Showing posts with label valuation. Show all posts
Showing posts with label valuation. Show all posts

September 21, 2017

An Infographical Survey of the Bitcoin Landscape


Josh Wardini sent me information on a new Bitcoin infographic that serves as a survey of events over the last 10 years in the world of Bitcoin development and legal regulation. Many interesting factoids in this graphic, some of which were unbeknownst to me. In the next few paragraphs, I will discuss my impressions that are brought to bear by each subset of factoids.




The relationship between blockchain and mining is an interesting one, and underscores the power of blockchain as both a data structure and a secure transaction system. Bitcoin is also its own economic system, complete with social interactions. In particular, the competitive and cooperative aspects of cryptocurrency can serve as a model for understanding the social structure of markets.







This is another interesting feature of bitcoin: the network has computational power to both unlock the value of existing blockchain as well as to create new currency. Bitcoin mining has always been a bit of a black box to me [1], but it seems as though it has potentially two roles in the bitcoin economy. In a Synthetic Daisies post from 2014, I mentioned that the supply of bitcoin is fixed (in the manner of a precious metals supply), but it turns out that it is not that simple. Of course, since then blockchain technology has become the latest hot emerging technology in a number of areas unrelated to Bitcoin and even the digital economy [2].



It turns out the computational systems (unlike people) is not all that hard to understand. However, digital currency, which is based on human systems, is much harder to understand (or at least fully appreciate). In 2013, I did a brief Synthetic Daisies mention of a flash crash on one of the main Bitcoin exchanges. There is a lot of opportunity to use blockchain and even perhaps cryptocurrency in the world of research. If ways are found to make these technologies more easily scalable, then they might be applied to many research problems involving human social systems [3].


NOTES:
[1] So I sought out a few introductory materials on Bitcoin mining to clarify what I did not know: 

a) startbitcoin (2016). Beginner's Guide to Mining Bitcoins. 99 Bitcoins blog, July 1.

* mining consists of discovery blocks in the blockchain data structure, the discovery of which is rewarded through a "bounty" of x bitcoins. From there, inequality emerges (or not).

b) Mining page. Bitcoin Wiki.

* the total number of blocks is agreed to by the community, as is the total amount of computational power of the network. This makes the monetary supply nominally fixed, but is not required by the technology.

c) Hashcash Algorithm page. Bitcoin Wiki.

Despite the clear metaphoric overtones, Bitcoin mining is essentially like breaking encryption in that it requires a massive amount of computing power thrown at a computationally hard problem, but is also has elements of an artificial life model (e.g. competition for blockchain elements).

Water-cooled rigs probably maximize your investment margin....

[2] Of course, there has been innovation in the use of blockchain for Bitcoin and more general cryptocurrency transactions. For more, please see:

Portegys, T.E. (2017). Coinspermia: a cryptocurrency unchained. Working Paper, ResearchGate, doi:10.13140/RG.2.2.33317.91360.

Brock, A. (2016). Beyond Blockchain: simple scalable cryptocurrencies. Metacurrency project blog, March 31.

[3] A few potential examples:

a) Data Management. 1  2




July 2, 2017

Excellence vs. Relevance

Impetus for this blog post. Twitter h/t to Alex Lancaster (@LancasterSci).

In academia, the term excellence is often used in the context of scarcity and competitive dynamics (e.g. publications, career promotion), and as a result can be used quite arbitrarily [1]. In [1], a distinction is made between excellence and soundness. Excellence is seen as a subjective concept, while soundness (enabled through completeness, thoroughness, and an emphasis on reproducibility) is the adherence to clearly defined and practiced research standards. While it may also be true that the concept of soundness can suffer from the same subjective limitations, it is probably an improvement over the current discussions surrounding excellence.

Another term we rarely refer to, but may be of even greater importance, is the relevance of scientific research. In a previous post, I brought relevance theory to bear on potential biases in scientific selectivity. One way to think of relevance is as the collective attentional focus of a given research group, community, or field. Collective attention (and thus relevance) can change with time: papers, methods, and influences rise and fall as research ideas are executed and new information is encountered [2]. As such, relevance defines the scope of scientific research that defines a particular field or community of researchers. Given a particular focus, what is relevant defines what is excellent. In this case, we return to the biases inherent in excellence, but this time with a framework for understanding what it means in a given context.

There is also an interesting relationship between soundness and relevance. For example, the stated goal of venues like PLoS One and Scientific Reports is to evaluate manuscripts based on methodological soundness rather than merely on field-specific relevance. To some extent this has eliminated issues of arbitrary selectivity, yet reviewers and editors from various fields may still surruptitiously impose their own field-specific conventions to the review process. Interestingly, soundness itself can be a matter of relevance, as the use of specific methodologies and modes of investigation can be highly field-specific.

Sometimes relevance is a matching problem between an individual researcher and the conventions of a specific field. Relevance can be represented as a formalized conceptual problem using skillset geometries [3]. In the example below, I have shown how the relevance of a specific individual overlaps with what is considered relevant in a specific field. In this case, the researcher has expertise in multiple areas of expertise, while the field is deeply rooted in a single domain of knowledge. The area of overlap, or Area of Mutual Relevance, describes the degree of shared relevance between individual and community (sometimes called "fit").


How relevant is a single person's skillset in the context of a research community, and how do we leverage this expertise in an inclusive manner? The mutual relevance criterion might provide opportunities in cases where there seems to be a "lack of fit". Understanding the role of collective attention within research communities might allow us to consider how this affects both the flow of new ideas between fields and the successful practice of interdisciplinarity.


NOTES:
[1] Moore, S., Neylon, C., Eve, M.P., O'Donnell, D.P., and Pattinson, D. (2016). Excellence R Us: university research and the fetishisation of excellence. Palgrave Communications, 3, 16105. doi:10.1057/palcomms.2016.105.

[2] Wu, F. and Huberman, B.A. (2007). Novelty and collective attention. PNAS, 104(45), 17599-17601. doi: 10.1073/pnas.0704916104.

[3] First introduced in: Alicea, B. (2017). A peripheral Darwin Day post, but centrality in his collaboration graph. Synthetic Daisies blog, February 16.

January 10, 2017

How to Kick-Start a Crypto-Currency

Here is an infographic (see below) I received from interested reader Steve Rogen, which follows up on a critique of Bitcoin I published back in 2014). He pointed me to a blogpost by Dinar Durham (a Financial Tech startup) explaining the concept of an initial coin offering (ICO). 

An ICO is a way for a new crypto-currency to distribute its coinage across a broader number of users than the more standard Bitcoin approach, and eliminates severe favoritism towards early adopters. The infographic itself demonstrates the process of public offering for a new coin. 

According to Dinar Durham's blogpost, ICOs have a mixed track record of success; while some are successful, others are not. However, they are becoming more popular as the number of altcoin types increases



October 22, 2015

Arriving at October 21, 2015...... and beyond

Last year I marked the date, and this year it became a "thing" (at east on the internet). So here are a few links to celebrate the famous date from the "Back to the Future" trilogy.

Billings, L.   Time Travel Simulation Resolves "Grandfather Paradox". Scientific American, September 2 (2015).

"What 'Back to the Future, Part II' Got Wrong (and right)", from the University of Illinois, Urbana-Champaign.


A welcome to the future, from Doc Brown himself:

COURTESY: Universal Studios.

And now..... a bit farther into the future...... The Economics of Star Trek, which is a really active area of internet scholarship:

Transcript of the recent New York ComicCon panel on Trekonomics.

Podcast on the "Economics of Star Trek", courtesy of FW: Thinking.

A few other takes on the Star Trek economy from Noahpinion, Joseph Dickerson, and Slate.

In the future, Spock is on the money. COURTESY: Rick Webb, The Economics of Star Trek: the proto-post scarcity economy.

December 18, 2014

Piketty Reviews: the year in review


Thomas Piketty's book "Capital in the Twenty-first Century" became quite the phenomenon this year. Originally published in French, it was translated into English in 2014 and has since elicited a large amount of feedback. I have collected a series of book reviews over the course of this year that provide a bit of perspective on the book. This could either prove to be prophetic, or another "End of History and the Last Man". The diversity of responses presented here suggests that the relationship between inequality and economic growth will become a defining social issue in years to come.

Even at 696 pages and a large number of graphs, it is quite a captivating read. Piketty synthesizes data from multiple sources and arrives at a fundamental set of relationships between concentrations of capital (e.g. inherited wealth) and economic growth (e.g. the diffusion of capital into the broader economy). Based on this intellectual synthesis, Piketty's presents two laws of inequality [1, 2]. These laws are drawn from the cross-national and historical data analyses. In particular, the second law serves as shorthand for the book's main thesis. While people might debate how exactly to define "wealth" and "growth" or how well this framework describes the macroeconomic present, Piketty's book gives us the conceptual tools to discuss these issues more clearly.

Piketty's insight is quite simple: there is a proportional and often unbalanced relationship between wealth and growth that transcends both nation and historical era. When the returns on inherited wealth exceeds income growth generated by resource exploitation, entrepreneurship, or innovation, high degrees of social and economic inequality result (W > G -- see Figure 1). This often occurs when growth is slow or nonexistant, and the rate of return on inherited capital exceeds growth by default. In terms of social relations, the W > G scenario allows for inherited wealth to triumph over social mobility and new wealth creation. By limiting social mobility, a host of related factors act to reinforce income inequality [3]. Yet this relationship does not always hold. For example, historical periods during which opportunities for economic expansion and social mobility exceed the power of inherited wealth (such as the latter half of the 20th century) tend to be characterized by high rates of conventional growth (e.g. increases in GDP). While the power of inherited wealth is curbed by growth, it might also be curbed by taxation policy. In any case, the second half of the 20th century scenario can be formulated as G > W, or growth exceeding wealth.

Figure 1. Extreme inequality, shown in both artistic and symbolic logical form.

Piketty arrives at this conclusion by using historical data. These data suggest that the slow growth and high levels of inequality which characteerize the early 21st century will recapitulate a pattern typical of the 19th century or even the European middle ages. The predominance of rentier behavior amongst the 21st century elite is indeed reminiscent of the medivel era, where the primary source of wealth generation came from rents paid to a landed gentry [4]. While the mode of wealth generating is variable from century to century, the basic tension between inherited versus newly-generated wealth is predicted to govern economic dynamics. And in this context, inequality can inflence a host of societal characteristics, from social stratification to technological innovation [5].

Perhaps these consequences of inequality are simply a consequence of an over-domineering financial industry, which provides massive returns to investment income relative to labor productivity. In this sense, history is more contextual than cyclical. But history can also parallel broadly-stated theoretical predictions. This state of affairs can be compared with the prediction made by Karl Marx with respect to the end of capitalism itself [6]. As capitalism matures (so-called "late stage" capitalism), we can expect most forms of labor to become devalued. While this is not something that Piketty predicts for the future, this devaluation is due to both various resource consolidations promulgated by the owners of capital and a by-product of technological innnovation (particularly automation -- see [7]). Piketty's solution to countering this type of structural inequality is wealth redistribution, which is something America pioneered [8], but is needed on a global scale to avoid the predicted negative consequences of economic growth stagnation [9].



Here is my collection of Piketty reviews



Introducing Piketty:
Galbraith, J.K.   Kapital for the Twenty-first Century? Institute for New Economic Thinking blog, March 31 (2014).

Frankel, J.   Piketty's Fence. Jeffrey Frankel's blog, September 22 (2014).

Yglesias, M.   The Short Guide to Capital in the 21rst Century. Vox blog, April 8 (2014).

Dorman, P.   Piketty for Dummies. EconoSpeak blog, April 26 (2014).

Wolf, M.   "Capital in the Twenty-first Century", by Thomas Piketty. FinancialTimes.com, April 15 (2014).

R.A.   Thomas Piketty's "Capital", summarized in four paragraphs. Economist, May 4 (2014).

Cowen, T. and de Rugy, V.   Why Piketty's Book Is a Bigger Deal in America Than in France. NYTimes The Upshot, April 29 (2014).

Eakin, E.   Capital Man. Chronicle of Higher Education, April 17 (2014).


Broader Economic Implications:
An Interview with Adair Turner: "Which Capitalism for the 21rst Century?". Institute for New Economic Thinking blog, November 12 (2013).

Hutton, W.   Capitalism simply isn't working and here are the reason why. The Guardian, April 12 (2014).

Boucoyannis, D.   Adam Smith is not the antidote to Thomas Piketty. WaPo Monkey Cage blog, April 22 (2014).

Cassidy, J.   Forces of Divergence: is surging inequality endemic to capitalism? The New Yorker, March 31 (2014).

Krugman, P.   Why we're in a New Gilded Age. New York Review of Books, April 10 (2014).

Shenk, T.   Thomas Piketty and Millenial Marxists on the Scourge of Inequality. The Nation, April 14 (2014).

Faux, J.   Thomas Piketty Undermines the Hallowed Tenets of the Capitalist Catechism. The Nation, April 18 (2014).

Rosenberg, P.   Thomas Piketty terrifies Paul Ryan: Behind the right’s desperate, laughable need to destroy an economist. Salon, April 30 (2014).

Ritholtz, B.   Piketty vs John Stuart Mill’s Marketplace of Ideas. The Big Picture blog, May 1 (2014).

Kaminska, I.   Inequality and Hyperinflation. Dizzynomics blog, April 25 (2014).


Criticisms: In May, there was a post on the Financial Times' Money Supply blog that claimed to find flaws in Piketty's data analyses and basic aproach to studying inequality. The following articles are a rebuttal to these claims.

Piketty, T.   Appendix to Chapter 10. Inequality of capital ownership. Addendum: response to FT. May 28 (2014).

Buchanan, M.   Economists, Show your Assumptions. Bloomberg View, May 6 (2014).

Irwin, N.   Everything You Need to Know About Thomas Piketty vs. The Financial Times. NY Times The Upshot, May 30 (2014).

Winship, S.   Financial Times vs. Piketty on US. Smoke, No Fire. Forbes, June 2 (2014).


Return to an Ancien Regime?
So is growth truly over? Or are we transitioning to a new mode of production? Perhaps it is not the nature of growth that guides thinking about this but the existential need for a powerful ruling class. Such a desire for oligarchy mirrors many popular interpretations of Piketty's main thesis, but in a more fatalistic manner. This hidden cultural theme might explain the recent (and disturbing) trend towards neo-reactionary thought amongst certain segments of Western society [10, 11]. So-called neo-reactionary thinking involves a combination of radical libertarianism with dictatorship. In and of itself, this would be a fairly predictable reaction to a period of great social and economic change. Yet this movement even has legions in the technology industry, a social milieu that a) represents the "new" economy and a prime source of future economic growth, and b) represents an industry that could help us overcome the limitations of traditional growth. This might reflect an inability to think innovatively about social and cultural change, or perhaps it shows how inerred we truly are to old ideas.


NOTES:
[1] Galbraith, J.K.   Unpacking the First Fundamental Law. Economist's View blog, May 25 (2014). AND Krussell, P. and Smith, T.   Is Piketty's "Second Law of Capitalism" Fundamental? Vox blog, June 1 (2014).

[2] von Schaik, T.   Piketty's laws with investment replacement and depreciation. Vox blog, July 6 (2014).

[3] Krugman, P.   Piketty Day Notes. Conscience of a Liberal blog, April 16 (2014).

[4] Kaminska, I.   The Tyrrany of Land. Dizzynomics blog, February 5 (2014).

[5] Hanlon, M.   Why has human progress ground to a halt? Aeon Magazine, December 3 (2014).

[6] Jeffries, S.   Karl Marx's guide to the end of capitalism: a primer. The Guardian, October 20 (2008).

[7] Gordon, R.J.   Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds. NBER Working Paper No. 18315 (2012).

[8] Geier, K.   Taking Aim at Inequality. Blog of the Century, March 12 (2014) AND Yglesias, M.   If growth is dead, we need radical redistribution. Moneybox blog, October 7 (2013).

[9] Cowen, T.   "Unified Growth Theory" by Oded Galor. Marginal Revolution blog, June 9 (2011) AND Kuznets Curve. Wikipedia. December 30 (2013).

[10] Pein, C.   Mouthbreathing Machiavellians Dream of a Silicon Reich. The Baffler, May 19 (2014).

[11] Brin, D.   "Neo-Reactionaries" drop all pretense: End democracy and bring back lords! Contrary Brin blog, November 26 (2013).

October 31, 2014

Introducing the Evolution of Inequality Project

The study of income and resource inequality has been the academic topic du jour this year, highlighted by Thomas Piketty's economic history opus [1] and a special issue of Science [2]. There was even a paper on the 1% vs. the 99% of academic publishing [3] which argues that in terms of citations, the rich tend to get richer. But how do these patterns emerge and evolve? Is it a merely a statistical artifact, or a reflection of how complex, hierarchical societies tend to evolve? For example, we might assume that extreme inequality is maladaptive. But upon simulating a range of artificial societies of different population sizes, initial degrees of stratification, and behavioral features, we might find that extreme inequality tends to occur under specific conditions.


This is the motivating factor for my interest in the topic. Unlike many of the more well-versed approaches to the topic, an alternative view of inequality is a cross between statistical distributions, nuanced views of human sociobiology, and the aftermath of social change. While most economists have taken a materialist view of inequality, I feel that evolutionary perspectives would be helpful in teasing out questions of inequality's origins. This might involve data as diverse as historical data, ethnographic data, evolutionary modeling, and behavioral/neurophysiological data. In the end, we will be able to provide a conceptual alternative to the usual discussion at the intersection of behavior, biology, and social change. An inclusive, multidisciplinary approach is a core component of this project.

My research organization (Orthogonal Research) is trying to initiate work on a project called "The Evolution of Value and Inequality". This project is an attempt to understand the emergence of these social inequalities as a set of evolutionary and biobehavioral phenomena. While the argument can be made that an evolutionary perspective might be helpful in understanding unequal allocations of resources, it is sorely lacking in the general discussion. The broadness of the initiative is necessary to make the connection between the pure inferential approach of evolutionary science coherent public policy outcomes. An initial grant application to the Washington Center for Equitable Growth (submitted January 2014) did not get funded, one reason being that the idea needed more conceptual and empirical fleshing out.


So upon doing some more conceptual refinement, I just finished submitting a second version of this proposal to the Institute for New Economic Thinking (INET). While I will not get into the technical details here, the basic idea is to construct adaptive computational models that mimic a social hierarchy (so-called hierarchical network models). Each node of these directed graphs are informed in their behavior by neuroimaging and other physiological sources of data on human behavior.

The project is focused around evolutionary models of social change (social and cultural change), the underlying assumptions of which can be verified by the collection of biobehavioral data (e.g. neuroimaging experiments). The empirical component is meant to test assumptions of individual and social behavior, and serves as an alternative to the rational expectations assumption that dominates much of conventional economics. However, it also refines many of the model-free findings that
characterize behavioral economics [4].

The evolutionary aspects of this work are also quite interesting. Because we are bridging the short-term (behavioral) and the longer-term (social evolution), there are at least three forms of adaptation: a social learning mechanism, a cultural evolutionary form of selection, and a neurophysiological imperative that satisfies various material, social, and existential needs of an individual. This gives us a fitness and selection criterion that is tangentially related to reproductive success. Subsequent evolutionary algorithms and simulations may bear out the evolutionary dynamics of value construction and social stratification.

Another contribution of this project is to link the statistical aspects of inequality with an evolutionary and demographic framework. The oft-referenced phrase the "1%" or the "0.01%" has its roots in an exponential (non-normal) statistical distribution called the power law. Power laws of various size tend to describe observed income distributions in many different types of society. As inequality increases sharply in a single society, or as different degrees of inequality are observed in different contexts, the power law and in conjunction with various stable states can be used as selection criteria.

Schematic of expected results. Both the C and L parameters refer to operations on intra- and bi-level hierarchical networks dynamics, respectively.

As proposed in the first part of this post, the resulting evolutionary algorithms and experimental inquiries provide us with a possibility space for given outcomes. Given a set of initial conditions, we can observe the tendencies of inequality of resource allocation. If there are common outcomes relative to a number of different initial conditions, this could tell us something cross-cultural and fundamental about the nature of inequality. Ultimately, the outcomes of this project could help to identify and predict opportunities to head off crises and as an architecture for achieving sustainable economic growth.

NOTES:
[1] Piketty, T.   Capital in the 21rst Century. Harvard University Press (2014).

[2] Citation for the special issue: Science, 344, May 23 (2014). One article with particular relevance to social evolution is: Pringle, H.    The Ancient Roots of the 1%. Science, 344, 822-825 (2014).

[3] Ioannidis JPA, Boyack KW, Klavans R.   Estimates of the Continuously Publishing Core in the Scientific Workforce. PLoS One, 9(7), e101698. doi:10.1371/journal.pone.0101698 (2014).

[4] Camerer, C.F. and Loewenstein, G.   Behavioral Economics: past, present, future. In "Advances in Behavioral Economics". C.F. Camerer, G. Loewenstein, and M. Rabin eds. Chapter 1. Russell Sage Foundation (2004).

Behavioral Economics Reading List. Russell Sage Foundation blog, March 23 (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).


IMAGES:
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.

January 24, 2014

On Bet-hedging and Evolutionary Futures

When someone mentions "bet-hedging", the first thing that comes to mind is an economic investment or gambling strategy, one that maximizes a human's return on their investment. This necessitates cognitive mechanisms for decision-making and valuation. For example, a bet-hedger might keep their investments in two places (e.g a rowboat and a hang-glider). When the return on one potential source of income is exhausted (e.g. rowboat goes over the falls), the other investment can be drawn upon to pick up the slack. Overall, total losses are minimized and potential gains are maximized.

Bet-hedging as a human investment strategy. Hence the rowboat and hang-glider analogy.

But bet-hedging has also been used as a means to explain biological "decision-making" with respect to adaptive changes in genotype and/or phenotype. In this case, decision-making refers to directed changes in a lineage that emerge from stochastic mechanisms. There is no need for a set of formal cognitive mechanisms (or a designer), because in this case natural selection plays the role of a brain (e.g. information processing). In the case of biological bet-hedging, an organism hedges between two or more phenotypic/genotypic states (or behavioral strategies), one becoming predominant only when encouraged by environmental conditions.

Figure 1. A statistical view of biological bet-hedging. COURTESY: Discussion of bet-hedging and adaptation to environmental stresses in [1].

To understand how biological bet-hedging might work, we can use a Venn diagram to take a statistical view of the process (Figure 1). Some form of switching (phenotypic, genotypic) is induced by a subset of environmental stimuli. A smaller subset of positive responses are triggered by environmental stress. This relatively small resulting set of adaptive responses (switching due to an environmental stress signal) should (in theory) be the outcomes of highest fitness value. 

There are many ways this bet-hedging can be accomplished, as the existing literature is quite diverse. I will focus on two candidate mechanisms from the literature: selection on random noise and selection on standing variation.

Selection on random noise is driven by inherent oscillations in gene expression. Gene expression differences (e.g. differential gene expression) are usually thought to define distinct biological processes and cell types. However, gene expression also exhibits wide-band fluctuations [2] and "bursty" changes over time [3], even within the same process or cell type. A recent review in Science on oscillating gene expression over time (e.g. gene expression noise) and biological bet-hedging [4] discusses these mechanisms in more detail.

Figure 2. Type A transcriptional oscillations, sensu [4]. One example of genotypic bet-hedging.

The first type of bet-hedging (type A) is indirect, and involves retaining two or more context-dependent mechanisms for the expression of a single gene. In Figure 2, the successful transcription of a downstream target gene depends on the synchronized activity of two upstream transcription factors. In this case, each upstream gene fluctuates with respect to time. Only when their fluctuations become coordinated in-phaseare they capable of activating a downstream target. In fact, in order to exhibit this selective synchronization, the upstream gene expression must be oscillatory. Otherwise, the downstream gene would not be sensitive to environmental context.

Figure 3. Type B transcriptional oscillations, sensu [4]. Another example of genotypic bet-hedging.

By contrast, type B bet-hedging (direct) is a matter of selecting between alternating genotypic or phenotypic states. In this case, upstream genetic mechanisms create a bistable switch which allows the organism to switch between states given an environmental signal. In the case of stochastic bet-hedging, switching can be like a roulette wheel, in which all possible states are generated spontaneously. The most (as opposed to transient) stable states are those which are most strongly supported by the environment. This is a candidate model for how cells acquire and maintain their identity in microenvironmental niches. Yet it is also relevant to evolutionary change.

Instead of individual phenotypes being the focus of selection, perhaps the ability to switch between phenotypes itself is selected as a trait. For example, stochastic phenotype switching in bacteria results in persistence in the face of rapid environmental variation [5]. Using Pseudomonas fluorescens lineages, phenotypic switching can be experimentally evolved. In fact, the capacity for switching can be isolated to a single gene mutation (CarB), which is both sufficient and necessary for the colony switching trait.

Although stochastic switching is a single trait, it is by no means a simple one. Not only does CarB enable colony switching, but lineages that carry this mutation have a higher fitness compared to those that do not among mixed cultures in a static environment. In the experiments of [5], it took multiple rounds of selection to evolve the CarB mutant. This may also be due to enabling mutations and lineage-specific dependencies which set up the transition to a CarB mutant phenotype.

CarB mutation, examined within a single bet-hedging lineage. TOP: fitness, MIDDLE: genotypes, BOTTOM: phenotypes.


Now we will jump to a highly-speculative infographic [6] on future events in Earth's history. "Timeline of the Far Future" proposes events from the present to Earth's best-case scenario astrological death. In 100 quadrillion (1020) years, the Earth's orbit is predicted to fall into the Sun, if the red giant Sun does not engulf the Earth before then.


In any case, five events on this timeline are relevant to the future of life and worthy of discussion. Keep in mind that this infographic is not sequentially consistent, as it is based on multiple sources of data and overlapping potential scenarios. These events include:

* end of Eukaryotic life, in 1.3 billion years.

* end of Prokaryotic and Archean life, in 2.8 billion years.

* end of C3 photosynthesis, in 600 million years.

* end of C4 photosynthesis, in 800 million years.

* Earth's temperature rises to 47C due to an increase in solar luminosity, in 1 billion years.

Some of the predictions are provocative, such as the extinction of the Y chromosome [7]. However, one set of predictions intersect with the literature on bet-hedging: the end of photosynthesis. What we know about the possible end of photosynthesis comes from studies [8, 9] that examine the instability and breakdown of photosynthetic reactions at high temperatures. A primary producer's photosynthesis rate becomes unstable above a threshold temperature [8]. When temperature is lowered, this rate returns to normal.

When applied in a laboratory or experienced during heat waves, severe heat stress can cause cellular damage. In fact, large-scale process-based models of photosynthesis assumes that the rate returns to normal after environmental shocks [9]. However, what happens in cases where the average temperature reaches or exceeds this threshold? In such cases, extreme temperatures are not experienced as shocks, but as the physiological set-point.

View of a "red giant" Sun from a now-barren (far future) Earth. Humans, the oceans, and perhaps all extremophiles are all gone at this point.

According to the infographic's predictions, in 0.6 to 2.8 billion that will be that (although I'm not sure why primary producers would be the first type of life to die out). Yet there might be three fundamental changes to life's complexity as we reach a red giant Sun that enable it to survive until perhaps the physical end of the Earth (and/or Sun): 

* the radical restructuring of organismal physiology to suit temperatures that will approach the boiling point of water. This might include hard insulating shells, smaller areas of exposed surface, and "interesting" changes to metabolism. The hedging concept could come into play here, as the expression of genes and phenotypes associated with metabolic function in all organisms (not just single-celled ones) could fluctuate significantly across life-history.

* the radical restructuring of food webs so as to reduce any one source of primary or secondary production. Bets would be hedged in order to survive ever increasing extreme conditions. The increase of energy in the biosphere could lead to more energy being available in general. But of course there could be biospheric tradeoffs (atmospheric composition) which could limit ecological complexity. The sources of primary production would of course need to adapt to take advantage of this situation.

* the evolution of primary production itself. Like complex organisms, we should not expect photosynthesis to simply disappear. Recall that C4 photosynthesis was dominant in the Paleozoic era, only to be eclipsed by the C3 variety during the Mesozoic era. And this transition occurred despite C4 photosynthesis being more efficient than C3 in times of heat stress and draught [10]. It might turn out that a new type of hyper-efficient and multiphasic photosynthesis could evolve that takes advantage of late solar system conditions.

These points are speculative as well, but remember -- fully-functioning ecosystems that are gradually exposed to extreme conditions will likely adapt instead of coming to an abrupt end. It would be hard to transplant existing organisms (even extremophiles) into these conditions, but it might not be as hard to evolve responses to the extremities of late Earth. 

NOTES:
[1] Mostowy, R.   Evolution of Stress Response in the Face of Unreliable Environmental Signals. Rafal Mostowy blog, August 20 (2012).

[2] Eldar, A. and Elowitz, M.B.   Functional roles for noise in genetic circuits. Nature, 467, 167-173 (2010).

[3] Goh, K-I. and Barabasi, A-L.   Burstiness and Memory in Complex Systems. arXiv:0610233.

[4] Levine, J.H., Lin, Y., and Elowitz, M.B.   Functional Roles of Pulsing in Genetic Circuits. Science, 342, 1193 (2013).

[5] Beaumont, H.J.E., Gallie, J., Kost, C., Ferguson, G.C., and Rainey, P.B.   Experimental evolution of bet hedging. Nature, 462, 90-93 (2009).

[6] Timeline of the Far Future. BBC Future, January 6 (2014).

[7] This is an example of a scientific debate disguised as persistent myth in the popular press. For two perspectives (the former Y-optimist, the latter Y-pessimist), please see:

a) Hughes, J.F. et.al   Strict evolutionary conservation followed rapid gene loss on human and rhesus Y chromosomes. Nature, 483, 82-86 (2012).

b) Aitken, R.J. and Marshall-Graves, J.A.   Human Spermatozoa: the future of sex. Nature, 415, 963 (2002).

[8] Sage, R.F. and Kubien, D.S.   The temperature response of C3 and C4 photosynthesis. Plant, Cell, and Environment, 30, 1086-1106 (2007).

[9] Huve, K., Bichele, I., Rasulov, B., and Niinemets, U.   When it is too hot for Photosynthesis: heat-induced instability of photosynthesis in relation to respiratory burst, cell permeability changes and H2O2 formation. Plant, Cell, and Environment, 34, 113-126 (2011).

[10] Liu, Z., Sun, N., Yang, S., Zhao, Y., Wang, X., Hao, X., and Qiao, Z.   Evolutionary transition from C3 to C4 photosynthesis and the route to C4 rice. Biologia, 68(4), 577-586 (2013).

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