Learn how playing the infinite game of science will improve your research, your collaborations, your career, and your life.
“If there are at least two players, a game exists. And there are two kinds of games: finite games and infinite games.
Finite games are played by known players. They have fixed rules. And there is an agreed-upon objective that, when reached, ends the game. Football, for example, is a finite game. The players all wear uniforms and are easily identifiable. There is a set of rules, and referees are there to enforce those rules. All the players have agreed to play by those rules and they accept penalties when they break the rules. Everyone agrees that whichever team has scored more points by the end of the set time period will be declared the winner, the game will end and everyone will go home. In finite games, there is always a beginning, a middle and an end.
“Infinite games, in contrast, are played by known and unknown players. There are no exact or agreed-upon rules. Though there may be conventions or laws that govern how the players conduct themselves, within those broad boundaries, the players can operate however they want. And if they choose to break with convention, they can. The manner in which each player chooses to play is entirely up to them. And they can change how they play the game at any time, for any reason….
Infinite games have infinite time horizons. And because there is no finish line, no practical end to the game, there is no such thing as ‘winning’ an infinite game” (Sinek 2019).
In Finite and Infinite Games, James Carse (1987) makes a number of statements about culture and nature, and about human endeavors that include a wide range (perhaps very wide) of everyday human situations.
Here we attempt to insert science as an endeavor into the scheme Carse outlined, with the purpose of grounding the norms of science—however these are described—within science’s infinite play with nature. Of course any scientist—as a biological organism—plays in nature at the same time she plays with nature. To play with nature as a profession is a privilege scientists all share. The finite games of finance or technology might bring in more (perhaps a lot more) money, but the struggle with universal unknowns has its own rewards. Science play, as we will see, also ties in complexity theories, emergent systems, explanation and narrative.
Today, science is an endeavor housed in organizations where we find game logics that are mainly finite (When is the next RFP coming out? What’s your H-Score?). This circumstance is in direct conflict with research needs that must—this is the main assertion here—include and support playing science.
The notion of the infinite play of science may seem foreign to scientists coached to win finite games to secure their careers. And yet all attempts to capture the normative culture of science hint at an underlying, non-finite home for science. What we find today is an academy trapped in the contradictions between these two mindsets: the poetry of discovery, the awe of nature, the joy of intellection, and the satisfactions of mentoring have been pushed aside, displaced by the rush for reputation in a now-harshly-competitive system of scarce resources and narrow opportunities.
These contradictions have been noted for decades in articles and books that contrast science’s putative norms with the observable organizational practices of science. Sociologists and critics of science practice point to the realities of doing science in today’s world. “Science claims X, but in practice we find Y.” Ziman (2002) makes this contrast more than seventy times. These observations now share the discourse with a chorus of observations about “bad science:” unreproducible findings, plagiarized and repetitive science articles, ersatz statistics (p-hacking, etc.), systemic biases and conflicts of interest in funding and advancement, public distrust of science findings, and a profiteering publishing industry.
The reality of doing science today seems fundamentally out of step with how good science needs to happen. “Real science” is still distinguished by normative behaviors and values that are regularly called upon to counter deviation into “bad science” (See: Zimring 2019; Accessed November 8, 2019). But when the incentives are perverse and pervasive, resistance is a challenge that overlays and undermines the challenges of doing infinite-game science. So, what happens when the reality of being a scientist fails to support “real science”?
“When you rely on incentives, you undermine virtues. Then when you discover that you actually need people who want to do the right thing, those people don’t exist because you've crushed anyone’s desire to do the right thing with all these incentives” (Barry Schwartz in Zetter 2009 <https://www.wired.com/2009/02/ted-barry-schwa/> Accessed 12/16/19).
Much of the “How” science is played is discussed within the philosophy of science, and the “What” of science fills books in the sociology of science. The infinite game of science explores the “Why” and the also the “just causes” (Sinek 2019) of science. The “Why” brings us a narrative of science up until this moment, which illuminates its horizon. Science’s just causes point us at the thousands of mysteries, the unknowns that scientists confront today; each mystery offers a bit of new knowledge to be discovered, and the benefits of new understanding. Every unknown also carries a moral load, and the need for judgement in pursuit of justice (more about this below), given that there are many consequences to new knowledge.
The book, Finite and Infinite Games goes into great detail to expose the two mindsets: finite and infinite. These are fundamentally different, and in ways that illuminate many of the issues plaguing the academy today. Here we note only a few key points. First, finite players (players with a finite mindset) play their roles in full seriousness, acquiring their parts as actual and necessary: even when they are always fully free to step out of their parts. They need to forget this freedom in order to play to win. This is really important to keep in mind: finite game players assume their roles as essential parts of who they are, even though they always have the freedom to abandon their role. This mindset lards the role of “scientist” with unnecessary seriousness.
On the other hand, infinite players play with the rules, instead of assuming roles. These rules are constantly changing as players move the boundaries of the game. Infinite play is rule-creating. The players do not need to accept a set of rules to play. Without stable rules, roles make no sense. Best practices do not apply. Every new experiment opens up its own horizon. In terms of complexity theory, infinite play demands that you probe, sense and respond (Laloux 2014, Kurtz and Snowden 2003) each time you play.
When an infinite player (someone with an infinite mindset) plays a finite game within infinite play, they do so fully understanding they are simply acting their role, and that they have the freedom to walk away. Yet they still have the capacity to play any finite game to its limit. They can accept the rules in order to play. However, winning or losing has no meaning for them. This may mean they play with greater freedom and abandon, improving their chance of winning.
To remind scientists that their research is a form of infinite play is to reconnect them to the “one long experiment” (Martin 1998) that is science. Recent organizational management theories (Sinek 2019) have put infinite play and sense-making for complexity (Snowden 2002; Kurtz and Snowden 2003; Ito and Howe 2016) at the center of their recommendations for 21st Century organizational governance. Getting good in the infinite play of science could also build skills that scientists can use to govern their labs, universities, and agencies. Infinite play also hones your ability to communicate through narratives.
“In the science world, introducing narrative early and in a substantial way will produce a whole new breed of scientist, able to communicate far more effectively among themselves, as well as with the public. They will also be less prone to subconsciously reach for false positives or present null results in such a boring way that they help perpetuate publication biases against such results” (Olson 2015).
This handbook will help you create new practices that can recenter your university’s values and vision around infinite play as a strategy for long-term success. Open science is a cultural platform that will connect infinite players across the globe. You and your organization can join this, or you can continue to play the same bullshit “excellence” games (Moore, et al. 2017; also Neylon <https://www.slideshare.net/CameronNeylon/excellence-is-bullshit>; Accessed Feb. 7, 2020) you take far too seriously today.
The infinite play of science was there with Aristotle and Plato, Bacon and Galileo. With Neuton and Boyle, Einstein and Feynman. And now here, this very moment, with every scientist in and out of the academy. Science play lies beneath the norms that Merton and others have used to delineate science’s core ethos. Infinite play stands behind every experimental hypothesis and laboratory method. Every time a scientist battles with some mystery of nature, the infinite game continues.
Science works toward horizons and not within boundaries. Scientists see boundaries around them and laugh as they violate these. They go beyond. Any scientist can change the horizon of science and modify the rules of science (for example, by improving a method of observation). That horizon, once stretched by a new idea or method, never returns to its former dimensions. Each change in the horizon of science changes the horizon of every scientist.
“The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of very great importance, I think. When a scientist doesn’t know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt” (Feynman, et al. 2005).
The scientist eats unknowns, and is never full. She sweats doubt. The products of science are not science. These can be destroyed or forgotten and science will continue. Science means challenging the known. Scientists understand how little science knows; that the mysteries they face are mighty. Each scientist picks her own mystery, her own just cause to pursue.
“The earth’s history has been only long experiment, poorly constrained in a reductionist’s eyes. How impoverished the earth would be if had been otherwise” (Martin 1998).
No single scientist speaks for science. No scientist speaks for nature. The speech given by the award winner at the annual convention is not any more scientific than the poster presented by the graduate student. The questions of a student can negate an entire history of discovery.
Unlike the history of society where politics is theatrical and works to close its history (against culture, which keeps this open), the history of science is always dramatic. It is formed by events that must repeat themselves again and again while remaining open to failure, open to a moving horizon that might, and probably will, change and render them false. After that, they join the past history of science and are merely theatrical. One can repeat a failed experiment only as historical theater. The science present moves on in dramatic fashion.
The goods of science inform the knowledge inventory of the world within which science is played. They push science to remake its horizons. They are not unimportant to science but they are not science in the infinite game. Finite-game science players want to own these products, in order to garland themselves with prizes. Prized science goods require durability for the value of their prizes to endure. Finite-game science players choose to defend their own goods by silencing others and gathering supporters. They seek a past that is closed and known, with their own goods at its front end.
“One must keep in mind that senior faculty probably hold their current positions through their success in the game, which may or may not have been achieved by using the most ethical ways” (Chapman C.A., et al. 2019).
Infinite science players—who know their own research best—interrogate their own findings in search of a larger knowledge horizon. They push the playing forward and their egos to the side. They open up to collaborations and seek out conversations with those who disagree with their findings.
Prizes bind science to a known past. This past is carried by science institutions, such as those learned societies that sponsor prizes. These societies also need to endure so that the prizes of finite-game science players retain their value. Prize winners and “fellows” carry the weight of ensuring the society persists, warranting the currency of their prizes. However, the continuity of science is not based on an attachment to its past or even it current goods—on the closing of its history—but rather, on a continual openness to surprise, to experiment (Schulz 2011). Science is based on the nearly universal ephemerality of its findings. Science has always been the child of an open history that will never close.
“[W]hat resounds most deeply in the life of Copernicus is the journey that made knowledge possible and not the knowledge that made the journey successful” (Carse).
Science doesn’t just have a culture. Science also is culture (in Carse’s sense). Like any culture, science is “itself a poiesis, all of its participants are poietai—inventors, makers, artists, storytellers, mythologists. They are not, however, makers of actualities, but makers of possibilities. The creativity of [science] has no outcome, no conclusion” (Carse 1987; paraphrase).
Scientists are ImagiNatives. Poets of the natural world. Makers of possibilities. “It’s been said that science fiction and fantasy are two different things: science fiction, the improbable made possible; fantasy, the impossible made probable” (Rod Serling,“The Fugitive”. The Twilight Zone. Season 3. Episode 25. March 9, 1962. CBS.) Science is nature made into poetry.
“The physicist who sees speaks physics with us, inviting us to see that the things we thought were there are not things at all. By learning new limitations from such a person, we learn not only what to look for with them but also how to see the way we use limitations. A physics so taught becomes poiesis” (Carse).
You cannot do science alone in isolation; do science only in your own mind. This does not mean that you cannot be solitary in your imagination, but only that science happens when you share this with at least one other person. A poet who does not speak has no poetry to speak of. Science happens between and among infinite players.
The infinite play of science allows no personal power or authority. In a finite game, power always requires opposition and an audience. Neither is available within science. In finite games, winning silences the loser. The personal power that a title conveys; this authority means nothing to science, and usually far too much for scientists. Competition feeds arbitrary power in the academy and defeats science itself, silencing the many to praise the few.
Finite games of prestige in the academy are failings of the academy. Finite games of personal influence and authority contradict the inherent authority of science methods. Scientists are known by their names, not their titles. If your method is transparent and well-founded, your science goods need no amplification beyond their public sharing. The audience that power seeks is not found in science. An infinite game allows no audience. There is no vote that can elevate one science good above another.
Science does not belong to any one society. Science flows across the globe. Change for science has no location, it is always everywhere. Change is always surprising, and so never a surprise. “To be prepared against surprise is to be trained. To be prepared for surprise is to be educated” (Carse). To invite and welcome surprise is to do science. Science creates its activities through fluid consensus, not from any established doctrine, but in response to surprises that happen whenever science moves its horizon.
“[As it is in nature, so] also in [science]. Infinite players understand that the vigor of [science] has to do with the variety of its sources, the differences within itself. The unique and the surprising are not suppressed in some persons for the strength of others. The genius in you stimulates the genius in me” (paraphrase) (Carse 1987).
Every science effort begins and ends in surprise. Because the next instant of knowing is always open, the moment of discovery is always surprising. This is a source of joy for the scientist. If the object of research were already known or fully predictable, the research is unnecessary. Reproducibility means that the same effort must result in the same surprise. The first effort exposes the scientist to this surprise. The second time gifts this surprise to science.
In its infinite play, science invests more authority on the rigor of its methods than it does in the sagacity of its practitioners. The results of well-constructed experiments are all discoveries, even when the results are null. Each experiment extends the horizon of the game.
“If everything we write today already bears within it a future anterior in which it will have been demonstrated to be wrong-headed, we have the potential for a genuine exploration of a new path, one along which we develop not just a form of critical audacity but also a kind of critical humility” (Fitzpatrick 2019).
The final page of science will never be written. A new finding is lightly penciled in after the previous paragraph on the current page. Every infinite player brings their own eraser to this book. Chapters long settled and well considered can be erased in a single day. That is a joy for science. New pages open up then. New horizons emerge. The play accelerates. More players find ways to add new paragraphs to this ledger. Ever since Bacon, yesterday’s findings hold less knowledge than tomorrow’s.
Scientific surprise is mainly retold as serendipity. Serendipity, the unexpected confluence of curiosity and sagacity, is just another way of announcing that scientists are playing with/in the infinite mode.
“The paradox in our relation to nature is that the more deeply [science] respects the indifference of nature, the more creatively it will call upon its own spontaneity in response. The more clearly we remind ourselves that we can have no unnatural influence on nature, the more our [science] will embody a freedom to embrace surprise and unpredictability” (paraphrase) (Carse 1987).
“The notion that academic scientists have to be humble and disinterested… seems to contradict all our impressions of the research world. Scientists are usually passionate advocates of their own ideas, and often extremely vain” (Ziman 2002).
We have all read studies and stories of “actual” science that highlight how scientists live and work in the “real world.” Today, scientists labor within the pragmatic circumstances of the increasingly neo-liberal academy—surrounded by an increasingly neo-liberal global economy: a world of intense competition for fame and funding, a space of cumulative advantages for a few, and increasing precarity for the rest. Yet the moments of science, the methodic but often serendipitous event of discovery, yank the scientist back into a different “real”—the real task of uncovering new knowledge about the actual “real world”—the natural universe. To do science is to play with nature.
The distinction between playing with an infinite mindset and playing with a finite game mindset allows us to unpack how a scientist might bring an infinite mindset to “actual science:” to all those finite games of power and scarce resources scientists play today in the academy. This handbook is all about changing cultures in the academy. Some of these changes are steps forward into new opportunities, others are steps back into the core of how science was done before the recent neoconservative managers arrived on campus. Let’s look at two personas: one, a die-hard finite game player, and the other, a scientist deep into infinite play.
Below are some signs you might be playing science with a finite-game mindset.
The role of “The Scientist” is just a role. You are free to throw this aside at any time. You always have this freedom. At some point you forgot this fact.
By forgetting you create a necessity to this role in your life: not for science, however, but for being The Scientist.
Entering grad school on a fellowship, you identified avenues of influence you could tap into: you picked a famous scholar to be your committee chair and selected a hot research topic, instead of one from your own interests.
You had three papers published by the time you completed your PhD. At least one of these used text “borrowed” from a colleague.
Your old committee chair had an inside track to a funding agency that you learned about and cultivated as a post-doc.
You jumped into an entry tenure-track position at a different university when your first research proposal was funded, taking your funding with you.
At annual meetings of your learned society, you work the publisher booths to find a sympathetic editor at a high-prestige journal.
You push your funded research team of grad students, post-docs, and research staff to make discoveries, or hack the data, to fit the needs of the field’s top impact-factor journal.
You ignore those students who don’t perform to your demands, and especially self-funded graduate students, who should realize they don’t belong.
You shift your lab’s research focus in response to the funder’s new five year strategic plan.
When you take on peer review assignments, you are particularly harsh on any work that intersects your own but doesn’t cite you, while you soaked up any useful information about their research methods and findings.
You leverage your funded research to minimize your teaching load, and you weasel the chair into handing over your undergrad survey class to adjuncts.
You use the same textbook for your upper division class that you had as an undergrad.
You grade easy to avoid hassles with undergrads.
Your graduate methods seminar class promotes your own methods, and critiques others.
The platform of your early career wins became a launching pad to grab career advantage over your peers. You search for other early-career winners, and avoid those who aren’t.
You constantly eye openings for jobs at higher ranked universities. You make sure you schmooze their department heavy-weights at learned society meetings.
You worm your way into volunteer leadership jobs at your learned society, hoping to fast-track recognition.
You sit on a couple of major campus-wide strategy committees, instead of curricular or other social committees.
Your chancellor gives you opportunities to speak at campus events, where you highlight the research findings you’ve maneuvered to be most glamorous.
You mold a social media persona around popular science issues.
You craft a high H-index by having your grad students write review articles, which you attach your name to as first author.
You haven’t done fully original research or used a new methodology in five years.
You carefully hoard your lab’s data, and only publish in journals that do not require open data.
You evaluate your colleagues as winners or losers, and steer clear of the latter.
You talk about meritocracy in the academy, and believe that’s why you got tenure.
You laugh off talk of “work-life balance.” Your work is your life.
You fit fully into the role of “The Scientist.” As it colonizes your future, the role of “The Scientist” becomes everything you are and ever wanted. But then you realize you haven’t yet been elected a fellow of your learned society. You worry that you haven’t spent enough time cultivating connections society board members.
You’ve never reflected on how your need to harvest your cumulative advantage impacted the quality of your science outputs, nor the career costs of the grad-students you’ve abandoned because they didn’t follow your lead. You never stopped to count the dreams you killed along the way.
Because you feel you must play The Scientist continually, you are unable to play infinite science. There is no joy in your work. There is a constant fear that your research results will be proven illegitimate.
A few of the the above activities might be pursued as a finite game by a scientist with an infinite mindset (See: Learning infinite science play). Every scientist is confronted by an academy infested by conflicts of interest and internally validated perverse incentives. As open science works to change the culture, scientists must still forge their careers while knowing that what they should be doing is not getting done: “Caught up in being neoliberal subjects who operate within the terms of dominant discourses does not suit academics very well. It runs counter to intellectual work. It places us in the impossible situation of existing in a context where what we know we should do is scoffed at as a romantic dream, a fantasy, an indulgence of the past—a love like Othello’s of Desdemona” (Davies 2005).
For every finite-game science player who “wins,” dozens more need to “lose.” Scarcity in the system demands this. “Losers” have their careers side-tracked at some point. Their dissertations do not result in high-impact journal articles. Their post-doc opportunities (if available) become dead-ends. The funding agency denies their last-chance research proposal. They migrate away from research institutions to other jobs in and out of academia. The enthusiasm and hope they brought with them as students no longer sustains the energy they need to compete in the finite games of science. They go off and do other work. This is one reason why science loses every time finite-game science player wins.
“It took a flight across the equator, a perilous crossing of the Andes and three days down the river in a dugout canoe to bring me to the heart of the rain forest….There is no word but awe for the biological excess of that place, the profusion of life, vivid and complex beyond our grasp. At every turn of a leaf, there are mysteries. There are life forms here that occur nowhere else on the planet and intricate relationships evolved over eons. You might take care not to step on them” (Kimmerer 2003).
Freedom of thought is a fundamental academic freedom. Because science is always shared, this first freedom includes freedom of speech. Freedom is central to infinite play, where boundaries and horizons, rules and roles, histories and futures are all in flux. Freedom of thought is the infinite-minded scientist’s chief weapon against the silence of nature. Like water, science flows against nature and finds the low spots where new knowledge lurks. Freedom interrupts scientific rigor and intention with the serendipitous discovery.
The infinite player is fully aware that a finite academy game she agrees to play carries a role she admits only to others. She is never “The Scientist” even when she plays one. She does science. She wears the white coat. She shares her findings, her data, her methods, her ideas. She teaches classes that open up infinite play to her students. She talks about awe and about doubt, about method and precision, and how doing science is something more than doing anything else; and it is more, because she plays with/in the infinite. And when her corner of nature’s mysteries remains silent over months and years, she persists. She knows the playing will last when she is gone.
There are finite games in which she has zero interest (to the annoyance of her Chair). She sees no point in crafting a “sexy” P-hacked paper for a high-impact journal. At conferences, she spends most of her time on conversation with students at their posters, or with a few colleagues who occupy the same corner of nature as does she. Chancellors and deans fail to recruit her to campus committees. She risks tenure by focusing on her teaching and her idea of research, on her students and their needs, and on the infinite play that fills her mind day and night. If she must leave this university, she will seek out a college somewhere, with the help of her ex-students, and continue to play.
Still, the innovative thrust of her experiments, the transparent rigor of her methods, the quality of her data (which she freely shares) and the unexpected results these reveal keep getting noticed. Despite her inattention to them, her metrics are stellar. In her tenure path, she is a “maverick” and a “connector:” her career is intentionally boundaryless (Dowd and Kaplan 2005). Her generosity is widespread, and well known. She simply lets go of the science goods that are most important to her, knowing that others will remember, and send her new ideas to try out. She is a giver, a genius-maker:
“In Multipliers, former Oracle executive Liz Wiseman distinguishes between geniuses and genius makers. Geniuses tend to be takers: to promote their own interests, they ‘drain intelligence, energy, and capability’ from others. Genius makers tend to be givers: they use their intelligence to amplify the smarts and capabilities’ of other people, Wiseman writes, such that ‘lightbulbs go off over people’s heads, ideas flow, and problems get solved’” (Grant 2013).
She was denied tenure at her university for ignoring many of the hoops through which she was expected to jump; and immediately hired with tenure at a different university, on the weight and the promise of her research, and the stories about her teaching and mentoring, volunteered from her ex-students. She commonly refuses awards and honors; she calls them distractions.
Even the awe and joy of infinite play can be easily forgotten; scientists can get lost when they play only finite games with scientific methods and organizational power. These finite games pull their logics from other finite games outside of the academy. These logics tear the academy away from the freedoms that science needs to pursue infinite play. The more that the academy is trapped into finite games, the less it gains through open sharing and new opportunities for collaboration and innovation.
The Just Cause(s) of Open Science
“In life, unlike chess, the game continues after checkmate” (Isaac Asimov).
Open science exists to return the everyday life of scientists to infinite play, to find paths to justice, and to support teaching and research opportunities for scientists everywhere on the planet, in any open institution that will house their work. Open science builds academy commons (plural) where scientists can govern themselves and their resources, maintain and care for their goods and each other, provision their work, and build an abundant future for infinite science across the globe.
Infinite play requires and rewards, demands and builds, encourages and exercises practical wisdom inside science. This type of caring, pragmatic wisdom can carry a scientist, a science team, a laboratory, a school, a university, an agency: any all academy organizations, toward open science, where sharing and caring are not reserved for losers. Where there are no winners, only players. And that is the whole point.
“…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.themarginalian.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).
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.
“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. 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.
“If you compromise your integrity and principles on minor issues, it gets easier to make bad choices on the big issues” (Dunwoody and Collins 2015).
“Do the right thing. It will gratify some people and astonish the rest” (Mark Twain, attrib.).
The current research on “wisdom” defines this in approximately as many ways as there are wisdom researchers (Sternberg 1990). Fortunately, we do not need to lock down a definition of wisdom here to see its lack in the academy, and the benefits of promoting this as one more measure of what it means to do science and to be a scientist today. Most of the research on practical wisdom confirms what you already think about this. People who are wise in this way know how and when to do the right thing in a broad range of circumstances.
It’s not all that simple, of course. Doing the right thing is not merely “being right,” and most certainly is not thinking you’re right, and convincing everyone else how wrong they are. Practical wisdom operates through the infinite play of open, complex intellectual and normative choices, and conflicting and ambiguous circumstances.
The “right thing” is rarely an easy binary operation and it often requires insights and emotional commitments beyond a simple logic. It may also be an action that goes against the immediate best interests of others or the wise person. “[M]any who have written on wisdom have identified it with the ability to develop and defend good judgments about the difficult, wicked-decision problems characteristic of adult life” (Kitchener and Brenner 1990).
Mostly, practical wisdom examples involve interpersonal or socioeconomic decisions. These examples map how individuals wend their life together with others and the world. This part of practical wisdom encompasses careers in and out of the academy. You can explore the more general forms of practical wisdom literature elsewhere (Practical wisdom: The right way to do the right thing by Schwartz and Sharpe (2010) is a good place to start, also Barry Schwartz’s TED talk [Accessed January 7, 2020] or his WIRED interview). So, what forms of practical wisdom are peculiar to doing science?
In finite games, where arbitrary scarcity and external incentives warp the moral fabric of interaction, practical wisdom often gives way to self-promotion strategies. Somebody has to lose. Actually, most people need to lose in order for winning to matter. In the infinite play of science, nobody wins or loses, and the main strategies include sharing knowledge and adding new players to the mix. Science loads additional wicked-problem solving on top of the adult life problems of work and home. Wise researchers confront the silent unknowns of a complex and emergent natural world.
The ability to start from observations and data about the world and transform these into information, knowledge, and understanding is an inherently moral activity; each bit of new knowledge—big or small—changes existing rules for every scientist and expands the envelope of possible human action. Medical science practice is often cited as a key discipline in need of practical wisdom because of its everyday moral decision making (See: Branch and Mitchell 2011; Kaldjian 2010; and Jeste, et al. 2019). All science domains are similarly implicated when they enter into infinite play.
Judith Glück (2017) makes a case for practical wisdom in the academy. The first behavior is a desire for a deep understanding of complex emergent systems, instead of a personal claim about some potentially universal truth. This is precisely the difference between the infinite play science, and playing a finite zero-sum game. “Over time, wise researchers’ desire to thoroughly understand should lead them to develop an extraordinary amount of knowledge: a broad and deep integrative understanding of a subject matter that includes a keen awareness of what they do not (and may never) know” (ibid). In short, they become intellectually humble.
“[Humble intelligence is] a method of thinking. It’s about entertaining the possibility that you may be wrong and being open to learning from the experience of others. Intellectual humility is about being actively curious about your blind spots. One illustration is in the ideal of the scientific method, where a scientist actively works against her own hypothesis, attempting to rule out any other alternative explanations for a phenomenon before settling on a conclusion. It’s about asking: What am I missing here?” (Resnick 2019; Accessed June 7, 2019).
Practical wisdom in the academy is built upon the humble intelligence of each scientist. As science becomes more collaborative, networking and teamwork bring new demands on the practical wisdom of every member. To review, Tangney (2000) proses that intellectual humility requires five abilities:
A. the ability to acknowledge mistakes and shortcomings;
B. openness to perspective and change;
C. an accurate view of the self’s strengths;
D. ability to acknowledge and experience life outside the direct consciousness of the self; and,
E. the ability to appreciate the worth of all things.
Each of these abilities adds value to any academy collaboration effort. Open science captures this value by promoting equality of access and networked collaborative opportunities.
The second wise-researcher behavior is a high level of concern and care for the “greater good”: for the welfare of the entire “Republic of Science” (Polanyi 1962), of the next generation of scientists, and of the planet:
“Wise researchers will be concerned with the well-being of others, ranging from their students to the world at large. Inside the university, wise researchers will care about the quality of their teaching. … Wise researchers are also caring, generative mentors who seriously work on supporting the career development of their mentees and genuinely enjoy their success” (ibid).
The care that wise researchers bring to their science reflects their ability to set their own ego aside. Many of the distinctions between practical wisdom and general intellectual accomplishment pivot on this attention to the greater good: to the underlying reasons, the overarching effects, the larger, messier, more complex consequences of new knowledge.
Open-access science publishing can support the wise researcher looking to share their whole research in exchange for access to the research of their colleagues. New evaluation tools that reward ethical behaviors and a much greater variety of goals and social activities would support the efforts of those with a recognized concern for the welfare of their mentees and for the research success of others. Assessment tools that capture a diverse and incremental landscape of work outputs and socially-supportive activities are already being developed in the workplace outside the academy (See: Buckingham and Goodall 2019).
Practical wisdom is also an antidote to assholes in the academy. The Handbook deals with assholes elsewhere (See: The Zero-Asshole Zone). Most wisdom research concludes that you cannot have practical wisdom and also be an asshole or evil (See: Stanford Encyclopedia of Philosophy: Wisdom, 2002). In part, this is because you cannot have practical wisdom without actually using your practical wisdom. As noted elsewhere in the Handbook, much of the assholic behavior in the academy is learned and rewarded today; and so, most academy assholes are not irredeemable. They learn and use bad behavior to win the finite games currently infecting the academy. They can unlearn these behaviors and gain some practical wisdom over time. Some, however, are true assholes. These will resent and resist change when your new open-science cultural practices no longer support their bad behavior.
How do we build the practices of humble intelligence and a care for the greater good into the academy? Such is the project of this Handbook, a guide to changing culture in your precinct of the academy, wherever this is. Open science builds fierce equality into the academy as a normative behavior that expresses intellectual humility and inclusiveness. In the infinite play of science new knowledge can be found by anyone in the Republic of Science. Open science uses demand sharing to support the greater good. As a sharing economy, the academy’s goods gain value across time and space.
The starting point for culture change is, as always, changing yourself. Everyone can gain practical wisdom, even tenured faculty. Start today. Be wiser tomorrow.
Some researchers point out that a fair amount of practical wisdom is learned throughout childhood, mostly during play (See: Feist 2006; Brown 2009; Carlson and White 2013; and Sharma and Dewangan 2017). If you are no longer a child as you read this, do not worry, you can still catch up. To begin with, play is always available even for adults (See: <https://eachother.org.uk/the-right-to-play-adults/> Accessed January 5, 2020).
While the “skills” of practical wisdom—and wisdom in general—cannot be gained through the same type of specific practice as, say, a violin, or a golf swing, practical wisdom is similarly experience-based (Cantrell and Sharpe 2016). Like most cultural practices, you can get better at practical wisdom through practice, it is not an inherent trait. You are not born with all the wisdom you can have nor what you might really need to use for your career in the academy.
Glück and Bluck (2103) demonstrate a model for acquiring personal wisdom based on a “strong sense of mastery, high levels of openness, a reflective attitude, and emotion regulation skills combined with empathy.” This “MORE” model can be used in “leadership” curriculum development at the undergraduate level (Sharma and Dewangan 2017). The Phronesis Project [Accessed January 8, 2020] at the University of Virginia School of Medicine is pioneering practical wisdom training in its curriculum. Just remember that you can also have fun while becoming wiser. You enjoy learning, after all, so learning practical wisdom is an opportunity to master another life skill:
“Another value of science is the fun called intellectual enjoyment which some people get from reading and learning and thinking about it, and which others get from working in it” (Feynman, et al. 2005).
Schwartz and Sharpe (2010) find that institutions that manage behaviors through rigid rules or laws crowd out opportunities for members to gain wisdom from self-governed interactions. “[I]t’s important to resist those rules and incentives that eviscerate discretion and threaten wisdom. That’s why we need to reform those institutions that are driving wisdom out.” It could be that your institutions would benefit from more democracy and healthy arguments, and fewer rules. The Handbook sections on Learning Organizations are a good place to start.
“Innovations in how we conduct conversations should be treated as art” (Schein 2013).
“Focusing on conversation highlights the need for generosity to be continually renewed in order to function. Moreover, it points to the things we owe one another, the things we owe our colleagues, and also the things we owe those publics whom we hope to engage. Conversation imposes an obligation that cannot be easily concluded, that asks me to open myself again and again to what is taking place between us. Conversation thus demands not that we become more giving, but instead that we become more receptive. It requires us to participate, to be part of an exchange that is multidirectional. It disallows any tendency to declare our work concluded, or to disclaim further responsibility toward the other participants in our exchange” (Fitzpatrick 2019).
First we turn to look at “knowing,” which is a practice intrinsic to scientific innovation and creativity. “Knowing,” as it is used here, has its own literature in the business-management world. Like “culture,” knowing is always shared. Elsewhere we learned about celebrations of your open-science culture. Here we will look at what Isaac Asimov called “cerebration sessions”: events planned to encourage the “folly of creativity,” in small, informal groups. These events trigger (when successful) shared knowing. Note: Asimov also noted that the subsequent written outputs for these sessions are incidental. What matters is the content of conversations in the room.
“Joviality, the use of first names, joking, relaxed kidding are, I think, of the essence—not in themselves, but because they encourage a willingness to be involved in the folly of creativeness….
I would suggest that members at a cerebration session be given sinecure tasks to do—short reports to write, or summaries of their conclusions, or brief answers to suggested problems—and be paid for that, the payment being the fee that would ordinarily be paid for the cerebration session. The cerebration session would then be officially unpaid-for and that, too, would allow considerable relaxation” (Issac Asimov 2014: MIT Technology Review; Accessed March 8, 2020).
The recent work of John Seely Brown and others, coming out of organizational knowledge theories in the mid 1990s (See: Boland Jr. and Tankasi 1995), has added (or recovered) a cultural angle on knowledge management which includes not only knowledge, but knowing: because “the interplay between knowledge and knowing can generate new knowledge and new ways of knowing” (Cook and Brown 1999). Instead of organizations stewarding an inventory of knowledge objects, what they need to do is open up contexts and spaces: events for knowing (Thomas and Brown 2011).
“In this way, conversation affords more than an exchange in which the net sum of knowledge remains the same; it dynamically affords a generative dance within which the creation of new knowledge and new ways of using knowledge is possible.
Engaging in such conversation is a practice that does epistemic work; it is a form of knowing. Knowing entails the use of knowledge as a tool in the interaction with the world. This interaction, in turn, is a bridging, a linking, of knowledge and knowing…[Which] makes possible the generative dance, which is the source of innovation. The generative dance, within the doing of work, constitutes the ability to generate new knowledge and new ways of using knowledge—which knowledge alone cannot do. And which the organizations of the future cannot afford to neglect” (Cook and Brown 1999).
These events for knowing are where organizations do their “sense-making” activities, and where scientists collaborate in conversation to solve—to make sense of—the emergent complexities of nature. Scientists use knowing practices every time a new experiment is made. Each time the scientist creates a new test to interrogate a piece of unknown nature, she hopes to distill a bit of new knowledge and a ray of understanding that might lead to new knowing. They share this knowing in conversations with their colleagues.
While the “official record” for a new discovery might be a published paper, open science works to accelerate sharing by promoting preprints that open up immediate opportunities for scientific conversations across the internet.
“[N]etworked markets get smart fast. Metcalfe’s Law, a famous axiom of the computer industry, states that the value of a network increases as the square of the number of users connected to it—connections multiply value exponentially. This is also true for conversations on networked markets. In fact, as the network gets larger it also gets smarter. The Cluetrain Corollary: the level of knowledge on a network increases as the square of the number of users times the volume of conversation. So, in market conversations, it is far easier to learn the truth about the products being pumped, about the promises being made, and about the people making those promises. Networked markets are not only smart markets, but they’re also equipped to get much smarter, much faster, than business-as-usual” (Levine, et al. 2009 . Emphasis added.)
The very first one of the ninety-five theses of the Cluetrain Manifesto (ibid) says this: Markets are Conversations. The “markets” for research knowledge in open science connect to the emergent abundance of research artifacts in repositories across the globe. But the knowledge that powers discovery right now lives only in the conversations available across networks of scholars. Buckheit and Donoho (1995) make the point that scientific articles rarely hold the scholarship they claim to convey: rather they are “merely advertising of the scholarship.”
The solution is two-fold: better ways of publishing results that reproduce more of the method, data, software, and ideas (open science looks to go “beyond the PDF”); and more conversations quicker and across a wider range of internet-enabled media, including online direct conversations among peer-to-peer networks. As we will soon see (Science happens elsewhere), these networks create virtual “rooms” that are smarter than any of their inhabitants. Following the Cluetrain Corollary, we can assert the following:
“The level and quality of current knowing in any science discipline increases as the square of the number of scientists times the amount of available conversation.”
Through Demand Sharing and Fierce Equality, open science resets the norms for research conversations across the planet. Today, virtual science organizations can be easily bootstrapped through platform cooperatives to support active collaborations across institutions and continents.
Extra Credit: For those of you who follow recent French philosophy, these knowing events are the center of the process from which truths emerge in the philosophy of Alain Badiou. “[A] truth is sparked by an event and spreads like a flame fanned by the breath of a subjective effort that remains forever incomplete. For truth is not a matter of theory but is a ‘practical question’ first and foremost: it is something that occurs, a point of excess, an evental exception, ‘a process from which something new emerges’…” (Bensaïd 2004; Accessed March 10, 2020).
Of course you do not need to read French philosophy to understand what Asimov and Badiou are telling you: one great conversation (perhaps over beer at a conference, or online on a teleconference) with a colleague about the intersections of your research can be more valuable—can spark more truths about your object of study—than any article or book in your library.
“The smartest person in the room is the room itself: the network that joins the people and ideas in the room, and connects to those outside of it” (Weinberger 2011).
Below, you will discover the “double-loops” of organizational learning, and the cultural practices that build and sustain these. But why are these important for science and the academy? Can’t we just keep doing things the old way? In fact, you can bet there will be two kinds of academy organizations going forward: those standing still (or worse: See: Is my learned society obsolete?, below), and those diving into the networked opportunities of open science.
Goldman and Gabriel (2005) penned the phrase: “Innovation happens elsewhere” to capture the value of open-source software communities. In the academy, it doesn’t matter if you are at Oxford or in Oxnard, almost everything you need to know to make the next step in your research is also being considered at this moment: somewhere else.
In the academy, this “beyond” is a global intellectual commons now becoming abundant with open data and accessible—and reproducible (Crick et al 2015)—research results. Using online peer production methods (Benkler 2016), the academy can optimize the value of this commons for innovation, knowledge, and growth. “[P]eer production practices [are] highly adept at learning and experimentation, innovation, and adaptation in rapidly changing, persistently uncertain and complex environments” (ibid).
The only competition your academy organization has is within itself. As other institutions—including new virtual science organizations—work to continuously improve on their work, your team needs to focus on leveraging the learning engine of double-loop governance to get better than your yesterday. In Learning infinite science play, winning means accelerating your team’s learning and sharing capacity through what Hagel and Brown (2011) call a “creation net” for open innovation. Standing still is not an option when the research world is exploding somewhere else. This “explosion of creativity is taking over more and more of our world. Everyone involved in it is at the same time a producer and a consumer, a worker and a manager…. Progress in most academic disciplines now seems to move at the speed of ‘instantaneous,’ with discoveries building atop one another at a dizzying pace” (Ito and Howe 2016).
A creation network is enabled by a certain quality of learning within social interactions, a greater quantity of information flows (and/or a greater attention to these), an availability of interpersonal trust (based on demonstrated skills and commitment), and an environment of reflexive involvement: all benefits of belonging to a community-led double-loop governance. “[I]nstitutions will need to become much more selective in their efforts to protect existing stocks of knowledge and much more adept in using their stocks of knowledge to contribute more actively in creation nets and to plug into promising flows of knowledge” (Hagel and Brown 2008). Data-intensive science (Hey et al 2009) in a whitewater world of global research demands a nimble governance for its teams, labs, networks, societies, universities, and agencies.
When your academy organization looks to innovate—or when your personal research is looking to find the right question to ask—in a world where multiple/large data/information inputs, and international science discoveries are coming on line, how can you stay ahead of this emergent complexity? One way to look at this problem is through Ashby’s principle/law of requisite variety, coming from cybernetic management. Ashby’s law notes that unless the control system has at least the variety of the environment it controls, it will fail; which actually means that some part of the environment will be controlled elsewhere.
You need find ways to join the greater “science elsewhere.” Elsewhere is where other science teams are now using their infinite play mindsets in collaboration, asking the questions that their networked teamwork generates. Elsewhere there are flows of information being shared across the planet. That is a great reason for new creation networks in the academy: for open science sharing across the academy.
Elsewhere is where innovation happens; because unless you can corral the inherent variety of the problem you face, it will be too complex for your team to innovate a response. If you are not engaged with the open-science elsewhere that is opening up today, your team will suffer. You can either go out and hire a bigger team (good luck talking your chancellor or the NSF into that), or you can borrow enough requisite variety just long enough to bring your own team up to speed by starting up or hooking into an online creation network. You can join the sharing economy, play infinite science, and get better at it every day. Or you can rest on your (bullshit) reputation and keep on thinking the world will come to you.
When members are given license to form working teams across organizations, they also expand the extent of where their research adjacent possible is found; creative interactions and new knowledge become predictable outcomes. The larger the room, the smarter it gets. Find the room to nurture your research.
The “adjacent possible” is a notion that comes from biological theories of coherent change. It describes how the surrounding environment tucked between stasis and chaos provides a resource of available change. The adjacent possible enables, and almost guarantees, certain changes (while ruling out others) out of potentially infinite play of innovation.
“Biospheres, on average, may enter their adjacent possible as rapidly as they can sustain; so too may econospheres. … [T]he hoped-for fourth law of thermodynamics for such self-constructing systems will be that they tend to maximize their dimensionality, the number of types of events that can happen next” (Kauffman 2000). Every new piece of information, each new proto-fact, expands the horizon of infinite science play. The more scientists that add this new fact to their knowledge, the larger their mutual adjacent possible becomes.
“...the love he really lived for was knowing.
That thrill of first discovery returned a handful of times the next twenty years, in diminished forms. He pushed himself forward on the pleasure of first: first place, first to lay eyes on, firsts the hearts of his peer reviewers. But he wanted more than simple primacy. First was just a sporting bagatelle. To look on a thing had been true since the start of creation but never grasped until you made it so: no euphoria available to the human brain could match it. Cleaner than drugs, broader and more powerful than sex—Huxley's “divine dipsomania." Anyone who tasted it once would spend the rest of his life trying for more.
Science fit the very folds of Tom Kurten's brain. Its exuberance tempered the tedium of daily lab work, kept him alert, overrode fatigue, and rendered risks trivial. And the goal of scientific exuberance, like the goal of life, which it helped to propel, was to replicate itself (Powers 2009).”
Open science is so much more than open access
“Science functions best when scientists are motivated by the joy of discovery and a desire to improve society rather than by wealth, recognition, and professional standing. In spite of current pressures, it is perhaps remarkable that many scientists continue to engage in selfless activities such as teaching and reviewing, decline to publish work that doesn’t meet stringent standards for quality and importance, freely share reagents and knowledge without worrying about who gets the credit, and take genuine pleasure in supporting the efforts of other investigators. Such individuals should be recognized and emulated” (Casadevall and Fang 2012).
“Numbers have many charms, unseen by vulgar eyes, and only discovered to the unwearied and respectful sons of Art. Sweet joy may arise from such contemplations.” Charles Babbage circa 1825, quoting French Mathematician Élie de Joncourt, circa 1735 (Gleick 2011).
“Making a better, more sustainable institution, in other words, requires us to move away from quantified metrics for meritorious production — in fact to step off the Fordist production line that forever asks us to do more — and instead to think in a humane fashion about ways that we can do better. Better often in fact requires slowing down, talking with our colleagues and our communities, and most importantly, listening to what others have to say. Better requires engagement, connection, sharing, in ways that more nearly always encourages us to rush past. Turning from more to better goes against some of the ingrained ways of working we’ve adopted, but that turn can help us access the pleasures — indeed, the joys — of our work that life on the production line has required us to push aside” (Fitzpatrick, April 26, 2020; Accessed September 2, 2020).
While writing this handbook, it became clear that, as a life-way—as a career that is also an avocation—science today needs to rekindle the internal emotional goods that have long been wellsprings for creativity and innovation across the lifetime of the scientist. Science is the hardest thing humans can do, in terms of the challenges it faces, and the obstacles to resolve these. There is no shortage of hard work, long hours, and disappointments available to the scientist. These have been with science since the time of Francis Bacon. Science is serious. We can take that as given. Science faces many of the hardest and most meaningful questions humans have managed to ask themselves and the universe. What is the origin of life? What is matter made from? Why must we die as we do? But science was never only “serious” in its practice. Scientists get to learn infinite play in their job, a pursuit that opens up to awe and wonder—and joy—at any time. “Joy has a component, if not of morality, then at least of seriousness. It signifies a happiness which is a serious business. And it seems to me the wholly appropriate name for the sudden passionate happiness which the natural world can occasionally trigger in us, which may well be the most serious business of all” (McCarthy 2015). Yes, science is serious, but so too is joy.
“Too much of the OA [open access] discussion is grim, utilitarian, and problem-oriented. We should complement it with discussion that is joyful, curious, and opportunity-oriented. Serious problems don’t rule out beautiful opportunities, and one of the most beautiful opportunities facing OA is that certain strategic actions will solve serious problems and seize beautiful opportunities at the same time.” Peter Suber (2012)
Open science will almost necessarily be more joy-full than the science you’ve been doing. Some of this comes from its inherent generosity, and the gratitude you feel at the generosity of others when they share openly the science findings that help your research. “Gratitude is a powerful emotion. We declare that we are satisfied. We can drop our search for more; in this moment, we have everything we need. Out of that fullness, other emotions naturally bubble up. We tend to get in touch with joy and generosity, and we treat others with love and care” (Laloux 2014).
Open science enables infinite play. You will be challenged there when nature stays silent to your questions, but you will also find joy. “There can be occasions when we suddenly and involuntarily find ourselves loving the natural world with a startling intensity, in a burst of emotion which we may not fully understand, and the only word that seems to me to be appropriate for this feeling is joy” (McCarthy 2015) (See also: Popova; Accessed May 31, 2020).
Certainly, there is some little joy in those finite games that scientists play today. However, this type of joy is kept scarce through the logic of “science as a race.” This logic informs scientific research as a series of races, each one ending in the form of some achievement that can be owned by the scientist, and in extension by their home organization. “The contradiction of finite play is that the players desire to bring play to an end for themselves” (Carse 1987). In order to win, and to feel this variety of joy, they need to hold up their discovery—as a distinct, independent object—for public notice. They seek an audience, and arenas—certain journals, learned society prizes, funding agencies, campus administrators—where their personal winning can be acknowledged with some special notice or title. “If finite players acquire titles from winning their games, we must say of infinite players that they have nothing but their names” (ibid). The winner’s joy is amplified by the number of losers around them. They have succeeded where so many others failed. But the joy of these distinctions is momentary. The next race has already begun.
“The revelation of suddenly seeing what I was blind to only moments before is a sublime experience for me. I can revisit those moments and still feel the surge of expansion. The boundaries between my world and the world of another being get pushed back with sudden clarity an experience both humbling and joyful” (Kimmerer 2003).
The joy of open science is like the joy of a choir while creating the music they sing together. This joy is fully shared, as a form of collective virtuosity (Accessed June 9, 2020). The better the singers’ voices blend into a single voice, the finer their song sounds. Should the choir grow larger, the joy only multiplies. Should the song grow longer, the joy only expands. “The paradox of infinite play is that the players desire to continue the play in others. The paradox is precisely that they play only when others go on with the game. Infinite players play best when they become least necessary to the continuation of play. It is for this reason they play as mortals. The joyfulness of infinite play, its laughter, lies in learning to start something we cannot finish” (Carse 1987). Open science is a song with a choir anyone is welcome to join (with some real training behind them), a song that doesn’t end when any one voice becomes silent.
“Specifically, joy may be thought of as delight that arises in response to a source of meaning or value in life. Delight describes a pleasant emotion, conveying the positive valence of joy. Connecting this to a matter of meaning or value differentiates joy from other positive emotions, such as happiness, a more general case in response to anything pleasant; amusement, in response to something entertaining; gratitude, in response to receiving something; pride, in response to accomplishing something; interest, in response to engaging in something, etc.” (Krumrei-Mancuso 2019).
Under certain circumstances, doing science opens up opportunities—events—for a delight that comes from connecting to nature. "A lot of the time, when you do Math, you’re stuck. But you feel privileged to work with it. You have a feeling of transcendence and feel like you’ve been part of something really meaningful." Akshay Venkatesh (ICM 2018; Accessed June 1, 2020). Science organizations can be governed to encourage, enable, and celebrate these events.
More importantly, individual scientists need to develop their capacity for joy, a capacity they might have had as a child only to lose during their schooling. Like practical wisdom (The practical wisdom of doing science), you can get better at finding moments of joy in your research and teaching. Of course, going to work in an open-science culture organization does not mean you simply step up to a day of joy. The labors of science are infinite. The disappointments and the setbacks are legendary. All the joys offered in science are earned. “The joy of science lies in pondering the magnificent and seeking answers to the unknown. Indeed, Stephen Hawking’s advice to ‘Look up at the stars and not down at your feet ... Be curious’… is not far from what other scientists have noticed drives many scientific discoveries: the experience of awe”(McPhetres 2019).
One of the goals for culture change in open science needs to be an acknowledgement of the role of intrinsic motivations, and a cultural devaluing of the external, often perverse (Binswanger 2015), incentives that create so many conflicts of interest today in science. “We call for a cultural change in which scientists rediscover what drew them to science in the first place. In the end, it is not the number of high-impact-factor papers, prizes, or grant dollars that matters most, but the joys of discovery and the innumerable contributions both large and small that one makes through contact with other scientists”(Casadevall and Fang 2012). Like other psychosocial skills, joy increases across time when you work at it. Fun, however, can erupt at any moment when two or more scientists get into a conversation (or a “cerebration”(Asimov; Accessed May 1, 2020)) about their research.
“For best purposes [for creativity in a group], there should be a feeling of informality. Joviality, the use of first names, joking, relaxed kidding are, I think, of the essence—not in themselves, but because they encourage a willingness to be involved in the folly of creativeness. For this purpose I think a meeting in someone’s home or over a dinner table at some restaurant is perhaps more useful than one in a conference room” (Asimov; Accessed June 8, 2020).
There is a whole literature on “play” and and its valuable role in creativity and imagination from childhood to the board room. Linder, Roos, and Victor (Working Paper 2001; Accessed June 4, 2020) do a good job of surveying this literature. Their Institute proposed “serious play” as the foundation for strategic thinking. Thomas and Brown (2007) look more specifically at the collective knowing that multiplayer games produce, and how this might inform new theories of learning. Csikszentmihalyi’s work on autotelic, optimal experiences (what he calls “flow”) describes how play—in a wide range of environments, including the workplace—offers its own very important incentives and rewards. His 2004 TED talk is a good starting place. Caron (2017) looks at how the study of society can be based on the cultures of games, as emergent, open-ended, strategic play. Here is it helpful to not contrast “play” with “serious”. Play can be terminally serious; look at sword-play. Play can be artistically virtuosic: as in word-play. Consider open science as full of play: data-play, theory-play, methods-play. Remember too, play is fun. That is a bonus.
Open-science-culture governed organizations can intentionally, and reflexively promote events where creative folly—the play of intellection—is more likely to occur on a more regular basis. When they abandon the finite games of metrics-turned-into-goals, these organizations will find new spaces and time for serious play. Fun is guaranteed as a first-order outcome, together with more creative imagination, an increase of shared knowing, and the likelihood of better problem solving. Asimov (above) noted that bosses and funders might not fully appreciate the level of fun involved in events of group creativity. He suggested that participants be given “sinecure” tasks—to write a white paper, say, or a final report—something perfunctory to keep the funders happy and the bosses complacent. Never confuse these tasks with the real work, and the serious play of scientific conversations.
It’s clear from the literature that talking about play and fun in the workplace surfaces a tension between two models of academic work. The first model tells us (and our funders) that work needs to be endured. And for it to be endurable in a meaningful fashion, it must be difficult and arduous. No fun allowed. Neoliberal managerial practices serve here to ratchet up demands and metrics to be sure that next year, or next week, you will need to work harder than today. So, buckle up and buckle down, because somewhere else, others are working harder than you are, and you will be left behind, unfunded, and tenure-less.
The second model tells us that each scientist has—through many years of learning and striving—earned the right to join the infinite play of science, which has no clock, and runs on shared knowing and ubiquitous doubt. This work is no less arduous. However, there is also laughter and joy, and a love for the process of doing science and for the object of study. “A love of knowledge, the most valuable resource in Universities, is being squandered by policies designed for the market place” (Rowland 2008). Open science culture change can move your team and your organization from the first model to the second one. This Handbook will help.
“The lesson here for Open Scholarship may be that an inherent personal love of science and discovery must be nourished…, and communities that can affect the principles of Open Scholarship must also be cultivated around this” (Tennant, et al. 2020 A tale of two 'opens’; DOI:10.31235/osf.io/2kxq8.).
In the end, you cannot talk about open science without adding how this enables new/old practices that show the love of science and the love of nature through science. And you can’t really talk about changing cultures in your workplace without asking the question: does this workplace nurture the love that its workers might find, the joy they can feel and share, and the fun they can generate through their work and with their time here? As Roland (2008) notes: “[I]t is somewhat ironic if academics consider a term such as a love of knowledge—or ‘intellectual love’—should not be taken seriously. It is strange that a phrase such as ‘the delivery of learning outcomes’ is taken to be serious and meaningful, but not ‘inspiring a love of learning’. Has talk of such love no place in the language in which academics write about their work?”
One of the cultural aspects of open science that deserves more conversation is the replacement of external incentives with those internal incentives that have long been a part of science, but which have been demoted and shunted in the pursuit of finite games and the logic of competition. The love of science is a lifetime affair. It probably started for you in childhood. “As researchers we sometimes need to be reminded that we are contributing to an astonishing human effort, which transcends an individual’s lifetime (Frith 2019). It is a big “why” for those career choices you’ve made. It also promotes trustful, caring relationships with other scientists. There is no room for: “I love science, it’s scientists I can’t stand” (See: Kindness, Culture, and Caring). A love of science also opens up an avenue for “slow science.” “We need to engage at every level to accomplish a reconceptualization of university time. Creating spaces for new modes of scholarship and intentional communities helps us to move from individually-focused solutions to solutions with potential to create institutional and structural changes that nourish and support slow scholarship” (Mountz et al. 2015).
“Science needs time to think. Science needs time to read, and time to fail. Science does not always know what it might be at right now. Science develops unsteadily, with jerky moves and unpredictable leaps forward—at the same time, however, it creeps about on a very slow time scale, for which there must be room and to which justice must be done.”
From The Slow Science Manifesto (2010).
Open science is not only slow science, indeed, open science looks to accelerate knowledge sharing, but it does foster slow science, through conversations and collegiality, as a part of the future of how science is done. “This slowing down represents both a commitment to good scholarship, teaching, and service and a collective feminist ethics of care that challenges the accelerated time and elitism of the neoliberal university” (Mountz, et al. 2015).
Open science is all about collaboration: “[I]n order for collaboration to work well, it emerges locally in conversations between people… …Collaboration is about thinking together. And undertaken in that spirit, collaboration can allow us to challenge neoliberal models of higher education and the remasculinization of the academy” (Berg and Seeber 2016).
What if each scientist is limited to one published paper a year (with unlimited preprints, blogs, etc.)? What if each scientist can only receive three external grants in their career—and their home organization was responsible to work with funders and the public to increase the general, sustained, in-house support for science research? What if teaching the love of science were a large part of career advancement? The cultures of open science will foster emergent practices that can move the academy away from the current the neoliberal university model.
“Will Rogers used to say that people don’t so much fall in love as step in it. I think the same may often be said of science. Even those who know from their third birthday that they will be a scientist can’t tell you precisely how they got to be doing exactly what they are doing. They try this or that, run into a professor or a graduate student who takes him or her under their wing and infects them with their mystery, and that’s it” (Firestein 2012).
You could argue that “rationality” is central to instrumentalist practices in science and dominates the explanatory prose of science reports. Useful it is, we will all admit, within its domain. No disagreement here. All of the arguments for precision and intellectual rigor, for “doing the math,” and being grounded in the methods: these are a given. There is no anti-rational basis nor bias in open science. The real issue is where rationality fits into the larger practice of science. What are the limits of instrumentalism? Where does imagination and serendipity show up? When do explanations fail? “Indeed, the exclusive attachment to purpose, consistency and rationality may be inappropriate in organizational situations that actually require reason’s ‘non-rational’ cousins, including impulse, intuition and lived bodily experience” (Jacobs and Statler ,2004. Working Paper; Accessed June 8, 2020). You can be coldly rational with your research strategies, logical with your data, rigorous with your methods and still be kind, caring, and ego-free with your team.
Scientists regularly probe beyond what they can currently explain, hoping to extend the limits of explain-ability. As Carse reminds us, there are other forms of writing better suited to some of these unknown domains: “Explanations settle issues, showing that matters must end as they have. Narratives raise issues, showing that matters do not end as they must but as they do. Explanation sets the need for further inquiry aside; narrative invites us to rethink what we thought we knew” (Carse 1987). In its infinite play, science goes beyond explanation and steps into narrative.
Background: descriptions of hyper-rationality in the state (and the academy) flow through “poststructuralist” social theories, and discussions about poststructuralist theories, in the late 20th Century (Harvey 1989; Bhabha 1990; Best 1991; Best and Kellner 1997). Michel Foucault’s lectures in the late 1970s (Foucault 2008) are a fountain of these descriptions. As an open scientist, you can make a quick note that you are not alone in seeking a better way to build teams, share knowledge, and collaborate with your peers. You knew this without ever reading Foucault.