literally, as in Keynes’ (1936) phrase, taking into account “what average opinion expects the average opinion to be.”
page 46
…perfect rationality in the market cannot be well defined. Infinitely intelligent agents cannot form expectations in a determinate way.
This type of behavior–coming up with appropriate hypothetical models to act upon, strengthening confidence in those that are validated, and discarding those that are not–is called inductive reasoning.
page 47
We see immediately that the market possesses a psychology. We define this as the collection of market hypotheses, or expectational models or mental beliefs, that are being acted upon at a given time.
page 48
the first(?) mention of a genetic model in the book
The ALife conferences are the major meeting of the artificial life research community since 1987. For its 15th edition in 2016, it was held in Latin America for the first time, in the Mayan Riviera, Mexico, from July 4 -8. The special them of the conference: How can the synthetic study of living systems contribute to societies: scientifically, technically, and culturally? The goal of the conference theme is to better understand societies with the purpose of using this understanding for a more efficient management and development of social systems.
A fascinating tool for exploring how, where and when diets evolve. Foodwise, what unites Cameroon, Nigeria and Grenada? How about Cape Verde, Colombia and Peru? As of today, you can visit a website to find out. The site is the brainchild of Colin Khoury and his colleagues, and is intended to make it easier to see the trends hidden within 50 years of annual food data from more than 150 countries. If that rings a bell, it may be because you heard the episode around three years ago, in which Khoury and I talked about the massive paper he and his colleagues had published on the global standard diet. Back then, the researchers found it easy enough to explain the overall global trends that emerged from the data, but more detailed questions – about particular crops, or countries, or food groups – were much more difficult to answer. The answer to that one? An interactive website.
While this seems a short and simple episode with some engaging conversation, it’s the podcast equivalent of the floating duck–things appear smooth and calm on the surface, but the duck is paddling like the devil underneath the surface. The Changing Global Diet website is truly spectacular and portends to have me losing a day’s worth of work or more over the next few days.
I’d be curious to see what some of the data overlays between and among some of these projects looked like and what connections they might show. I suspect that some of the food diversity questions may play into the economic complexities that countries exhibit as well.
If there were longer term data over the past 10,000+ years to make this a big history and food related thing, that would be phenomenal too, though I suspect that there just isn’t enough data to make a longer time line truly useful.
References
[1]
D. Hartmann, M. R. Guevara, C. Jara-Figueroa, M. Aristarán, and C. A. Hidalgo, “Linking Economic Complexity, Institutions, and Income Inequality,” World Development, vol. 93. Elsevier BV, pp. 75–93, May-2017 [Online]. Available: http://dx.doi.org/10.1016/j.worlddev.2016.12.020
[2]
S. Ronen, B. Gonçalves, K. Z. Hu, A. Vespignani, S. Pinker, and C. A. Hidalgo, “Links that speak: The global language network and its association with global fame,” Proceedings of the National Academy of Sciences, vol. 111, no. 52. Proceedings of the National Academy of Sciences, pp. E5616–E5622, 15-Dec-2014 [Online]. Available: http://dx.doi.org/10.1073/pnas.1410931111
This book highlights cutting-edge research in the field of network science, offering scientists, researchers and graduate students a unique opportunity to catch up on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the fifth International Workshop on Complex Networks & their Applications (COMPLEX NETWORKS 2016), which took place in Milan during the last week of November 2016. The carefully selected papers are divided into 11 sections reflecting the diversity and richness of research areas in the field. More specifically, the following topics are covered: Network models; Network measures; Community structure; Network dynamics; Diffusion, epidemics and spreading processes; Resilience and control; Network visualization; Social and political networks; Networks in finance and economics; Biological and ecological networks; and Network analysis.
DOI: 10.1007/978-3-319-50901-3;
Part of the Studies in Computational Intelligence book series (SCI, volume 693)
Recent advances suggest that the concept of information might hold the key to unravelling the mystery of life's nature and origin. Fresh insights from a broad and authoritative range of articulate and respected experts focus on the transition from matter to life, and hence reconcile the deep conceptual schism between the way we describe physical and biological systems. A unique cross-disciplinary perspective, drawing on expertise from philosophy, biology, chemistry, physics, and cognitive and social sciences, provides a new way to look at the deepest questions of our existence. This book addresses the role of information in life, and how it can make a difference to what we know about the world. Students, researchers, and all those interested in what life is and how it began will gain insights into the nature of life and its origins that touch on nearly every domain of science.
Hardcover: 514 pages;
ISBN-10: 1107150531;
ISBN-13: 978-1107150539;
Chapter 2 is a nice piece on the El Farol Problem which is a paradox which “represented a decision problem where expectations (forecasts) that many would attend [the El Farol bar] would lead to few attending, and expectations that few would attend would lead to many attending: expectations would lead to outcomes that would negate these expectations.”
Zhang and Challet generalized this problem into the Minority Game in game theoretic form.
Page 31:
There are two reasons for perfect or deductive rationality to break down under complication. The obvious one is that beyond a certain level of of complexity human logical capacity ceases to cope–human rationality is bounded. The other is that in interactive situations of complication, agents cannot rely upon the other agents they are dealing with to behave under perfect rationality, and so they are forced to guess their behavior. This lands them in a world of subjective beliefs and subjective beliefs about subjective beliefs. Objective, well-defined, shared assumptions then cease to apply. In turn, rational, deductive reasoning (deriving a conclusion by perfect logical processes from well-defined premises) itself cannot apply. The problem becomes ill-defined.
This passage, though in an economics text, seems to be a perfect statement about part of the problem of governing in the United States at the moment. I have a thesis that Donald Trump is a system 1 thinker and is generally incapable of system 2 level thought, thus he has no ability to discern the overall complexity of the situations in which he finds himself (or in which the United States finds itself). As a result, he’s unable to effectively lead. From a complexity and game theoretic standpoint, he feels he’s able to perfectly play and win any game. His problem is that he feels like he’s playing tic-tac-toe, while many see at least a game as complex as checkers. In reality, he’s playing a game far more complex than either chess or go.
The overall problem laid out in this chapter is an interesting one vis-a-vis the issues many restaurant startups face, particularly in large cities. How can they best maximize their attendance not only presently, but in the long term while staying afloat in very crowded market places.
Page 38:
The level at which humans can apply perfect rationality is surprisingly modest. Yet it has not been clear how to deal with imperfect or bounded rationality.
Chapter 3 takes a similar problem as Chapter 2 and ups the complexity of the problem somewhat substantially. While I understand that at the time these problems may have seemed cutting edge and incomprehensible to most, I find myself wondering how they didn’t see it all from the beginning.
This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance.
The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering.
ISBN: 978-3-319-43221-2 (Print), 978-3-319-43222-9 (Online)
I'm giving a talk at the Stanford Complexity Group this Thursday afternoon, April 20th. If you're around - like in Silicon Valley - please drop by! It will be in Clark S361 at 4 pm.
Here's the idea. Everyone likes to say that biology is all about information. There's something true about this - just think about DNA. But what does this insight actually do for us? To figure it out, we need to do some work.
Biology is also about things that can make copies of themselves. So it makes sense to figure out how information theory is connected to the 'replicator equation' — a simple model of population dynamics for self-replicating entities.
To see the connection, we need to use relative information: the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Then everything pops into sharp focus.
It turns out that free energy — energy in forms that can actually be used, not just waste heat — is a special case of relative information Since the decrease of free energy is what drives chemical reactions, biochemistry is founded on relative information.
But there's a lot more to it than this! Using relative information we can also see evolution as a learning process, fix the problems with Fisher's fundamental theorem of natural selection, and more.
So this what I'll talk about! You can see slides of an old version here:
http://math.ucr.edu/home/baez/bio_asu/
but my Stanford talk will be videotaped and it'll eventually be here:
https://www.youtube.com/user/StanfordComplexity
You can already see lots of cool talks at this location!
#biology
Wondering if there’s a way I can manufacture a reason to head to Northern California this week…
A country's mix of products predicts its subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries exporting complex products (as measured by the Economic Complexity Index) have lower levels of income inequality than countries exporting simpler products. Using multivariate regression analysis, we show that economic complexity is a significant and negative predictor of income inequality and that this relationship is robust to controlling for aggregate measures of income, institutions, export concentration, and human capital. Moreover, we introduce a measure that associates a product to a level of income inequality equal to the average GINI of the countries exporting that product (weighted by the share the product represents in that country's export basket). We use this measure together with the network of related products (or product space) to illustrate how the development of new products is associated with changes in income inequality. These findings show that economic complexity captures information about an economy's level of development that is relevant to the ways an economy generates and distributes its income. Moreover, these findings suggest that a country's productive structure may limit its range of income inequality. Finally, we make our results available through an online resource that allows for its users to visualize the structural transformation of over 150 countries and their associated changes in income inequality between 1963 and 2008.
One of America’s foremost philosophers offers a major new account of the origins of the conscious mind.
How did we come to have minds?
For centuries, this question has intrigued psychologists, physicists, poets, and philosophers, who have wondered how the human mind developed its unrivaled ability to create, imagine, and explain. Disciples of Darwin have long aspired to explain how consciousness, language, and culture could have appeared through natural selection, blazing promising trails that tend, however, to end in confusion and controversy. Even though our understanding of the inner workings of proteins, neurons, and DNA is deeper than ever before, the matter of how our minds came to be has largely remained a mystery.
That is now changing, says Daniel C. Dennett. In From Bacteria to Bach and Back, his most comprehensive exploration of evolutionary thinking yet, he builds on ideas from computer science and biology to show how a comprehending mind could in fact have arisen from a mindless process of natural selection. Part philosophical whodunit, part bold scientific conjecture, this landmark work enlarges themes that have sustained Dennett’s legendary career at the forefront of philosophical thought.
In his inimitable style―laced with wit and arresting thought experiments―Dennett explains that a crucial shift occurred when humans developed the ability to share memes, or ways of doing things not based in genetic instinct. Language, itself composed of memes, turbocharged this interplay. Competition among memes―a form of natural selection―produced thinking tools so well-designed that they gave us the power to design our own memes. The result, a mind that not only perceives and controls but can create and comprehend, was thus largely shaped by the process of cultural evolution.
An agenda-setting book for a new generation of philosophers, scientists, and thinkers, From Bacteria to Bach and Back will delight and entertain anyone eager to make sense of how the mind works and how it came about.
This book originated from a series of papers which were published in "Die Naturwissenschaften" in 1977178. Its division into three parts is the reflection of a logic structure, which may be abstracted in the form of three theses:
A. Hypercycles are a principle of natural self-organization allowing an integration and coherent evolution of a set of functionally coupled self-replicative entities.
B. Hypercycles are a novel class of nonlinear reaction networks with unique properties, amenable to a unified mathematical treatment.
C. Hypercycles are able to originate in the mutant distribution of a single Darwinian quasi-species through stabilization of its diverging mutant genes. Once nucleated hypercycles evolve to higher complexity by a process analogous to gene duplication and specialization. In order to outline the meaning of the first statement we may refer to another principle of material self organization, namely to Darwin's principle of natural selection. This principle as we see it today represents the only understood means for creating information, be it the blue print for a complex living organism which evolved from less complex ancestral forms, or be it a meaningful sequence of letters the selection of which can be simulated by evolutionary model games.
Understanding the emergence and robustness of life requires accounting for both chemical specificity and statistical generality. We argue that the reverse of a common observation—that life requires a source of free energy to persist—provides an appropriate principle to understand the emergence, organization, and persistence of life on earth. Life, and in particular core biochemistry, has many properties of a relaxation channel that was driven into existence by free energy stresses from the earth's geochemistry. Like lightning or convective storms, the carbon, nitrogen, and phosphorus fluxes through core anabolic pathways make sense as the order parameters in a phase transition from an abiotic to a living state of the geosphere. Interpreting core pathways as order parameters would both explain their stability over billions of years, and perhaps predict the uniqueness of specific optimal chemical pathways.
H. Morowitz and E. Smith, “Energy flow and the organization of life,” Complexity, vol. 13, no. 1. Wiley-Blackwell, pp. 51–59, 2007 [Online]. Available: http://dx.doi.org/10.1002/cplx.20191
Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.
This is a nice little general interest article by Philip Ball that does a relatively good job of covering several of my favorite topics (information theory, biology, complexity) for the layperson. While it stays relatively basic, it links to a handful of really great references, many of which I’ve already read, though several appear to be new to me. [1][2][3][4][5][6][7][8][9][10]
While Ball has a broad area of interests and coverage in his work, he’s certainly one of the best journalists working in this subarea of interests today. I highly recommend his work to those who find this area interesting.
References
[1]
E. Mayr, What Makes Biology Unique? Cambridge University Press, 2004.
[2]
A. Wissner-Gross and C. Freer, “Causal entropic forces.,” Phys Rev Lett, vol. 110, no. 16, p. 168702, Apr. 2013. [PubMed]
[3]
A. Barato and U. Seifert, “Thermodynamic uncertainty relation for biomolecular processes.,” Phys Rev Lett, vol. 114, no. 15, p. 158101, Apr. 2015. [PubMed]
[4]
J. Shay and W. Wright, “Hayflick, his limit, and cellular ageing.,” Nat Rev Mol Cell Biol, vol. 1, no. 1, pp. 72–6, Oct. 2000. [PubMed]
[5]
X. Dong, B. Milholland, and J. Vijg, “Evidence for a limit to human lifespan,” Nature, vol. 538, no. 7624. Springer Nature, pp. 257–259, 05-Oct-2016 [Online]. Available: http://dx.doi.org/10.1038/nature19793
R. Landauer, “Irreversibility and Heat Generation in the Computing Process,” IBM Journal of Research and Development, vol. 5, no. 3. IBM, pp. 183–191, Jul-1961 [Online]. Available: http://dx.doi.org/10.1147/rd.53.0183
N. Perunov, R. A. Marsland, and J. L. England, “Statistical Physics of Adaptation,” Physical Review X, vol. 6, no. 2. American Physical Society (APS), 16-Jun-2016 [Online]. Available: http://dx.doi.org/10.1103/PhysRevX.6.021036 [Source]
[10]
S. Still, D. A. Sivak, A. J. Bell, and G. E. Crooks, “Thermodynamics of Prediction,” Physical Review Letters, vol. 109, no. 12. American Physical Society (APS), 19-Sep-2012 [Online]. Available: http://dx.doi.org/10.1103/PhysRevLett.109.120604 [Source]
Daniel C. Dennett