In Thinking, Fast and Slow, Daniel Kahneman develops the idea of “what you see is all there is.” He makes the case that we use two rather different mental approaches, which he calls System 1 and System 2. System 1 is active, impulsive, reflexive, and readily jumps to conclusions. System 2 is “lazy,” rule-based, suspicious, analytical. The differences between these approaches to thinking can be seen in things like face recognition–system 1 cannot help but see the face of your friend and know who it is–and in math problems–

2 x 2 =

is a system 1 problem–the answer jumps out–while

17 x 33 =

is a system 2 problem. System 1 sees the pattern–it is a multiplication problem!–but has no idea of the answer. Your System 2 knows how to work the problem, but isn’t about to do so unless it has to–which it doesn’t for the purpose of reading this article, so it won’t. Right?

System 1 and System 2 are interrelated. System 1 matches patterns, so when it perceives something that matches, it will enlist System 2 to construct a narrative that supports its match or conclusion or sensibility. In discussing System 1, Kahneman writes (my emphasis):

An essential design feature of the associative machine is that it represents only activated ideas.  Information that is not retrieved (even unconsciously) from memory might as well not exist.  System 1 excels at constructing the best possible story that incorporates ideas currently activated, but it does not (cannot) allow for information it does not have.  The measure of success for System 1 is the coherence of the story it manages to create.  The amount and quality of the data on which the story is based are largely irrelevant.  When information is scarce, which is a common occurrence, System 1 operates as a machine for jumping to conclusions. (85)

The combination of a coherence-seeking System 1 with a lazy System 2 implies that System 2 will endorse many intuitive beliefs, which closely resemble the impressions generated by System 1.

Jumping to conclusions on the basis of limited evidence is so important to an understanding of intuitive thinking, and comes up so often in this book, that I will use a cumbersome abbreviation for it:  WYSIATI, which stands for what you see is all there is.  System 1 is radically insensitive to both the quality and the quantity of information that gives rise to impressions and intuitions. (86)

Later in the book, Kahneman introduces the idea of rhetorical frames in which a decision is based. When people are asked to make decisions, psychologists have found that they reason from the frame using their System 1 apparatus.   Kahneman gives this example for a surgery decision:

1 month survival rate is 90%
10% chance of death in the first month

The first option, if presented, triggers a “good” response from System 1, while the second triggers a “bad” response. This, even from surgeons! Further (more of my emphasis):

Reframing is effortful and System 2 is normally lazy.  Unless there is an obvious reason to do otherwise, most of us passively accept decision problems as they are framed and therefore rarely have an opportunity to discover the extent to which our preferences are frame-bound rather than reality-bound.  (367)

Your moral feelings are attached to frames, to descriptions of reality rather than to reality itself. The message about the nature of framing is stark:  framing should not be viewed as an intervention that masks or distorts an underlying preference….there is no underlying preference that is masked or distorted by the frame.  Our preferences are about framed problems, and our moral intuitions are about descriptions, not about substances. (370)

This is quite the conclusion. Kahneman argues that the evidence shows we reason intuitively (System 1) about fairness and safety and advantage from the descriptions we have of things, the frames, not from a broader underlying reality we might observe and consider. Our morality, our allegiances, our preferences are triggered by the frames. Without them, we do not have an easy moral compass, but rather analytical reasoning lacking the comfort of such a narrative.

From this, one might see how important the combination of WYSIATI and reasoning from simple narratives about decisions can combine to create a powerful rhetorical effect. There’s a bunch more to it, and I won’t cover it all here, but these ideas about different strategies of thinking suggest a couple of things going on in the rhetoric of metrics surrounding technology transfer practice.

First, if one aims to exploit WYSIATI, then in presenting a frame, one aims to create a narrative that triggers the moral preference that one wants to elicit. Thus, something like the “linear model” of research to invention to patent to license to product serves well. System 1 recognizes the pattern, sees an obvious progression, and jumps to a conclusion. Anything else takes real work and sounds like needless complication. Once the pattern is in place, it is darned hard to dislodge it. Matt Ridley argues in Chapter 8 of The Rational Optimist that in the industrial revolution, most of the technological advances were not made by scientists but by tinkerers in weaving factories and the like, and that science was largely the beneficiary of technology change, not its driver (see pp. 255-58 in the paperback edition). This argument flies against the affinity one already has for the linear model, which insists that science comes first.

More so, if one aims to keep things in place, the way they are, then one sets up that thing as a sure thing, and any change as a big gamble. In terms of such framing, System 1 is sure to want the sure thing. On the other hand, if you want to move someone off that position, then presenting the present as a sure loss, and the future as a possible gamble with an upside is going to cause a lot of people to reason away from the sure loss and go for the gamble, even if mathematically, the odds (or the financial outcome) favors taking the sure loss.

If one considers metrics, then, about something like “technology transfer”, one use of metrics is to demonstrate that the present is doing just fine. One selects metrics that confirm the model, the frame, and supports the affinities for the frame that one anticipates are already in place, thanks to System 1 constructing the best story to accommodate the information. The reporting of such metrics becomes a kind of signalling of those who accept the framing account and agree to keep it in place.

So, for instance, one might report the number of patents filed, the number of licenses, the royalty income. System 1 cannot help itself–“those numbers look pretty good, you must be doing something right, keep at it, it can only get better.” And this is more than rational–it is a moral affinity, a world view, for which System 2 is enlisted to argue in favor of keeping. Reasoning, in this context, means telling more stories that rationalize the deeply held belief that is signaled and triggered and affirmed by the metrics.

If I come up with alternative metrics, such as the number of ideas removed from immediate circulation at a critical moment in their existence by an institutional patent claim, the number of unlicensed patents that therefore block practice and development of all sorts, and moribund exclusive licenses that are likely to turn into opportunities to troll practice as industry catches up to the once-new technology, then you might go, “oh wow, the universities are taking a huge chunk of new work and that is undermining the value of publications and personal initiative, and the unlicensed patents are simply barriers to entry in the US and all but handing the advantage in new technology to all those regions in which the universities are not routinely filing, such as China or Brazil or Germany–that’s some national innovation program–blocking nearly everything that might have an economic benefit in the near term for a shot at one or two royalty-generating deals per decade per institution.”

Well, that’s System 1 again, but aligning itself with a different frame, and telling a different story. As long as we are going to reason from our intuitive desires, we should expect that frames–the stories we tell ourselves, the WYSIATI–will play a large role in deciding whether we should keep things the same or take a gamble on change. And this is at the heart of innovation, relative to the status quo, convention, the powers that be.

If a discussion of approaches to university technology transfer is merely about comparing notes on a shared WYSIATI narrative (like the linear model) and supporting metrics (that serve to confirm this model and do not challenge the WYSIATI narrative), then the discussion is about acceptable rationalizations for the work as it is. There will be change, progress, efficiencies in the approach, but not innovation, which would be a “bad” thing.  An odd prospect, for a profession that has been built around support for innovation, but reacts strongly against such a thing when the prospect arises in its own activities–whether patent reform or free agency for faculty or revisions to Bayh-Dole.

Benoît Godin and Joseph Lane have just published an essay in Science Progress in which they argue that US innovation policy is limited by ideology, not reality. They argue that there are three areas of practice:  not just research, but also prototype creation and product development, and that each of these activities should be considered in a broader program of national innovation support.

I might add that this means breaking up the idea of the linear model, that piling up the science will produce more prototypes and eventually some product. This linear model is great for making the case for university technology transfer’s two apparent real functions:  create a patent barrier to transfer (and therefore get credit and payment for transfers that do take place); and pass the burden of blame for lack of success to investors and industry unwilling to pay for transfers and unwilling to support the inventions produced by academic research. Once this frame is in place, System 1 pounces on it (it cannot help itself, things are obvious, they make so much sense) and confirms the story. It wants to find supporting evidence, and university technology licensing offices are only too kind to assist.

Instead, what Godin and Lane are making (as I see it) two key points. First, that these three activities of research, prototypes, and development exist somewhat independently, and that there is no need, other than a prevailing ideology (or frame story), to expect that there is a necessary flow of potential from science to product passing through each stage in turn. No doubt this does happen. But it is not the only thing that happens, or the primary thing that happens, or the most effective thing that happens. It is a WYSIATI kind of moment to realize that the route to a new product might start with a spin-out from a product development activity passed to a alternative prototype that would move in a new direction, and from there over to some science to confirm an unnoticed effect, and from there to development of an entirely different product based on taking advantage of that effect. Development to prototype to science to development. Any number of other combinations are possible, including everyone makes his own prototype and no one having need of product at all (consider, for instance, custom instrumentation or disease assays). This first point takes on the ideology that positions university patenting and licensing as a critical step in any broader plan for economic development through technology change. Bound to cause howls of protest from anyone really attached to their System 1 framing story that accounts for a limited view WYSIATI.

The second point is even more profound:  the federal government’s throwing money at universities for research, using a research vocabulary and research metrics, is potentially misplaced. Not that there should not be research funding, but that it must be balanced with funding for prototypes, and for development, and that this funding may be independent of the “progress” of scientific inventions through a linear model of ideology-mandated expectations. In simple terms, this means, shift funding to a better balance among these three functions, and do not expect that the arrows all point from research to product. As Ridley argues, it may be that technology or social change would be a huge boost to science, much more than anything money can buy in the present emphasis on peer-reviewed research proposals. Godin and Lane’s point is that this is an ideological problem, baked into a narrative that keeps things in place. It is not necessarily a problem of funding, or inherent in the difficulty of the areas of inquiry.

As Kahneman might argue, however, it takes a ton of effort to look for what has been left out by a persuasive, rationalized System 1 created frame. A change in ideology itself would amount to a conversion experience, an epiphany… an innovation. It takes one’s breath away, for all the talk about the necessity for innovation, and to be told that the block to it all may be the lack of innovation in innovation thinking and policy!

How to go about it?  Godin and Lane point out this:

Shifting the units of innovation analysis from inputs and outputs to outcomes and impacts is the single-most obvious yet entirely ignored change confronting U.S. government agencies and academic institutions engaged in technological innovation.

That is, change the metrics from the academic attributes of scientific research–number of grants, research budgets, inventions made, patents issued, publications–and look instead at what happens in each of the three targeted areas of activity–who and what makes any difference in the economy? That is, what are the impacts in the context of a broad set of information? This is territory where there may be no simple stories for our moral sensibilities to attach to and do our reasoning for us, pattern recognition System 1 style. Or put another way, we would have to learn to recognize a new set of patterns if we preferred to reason primarily from a frame rather than bare reality. It may also be that the stories are there for us, just not within the ideology that predominates discussions in the U.S.

There is a discussion presently in the US around whether we were better off with faculty using their academic freedom to spark technological change, or whether the present “system” that imposes institutional controls and overhead should remain. In one regard, this discussion is about whether we went up a dead end and didn’t recognize it, an “anti-pattern” from programming design. Michael Crow at ASU appears to be thinking along this line, that we have limited our ability for technological change by the design of our approach to grants and research and the like–by our ideology, by a WYSIATI focus.

In another regard, this discussion is about whether the present dominant system with its self-confirming stories and metrics should be disturbed by data and stories that unsettle its claims and call into question not so much its efficacy but rather then scope of its application–a comprehensive, compulsory, own-all and dedicate to a commercial paywall for industry may “work” for some few things, but not for most. That point of view, from the pure self-interest of folks who like the story they tell one another and the public and especially policy-makers, would appear to be terrible, intolerable. From the point of view of national policy, however, the self interest of a few thousand patent licensing officers might not count for much against the interests of hundreds of thousands of people involved in prototyping and development, or the millions of workers who would like to get at something in the here and now rather than wait for someone using tweezers to carry ore out of the mine. In this regard, the debate is about whether there should be a debate.  The answer here, I think, is that the debate is critical.

There’s a third reason for debate, apart for whether universities turning patent licensing into an industry or whether that industry has only limited scope and efficacy, and that’s to get outside the WYSIATI frame. Doing so is a form of exploration. It opens up new things to be observed and considered. This is why exploration, travel, has been so potentially revolutionary, why innovators seem to have a better affinity for change after they immigrate–that we move outside of comforting frames, push off from WYSIATI, and give ourselves half a chance to develop something outside a prevailing ideology that tells us, on the advice of experts and with full confidence, the best story that accommodates the limited range of information that anyone will permit to circulate.

I’m not interested in finding data to make the prevailing linear model look good.  I don’t see my role in life to make the university tech transfer story “look good.” The role for faculty is not to “contribute to the success of the technology transfer effort.” Nor is it for them to change from academic caterpillars into entrepreneurial butterflies, (or from free-flying butterflies into closed, proprietary pupae that break down their living tissues in preparation for the slow crawling stage of patent licensing–pick your frame and let your System 1 moralizing affinities go wild). No, what we are after is not settling an ideology, or overthrowing it in favor of another one, just as limited but with different proprietors. We are after putting things in a broader context, challenging a System 1 thinking that needs to innovate–that is, we need to see the limits of the prevailing ideology and move outside it to explore what is possible, and do something in that new space rather than stagnate in the service to a really seductive thirty-year story that has gobbled up opportunity and resources and staked its claim to be the primary agent and chief beneficiary for some trillion dollars or so of public investment in university research.

To get on with things, we need to have the discussion directed at exploration. We need to loosen up the ideology and get past mere incremental changes that preserve the status quo of technology transfer and technology change. That is, we need innovation in innovation management. Something for System 2 to really sink its teeth into and make sense of, new things that break up the present WYSIATI and, yes, reposition the present ideology for a new role, perhaps more limited, perhaps in history books as a dead end. If we do this, there will may be jobs for folks in IP, for technology transfer, and there’s a good chance they will be tremendously more interesting, challenging, and rewarding than the jobs folks have now. If we stay with the status quo, what AUTM is rallying around, we stagnate and die. It’s a sure thing, folks.  Count on it while you ride the blimp into the ground. Now, use your System 1’s to good effect and take the gamble on a future that is iffy, uncertain, but with an upside way, way better than the sure thing you have going now, which is sure to fail and take with it the work and potential of many good people who otherwise would have a positive impact for society.

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