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Learning the infinite play of science

Published onMar 10, 2021
Learning the infinite play of science
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“…The tools we make to build our lives:
our clothes, our food, our path home…
all these things we base on observation,
on experiment, on measurement, on truth.
And science, you remember, is the study
of the nature and behaviour of the universe,
based on observation, experiment, and measurement… The Mushroom Hunters. Neil Gaiman
See: Brainpickings <https://www.brainpickings.org/2017/04/26/the-mushroom-hunters-neil-gaiman/> Accessed December 24, 2019.

Albert Einstein describes his infinite play:
“The words or the language, as they are written or spoken, do not seem to play any role in my mechanism of thought. The psychical entities which seem to serve as elements in thought are certain signs and more or less clear images which can be ‘voluntarily’ reproduced and combined.
“There is, of course, a certain connection between those elements and relevant logical concepts. It is also clear that the desire to arrive finally at logically connected concepts is the emotional basis of this rather vague play with the above-mentioned elements. But taken from a psychological viewpoint, this combinatory play seems to be the essential feature in productive thought…” (Einstein 1960; emphasis added).

You Get to Play with Infinity

After years of study and learning how to learn, you are active in pursuit of the unknowns of the universe. You have acquired all the accumulated information, all the theories, facts, and guesses about your particular object of study. You have mastered the methods, the instruments and the code, you need to query this object, which is now the last teacher you will ever fully need. You have entered the zone of infinite play. You are a scientist, just like Albert.

How do you learn infinite science play? “Infinite science play” may seem like a metaphor for something more “serious”: “tackling a complex problem,” or “stretching the envelope of our knowledge.” This is not so. The use of “play” here is accurate, in the sense that play often: 1) builds and rewards skilling; 2) uses rules and shared limits (time and space); 3) and, is open-ended: its outcome cannot be predicted. “Infinite” play points to the universe around us, and our place in this and notes that this particular type of play is fundamentally different from any finite game play.

In infinite play, rules and horizons can and will change. Boundaries are broken. Roles are just labels. Infinite play prohibits winning and losing. Players come and go. Every player will go at some point, but the play moves on. Evolution is one way nature learns its own infinite play. Species come and go. The ecosystem moves on.

Once these particulars are known, then the strategy for playing shifts away from tactics based on winning, toward cooperation, and to efforts to make the play more interesting, to play longer and include more players, to go deeper, to dive into playing. The unknowns you seek to understand are linked in the same way that natural philosophers and scientists have been playing for centuries. Now it’s your turn.

As you cannot win infinite play, a good tactic is to discover more intrinsic rewards for playing. Fortunately, the better you get at playing, the more fun you can have. This is something of a secret that your thesis advisor may not have told you: the more fun you have, the more you will play, and the better you will get, and the more fun you can have. Playing better, when it comes to your research, means more innovation, better insights, and improved results. Just ask Albert (ibid) — or Arthur, Paula, Thomas, Steven, or Johannes <https://www.brainpickings.org/2013/08/14/how-einstein-thought-combinatorial-creativity/>.

Kevin Kelly <https://kk.org/>, the “senior maverick” at Wired Magazine, understood how the “infinite game” enables technology innovation way back in 2005:

“Our humanity is actually defined by technology. All the things that we think that we really like about humanity is being driven by technology. This is the infinite game. That’s what we’re talking about. You see, technology is a way to evolve the evolution. It’s a way to explore possibilities and opportunities and create more. And it’s actually a way of playing the game, of playing all the games. That’s what technology wants. And so when I think about what technology wants, I think that it has to do with the fact that every person here—and I really believe this—every person here has an assignment. And your assignment is to spend your life discovering what your assignment is. That recursive nature is the infinite game. And if you play that well, you’ll have other people involved, so even that game extends and continues even when you’re gone. That is the infinite game. And what technology is is the medium in which we play that infinite game. And so I think that we should embrace technology because it is an essential part of our journey in finding out who we are” (Kelly 2005 <https://www.ted.com/talks/kevin_kelly_on_how_technology_evolves> Accessed April 12, 2019).

Substitute “science” for “technology” in the above and you will understand why you play.

Look inside for your incentives

“In academia, a special motivation called ‘taste for science’ exists…, which is characterized by a relatively low importance of monetary incentives and a high importance of peer recognition and autonomy. People are attracted to research for which, at the margin, the autonomy to satisfy their curiosity and to gain peer recognition is more important than money. They value the possibility of following their own scientific goals more than financial rewards …. The preference for the autonomy to choose one’s own goals is important for innovative research in two ways. Firstly, it leads to a useful self-selection effect of creative researchers. Secondly, autonomy is the most important precondition for intrinsic motivation, which in turn is required for creative research…” (Osterloh and Frey 2011).

One of the motivations that “money cannot buy” is the experience of scientific discovery. Whether this is an “aha” moment in the shower or on the bus, a visual experience from an observation, or the result of a computation on data, you get to be the person/team that—right now, this moment—knows something the rest of the world does not. And sure, this new bit of knowing will need confirmation and validation, but in this moment, your passion is rewarded and you find yourself in what social psychologists call an “optimal experience.”

This is not an accident. You have worked really hard to get here. This is why you are driven to be a scientist; “As we have seen, many of the most active participants in these creation spaces are driven by intrinsic motivations—the passion they have for the domain, the satisfaction they feel when solving difficult problems and helping others, or a desire to build their skills and experience base” (Hagel, et al. 2012).

This is an experience that can only come from being skilled, from knowing what you have learned over the years, and from risking failure commensurate to your skilling. Another word for this experience is “flow;”“Flow is found in using a full measure of commitment, innovation, and individual investment to perform real and meaningful tasks that are self-chosen, limited in scope, and rewarding in their own right” (Mitchell, in Csikszentmihalyi 1992).

How much flow you can experience depends on your own demeanor, on the circumstances of your research employment, and how your organization is governed. Your intrinsic motivations easily can get crowded out when money enters the equation:

“Crowding-out of intrinsic motivation by stick and carrot: Carrots and sticks replace the taste for science (Merton 1973) which is indispensable for scientific progress. A scientist who does not truly love his work will never be a great scientist. Yet exactly those scientists who are intrinsically motivated are the ones whose motivation is usually crowded out the most…. [A] lot of potentially highly valuable research is crowded out along with intrinsic motivation…” (Binswanger 2014).

It’s not simply flow that gets crowded out. Money comes with a load of conflicted interests that warp how you configure your science practice. The crowding-out impacts of adding money to (previously straight-forward) moral-choice situations have been experimentally verified (See: Bowles and Polanía-Reyes 2012; Osterloh and Frey 2015; Benkler 2016).

This very common combination of zero-fun—what they call “low flow”—and delayed moral choices—“I know this is wrong, but it makes economic sense to me right now”—describes the state of science when infinite play is interrupted by the logic of the neoliberal marketplace. It probably describes your own lab or department today (Binswanger, M. 2014).

Why should your research be held hostage by perverse incentives that hijack all the fun too? You’ve worked too hard and know too much to miss the intrinsic joy of infinite play. You need to get the taste for science back into your head, and in the minds of your team. This is why open-science culture change is important.

“The legacy of traditional ecological knowledge, the intellectual twin to science, has been handed down in the oral tradition for countless generations. It passes from grandmother to granddaughter gathering together in the meadow, from uncle to nephew fishing on the riverbank, and next year to the students in Big Bear’s school. But, where did it first come from? How did they know which plant to use in childbirth, which plant to conceal the scent of a hunter? Like scientific information, traditional knowledge arises from careful systematic observation of nature, from the results of innumerable lived experiments. Traditional knowledge is rooted in intimacy with a local landscape where the land itself is the teacher” (Kimmerer 2003).

You must play to get better at playing

Playing to learn the infinite play of science

You must play to get better at playing. The practice of science builds the praxis of science. [“A praxis is a practice that contains the purpose in itself, and is, therefore, the good to strive for”(Klamer 2017)]. Through play you will develop strategies, tactics, processes, and practices, just as you would in a finite game. However, infinite play has its own flavor for these practices: they are durable, non-destructive, and encourage wider play. Seth Godin (2019 <https://www.akimbo.me/blog/s-3-e-14-waiting-for-godiva> Accessed April 16, 2019) provides four key rules (paraphrased here), that apply well to infinite play:

1. Repeatability: what you propose to do needs to be repeatable, not a one-off. Ask yourself: can I keep on doing this? Remember that infinite play has no ending. You research methods must be repeatable to be verifiable, and also falsifiable. You are also repeating what others have done. They have passed on their knowledge. Your turn is now. Tag, you are it. Others will come after you. You need to let go of what is most important to you. Your job is to contribute. You invest in open science and others will build on your work.
2. Non-harmful to others: what you propose to accomplish cannot harm others or the planet in the process. This feature is connected to repeatability, of course, but also to a general moral code. “Do no harm.” It means non-harm to the careers of other scientists, and neutral or better, positive, impacts on the environment humans need to thrive.
Infinite play is not a zero-sum event. Your success should not be at someone else’s expense. The academy needs to refactor over-competitive practices (in funding and promotion) into collaborative ventures. Open science play is not extractive. The opportunities for discovery are abundant.
3. Additive: This is connected to complexity theory and the need for practices to experiment, iterate, and learn. New knowledge is produced in the process. You are impacting the evolution of the infinite—of nature and of the field of knowledge—as you play with this. New complexities emerge. While you are “repeating,” each repeat has new results. You experiment and iterate. That’s how science is done.
Infinite science play is generative. Its goods are anti-rivalrous. Getting “scooped” is not your problem. Obscurity is your problem. Your process or practice needs to offer a learning curve. You get better at it. You train others in it. They go off and improve the process. Then they can teach you new things.
4. Non-secretive: If you need to keep your process or practice a secret for it to work, then it will fail. Infinite play means inviting others to join. Secrets are for finite games. Infinite play runs on sharing. Open science is democratic at its core. Fierce equality means sharing with everyone. Open science is generous.

This is not all of what you need to play with/in the infinite. Just a taste. Ahead, you will see how open-science based infinite play restores science’s normative drivers, marginalizes perverse incentives, embraces emergent complexity, nourishes practical wisdom in the academy, and fosters innovative serendipity.

“The men go running on after beasts.
The scientists walk more slowly, over to the brow of the hill
and down to the water’s edge and past the place where the red clay runs.
They are carrying their babies in the slings they made,
freeing their hands to pick the mushrooms.” The Mushroom Hunters. Neil Gaiman
See: Brainpickings <https://www.brainpickings.org/2017/04/26/the-mushroom-hunters-neil-gaiman/> Accessed December 24, 2019.


Bibliography: Open Scientist Handbook References

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