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.