showing the types and examples of naive and informed theories.




Cults, Philosophies based on simple principles

Pop-psychology and pop-science


Conspiracy theories

Scientific theories

Naive and informed theories can also be distinguished by their degree of complexity. As they are based on uninformed intuition, naive theories are self-evident and self-complete, perhaps too much so. Fundamentalist religious belief and denialist-based political philosophies are based on simple sets of principles and are said by some to be tightly self-referential [4]. This inflexible self-referential capacity these theories rely on common sense over social complexity. Conspiracy theories and denialist tendencies are deeper versions of naive theories [5], but unlike their informed counterparts, do not get by on objective data, and are particularly resistant to updating [6]. By contrast, formal theories are based on abstractions and possess incompleteness-tolerance. This is often by necessity, as we cannot observe every instance of every associated process we would like to understand.

Sometimes the deepest naive theories lead to conspiracies. I have it on the highest authority.

Theory of Ontological Theories?
This leads us to an interesting set of questions. One, are the informed theories that currently exist in many fields of inquiry inevitable outcomes? Second, why are some fields more theoretical than others, and why are theory and data more integrated in some fields but not others? This is a question of historical contingency vs. field-specific structure. Is the state of theory in different areas of science due to historical context or a consequence of the natural laws they purport to make sense of? To answer these three questions, we will not briefly examine five examples from various academic disciplines. Underlying many of these approaches to informed theory is an assumption: theories are a search for ontological truths rather than the product of interactions among privileged experts. This is where informed theories hold an advantage -- they can change gradually with regard to new data and hypotheses while also remaining relevant. This is an ideal in any case, so let us get to the examples:

1) Economics has an interesting relationship to theory. Formal macroeconomic theory involves two schools of thought: freshwater and saltwater. The former group favors the theories of the free-market, while the latter group adhere to Keynesian principles. However, there are also adherents of political economy, who favor models of performativity over formal mathematical models. Since the financial crisis of 2008, there has been a rise of interest in alternative economic theories and associated models, perhaps serving as an example of how theories change and are supplanted over time. And, of course, a common naive theory of economics is based on confounding micro- (or household) and macro- (or national-scale) economics.

2) Physics is though of as the gold standard of scientific theory. For example, "Einstein" is synonymous with "theory" and "genius". The successes of deep, informed theories such as relativity and quantum mechanics is well-known. Aside from explanation and prediction of physics theory are logical consistency and grand unification as an enterprise that can often be separated from experimentation. As the gold standard of scientific theory, physics also provides a theoretical conduit to other disciplines, sometimes without modification. We will discuss this further in point #5.

 This book [7] is a statement on self-anointed "bad" theories. The statement is: although string theory is structurally elegant, it is not functionally elegant like quantum gravity. But does that make quantum gravity a superior theory?

3) In neuroscience and cell biology, theories are as often deemed superfluous and inherently incomplete in lieu of ever more data. This is partially due to our level of understanding relative to the complex nature of these fields. Yet many naive and informed social theories exist, despite the complexity of the social world. So what is the difference? It could be a matter of neuroscientists and cell biologists not being oriented towards theoretical thinking. This may explain why computation neuroscience and systems biology exist as fields quite independent of their biological counterparts.

4) Theoretical constructs associated with evolution by natural selection are the consensus in evolutionary biology. This wasn't always the case, however, as 19th century German embryologists and 18th century adherents to Lamarkian theory had competing ideas of how animal diversity was produced and perpetuated. However, Darwinian notions of evolution by natural selection did the best job at synthesizing previous knowledge about natural history with a formal mechanism for descent with modification. In popular culture, there has always been a resistance to Darwinian evolution. Usually, these divine creation-inspired naive theories are embraced as a contrarian counterbalance to deep, informed theory advocated by scientific authorities. In this case, theories have a social component, as Social Darwinism (a social co-option of Darwinian evolution) was popular in the 19th and early 20th centuries.

5) Because informed theories can explain invariants of the natural world, they often cross academic disciplines. Sometimes these crosses are direct. Evolutionary Psychology is one such example. Evolutionary theory can explain biological evolution, and as we are the products of evolution, the same theory should explain the evolution of the human mind. A simple analogical transfer, but much harder to yield the same results. But sometimes theories cross into domains not because of their suitability for the problem at hand, but because they are mathematically rigorous and/or have great predictive power in their original domain. The "quantum mind" is one such example of this. Is "quantum mind" theory any better or more powerful than a naive theory about how the mind works? It is unclear. However, this co-option suggests that even the most reputable informed theories can be cultural artifacts. A real caveat emptor.

Roger Penrose et.al [8] will tell us about everything, in the spirit of physics and mathematics.

Properties of the Theory of Theories
The inherent dualisms of the theory of theories stems from deeper cognitive divisions between matter-of-fact and abstract thinking. As cultural constructs, matter-of-fact theories are much more amenable to narrative structures that permeate folklore and pseudo-science. This does not mean that abstract theories are "better" or any more "scientific" than matter-of-fact formulations. In fact, abstract theories are more susceptible to cultural blends [9] or symbolic confabulation [10], as these short-cuts aid us in conceptual understanding.

Scientific theories tend to be abstract, informed ones, but scientific theories that are more well-known by the general public have many features of naive theories. Examples of this include Newtonian physics and the Big Bang. There is a certain intuitive satisfaction from these two theories that are not offered by, say, quantum theory or Darwinian evolution [11]. This satisfaction arises from consistency with one's immediate sensory surroundings and/or existing cultural myths. Interestingly, naive (and mythical) versions of quantum theory and Darwinian evolution have arisen alongside the more formal theory. These faux-theories use their informed theory counterparts as a narrative template to explain everything from the spiritual basis of the mind (Chopra's Nonlocality) to social inequalities (Spencer's Social Darwinism).

But what about beauty in theory? Again, this could arguably be a feature of naive theorizing. Whether it is the over-application of parsimony or an over-reliance on elegance and beauty [7], informed theories require a degree of initial convolution before such features can be incorporated into the theory. In other words, these things should not be goals in and of themselves. Rather, deep, informed theories should be robust enough to be improved upon incrementally without having to be being completely replaced [12]. The beauty of parsimony and symmetry should only considered to be a nice side-benefit. There is also a significant role for mental and statistical models in theory-building, but for the sake of relative simplicity I am intentionally leaving this discussion aside for now.

Tides go in, tides go out. When it's God's will, it's a short and neat proposition. When it's more complicated, then it's scientific inquiry. COURTESY: Geekosystem and High Power Rocketry blogs.

In a future post, I will move from the notion of a theory of theories to the need for an analysis of analyses. Much like the theory of theories, a deep reconsideration of analysis is also needed. This has been driven by the scientific replication crisis, the proliferation of data (e.g. infographics) on the internet, and the rise of big data (e.g. very large datasets, once again enabled by the internet). 

[1] Here are a few references on the cognition of sense-making, particularly as it related to theory construction:

a) Klein, G., Moon, B. and Hoffman, R.F.   Making sense of sensemaking I: alternative perspectives. IEEE Intelligent Systems, 21(4), 70–73 (2006).

b) Pirolli, P., & Card, S.   The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Proceedings of the International Conference on Intelligence Analysis (2005).

[2] Here are some references that will help you understand the "hows" and "whys" of theory competition, with particular relevance to what I am calling deep, informed theories:

a) Steiner, E.   Methodology of Theory-building. Educology research Associates, Sydney (1988).

b) Kuhn, T.   The structure of scientific revolutions. University of Chicago Press (1962).

c) Arbesman, S.   The Half-life of Facts. Current Press (2012).

[3] sometimes, naive theorists will accuse deep, informed theorists of being "stupid" or "irrelevant". This is because the theories generated do not conform to the expectations and understandings of the naive theorist.

Paul Krugman calls one such instance "the myth of the progressive economist": Krugman, P.   Stupidity in Economic Discourse 2. The Conscience of a Liberal blog, April 1 (2014).

[4] Religious fundamentalist and  denialist groups also seem to theorize in a deep naive manner, using a tightly self-referential set of theoretical propositions. In these cases, however, common sense is replaced with a intersubjective (e.g. you have to be part of the group to understand) self-evidence. The associated logical extremes tend to astound people not in the "know".

a) Example from religious fundamentalism: Koerth-Baker, M.   What do Christian fundamentalists have against set theory? BoingBoing, August 7 (2012) AND Simon, S.   Special Report: Taxpayers fund creationism in the classroom. Politico Pro, March 24 (2014).

For a discussion of Nominalism (basic math) vs. Platonism (higher math) in Mathematics, please see: Franklin, J.   The Mathematical World. Aeon Magazine, April 7 (2014).

b) Example from climate change denialism: Cook, J. and Lewandowsky, S.   Recursive Fury: facts and misrepresentations. Skeptical Science blog, March 21 (2013).

[5] for one such example, please see: Roberts, D.   Conservative hostility to science predates climate science. Grist.org, August 12 (2013).

For a more comprehensive background on naive theories (in this case, the development of naive theories of physics among children) please see the following:

a) Reiner, M., Slotta, J.D., Chi, M.T.H., and Resnick, L.B.   Naive Physics Reasoning: a commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1-34 (2000).

b) Vosniadou, S.   On the Nature of Naive Physics. In "Reconsidering Conceptual Change: issues in theory and practice", M. Limon and L. Mason, eds., Pgs. 61-76, Kluwer Press (2002).

For the continued naive popularity of the extramission theory of vision, please see the following:

c) Winer, G. A., Cottrell, J. E., Gregg, V., Fournier, J. S., & Bica, L. A. (2002). Fundamentally misunderstanding visual perception: Adults' beliefs in visual emissions. American Psychologist, 57, 417-424.

[6] sometimes, theories that are denialist in tone are constructed to preserve certain desired outcomes from data that actually suggest otherwise. In other words, a narrative takes precedence over a more objective understanding. Charles Seife calls this a form of "proofiness".

For more, please see: Seife, C. Proofiness: how you're being fooled by numbers. Penguin Books (2011).

[7] Smolin, L.   The Trouble with Physics. Houghton-Mifflin (2006).

[8] Penrose, R., Shimony, A., Cartwright., N., and Hawking, S.   The large, the small, and the human mind. Cambridge University Press (1997).

[9] Fauconnier, G.   Methods and Generalizations. In "Cognitive Linguistics: foundations, scope, and methodology". T. Janssen and G. Redeker, eds, 95-128. Mouton DeGruyter (1999).

[10] Confounds are a psychological concept that identifies when ideas and deep informed theories are confused or otherwise condensed for purposes of superficial understanding or misinterpretation. In the case of creationists, such intentional confounds are often used to generate doubt and confusion of subtle and complex concepts.

a) Role of confabulation in cognition (a theory): Hecht-Nielsen, R.   Confabulation Theory. Scholarpedia, 2(3), 1763 (2007).

b) Example of intentional confounding from anti-evolutionism: Moran, L.A.   A creationist tries to understand genetic load. Sandwalk blog, April 1 (2014).

[11] By "conforming to intuitive satisfaction", I mean that Newtonian physics explains the physics of things we interact with on an everyday basis, and the Big Bang is consistent with the idea of divine creation (or creation from a singular point). This is not to say that these theories were developed because of these features, but perhaps explains their widespread popular appeal.

[12] Wholesale replacement of old deep, informed theories is explained in detail here: Kuhn, T.   Structure of Scientific Revolutions. University of Chicago Press (1962).

April 1, 2014

Carnival of Evolution #70: the game of evolution

"It's not whether you win or lose, it's how you play the game" -- Grantland Rice, in an oddly prescient and hypothetico-deductive capacity.

This month's Carnival of Evolution (#70) theme will be evolutionary games, broadly defined. Games are generally associated with strategy and intentionality (e.g. having a brain). In fact, formal game theory (the kind developed by John von Neumann and John Nash) arises from the mathematical study of human decision-making and economic theory [1]. However, game theory has also been applied to biological systems such as the dynamically stable behavioral states exhibited by E. coli [2] and viruses [3]. Game theory can also be applied to many proximal animal behaviors. While perhaps not the objects of selection themselves, these proximal behaviors can be better understood in the context of an adaptive game. In this post, I will not advocate for any one approach to evolutionary game theory, but will offer a guided tour exploring the possibilities for this approach. The month's posts will be presented at various points in this discussion.

The outcome of evolutionary games? TOP: tree of life (sensu Woese). BOTTOM: evolution of complexity (sensu Gould).

Game theory traditionally quantifies the outcomes of intentional actions. In evolutionary game theory, we are quantifying the discrete interactions between individuals. This does not require formal cognitive mechanisms, only biological units (e.g. genes, organisms, or even populations) that interact over time. Evolutionary game theory bears a striking conceptual resemblance to population genetics. But instead of using a gene metaphor, the metaphor of strategy is used. When these strategic interactions are shaped by natural selection and population processes, the results are evolutionary dynamics. Evolutionary dynamics shape not only shape microevolution, but have an influence on macroevolution as well.

Early game theory afficionados, in the pursuit of GOFAI.

The month's posts, part 1
In the post "Fixing on the Nitrogen fixation problem" at Mermaid's Tale, Anne Buchanan presents a post on the biology of Nitrogen fixation in plants, and poses it as an long-term scientific research problem with far-reaching consequences. Holly Dunsworth, also writing at Mermaid's Tale, discusses the challenges of teaching evolutionary concepts in her post "Are we removing the wisdom along with the teeth?". John Wilkins from Evolving Thoughts enlightens us about species concepts and the history of biology in "The Origins of Speciation". PZ Myers presents a post at Pharyngula called "Pathways to Sex", which is a comprehensive review on the evolution, diversity, and genetics of sexual dimorphism. Jonathan Richardson highlights a new Trends in Ecology and Evolution paper on the blog Eco-Evo-Evo-Eco (Eco-evolutionary Dynamics). As one of the co-authors, he provides a discussion of local adaptation, or adaptation at very small geographic scales [4].

Examples of evolutionary dynamics. COURTESY: Box 1, Figure 1 in [5].

How does game theory fit into evolutionary theory? Here are some definitions and their broader implications in the context of evolutionary game theory:

Decision: Decision-making is not always a cognitive function. In evolutionary game theory, decision-making can relate to the replication of genes or behaviors, which is a prime imperative of life. Replicator dynamics, then, are the results of making a decision over and over again. After each decision is made, a payoff can be assigned to the outcome. In evolutionary game theory, the payoff (positive or negative) has a fitness consequence. These consequences provide feedback to the player (e.g. organism) to act upon during subsequent decisions.

Outcome of the Hawk-Dove game in terms of population dynamics. COURTESY: Figure 1 in [6]

Strategy Suite: The player (in this case, an organism) must choose a strategy to counter responses by conspecifics, predators/prey, or even environmental stimuli. A pure strategy is a single strategy played at a given time-point. A mixed strategy involves choosing from a number of strategies at a given time point [7]. In the case of an evolutionary stable state (ESS), each player's mixed strategy suite converges to a pure strategy over time [8]. These pure strategies are optimal in the sense that established pure strategies cannot be beaten by upstart strategies that might emerge in a population over time.

Outcome of a mutualistic relationship (legume-bacterium) modeled as a Prisoner's Dilemma game and outcomes shown in terms of population dynamics. COURTESY: Figure 3 in [6]

Strategy as variation: The existence of pure or mixed strategies may be tied to genetic variation. However, the evolution of these strategy suites (e.g. how they are deployed) is a function of natural selection. One example of this is when a strategy becomes evolutionarily stable in a population. Once a given strategy is fixed in the population, natural selection can maintain its dominance, even as lower-frequency mutant strategies emerge [9]. In this sense, strategies behave like loci in population genetics theory.

Maynard-Smith on the origins of Evolutionary Game Theory [10]. COURTESY: Web of Stories.

In evolutionary games, strategies can be defined as heritable phenotypes [11], which range from well-defined behaviors to morphological characters. A strategy is evolutionary feasible if it is either an extant (current existing) variant within a population or a recurring mutant in that population. The strategies themselves can range in frequency from very rare to dominant. A player (organism) may be a carrier for latent strategies. Such strategies may have a low payoff in one environment while having a much higher payoff in another environment.

But how can an organism not "show all of its cards" as it were? According to [12], evolutionary game theory exists of an inner game and an outer game. The inner game is more akin to classical (e.g. economic) game theory where there are payoffs for intentional strategies. However, the evolutionary version also consists of an outer game that is dynamically linked to the inner game. This linkage allows the outer game to take the form of translating these payoffs into changes in phenotypic frequencies. This allows us to bridge proximal and ultimate causes of adaptive change in a population.

The month's posts, part 2
Jeremy Yoder from Nothing in Biology Makes Sense! reviews the Festival of Bad Ad-hoc Hypotheses. In "BAH! This looks amazing", Jeremy introduces us to the quest to discover the best "well-argued and thoroughly researched but completely incorrect evolutionary theory". Then, writing at Molecular Ecologist, Jeremy discusses the occurrence of soft selective sweeps in bacterial populations of the gut. Adam Goldstein of The Shifting Balance of Factors critiques scala naturae views of evolution in "March of Progress, reloaded". Ed Yong from Phenomena  presents a new paper that highlights the role of doublesex, which enables mimicry in the female common mormon butterfly (Papilio polytes). Here's an interview of Baba Brinkman by Kylie Sturgess at CSI's Curiouser and Curiouser blog. Baba Brinkman raps about evolution on a regular basis. You will have to go to the post to find out more. And at the BEACON Center blogDanielle Whittaker introduces us to the work of Tyler Heather, who works on the role of gene-phenotype interactions in the speed of adaptation, and Raffica LaRosa, who measures natural selection in flowers.

 "Time (evolution) is a game played beautifully by children (juveniles)" -- Heraclitus, perhaps anticipating the rise of Evo-Devo

Hawk-Dove Games

Hawk-Dove games are the traditional two-player zero-sum games most people are familiar with. A game with the simplest type of outcome, hawk-dove produces a winner and a loser. Only winning is stable (e.g. winner-take-all), so such games often result in arms races and necessitate conflict. While a pure "hawk" strategy is stable in the short term, it may not be evolutionarily stable.

Illustration of Hawk-Dove dynamics. COURTESY: Evolutionary Game Theory Wikipedia page.

In an evolutionary context, Hawk-Dove can also be characterized as the well-known Red Queen (a special instance of zero-sum game theory) [13]. The Red Queen, which characterizes co-evolutionary arms races, provides a means for the emergence of complex evolutionary dynamics between two species (e.g. players).

The month's posts, part 3
Razib Khan, writing at Unz Review, considers what can be learned from the re-analysis of open-access genome data in a post entitled "Reanalyzing Data: it does a mind good". Lesson: re-analysis is a highly fruitful endeavor. Dan Graur from the Judge Starling tumblr brings us a discussion of the ENCODE project in relation to the gene concept in "Mutons, Cistrons, Recons, and Nuons: News Concerning the Death of “Gene” are Greatly Exaggerated". At Genealogical World of Phylogenetic Networks, David Morrision leads a bibliometrics-based discussion on the emergence and current state of phylogenetics research as a subfield of evolutionary biology in "Has phylogenetics reached its apogee?". Moving from analysis to modeling, Artem Kaznatcheev at the Theory, Evolution, and Games group blog discusses a recent evolutionary-oriented theoretical Computer Science conference in "Computational theories of evolution" and "Algorithmic Darwinism". 
 Two player games with complexity.

Rock-paper-scissor Games

Rock-paper-scissor games are defined by their non-transitive outcomes. In Sinervo and Lively [14], male side-blotched lizard phenotypes give rise to three behavioral strategies. While competitive, these behaviors do not result in a definitive winner. For example, while there is a clearly dominant strategy (blue-throated guarders) that provides the highest payoff, alternate strategies (yellow-throated sneakers and orange-throated usurpers) can also be stable. Rather than converging to a pure strategy where competition would be winner-take-all, multiple strategies can co-exist at varying frequencies indefinitely.

Example of Rock-Scissors-Paper in Side-blotched Lizard. COURTESY: Sinervo Lab (UCSC).

The month's posts, part 4
John Hawks, writing at his weblog, provides information and his own insights on "A new early modern human genome from Siberia", which was isolated and sequenced from a 45,000 year-old femur. Moving from ancient genomics to theory, we have two posts on mechanisms and misunderstandings. The first is Philip Ball's (Homunculus blog) take on the "Molecular mechanisms of evolution". The second is from Larry Moran of Sandwalk blog, who introduces us to "A chemist who doesn't understand evolution". Returning to human evolution, but moving on to analysis, The Olduvai Gorge tumblr site provides a preview of and link to the new article "The Doubly-Conditioned Frequency Spectrum does not distinguish between ancient population structure and hybridisation". The paper itself is a critique of a popular method used in studies of phylodemography.

Prisoner's Dilemma and Snowdrift Games

Most biologists are familiar with the Prisoners' Dilemma (PD) -- in fact, this is the canonical game for demonstrating the evolution of cooperation [15]. A slightly less familiar variant of the PD game is the snowdrift (or cooperation) game. In both PD and snowdrift games, the maximal payoff results from cooperation and coordination between players rather than competition.

Payoff matrix for the PD game using a generic example. A 2x2 payoff matrix. COURTESY: Animalbehavioronline.com

Using the snowdrift game as an example, two players are confronted with the task of clearing away a snowdrift. If completed, the work benefits them both. However, if only one player decides to undertake the task, the second player can benefit without contributing (e.g. free-riding). But since the first player is unlikely to put up with free-riding over repeated plays of the game, the highest payoff for both players over repeated plays is attained from full cooperation in performing the work. As this strategy is replicated over evolutionary time, it becomes the dominant strategy. Thus, players converge upon this pure strategy through the maximization of payoffs [16], and it becomes evolutionarily stable.

The month's posts, part 5
Bjorn Ostman from Pleiotropy presents a review of evolutionary dynamics in holey fitness landscapes. Charles Goodnight from the excellent Evolution in Structured Populations blog gives us three tutorial-esque posts the month: "Mating structure, Interaction structure, and Selection Structure", "Griffing, Associate Effects, and Heritability", and "Measuring the Heritability of Contextual Traits". The population biology preprint blog Haldane's Sieve features a new paper (now accepted at PLoS One) called "The Arrival of the Frequent: how bias in genotype-phenotype maps can steer populations to local optima". Using both simulation and genotype-phenotype maps, this paper demonstrates that as rare variants, the fittest organisms in a population often do not survive to be fixed or otherwise represented at evolutionary timescales. And in the spirit of evolutionary computation, IEEE Spectrum has a feature on how bug-ridden computer code is being refactored and otherwise fixed using genetic algorithms derived from evolutionary theory.
A mans friendships are one of the best measures of his worth” -- Charles Darwin

Stackelberg and Pursuit-Evasion Games

These types of games are not as familiar to biologists. However, in their instantiated form, they appear to be quite useful to the evolution of biological complexity.

Stackelberg (or first-mover) games [17] might explain much about the emergence of evolutionary constraints and biological complexity. One simple example of such a game is the leader-follower game. The leader moves first by choosing from a mixed strategy suite, usually in a way that maximizes the payoff. The second player must then continually respond to the actions of the first move, as they are constrained from using a full set of possible strategies. While the second player gains information from the first-mover's strategy, it only allows them to maximize their payoff from a subset of strategies. This might explain the emergence of symbiotic relationships, or perhaps the emergence of social dominance hierarchies.

Pursuit-evasion (or cops and robbers) games might explain the emergence of predator-prey relationships. As is the case with Stackleberg games, the order in which turns are taken becomes an important determinant of the payoff. In pursuit-evasion, however, the first mover (evader) is constrained by what it takes to successfully avoid the pursuant (second mover) [18]. These types of games are generally zero-sum, although they need not be.

Leader-follower dynamics, presented as an abstract model. COURTESY: Evolutionary Bilevel Optimization.

Predator-prey dynamics in a two-state system. COURTESY: Wolfram Demonstrations Project.

The tic-tac-toe (a.k.a. naughts and crosses) game is an example of how leader-follower dynamics can produce stable equilibria. In tic-tac-toe, there are first movers and second movers. While optimal play by both players will result in a tie, the first move can often win the game if the second mover makes a suboptimal move.

Tic, tac, toe! Sometimes learning how to play games are a matter of life and death.

Games Against Nature

Games against nature are 1-player games where the sole player implements a strategy against a random process. The payoff is determined by how well the intentional player fares against the random process. The obvious extension of this is an organism adapting in the face of natural selection. One example of a game against nature can be found in cellular automata [19]. Cellular automata operate using simple rules imposed of a single cell by both its neighbors and stochastic processes that lead to emergent patterns across a grid of cells. While such games do not rely on competition nor cooperation, they do produce coordinated outcomes. In Conway's Game of Life, each cell is "born" or "killed" based on the states of its neighbors. The result is not a formal payoff matrix, but rather a set of patterns that persist or die off. Unlike zero-sum or conventional cooperation games, the outcome of the game is non-deterministic.

A cellular automata game against nature, played during development.

The month's posts, part 6
Carlos Araya from CEHG Blog reviews the latest findings in the area of experimental evolution in a post called "Dissecting the dynamics of adaptation with experimental evolution". Henry Gee from The End of the Pier Show brings us a paleontologically-inspired tale entitled "Careful with that Amphiooxus, Eugene". Aeon Magazine has a feature this month on selfish gene theory as a takeoff on the blogosphere kerfuffle started by David Dobbs with his article "Die, Selfish Gene, Die!". Their roundtable includes David Dobbs, Robert Sapolsky, Laura Hercher, Karen James, and John Dupre (a writer, a genetic counselor, two biologists, and a philosopher). For further critical assessment of this roundtable, see posts by Jerry Coyne at Why Evolution is True and  Larry Moran of Sandwalk
John Conway, on the origins of his "game of life" (a game against nature). COURTESY: Numberphile.

"There are no shortcuts in evolution" -- Louis D. Brandeis, who was not a biologist.

Many modern video games (such as first-person shooter games) are essentially games against nature. In this conception, nature is an artificial agent that presents challenges to a player, which can be overcome through either inherent skill or an adaptive solution. What if we could replace the goal-directed behaviors of a player with evolutionary imperatives?

Evolutionary Simon: a plot device devised for this post, but does the model fit the data?

To model this possibility using a formal game model, I introduce something called Evolutionary Simon. Simon is a programmed board game developed in the 1970s that might also be used to model the proximate effects of behavioral selection. Recall that the Simon game presents a sequence of lighted tiles (e.g. blue, blue, yellow, blue, red, green, red) that is generated by a computer program. The player must then imitate this sequence by pressing the right buttons in the correct order.

So far, this resembles a typical free recall (learning and memory) experiment. Now let us introduce a diversity of players, some with greater innate recall capacity, some with less. This innate capacity is improved upon by getting a correct answer. The payoff matrix for this 3x1 game:

Payoff matrix for Evolutionary Simon game. Payoffs are for strategies employed by a player (top row). ε is used to distinguish minimally correct response from incorrect. 


Partially Correct

Fully Correct

Simon Output


(1 – (1/cr)) + ε


Players respond to the output using either an entirely inappropriate response, a fully correct response, or a partially correct response (which exposes the limitations of their memory). Partially correct responses (cr) are scored by how many components of the original sequence they were able to recall. For every turn, an agent receives a payoff. The length of a Simon sequence can be used as a source of environmental selection.

In the end, the agents that end up with the largest payoffs over a wide range of generated patterns are the fittest. But we can end up with quite interesting evolutionary dynamics. For example, some agents might receive very high payoffs for specific patterns. And other agents might be able to garner a sizeable payoff for nearly every pattern presented.

The month's posts, part 7
The Cosmos reboot hosted by Neil DeGrasse Tyson is coming along nicely. Despite a few detail-oriented and denialism-related glitches, it has become a great opportunity to make science accessible to a broader audience (episode 2 was exclusively on evolution). I have been providing supplemental references on selected topics from each episode here on Synthetic Daisies. Here are the supplemental readings for the first episode (Section II of "Bits and Starstuff")second episode (Section II of "Futures of More Starstuff"), and third episode (Section II of "Ancien Regimes, Google Grokking, and Starstuff"). Larry Moran at Sandwalk provides his own insights into the factual and conceptual shortcomings of evolution, Cosmos-style. Greg Laden's Blog features a post called "Will Neil DeGrasse Tyson's Cosmos be a turning point in science denialism?", which considers the potential of the Cosmos reboot to combat science denialism. In the spirit of combating bad scientific ideas, Alex B. Berezow at Real Clear Science heeds us to "End the Hype over Epigenetics and Lamarckian Evolution", and does so by highlighting a new paper in Cell [20]. While there have been many interesting recent findings regarding the potential for short-term epigenetic heritability, it is also important to remember why Lamarck fell into disrepute in the first place (HINT: it has to do with long-term mechanisms). And finally, in the spirit of pop-science, here is an infographic from Visual.ly and Juan Martinez on the History of Life 
"Evolution is all about survival of the (your most stable equilibrium here)" -- one possible moral of our story

One lesson learned from modeling evolution as a game is that popular conceptions of evolution such as "survival of the fittest" are fundamentally incorrect. Indeed, modeling mixed strategy intra-specific competition using a rock-paper-scissors game [2] results in a "survival of the weakest". Another lesson is that evolutionary games are more than simply a matter of zero-sum competition or stable cooperation [21]. Despite the metaphor, evolutionary games are more about capturing interactions than direct intentionality. However, contemporary models focus on the role of natural selection in evolution. Yet due to their flexibility, evolutionary games could also be used to model neutral processes and other contributors to evolutionary dynamics.

There are other lessons to be learned as well, including the linkages between micro- and macroevolution and the evolution of sociality. Game-theory models can be combined with other concepts at the intersection of economics and evolutionary biology to understand behavioral signaling and other forms of informative communication. Examples include such as hedging (managing trade-offs), biological markets [22], and handicapping [23]. So is life just one big game? According to game theory and the application of game-inspired models, the answer is "yes".

This month's Carnival is also available in printable form (on Figshare) for teaching purposes. And don't forget to check out next month's Carnival of Evolution. Until then, enjoy this month's posts. And remember, the game is not over until evolution has occurred.

[1] von Neumann, J. and Morgenstern, O.   Theory of Games and Economic Behavior. Princeton Press (1947) AND Nash, J., Kuhn, J.W., Nasar, S.   The Essential John Nash. Princeton Press (2007).

[2] Kerr, B., Riley, M.A., Feldman, M.W., and Bohannan, B.J.M.   Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors. Nature, 418, 171-174 (2002).

[3] Turner, P.E.   Cheating Viruses and Game Theory. American Scientist, 93(5), 428-435 (2005).

[4] For more information, read the following paper: Richardson, J.L., Urban, M.C., Bolnick, D.I., and Skelly, D.K.   Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology and Evolution, 29(3), 165-176 (2014).

[5] Nowak, M.A. and Sigmund, K.   Evolution of Indirect Reciprocity. Nature, 437, 1291-1298 (2005).

[6] Cowden, C.C.   Game theory, evolutionary stable strategies, and the evolution of biological interactions. Nature Education Knowledge, 3(10), 6 (2012).

[7] Rasmussen, E.   Games and Information. Blackwell Publishing (2006).

[8] Weibull, J.W.   Evolutionary Game Theory. MIT Press (1995) AND Brown, J.S. and Vincent, T.L.   Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics. Cambridge University Press (2005).

[9] Nowak, M.A.   Evolutionary Dynamics: exploring the equations of life. Belknap Press (2006) AND Broom, M. and Rychtar, J.   Game-Theoretical Models in Biology. Chapman-Hall CRC Press (2013).

[10] Maynard-Smith, J. and Price, G.R.   The Logic of Animal Conflict. Nature 246 (5427): 15 (1973).

[11] Brown, J.S.   Fit of form and function, diversity of life, and procession of life as an evolutionary game. In "Adaptationism and Optimality", S.H. Orzack and E. Sober eds., Chapter 4 (1999).

[12] Vincent, T.L. and Brown, J.S.   Evolution of ESS Theory. Annual Review of Ecology and Systematics, 19, 423-443 AND Charlesworth, B.   Optimization Models, Quantitative Genetics, and Mutation. Evolution, 44(3), 520-538 (1990).

[13] Cohen, J. and Newman, C.E.   Host-parasite relations and random zero-sum games: the stabilizing effect of strategy diversification. American Naturalist, 133(4), 533-552 (1989) AND Perc, M. and Szolnoki, A. Coevolutionary games: a mini review. Biosystems, 99, 109-125 (2010).

[14] Sinervo, B. and Lively, C.M.   The rock–paper–scissors game and the evolution of alternative male strategies. Nature, 380, 240-243 (1996).

[15] Brembs, B.   Evolution of Cooperation. Brembs.net Evolution section.

[16] Shutters, S.T.   Punishment, Rational Expectations, and Relative Payoffs in a Networked Prisoners Dilemma. In "Social Computing and Behavioral Modeling", H. Liu, J. Salerno, and M.J. Young (eds.), pgs. 1-8 (2009).

[17] McNamara, J.M., Wilson, E.M.K., and Houston, A.I.   Is it better to give information, receive it, or be ignorant in a two-player game? Behavioral Ecology, 17(3), 441-451 (2006).

[18] Basar, T. and Olsder, G.J.   Dynamic Noncooperative Game Theory. Academic Press (1995).

[19] Sigmund, K.   Games of Life: explorations in ecology, evolution, and behavior. Oxford University Press (1993) AND Wolfram, S.   A New Kind of Science. Wolfram Press (2002).

[20] Heard, E. and Martienssen, R.   Transgenerational Epigenetic Inheritance: myths and mechanisms. Cell, 157(1), 95–109 (2014).

[21] Bendor, J. and Swistak, P.   Types of evolutionary stability and the problem of cooperation. PNAS, 92, 3596-3600 (1995).

[22] Noe, R. and Hammerstein, P.   Biological Markets: supply and demand determine the effect of partner choice in cooperation, mutualism, and mating. Behavioral Ecology and Sociobiology, 35(1), 1-11 (1994).

[23] Grafin, A.   Biological Signals as Handicaps. Journal of Theoretical Biology, 144, 517-546 (1990).