Charles Darwin meets Rube Goldberg: a tale of biological convolutedness

Charles Darwin studying a Rube Goldberg Machine (Freepik Generative AI, text-to-image)

For this Darwin Day post (2024), I will discuss the paper Machinery of Biocomplexity [1]. This paper introduces the notion of Rube Goldberg machines as a way to explore biological complexity and non-optimal function. This concept was first highlighted on Synthetic Daises in 2009 [2], while an earlier version of the paper was discussed on Synthetic Daisies in 2013 [3]. The paper was revised in 2014 to include a number of more advanced computational concepts, after a talk to the Network Frontiers Workshop at Northwestern University in 2013 [4]. 

Figure 1. Block and arrow model of a biological RGM (bRGM) that captures the non-optimal changes resulting from greater complexity. Mutation/Co-option removes the connection between A and B, then establishes a new set of connection with D. Inversion (bottom) flips the direction of connections between C-B and C-A, while also removing the output. This results in the addition of E and D which reestablishes the output in a circuitous manner.

Biological Rube Goldberg Machines (bRGNs) are defined as a computational abstraction of convoluted, non-optimal mechanisms. Non-optimal biological systems are represented using flexible Markovian box and arrow models that can be mutated and expanded given functional imperatives [5]. Non-optimality is captured through the principle of "maximum intermediate steps": biological systems such as neural pathways, metabolic reactions, and serial interactions do not evolve to the shortest route but is constrained (and perhaps even converge to) the largest number of steps. This results in a set of biological traits that functionally emerge as a biological process. Figure 1B shows an example where maximal steps represents a balance between the path of least resistance and exploration given constraints on possible interconnections [6]. The paths from A-E, E-B, and C-D are the paths of least resistance given the constraints of structure and function. In the sense that optimality is a practical outcome of physiological function, a great degree of intermediacy can preserve unconventional pathways that are utilized only spontaneously.

This can be seen in a wide variety of biological systems and is a consequence of evolution. Evolutionary exaptation, the evolution of alternative functions, and serial innovation all result in systems with a large number of steps from input to output. But sometimes convolution is the evolutionary imperative in and of itself. As fitness criteria change over evolutionary time, traces of these historical trajectories can be observed in redundant pathways and other results of subsequent evolutionary neutrality. One example from the paper involves a multiscale model (genotype-to-phenotype) that exploits both tree depth and lateral connectivity to maximize innovation in the production of a phenotype (Figure 2). While our models are based on connections between discrete states, bRGMs can also provide insight into the evolution of looser collections of single traits and even networks, where the sequence of function is bidirectional and hard to follow in stepwise fashion.

Figure 2.  A hypothetical biological RGM representing a multi-scale relationship. Each set of elements (A-F) represents the number of elements at each scale (actual and potential connections are shown with bold and thin lines, respectively). Examples of convolutedness incorporate both loops (as with E5,1 and E5,5) and the depth of the entire network.

The paper also features extensions of the basic bRGM, including massively convoluted architectures and microfluidic implementations. In the former, interconnected networks represent systems that are not only maximal in terms of size or length, but also massively topologically complex [7]. One example of this is cortical folding and the resulting neuronal connectivity in Mammalian brains. The latter example is based on fluid dynamics and combinatorial architectures that are more in line with discrete bRGMs (Figure 3). 

Figure 3. A microfluidic-inspired bRGM model that mimics the complexity of biological fluid dynamics (e.g. blood vessel networks). G1, G2, and G3 represent iterations of the system.

References:

[1] Alicea, B. (2014). The "Machinery" of Biocomplexity: understanding non-optimal architectures in biological systems. arXiv, 1104.3559.

[2] Non-razors, unite! January 30, 2009. https://syntheticdaisies.blogspot.com/2009/01/non-razors-unite.html

[3] Maps, Models, and Concepts: July Edition. Synthetic Daises blog. July 13, 2013. https://syntheticdaisies.blogspot.com/2013/07/maps-models-and-concepts-july-edition.html

[4] Inspired by a visit to the Network's Frontier....  Synthetic Daises blog. December 16, 2013. https://syntheticdaisies.blogspot.com/2013/12/fireside-science-inspired-by-visit-to.html

[5] when dealing with a large number of steps or in a polygenic context, these types of models can also resemble renormalization groups. For more on renormalization group, please see: Wilson, K.G. (1975). Renormalization group methods. Advances in Mathematics, 16(2), 170-186.

[6] this balance is as predicted by Constructive Neutral Evolution (CNE). For a relevant paper, please see: Gray et.al (2010). Irremediable Complexity? Science, 330(6006), 920-921.

[7] in the paper, this is referred to as Spaghettification, a term borrowed from the physics of gravitation. See this reference for an interesting implementation of this in soft materials:  Bonamassa et.al (2024). Bundling by volume exclusion in non-equilibrium spaghetti. arXiv, 2401.02579.

Heuristic Haystacks and the Messy Lesson

As an admitted and self-styled parsimony skeptic, I was interested to see a discussion in the blogosphere on the seductive allure of simple explanations [1]. This was in the context of economic policy and decision-making, with Paul Krugman even offering an H.L. Mencken quote: "For every complex problem there is an answer that is clear, simple, and wrong" [2]. Yet while parsimony was never brought up, I suspect that hypotheses and arguments related to the efficient markets hypothesis were always somewhat in mind.

There are, of course, broader parallels between seductive simplicity and parsimony. As I have pointed out before, I find parsimony to be a overly-seductive null model [3]. The simplest explanation often leads us not to the truth, but to what is most conceptually consistent. In some cases (where theory is well-established) this works out well. Intuition in support of serendipity and serendipity in support of discovery is an unassuming (and often underplayed) pillar of science [4]. However, in cases where our intuitions get in the way of objective analysis, this becomes problematic. And this seeming exception is actually quite common. In a related manner, this brings up an interesting problem of the relationship between parsimony as a decision-making criterion and the epistomology of a scientific phenomenon.

An appalling lack of faith in both Occam's and Einstein's worldviews. More horrifying details in my Ignite! talk on the topic.

This relationship, or more accurately inconsistency, is due to argumentatively-influenced judgments on a naturalistic search space. Even in children, it is observed that argumentation is rife with confirmation bias and logically arguing to absurd positions [5]. While argumentation allows us to build hypotheses, it also gets us stuck in a conceptual minimum (my own ad-hoc phrase). In a previous post, I pointed to recent work on how belief systems and associated systems of argumentation can shape our perception of reality. But, of course, this cannot will the natural world to our liking. In fact, it often serves to muddy the conceptual and theoretical waters [6]. Therefore, you often have a conceptual gap unrelated to problem incompleteness which we will flesh out in the rest of this post.

The first point to be made here is that such an inconsistency introduces two biases that shape how we think about the simplest explanation, and more generally about what is optimal. First of all, can we even find the true simplest explanation? Perhaps the simplest possible statement that can be constructed cannot capture the true complexity of a given situation. This is particularly true when there are competing dimensions (or layers or levels) of complexity. Secondly, and particularly in the face of complexity, simplicity can often be a foil to deep understanding. Unfortunately, this is often conceptualized of and practiced upon in a destructive way, favoring simple and homogeneous mental models over more subtle ones.

How to dream of complex sheep....

In the parlance of decision-making theory, parsimony is consistent with the notion of good-enough heuristics. In the work of Gigerenzer [7], such heuristics are claimed to be nearly optimal when compared to formal analysis of a problem. This can also be seen with statistical prediction rules that outperform human judgments in a number of everyday contexts [8]. But is this a statement of problem "wickedness", or a statement of superiority with respect to human cognition? When compared to problems that require needle in a haystack criteria, fast and frugal heuristics (and hence parsimony) is severely lacking.

So complexity introduces a secondary bias at best and serves as a severe limitation to achieving parsimony at worst. One might expect that experimentally verifying a prediction made in conjunction with Occam's Razor requires finding an exact analytical solution. Finding this proverbial "needle in a haystack" requires both a multi-criterion, algorithmically-friendly heuristic solution in addition to a formal strategy that often defies intuition. Seemingly, the simple solution cannot keep up.

I found it! It was quick, but I was also quite lucky,

NOTES:
[1] The Simplicity Paradox. Stumbling and Mumbling blog, September 9 (2014) AND Krugman, P.   Simply Unacceptable. The Conscience of a Liberal blog, September 5 (2014).

[2] This is not to equate parsimony with methodological snake oil -- in fact, I am arguing quite the opposite. But I am merely pointing out that parsimony is an incomplete hypothesis for acquiring knowledge.

[3] For more, please see this Synthetic Daisies post: Alicea, B.   Argument from Non-Optimality: what does it mean to be optimal? Synthetic Daisies blog, July 28 (2013).

[4] Kantorovich, A.   Scientific Discovery: Logic and Tinkering. SUNY Press, Albany (1993).

[5] I say "even" in children even though the latter (logically arguing to absurd conclusions) is often expected from children. But we see these things in adults as well, and such is the point of argumentation theory. For more, please see: Mercier, H.   Reasoning Serves Argumentation in Children. Cognitive Development, 26(3), 177–191 (2011).

[6] Wolchover, N.   Is Nature Unnatural? Quanta Magazine, May 24 (2013).

[7] While there are likely other (and perhaps better) examples, I am using a reference cited in [1]: Gigerenzer, G.   Bounded and Rational. In "Contemporary Debates in Cognitive Science", R.J. Stainton eds. Blackwell, Oxford, UK (2006).

[8] lukeprog   Statistical Prediction Rules Out-Perform Expert Human Judgments. LessWrong blog, January 18 (2011).

Argument from Non-Optimality: what does it mean to be optimal?

One of my theoretical interests, which has been previously featured on this blog, is something called non-optimality [1]. Non-optimality, as you might surmise, is the tendency for systems to not behave optimally or result in optimal outcomes. This is challenging to people to wrap their head around, as there are entire shelves of library books on optimization methods. Additionally, there have also been several attempts to focus on the nature of non-optimal outcomes in biology and human behavior [2].

Example of the perfect mousetrap, or example of the least optimal biological system: the laryngeal nerve in Giraffa camelopardalis (giraffe). COURTESY: NatGeo YouTube video.

Optimization, whether through approximation or through the use of multiple criteria, is the standard world-view in fields as diverse as economics and physics to engineering and computer science. However, as one moves towards the social and natural sciences, an interesting phenomenon emerges. While optimality criteria can be applied to specific outcomes using theoretical models, they may only sporadically describe general trends [3].

This is an example of ant colony optimization (ACO), an engineering technique derived from insect ethology (e.g. collective behavior in ants, soemtimes referred to as stigmergy). In nature, ants find an optimal (e.g. the shortest) path to a food source by integrating multiple environmental signals (e.g. a network of pheromones). Process of the engineering endeavor shown in A) optimal path-finding demonstration, B) algorithm to discover shortest paths, C) the amount of time it takes to discover a series of shortest paths (e.g. potential optima). COURTESY [3] -- A) Figure 1.7, B) Box 2.4, C) Figure 1.10.

So why is this state of affairs the case? Are social and natural too complex to be optimized, or are the prevailing models simply wrong? To get at this issue in a systematic fashion, I will conduct a point-by-point critique of optimality in evolutionary biology to demonstrate where optimality may truly exist and where the hypothesis may fall short.

We can begin with the classics. In 1990, Parker and Maynard-Smith [4] reviewed the use and usefulness of optimality models in evolutionary biology. While the application of optimality criteria to evolutionary systems is highly diverse, they boil down to five basic components:

1) A model of adaptation must be constructed. Adaptation (through natural selection) is assumed to occur in an optimal fashion. In this sense, optimization as an outcome of evolution is implicitly adaptationist [5]. This adaptationist model is also implicitly probabilistic. For example, what fitness values for x are most likely to result from natural selection? If those values are maximized in a systematic fashion, then it is assumed that natural selection is responsible.

2) Potential strategies related to obtaining an outcome must be defined, such as discrete behaviors or phenotypic variants. This component is biased towards behavioral ecology, but "strategy" can be thought of as a general tendency rather than an intentional behavior. In either case, there is an assumed causal relationship between the employed strategy and the outcome.

3) Must have a maximization (e.g. fitness) or minimization (e.g. energetic expenditure) criteria. In both cases, there is an expectation of a directional process. This process (the route to optimization) is adaptive by definition. Less clear is what constitutes asymptotic convergence of the optimization process. In other words, while a system might tend towards optimization, it does not follow that this automatically results in a given evolutionary system settling into an optimal equilibrium.

A graphical representation of the Prisoner's Dilemma (PD) game-theoretic model of cooperation.

4) Payoffs for pursuing various strategies must be defined (units of max/min criterion). For game-theoretic applications (e.g. PD), the payoff structure is intuitive. However, any direct consequence of the strategies discussed in point 2 has a payoff. This in turn drives adaptation -- the assumption being that equilibrium behavior is the natural outcome of long-term interaction [6].

5) This optimum can either be frequency-independent (individualistic) or frequency-dependent (population context). This means that either individual performance can become increasingly better over time, or that differential reproduction occurs in a population based on the trait in question.


Now that we have reduced optimality models to their basic components, I will now conduct a point-by-point critique of optimality approaches. Hopefully, this will bring us closer to a theory of non-optimality. For now, I will present off-the-cuff critical observations.

a) Not all evolutionary change is adaptive. For example, models of exaptation [7] and  neutrality have been proposed that account for non-adaptive evolutionary changes. Traits that arise by these mechanisms are not likely to be optimized. In fact, much like cultural traits [8], they may often be maladaptive. Alternately, highly adaptive traits may be built upon latent abilities that would be lost through strict optimization [7].

In [5], in silico metabolic reaction networks that evolved to metabolize glucose also allowed for viability using other carbon sources as well. The evolution of environmental generalization undercuts the argument that this system evolved towards an optimum   COURTESY: Figure 1 in [7].

b) What if the "strategies" that result in optimal behavior occur at different levels of organization conflict [9] with each other? For example, different strategies may be taken within the same organism in terms of behavioral ecology and gene expression. This might be understood in terms of the relatively clear mapping between gene expression and behavior in insects [10] versus the unyielding brain-to-behavior complexity observed in mammals.

c) What if the strength of selection is weak, or if selection is alternating across the evolutionary process? In cases of uniformly strong selection, we might expect strong optimization. In cases of sporadic or weak selection, however, this outcome may be highly nonlinear (e.g. small selective advantages can lead to large changes in fitness). This may or may not lead to optimal phenotypes. Such a claim assumes that we even know what the effects of optimization look like at the phenotypic level. It might be useful to review the concept of optimizing selection [11] and its effects on phenotype.

See this HHMI video on pocket mouse evolution for more information on the relationship between the strength of selection and resulting fitness dynamics.

d) Some strategies may have conditional or compound payoffs which may not translate into a global optimum. For example, there may be clear benefits to niche specialization or functional partitioning. Whether or not this consititutes an globally (e.g. whole-organism) optimal outcome is an open issue. In the case of niche specialization, niche construction [12] involves the modification of the environment, which leads to cultural and natural selective feedbacks. Achievement of the optimal outcome may depend on whether this feedback leads to environmental stability or further fluctuations. In addition, recent research may suggest that theoretical predictions of the PD model do not match the situational behavior of humans and other animals [13].

e) While the model of Parker and Maynard-Smith is a heuristic model for biological optimality [14], it is worth noting that in engineering, multiobjective criteria are often used to approximate the optimal properties of a complex system. Since determining biological optimality is also an exercise in approximation, we need to incorporate better ways of finding dynamic equilibrium using multiple attributes. One example of this involves the application of maximization principles to ecological simulations [15]. In this case, even though a system might evolve to maximize one thing, this may not translate into global optimization or even a maximization of fitness.

Or perhaps we can take a lesson from some quarters of economics [16] where Grossman and Stiglitz found that a competitive economy cannot always be in equilibrium, but rather an equilibrium degree of disequilibrium. This has been used as evidence against the efficient markets hypothesis, which relies (sans natural selection) on the concept of market optimization over time.

While I have not focused on more traditional critiques of optimization in the evolutionary biology literature, I hope that this exercise actually leads to a series of non-optimal mathematical models that can go toe-to-toe with traditional optimization models. But I'll leave that development to future posts.

NOTES: 

[1] see the Synthetic Daisies #non-optimality tag for more information. Not all are relevant to biology, but all feature various takes on this concept.

[2] Boyd, R.   Cultural Adaptation and Maladaptation: of kayaks and commissars. In The Evolution of the Mind: fundamental questions and controversies. S.W. Gangestad and J.A. Simpson eds. Guilford Press (2007). In this reference, maladaptation is defined in contrast to adaptation.

Crespi, B.   The evolution of maladaptation. Heredity, 84, 623–629 (2000). In this reference, maladaptation is defined as a deviation from adaptive peaks.

[3] Dorigo, M. and Stutzle, T.   Ant Colony Optimization. MIT Press, Cambridge, MA (2004).

[4] Parker, G.A. and Maynard-Smith, J.   Optimality theory in evolutionary biology. Nature 348, 27-33 (1990).

For a different take (with perspective from the evolutionary computation community and the cognition-as-computation debate), please see: Harvey, I.   Cognition is not Computation: evolution is not optimisation. ICANN97, 685-690 (1987).

Another perspective (from a discrete mathematician) can be found here: Kelk, S.   What mathematical optimization can, and cannot, do for biologists. Lorentz Center presentations.

[5] For more reading on this idea, please see: Orzack, S.H. and Sober, E.   Adaptationism and optimality. Cambridge University Press, Cambridge, UK (2001).

[6] For a take on this idea using human societies as an example, please see: Cremer, H., Marchand, M., Pestieau, P.   Investment in local public services: Nash equilibrium and social optimum. Journal of Public Economics, 65(1), 23–35 (1997).

[7] Barve, A. and Wagner, A.   A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature, doi:10.1038/nature12301 (2013).

[8] For an adaptationist perspective on this, please see: Logan, M.H. and Qirko, H.N.   An evolutionary perspective on maladaptive traits and cultural conformity. American Journal of Human Biology, 8(5), 615–629 (1996).

[9] for the role of genomic conflict and how it may potentially undercut optimization, please see: Werren, J.H.   Selfish genetic elements, genetic conflict, and evolutionary innovation. PNAS, 108(2), 10863-10870.

[10] For two takes on this, please see: Robinson, G.E., Grozinger, C.M., and Whitfield,C.W. Sociogenomics: social life in molecular terms. Nature Reviews Genetics, 6(4), 257-270 (2005) AND Boguski, M.S. and Jones, A.R. Neurogenomics: at the intersection of neurobiology and genome sciences. Nature Neuroscience, 7(5), 429-433 (2004).

[11] For more information on optimizing selection (which is similar to but distinct from normalizing or stabilizing selection), please see: Travis, J.   The Role of Optimizing Selection in Natural Populations. Annual Review of Ecology and Systematics, 20(1), 279-296 (1989).

[12] Olding-Smee, F.J., Laland, K.N., and Feldman, M.W.   Niche construction: the neglected process in evolution. Princeton University Press, Princeton, NJ (2003).

[13] Khadjavi, M. and Lange, A.   Prisoners and their dilemma. Journal of Economic Behavior and Organization, 92, 163-175 (2013).

[14] Sometimes, the application of heuristics does not translate into optimal behavior or tradeoffs. For an example from information-seeking behavior in an in silico model (ACT-R cognitive architecture), please see: Fu, W-T., Gray, W.D.   Suboptimal tradeoffs in information seeking. Cognitive Psychology, 52, 195-242 (2006).

[15] Ackland, G.   Maximization principles and daisyworld. Journal of Theoretical Biology, 227(1), 121-128 (2004).

[16] Grossman, S.J. and Stiglitz, J.E.   On the impossibility of informationally efficient markets. American Economic Review, 70(3), 393-408 (1980).

Maps, Models, and Concepts, July edition

Here are some maps, models, and concepts reposted from my micro-blog, Tumbld Thoughts. This is a mash-up of recent books and articles in my reading queue, plus recent features from around the web. The order is: I (Nearly-decomposable Systems), II (Rube Goldberg Mechanisms), III (a profile of Elon Musk's Hyperloop), and IV (Maps + Metadata).

I. Nearly-decomposable Systems

This concept was originally proposed in Chapter 4 of "Sciences of the Artificial" by Herbert A. Simon (Chapter 4 is entitled "Architecture of Complexity"). The focus is on a concept called "nearly-decomposable systems".

In the modern practice of algorithmic representation, decomposability enables one to represent a complex system as a strict hierarchy. By contrast, nearly-decomposable systems can be found in systems where short-run behavior is statistically independent but long-run behavior is dependent in an aggregate fashion. While discrete states appear to exist at static intervals, examining the dynamics reveal interactions (or overlap) between these states. 

One example of this can be seen in the above image, which is adapted from Figure 7. A system is partitioned into 8 spatially non-overlapping components (A1, A2, A3, B2, B2, C1, C2, C3). A sparse matrix (top left) can then be constructed to model the selective functional interactivity between these components (discrete states). In this case, short-run behavior is restricted to interactions within each state, while long-run behavior characterizes the interactions between states. Overall, intra-component linkages (e.g. interactions within A1) are greater than inter-component linkages (e.g. interactions between A1 and B2). 

In Chapter 4, Simon applies this concept to the behavior of diffusing particles in physiochemical systems. However, in systems with autonomous intelligence (e.g. social systems), agents (the equivalent of diffusing particles) can influence and communicate with each other. This concept can also be applied to hierarchical systems (e.g. social and biological complexity). In such cases, the distinction between "broad" vs. "narrow" hierarchies (e.g. hierarchical span) becomes important. 

For a related concept as applied to systems biology, please see: 

Alicea, B.   The Curse of Orthogonality. Synthetic Daisies blog, October 3 (2011).

Further Reading:

Agre, P.E.   Hierarchy and History in Simon's "Architecture of Complexity". Journal of the Learning Sciences, 12(3) 2003.

Bentley, J.L. and Saxe, J.B.   Decomposable searching problems I. Static-to-dynamic transformation. Journal of Algorithms, 1(4), 301-358 (1980).

Feigenbaum, E.A.   Retrospective: Herbert A. Simon, 1916-2001. Science, 291(5511), 2107 (2001).

Simon, H.A.   Near-decomposability and the speed of evolution. Industrial and Corporate Change, 11(3), 587-599.

Simon, H.A.   Sciences of the Artificial. MIT Press, Cambridge, MA (1969).

II. Rube Goldberg (e.g. convoluted, non-optimal) Mechanisms

Happy (posthumous) Birthday (July 4th) to Rube Goldberg, the father of the Rube Goldberg machine [1]. Rube Goldberg machines provide a convoluted way to accomplish something that is otherwise simple. For example, to get sand out of a pair of shoes, one could take their shoes off, turn them upside down, and tap them.

In the Rube Goldberg universe, however, you would have to build a complex machine with many degrees of freedom to accomplish the same feat. His creations were massively inefficient, and that's the whole point -- if your worldview is one of parsimony, you will find his comics humorously absurd.

Given that his birthday falls on July 4 (US Independence Day), the associated Google Doodle (from 2010) features a seven-step machine that lights a firecracker. But can Rube Goldberg machines be useful? For more on this, check out a few Synthetic Daisies blog posts [2] on the application of Rube Goldberg-like machines to systems biology and evolution.

In this case, we are evaluating viable function in the context of maximal convolution -- in other case, the more steps to accomplish a task, the better. Perhaps this [3] is a biologically-plausible alternative to the view of evolution as parsimony.

III. Conceptual Porn for Technology Visionaries

Tech visionaries unite! I have run across a critical mass of articles on Elon Musk's proposal to build a high-speed transportation system called the "Hyperloop" [4]. Musk has described it as "a cross between the Concorde, a railgun, and an air hockey table". Supposedly better than high-speed rail, airplanes, or electric vehicles.

The hyperloop concept seems to build off of a number of existing technologies [5], some more developed for commercial use than others. It is not a vacuum tube nor a conventional rail system, although it incorporates both of these design elements. A version of what Musk is envisioning is similar to the Evacuated Tube Transport system, patented by Daryl Oster (founder of ET3 technologies) [6].

How does it work and is it simply hype? See these popular news features from Gizmag.com, Autoblog

Green, and The Atlantic Wire for more information. Is this fringe science? Read the book "Physics on the Fringe" [7] and decide for yourself.

IV. Maps + Metadata Tell Interesting Tales

The first set of layered maps are from the NASA/NOAA Green project. The goal of this joint project is to make a remotely-sensed map of the earth's vegetation (land mass vegetation only) using data from the Suomi NPP satellite.

In this YouTube video, it is explained that land cover and vegetation changes can have an effect on weather variability. Viewing these processes over the course of a year (animated in the video) can help us understand interactions between the biosphere and atmosphere. 

Examples of this are shown above. The pictures (from top) represent: the red deserts of Australia (shown as white expanses), snowcover in North America, the grasslands in the Florida everglades, and deforestation in East Africa.

The second set of layered maps are brought to us in the form of a really cool video animation from Cube Cities and Google Earth. Specifically, this is a time lapse of Chicago's skyline and its growth from 1865 (perspective view) to 2014 (birds-eye view). To illustrate the differences, I have screen-captured selected years and placed them in series. The buildings are 3-D models superimposed on a 2-D map of the city. Quite impressive.

NOTES: 

[1] Leibach, J.   Rube Goldberg Mashup. Science Friday blog, July 4 (2013).

[2] Alicea, B.   Machinery of Biocomplexity, new arXiv paper. Synthetic Daisies blog, April 19 (2011) AND Non-razors, unite! Synthetic Daisies blog, January 30 (2009)

[3] Bottom figure (minimal biological model) is from: Alicea, B.   The "Machinery" of Biocomplexity: understanding non-optimal architectures in biological systems. arXiv: 1104.3559 [q-bio.QM] (2011).

[4] Basulto, D.   Is the Hyperloop the Future of Transportation? BigLoop blog, June 12 (2013).

[5] For more information on these technologies, please see the following list:

a) Maglev technology

b) Railgun technology

c) Group effect vehicle (GEV) technology

d) VHST technology. For more information, please see: Salter, R.M. The Very High-speed Transit (VHST) System. Rand Corporation (1972).

[6] Here is more information about the ET3 Maglev system, and here is a video of their technology.

See also the following patent document: Oster, D.   Evacuated tube transport. US5950543. USPTO (1999).

[7] Wertheim, M.   Physics on the Fringe: Smoke Rings, Circlons, and Alternative Theories of Everything. Walker (2013).

But will they pay for "jibberish"? (or jabberwocky)?

Here are more reflections on alternative funding mechanisms for science. Recently, a new model [1] for funding early-stage innovation came to my attention.

The investors at Allied Minds want to fill the "gap" between basic research and commercial development by partnering with University labs, providing everything from seed money to management expertise. This "gap" is a major reason why most basic research is funded via government initiative. Indeed, their basic research areas of interest tend to be skewed towards topics that can be most easily brought to commercial success.

The Allied Minds people view research that falls into this gap this as an underdeveloped asset class [2]. However, it is not clear what exactly the assets are and how they can be monetized (if this is even the appropriate way to look at things, as we will return to later).

Consider the goal of an Allied Minds-sponsored project. They will tend to fund only "breakout" innovation with potential for outsized returns (e.g. profits). However, if this is no different or perhaps even more selective than existing venture capital initiative, have they really solved the problem they set out to solve?

Perhaps the idea of bridging the gap between discovery and innovation is the wrong way to approach this problem. In fact, the problem may actually not be the level of development or degree of practicality, but a natural feature of innovation. That is, by using an economic model that only rewards of large and tangible returns, only a small fraction of research can ever be monetized.

Mark Changizi wrote a guest blog post for the Discover blog "The Crux" in which he coined the phrase the "jibberish" of discovery [3]. The jibberish involves the intellectual jumble, open-endedness, and circuitous route to benchmarks from which emerge the core innovations that can be had from scientific research. He has some good insights on this process. In particular, he notes that there is a mismatch between the reporting of scientific results, the discovery process, and the funding mechanisms that currently exist [4]. When you go outside of the traditional funding mechanisms (e.g. private funding, venture capital), it is hard to propose to make a discovery. This observation is obviously rooted in the realities his experiences with 2AI Labs, but from here on out I would like to think bigger and more in terms of how to tailor economic models to science (rather than the other way around).

Is discovery a process of jibberish? Or waste?

By framing the problem as one of jibberish (or, alternatively, waste minimization [5]), I feel he helps to perpetuate a number of stereotypes and conceptual problems that plague basic science as a self-sustaining economic concern. The consensus in the investment/business community seems to be that this jibberish (or other "waste") is clearly unnecessary, or at very least can be minimized. However, there are three converging misconceptions/economic shortcomings that govern this assumption:

1) The cult of efficiency governs much of the discussion on funding science and the process of discovery. It is a commonly-held assumption that the short, bullet-pointed processes are the most focused, the best worked out, and thus the "safest" investments. This assumption also holds that innovation proceeds in a straight line. Sadly for the optimizers among us, innovation tends not to proceed in this manner [6]. Funding only a small part of this process is efficient, indeed (sarcasm intended).

An alternative to this gap (which reflects the gap between early innovation and marketable product) is to invest in the arcanocracy [7]. The arcanocracy implies a jumble or kluge of ideas and expertise leading to breakthroughs (intagible goods fill the "gap"). Monetizing and formalizing a system of exchange within the arcanocracy would go a long way towards finding the true value of a basic scientific enterprise.

2) There is also a problem I like to call the jabberwocky principle [8]. Investors and other outsiders do not understand the process of discovery because they do not engage in it. In a similar manner, a visitor to another culture cannot truly understand all of its practices unless they become enculturated [9]. This is fundamentally different from the so-called epistemic closure that plagues many dynsfunctional organizational cultures -- rather, I am suggesting that decisions about what is waste and what is not be made from the perspective of the scientific community rather than outsiders (or what Cultural Anthropologists would term an etic perspective).

Value from the outside, or value by understanding the practice?

Part of evaluating scientific discovery from the outside or a classical economic perspective involves not only viewing the process as jabberwocky, but overcoming popular misconceptions of scientists and their practices. Would everyday science by more highly valued if it were not so alien to the everyday activities of society-at-large (in particular the business and commercial worlds)? The classic portrayal of scientists as transmogrifiers or evil geniuses in science fiction has an influence value systems whether we like admit to it or not. This is particularly true of emerging sciences such as genetic engineering or artificial intelligence. We pay large amounts of money for circuses and other forms of entertainment, but do not pay for basic research with the same enthusiasm.

3) There is also an economies-of-scale problem, which is a practical concern but can be overcome. When basic research occurs, it often occurs at small scales/scopes, in what equates to hyperspecialized niche markets. Of course, big science exists. But often, this enterprise (even when supported by the government) is winner-take-all, with elite institutions and groups all too often taking it all. And with the state of science funding in crisis, new solutions and approaches are needed.

This is a significant barrier to new innovation, particularly in light of the current job market for scientists [10]. So why engage in supply-side economics, when new models for the economic value of intangible goods can be developed and employed?


NOTES:

[1] Allied Minds, a firm that takes early concepts from seed funding to start-up.

[2] underdeveloped in the sense that very little of it is monetized or gets rewarded based on conventional capitalist market models.

[3] Changizi, M.   The Colossal Pile of Jibberish Behind Discovery, and Its Implications for Science Funding.
The Crux blog, November 14 (2012).

[4] While he only alludes to this in his blog post, the idea of stable allocations may be applied to the mismatch between what funding agencies/private investors want, and what the scientific enterprise (discovery) often provides.

[5] This content is cross-posted to my micro-blog, Tumbld Thoughts:

Here are a few articles on the limitations of academic science. I post them because, as critiques, they are beset with their own biases about "the system". The first article [A] is about Carver Mead with a critique of mainstream science and how prevailing thought hold back the course of innovation. The second article [B] is about the existence of "waste" in academic institutions.

Now the question is: even though the authors bring up valid criticisms of the current academic culture, are there suggestions the correct (or even workable) solutions? For example, while the mainstream tend to reject offbeat ideas, it may be due to valid concerns about viability and other factors. When can bias be said to be good or bad, and when does it serve us well? Furthermore, can we optimize when and where we employ these biases in a way that is best for scientific discovery?

[A] Myslewski, R.   Chip daddy Mead: 'A bunch of big egos' are strangling science. The Register,    

February 20 (2013). Here, biases is defined as framing problems in terms of obscure mathematics,   

invested ideas, and lack of vision.

If you are interested in reading more about the topic, please see this book:: "Physics on the Fringe" by Margaret Wertheim.

[B] Marty, T.   Clean up the waste. Nature, 484, 27-28 (2012). Here, waste is defined as big egos, 

the lack of modern management practices, and other things that stand in the way of "optimizing" academia. A recent letter to the Washington Post ("It's time to get serious about science") argues that we (the United States) must get serious about funding science in the face of foreign competition. But should we pour money blindly into the system, or make sure that we do not re-enforce clear instances of bias?

[6] Innovation, like evolution and creativity, do not proceed in a straight line -- in fact, the branching bush metaphor works very well here (even by using a somewhat tangential example from horse evolution). So the question is: how do we monetize and create a self-sustaining economy that values as many main branches and offshoots of this branching bush as possible?

[7] This content is cross-posted to my micro-blog, Tumbld Thoughts:

Ah, meritocracy [A]. Supposedly fair, examples throughout history have often produced a strange mix of outcomes [B] that are far from "the optimal outcome". Here is a comparison between the current system of meritocracy and an alternative model of power and reward, something I am calling "arcanocracy". An arcanocracy is an economy based on the rule of or ascendance of arcane [C] skills and ideas.

[A] By virtue of picking and rewarding the "best of the best", the meritocracy is supposed to offer an optimal system that transcends religious, crony, and ethnic bias. However, in practice it can also be quite brittle, with a number of faults that cannot be easily remedied.

[B] Here are two sets of opinion on the subject: "Down with Meritocracy" from The Guardian and  "Meritocracy and its Discontents" from The Economist. The second picture features covers from the following books: "The Rise of the Meritocracy" and "Twilight of the Elites".

[C] arcane skills and ideas exist when a person are very skilled in a single oddball activity, but are average overall. Averaged together across an economy of scale, these arcania converge to spread reward more evenly across society.

[8] Jabberwocky refers to a Lewis Carroll poem ("Through the Looking Glass") in which the protagonist (Alice) tries to navigate a strange and often nonsensical culture. Perhaps much of the jibberish that Changizi refers to in his post is actually Jabberwocky that needs to be viewed through an alternate looking glass.

[9] There is a significant literature on cultural practices related to the scientific method. The work of Bruno LaTour ("Science in Action") and Roy Bhaskar ("A Realist Theory of Science") are good starting points.

[10] According to the conventional rules of supply-and-demand, we are in a bust period for academic (and perhaps also industry) science. The conventional (or conservative) wisdom is that we have trained too many people, or have too many degree-granting outlets. But this ignores the simple fact that he have produced an enormous amount of expertise. While the conventional labor market might be unable to absorb the labor, they may still contribute to overall productivity with the proper kind of economic model underpinning their efforts.

A sparse, anti-fragile universe.....

This article is being cross-posted [1] from my micro-blog, Tumbld Thoughts.

Not a particularly sparse universe, by the way. But a home of sparse variables? Perhaps.

And now, dispatches from an antifragile universe [2]. Nassim Nicholas Taleb has a guest article in Wired (entitled "Beware the Big Errors of Big Data") in which he warns against the use of sparse variables [3] in so-called high-throughput datasets.

NOTES:

[1] also re-posted to the Mathematica group on Tumblr.

[2] N.N. Taleb  "Antifragile: things that gain from disorder". RSA lecture here. I've yet to decide whether Taleb is the Rasputin or the Tesla of analytics. Regardless, here is a video from the New York Public Library speaker series featuring Taleb discussing the concept of anti-fragility with cognitive psychologist Daniel Kahneman.

[3] variables that have a high signal-to-noise ratio. Or, why sometimes Watson gives ridiculous answers. Generally speaking, the more variables you have in an analysis, the greater the number of spurious correlations you must contend with. This is quite similar to Bellman's "curse of dimensionality".