Digital/Dirt Differences – Loose Ends

I’m not happy yet with my first attempt at a list of digital/dirt differences. It didn’t reflect the rigidity and fragility of computer systems; the intangibility of digital media, particularly their intrinsic non-rivalry and non-excludability; or the effortless copyability of digital stuff. There’s also the tension between the promise of personalization in software, and the fact that most extant code is, in fact, largely one-size-fits-all-with-a-little-slack-at-the-edges.

If I just added them, the list would grow to seven – which may be George Miller’s Magical Number, but is too many for me to remember:

1. Persistence
2. Findability
3. Opacity
4. Mutability
5. Scale
6. Brittleness (rigidity, fragility)
7. Abundance (intangibility, copyability, non-rivalry)

One can make three pairs of intriguing oppositions, with one left over:

• Persistence <> Mutability
• Findability <> Opacity
• Scale <> Brittleness
• Abundance

Better, but not quite there yet.

A few loose ideas that I’m still mulling, and that my dear readers may have opinions on:

Abundance is an important attribute of digital media. Economics is often defined as “the study of choice under scarcity.” If scarcity exists in digital systems (human attention, perhaps) it’s of a relatively novel kind in evolutionary terms. As I remarked in Why are virtual worlds so real?, synthetic worlds suppress much of the abundance of digital environments in order to be intelligible and enjoyable.

I’m not proposing that any of these attributes are brand new in human experience. I do claim, though, that individually and collectively they represent a phase transition in the situations we have to cope with. Each individual attribute gains its power from an amplification beyond what we’re used to:

Persistent artifacts aren’t novel. The pyramids have been around a while and will probably outlast most buildings in Second Life; and even intangibles like conversations last in people’s memories. However, the precision of recall and the volume of digital items that be recorded is sufficiently novel to drive a phase change.

On brittleness: We’re used to things breaking. However, the closest we’ve come to the brittleness of software until now has been legal documents where a misplaced comma can be very significant. However, in the legal case the “application” is “executed” in a court of law, where human concepts like intent and reasonableness can provide some resilience.

On scale: “The moment programs grow beyond smallness, their brittleness becomes the most prominent feature, and software engineering becomes Sisyphean.” – Jaron Lanier, Why Gordian Software has convinced me to believe in the reality of cats and apples.

Information overload can be seen as the consequence of combined excesses in Abundance, Findability, Persistence and Scale.

Another weakness in the analysis so far: it’s essentially descriptive, and to some extent explanatory. I have yet to demonstrate that it has predictive power. If I were a social scientist I’d try to extract normative prescriptions, but I’m still too much of a geek for that.

More cores than cognition

Intel's announcement of an 80-core chip reminds me the cognitive challenge entailed by parallel programming. According to David A. Patterson, a UC Berkeley microprocessor expert, “If we can figure out how to program thousands of cores on a chip, the future looks rosy. If we can’t figure it out, then things look dark.” (New York Times story 11 Feb 2007.)

Programming thousands of cores is a hard research question. The shopping list of open tasks in a Berkeley white paper on this topic indicates just how far we have to go. I've observed some work on parallel computing, and I suspect that the cognitive limitations that have lurked below the surface in programming so far (e.g. limits on the number of independent variables humans can think about simultaneously) will rise up with a vengeance in parallel programming. We simply may not be able to figure it out.

Programming tools are surely part of the picture, but tools have typically automated and accelerated activities that humans have already mastered. It's an open question whether we can conceive of 1000-core parallel processing sufficiently well to create tools.

Hardware proposes, software disposes. . . . Chip innovation has always been exponential, and software innovation linear. Many-core chips will make matters qualitatively as well as quantitatively worse.

This mismatch between hardware and software is a weakness in the singulatarian argument that exploding compute power will soon trigger a phase transition in humanity and culture. All those CPU cycles are useless if they cannot be programmed efficiently, and our brains may be the limiting factor. We may need a transhuman mind to get through the barrier to transhumanity. Anyone got a time machine?

Scale and opacity in social networks

A Tim O’Reilly post linked me to a column by Jon Udell which eloquently expresses the problem of scale in the digital world. It’s so easy to build and join a social network – and so attractive for us naturally gregarious humans – that overload is never very far away. Udell wrote:

“How many networks can one person join? How many different identities can one person sanely manage? How many different tagging or photo-uploading or friending protocols can one person deal with?”

In his reply, O’Reilly remarks on the opacity of the network:

“When one of the big communications vendors (email, IM OR phone) gets this right, simply by instrumenting our communications so that the social network becomes visible (and under the control of the user), it seems to me that they could blow away a lot of the existing social network froth.”

Udell was focusing on getting social networks to critical mass, and O’Reilly was pointing the way to more usable applications. However, any solution will also have to deal with the attributes of digital artifacts that make them hard to deal with given our innate cognitive endowment. Scale and opacity are two; others include persistence, findability, and mutability.

Not selling what they’re buying

At the root of the argument between content owners and copyright activists lies fundamentally different mental models. (Earlier posts on this topic: Media business beyond DRM, From copyfight to copytruce, King Content vs. the Copyfighters.)

To the activists, music (or other digital content) is a substance. When they hand over money, they feel they have bought this substance. This “stuff” is turned into entertainment through rendering, and they thus feel they have the right to render it anywhere, and anyhow.

Rights holders think they’ve sold a permission: the right for a customer to enjoy specific content in a particular way. The physical item, if there is one, is mostly a token that the customer has the right to play that music in a particular way. Customers, at least if they’re activists, aren’t buying what the rights holders are selling.

Nobody had to think about this much until now, because the music “substance” and the performance “permission” were inextricably tied to a physical medium. With intangible media, though, the consequences of the models diverge:

• To the activists, DRM is a hindrance to their right to enjoy the music-stuff they bought in any way they choose.
• To rights holders, Fair Use is a diminution of the permissions they own.

The activists’ model is grounded in the habits and customs associated with tangible media. You can take your CD to a friend’s house, and hear it on player, not to mention in the car: music is stuff you can take with you anywhere. You own the CD, and thus you own the music. The same can be said for sheet music. The rights holder view is more esoteric, and depends on abstractions far removed from everyday experience, like rights in non-rival goods. Their model has some similarities with live concerts. (This is ironic, since activists argue that bands should make money from live music, not media sales.) Buying a ticket to a show doesn’t entitle you to walk into a record store and just take the CD; you’ve only bought the right to enjoy that show, at that time, in that place. It’s widely understood that making fan recordings usually isn’t legal – viz. “bootleg tapes.”

Given these dissonant views among the experts, how do “ordinary consumers” think about the entertainment they buy? Do they see purchased music as a substance, as a set of permissions, some paradoxical mixture, or something else altogether? I doubt anyone has bothered to ask. The partisans don’t want facts to cloud the issue, and, as Peter Cowhey has explained to me, scholarship on public opinion and policy doesn’t care about the “what do they know” questions. Scholars are more interested in the heuristics that people use to decide complex issues – whose opinion can you trust on an issue you don’t know much about? – and in how the framing of an issue can determine outcomes. I think rights holders over-estimate the power of the permissions frame, which doesn’t resonate with the public’s assumptions about media; and activists over-estimate the degree to which the public experiences the DRM frame as a hindrance. I look forward to seeing the scholarship on this question. My hunch is that consumer thinking is closer to the activists’ view. (Aside: I’d be fascinated to see how many people who have an opinion about the term “DMCA” can explain why it’s a good or bad thing. I suspect even the experts will struggle.)

The dirt/digital differences may exacerbate the problem because digital media are so unlike the physical stuff humans have evolved to cope with. Perfectly copyable, persistent media are counter-intuitive, which accounts for rights holders’ terror of the “release once, gone forever” consequences of non-DRM content. (Not that DRM helps, since it will always be broken...) Opacity also plays a role: Digital media are hard to understand because they’re non-rival and non-excludable. DRM is deeply inscrutable, and even though the license terms sound simple, it’s often hard to understand why, or predict whether, media plays in some places and not in others. DRM tries to convert a non-rival, non-excludable information good into an excludable one. I’ve always found it hard to think about non-rival, non-excludable goods; perhaps I’m not alone.

The problem for rights holders is that there are few things in our everyday experience that fit their model of music as a rights-controlled good. It’s not that people can’t deal with the restrictions that come from “technological boundaries.” Brad Gillespie has pointed out to me that most people didn’t have problem moving from LPs to 8-track to cassettes to CDs. He observes that they can deal with restrictions based on physics – playing a CD on two different players at once, or making an LP play in a CD player – but they don’t accept “virtual restrictions.” The problem is that rights holders have over-estimated the persuasiveness of the permissions frame. They’ve also under-played analogies that might help them, like “buying digital music is like buying a book of concert tickets.”

Of course, as Brad Gillespie notes, legal restrictions like “don’t steal” are not imposed by physics. One thus has to confront the question of why humans obey laws. Rights holders have tried to appeal to consumer self-interest in ads where studio crafts people say that they wouldn’t have a job making movies if the pirates won. The problem is that there’s an over-supply of creators. That’s always been true (Q: "What does an English major say after graduation?" A: "You want fries with that?"), but finding an audience used to be hard. The distribution problem is now being solved by YouTube et al. The catch is that the Fat Rump still makes most of the money for Hollywood, and that the oversupply is in the Long Tail. Consumers like some Rump as part of a fully balanced media diet, but rights owners have trouble distinguishing between rump and tail.

Five Digital/Dirt Differences

I love clambering around water mills. They’re big old machines, and with little effort one can figure out how everything works: this gear driving that pulley moving that screw, grain kernels winched to the top floor in bags turning into flour on their way down.

Machines these days aren’t decipherable any more; you need to read a manual to operate a toaster oven, and men past a certain age bemoan the fact that they can’t work on their cars any more. Digital tools work in ways profoundly different from what we’ve come to expect from living in the dirt world. They are alien, not just opaque. Technology-mediated interactions are swamping our inherent cognitive capacities.

I’m working on a list of ways in which the digital world is different. It started off being descriptive, but I think it has some explanatory power. It may even eventually help predict gotchas in yet-to-be-seen systems. Here are the top five: persistence, findability, opacity, mutability, and scale [1].

danah boyd’s work on online social networks provided a very helpful basis for this list. My first four items were inspired by hers. Here’s danah in a WireTap interview with Kate Sheppard:

There are four functions that are sort of the key architecture of online publics and key structures of mediated environments that are generally not part of the offline world. And those are persistence, searchability, replicability, and invisible audiences. Persistence -- what you say sticks around. Searchability -- my mother would have loved the ability to sort of magically scream into the ether to figure out where I was when I'd gone off to hang out with my friends. She couldn’t, thank God. But today when kids are hanging out online because they've written [themselves] into being online, they become very searchable. Replicability -- you have a conversation with your friends, and this can be copied and pasted into your Live Journal and you get into a tiff. That creates an amazing amount of "uh ohs" when you add it to persistence. And finally, invisible audiences. In unmediated environment, you can look around and have an understanding of who can possibly overhear you. You adjust what you're saying to the reactions of those people. You figure out what is appropriate to say, you understand the social context. But when we're dealing with mediated environments, we have no way of gauging who might hear or see us, not only because we can't tell whose presence is lurking at the moment, but because of persistence and searchability.

1. Persistence

Things that used to be ephemeral have become enduring. Conversations used to linger only in memory, but now they’re recorded. Letters might have survived in shoeboxes, but email is archived indefinitely. Digital artifacts persist because they are easily captured and copied, and because storage is cheap. Thinking out loud in a late-night email or blog post can come back to haunt us.

Like many other items on the list, it’s tricky to discern whether we’re seeing a difference in degree, or in kind. Humanity has had a long and fretful relationship with the recorded word. Plato – didn’t those Greeks think of everything first? – seems to have preferred the spoken to the written word. In the Phaedrus he has Socrates say, “He who thinks, then, that he has left behind him any art in writing, and he who receives it in the belief that anything in writing will be clear and certain, would be an utterly simple person ... if he thinks written words are of any use except to remind him who knows the matter about which they are written” [2].

We’ve become habituated to the unnatural persistences technology gives us. Still, a residue of magic remains, just as we are occasionally disturbed by seeing ourselves in a mirror. Our old brains are not used to such uncanny permanence.

2. Findability

As danah points out, digital search tools can make the previously hidden suddenly available. Again, we get used to it; I’m finding it increasingly difficult to write off-line, when I can’t check assumptions or follow promising leads instantaneously. Information about the world, and about people, is at our fingertips.

Even though we all take Google for granted, I suspect that we don’t have an accurate mental model of the consequences for material that relates to us. People’s surprise at how easy it is to make a profile about them from online information is a staple of identity theft news reports. It may be related to our tendency to over-value near-term events, and down-play the distant future (hyperbolic discounting).

The five distinctions I’m working through complement and contradict each other. Here’s the first example: Findability makes persistence meaningful. Not only do people leave a permanent spoor; these shreds and scintillas can be traced – though the stories they tell may easily be misconstrued.

3. Opacity

I can seeeeeee you! – not. Asymmetric visibility is everywhere in digital spaces. In its social form, it’s danah’s “invisible audiences.” Anybody can watch you write yourself into being on the web, but you often can’t be sure who’s out there. Even if you do know, they may expect mutually incompatible things from you (parents vs. peers). As danah’s pointed out: until the web, only celebrities had to figure out how to deal with the problems of persistence, findability and invisible audiences.

Not only can’t we see the audience, we can’t see the system’s workings, either. We’ve been dealing with inscrutable intelligent agents for aeons, of course; other people, and the politics we’re immersed in. However, computer systems often don’t behave like humans, in part for the reasons I’m listing here. When they do try to emulate humans, it’s a thin veneer at best, and usually an embarrassing kludge.

Every computer user can list features that they find baffling. For example, I struggle to understand how various browsers implement saved RSS feeds. Often they don’t know that they don’t know. Many people with a non-technical background don’t understand what cookies do, and what information is being saved [3]. When they’re exposed to cookie management tools for the first time, they’re shocked to discover how prevalent cookies are. Younger people understand that they pose a threat to personal privacy, but are resigned to it. The power of cookies shows how the various differences augment each other: cookies are not only persistent, findable traces, but are also hidden from many users.

Mysterious mechanisms are understood as magic. I hoped to find an extensive literature on superstitions about computers and software, but didn’t find any articles on a first pass. Such studies would be a fertile source of information about mental models, and could help predict how users absorb new technologies.

4. Mutability

When the store layout changed in my local supermarket, everybody was disconcerted; people were still asking employees for directions months later. Magazine layouts change more often, and web designs more often still. In these cases one still has to do the redesign work and risk confusing your customers, but at least one doesn’t have to move all those atoms about. Digital artifacts are easier to change. This gives designers a great deal of flexibility, but at the price of destabilizing markers that are useful for navigation.

Change is also effectively invisible in the digital world. Flux, replication, multi-media, cross-over, impermanence and blending are rife on the web – but there are no palimpsests. The new blue pixel is as crisp and bright as the old green one. One can reveal a history, as wikipedia does, or track changes using databases, but it takes additional effort and/or money. Scholars using digital media are disconcerted by the lack of stable reference [4]. If the content cited can change invisibly, how do you know what you’re referring to? With paper references you could, for example, refer to the “reknowned 11th edition of Encyclopædia Britannica” and be sure what about the target content. I often link to Wikipedia, but there’s no guarantee that a link will in future provide the information I’m expecting. Uncertainty is the price we pay for currency.

Digital media can also be easily copied and morphed from one place to another. danah’s replicability attribute (see above), where someone can cut and paste a conversation out of IM into their LiveJournal, is an aspect of mutability. Mash-up, remix and plagiarism become easier as mutability increases. They have always been part of culture, but as the barriers to copying come down, their prevalence goes up. Materials don’t have the persistence they used to have in the Stone Age; can our neolithic instincts keep up?

Mutability is a curious dual of the persistence attribute. Digital artifacts are more persistent because they’re so easy to copy; embarrassing videos are replicated across the web faster than they can be taken down. At the same time, though, changes are just as easy to propagate. As the clones proliferate, they can mutate.

5. Scale

Information overload is not just hype; we deal with hugely more factual and social information than people only a few centuries ago. Dominic Foray points out in The Economics of Knowledge that the great eleventh century thinker Gerbert d’Aurillac had a library containing no more than twenty books, which was quite a lot in those days. A Google search for his name yields about 24,800 hits. . . .

Human society evolved in the context of small groups, and the number of people we can have genuinely social relationships with seems to be limited to around a hundred people. This wasn’t a problem until very recently in evolutionary time: “In the early 1500s one could hike through the woods for days without encountering a settlement of any size. Between 80 and 90 percent of the population ... lived in villages of fewer than a hundred people, fifteen or twenty miles apart, surrounded by endless woodlands. They slept in their small, cramped hamlets, which afforded little privacy ...” [5].

The limitation is probably built in. Dunbar has shown that “mating system[s] and/or mean group size are a function of relative neocortex volumes in primates. This relationship suggests that the size of the neocortex imposes some kind of constraint on the number of social relationships that an individual can keep track of within its social group” [6].

Email and online social networks enable us to interact with thousands of people. That’s not new, in one sense, humans have been able to coexist with thousands of strangers without either killing them or running away since the invention of cities in the neolithic. However, digital socialization provides an aura of intimacy which triggers behaviors optimized for face-to-face behavior. The number of relationships we’re expected to participate in grows, but our means of dealing with them doesn’t. This can be dangerous, as persistence, findability, opacity and scale conspire to confound expectations: one is speaking to a large, invisible audience, and one’s words don’t go away. Microsoft executive Jim Allchin had learned the hard way during the 1998 anti-trust trial that emailed words, like “We need to slaughter Novell before they get stronger” could come back to haunt him. Yet, in 2004, he wrote this to what seemed at the time to be a small circle of colleagues: “I would buy a Mac today if I was not working at Microsoft.”

Intractable scale also characterizes software engineering. It’s a truism among software engineers that very large interlocked code bases are beyond the grasp of to any individual mind. I’ll be coming back to this topic in future; for now, I’ll just note that limitations on working memory apply to software engineering (see Problems Revisited), and software’s intangibility combined with ballooning memory and processing capacity encourage developers to combine more and more functionality into a single package.

Computing tools can help humans work at previously impossible scales, but many of them feel uneasy about that. Computer-assisted proofs, such as that for the four-color theorem, have generated controversy among mathematicians. Lengthy computer-assisted proofs that could not be replicated by humans somehow didn’t feel “real” to the skeptics. Perhaps proponents of computer-assisted proofs just need to wait, as Thomas Kuhn suggested, until “its opponents eventually die, and a new generation grows up that is familiar with it” – or perhaps the concern now raised will continue to linger, just as Plato’s unease about the written word still finds echoes today.

----- Notes -----

[1] The list is still evolving. Here are some aspects of digital life which don’t fit yet: the rigidity and fragility of computer systems; the tension between personalization and the one-size-fits-all design of most software; the non-rival and non-excludable nature of digital media.

[2] Plato, Phaedrus 275c. See also William Chase Greene, The Spoken and the Written Word, Harvard Studies in Classical Philology, Vol. 60, 1951 (1951), pp. 23-59.

[3] Vicki Ha, Kori Inkpen, Farah Al Shaar, Lina Hdeib, “Work-in-progress: An examination of user perception and misconception of internet cookies,” April 2006, CHI '06 extended abstracts on Human factors in computing systems CHI '06

[4] Bob Stein, personal communication, 19 Jan 2007

[5] William Manchester, A World Lit Only by Fire, Back Bay Books, 1992, pp. 50-2. Cited by delanceyplace.com 01/25/07-life in the 1500s

[6] R. I. M. Dunbar, “Neocortex size and group size in primates: a test of the hypothesis,” Journal of Human Evolution, Volume 28, Issue 3, March 1995, Pages 287-296

Cognitive Inadequacy and Wickedness

I’m beginning to suspect that “wicked problems” result when social techniques are used to solve intractable cognitive problems.

The CogNexus Institute defines a wicked problem as one for which each attempt to create a solution changes the understanding of the problem. Wicked problems seem to be inextricably linked to social context (Jeffrey Conklin, Nancy Roberts, Jerry Talley). A sociological approach is used to solve complex problems, even in technical settings like software development.

The root of wickedness lies in difficulties understanding such problems. No one person’s brain (processing + experience) is big enough to encompass the entire conundrum. Social animals succeed by dividing up problems among many brains – the wisdom of crowds. Each brain can take in and process its small part, but the interactions between the parts are now externalized as politics and conflict.

If many of the systems problems facing software developers are wicked problems (DeGrace & Stahl), and if wicked problems are a social solution to human cognitive constraints, then the root cause of many pernicious software problems is cognitive – not social.

Evolved for Middle World

Richard Dawkins opens his 2005 TED talk (stream, download) with the JBS Haldane quote, “My own suspicion is that the universe is not only queerer than we suppose, but queerer than we can suppose.”

Dawkins observes, “We find real matter comforting only because we’ve evolved to survive in “Middle World” where matter is a useful fiction.” Middle World is “the range of sizes and speeds which we’ve evolved to feel intuitively comfortable with.” He argues that the world is much broader, extending it to sensory modalities that other animals can sense: sound for bats, and smell for dogs. What we see is a model of the world; the model is constructed to be useful to the body for which it is created. “The nature of the model is governed by how it is to be used.” He talks about “our brain’s evolutionary apprenticeship in Middle World.”

The challenge we face is that the uses to which we put our models of the world are changing radically, since we’re changing our world to contain not just solid matter, but increasing numbers of interacting abstractions. Our apprenticeship has not prepared us for this new world. We have to make crutches for our brains to succeed in it.

Problems Revisited

In Kinds of Hard (ctd.) I speculated that one could tease apart problems by looking at their analytical, social and cognitive content.

Analytical problems can be clearly formulated, if not necessarily easily solved. Scientific problems like “what is the age of the universe” and “how does evolution work” are prototypical. The vagaries of human nature are not explicitly involved, though since science is done by people activity, a human perspective is unavoidable.

Social problems are characteristic by conflicts between people. They occur when there are many stakeholders, and arguments ensue not only over what’s an acceptable solution, but also over what counts as “the problem.” An example: what should America do regarding immigration?

In cognitive problems, individual human frailty is the determining factor [1]. There are two aspects: capacity and bias.

The scale of some problems is beyond the brain’s processing capacity. Humans have a limited working memory, and a limited capacity to understand interactions between many variables. For example, we can handle at most four-way interactions between independent variables, e.g. discerning patterns in graphs of consumer preferences for cakes that are {fresh vs. frozen}, {chocolate vs. carrot}, {iced vs. plain} and {rich vs. low fat} [2]. This capacity limit is not a memory limit - all relevant information can be seen continuously - but a limit on how many relationships we can discern simultaneously. As another example, consider 3D block rotation, which is a common feature of IQ tests: the subject has to decide by looking at 2D projections whether one shape is the same as another, though rotated. I haven’t found studies on this yet, but I expect that humans would find it impossible to do 4D block rotation, that is, decide whether two 3D shapes represent a rotation of a 4D shape.

The second aspect is cognitive bias. Our in-born heuristics often yield results that are different from analytical solutions, or that are different depending on how choices are presented. Our loss aversion is evident in this example: Imagine that your city is preparing for an outbreak of a disease which is expected to kill 600 people. Let’s say the choice is between two vaccination schedules, Program A which will allow 400 people to die, and Program B which will let no one die with probability 1/3, and all 600 will die with probability 2/3. Most people will choose option B, though the two situations are identical in quantitative terms (0 x 1/3 + 600 x 2/3 = 400); the certain loss of life is more loathsome that probable losses. Caveat: Some researchers argue that the choice of “correct” solution, and the way in which the question is framed makes a big difference in outcomes; they argue that many alleged biases are largely unsupported [3].

Problems usually show more than one aspect. It gets interesting when there are overlaps between the facets:

Analytical x Cognitive

The Monty Hall problem asks whether, given a certain set-up, contestants should stick with a prior choice, or change their minds. Probability theory (and simulation) indicates that they should change their choice. For most people, that feels like the wrong answer – even for some of those who’ve worked through the math. This is an analytical problem, since it is well-formulated and can be solved using probability theory. However, given that so many people find the correct solution counter-intuitive, it’s also a cognitive problem.

Analytical x Social

Scientific paradigm shifts are good examples of the interplay between analytical and social problems. On the one hand, science engages with problems that are well-defined, with recognizable solutions; in other words, with analytical problems. On the other, the salient questions at any given moment are influenced by intellectual fashion and history, and the data that are used to test hypotheses are shaped by assumptions: “It is the theory that decides what we can observe” [4]. The seventeenth-century shift away from Aristotelian physics was an argument over whether natural phenomena should be explained in terms of mechanical interactions, or teleology. This social tussle framed the analytical questions.

Social x Cognitive

Decisions about large community investments – energy policy, for example – are politically fraught processes that are inherently social. There are conflicts over what needs to be done, and how to do it. Cognitive factors can play a role when risk assessment is involved. Lay people inexperienced with probabilities often come to different conclusions about risks to experts. For example, they overestimate the frequency of low-probability but dramatic hazards like nuclear power plant accidents, and under-estimate high-probability hazards that are less memorable, like everyday causes of death. In this case, discrepancies between analytical and subjective risk estimates can influence the social dynamics of a debate.

Analytical x Cognitive x Social

My research is headed towards the challenge of software development, where I suspect all three problem traits intersect and amplify each other. The development of large code bases clearly poses severe social problems, since there are many stakeholders with conflicting needs and views. Specifying a new product is an iterative and indeterminate process, and what counts as a solution – the product to be shipped – is the subject of intense conflict as feature triage intensifies towards the end. The analytical problems of choosing the best algorithm and producing efficient code are easily hidden by the foam of debate, but are nonetheless crucial to success.

Cognitive problems are perhaps the most obscure of all, and appear in both aspects mentioned above. Biases abound: teams become possessive about their features (endowment effect), executives only see information that confirms their preconceptions (confirmation bias), and managers rely over-much on one piece of information when making decisions (anchoring). At least some of the biases, like anchoring, are related to the overwhelming scale of the problem. Large code bases are beyond the grasp of any human, and therefore there is a limited capacity to reason about them – say, to trade off one design choice against another, or to imagine the consequences of a feature change.

Computer science studies the analytical problems, and theorists of industrial organization focus on social problems. The cognitive problems of producing software are less well understood – stay tuned.

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[1] The suitability of the word “cognitive” is debatable. Since all problems involve human thought, they’re all cognitive in some sense. However, the complications of cognitive capacity and bias strike me as different in kind from social and analytical constraints, so I’ll use the term “cognitive problems” until I find a better one.

[2] Halford, G. S., Baker, R., McCredden, J. E., & Bain, J. D. (2004). How many variables can humans process? Psychological Science, 16, 70-76.

[3] See Alexander Todorov (1997) , “Another Look at Reasoning Experiments: Rationality, Normative Models and Conversational Factors” Journal for the Theory of Social Behaviour 27 (4), 387–417. See also Judgment Under Uncertainty, UCSB Center for Evolutionary Psychology (Cosmides & Tooby?)

[4] Albert Einstein, from J. Bernstein, "The Secret of the Old Ones, II." New Yorker, March 17, 1973, cited in http://chem.tufts.edu/AnswersInScience/What-Is-Science.htm

The data dimension

The network-centric way of looking at the web has been bugging me for a while, but I’m still struggling to come up with an alternative to this analysis. I suspect the answer is a data-centric view. The network is already pretty pervasive, so “ubiquity” isn’t news. Moore’s Law is driving sophisticated packet processing into the core of the network, so “smarts” isn’t quite it, either. What is most important about its current development is that reach and processing are being leveraged by complex new content: the data dimension.

I’ve argued that one can see the IT developments of the last 25 years as three waves of commoditization:

80’s: Commodity Computing
90’s: Commodity Communications
00’s: Commodity Data

The current view of networking is packet- and processor-centric. A packet-centric view buys into the end-to-end worldview, where the network is dumb, and all the interesting stuff happens outside it, on the edge. This is of course not the case; cf. in-transit virus scanning, large data caches (Akamai), in-network codec transcoding, and deep packet inspection. The processor-centric view adds this edge and core computing to the picture. It assumes that packets flow largely unchanged across lines of communication between nodes. What matters are the lines and the nodes, and not what flows over and between them.

This approach under-values the importance of massive, distributed, evolving data sets. I’m not saying that processors and packets are unimportant. The three decades of commoditization I list are cumulative: one needs commodity computing and commodity communications to get the real value of commodity data. But the novelty is in the data, and that’s where the hardest new questions of policy and governance will arise.

I haven’t thought through all the implications of the data-centric view. But to get past the old mental image of glowing tubes connecting pulsing processors [1], imagine yourself as a data file:

You start life, let’s say, on Alice’s computer. You’re small and lonely, and when you look out at the world, all you see is Alice’s computer.

Suddenly you feel a little bigger, and you can see another computer: Bob’s. Alice has sent a copy of you to Bob. Now you’re a little bigger.

In fact, if you paid attention, there was a blink when the world seemed to flash by. the world temporarily got bigger – the flicker of many router processors as you passed over the network from Alice to Bob. You got briefly bigger as copies of you were cached along the way.

After a while you find yourself in a much bigger world; Bob copied you up to YouTube, and computers flicker in and out of existence around you as Youtube users download and watch you. You don’t feel much bigger, because nobody’s copying you. However, occasionally you grow and start morphing, as someone takes your original self and mashes it into a new version.

Then your world changes again. You can now see many PCs; Charlie got hold of you, and posted a bittorrent tracker to you.

More parts of you start morphing and growing as people incorporate you into other data files. You still feel as if these new existences are part of you, but they’ve also started to change on their own. Each of them opens up windows onto new host machines.

If you can’t resist thinking in terms of nodes and links (and who can, in this age of network theory), imagine that the nodes are data sets, and the links are semantic and genealogical connections. The meaning is in these nodes and links – not those boring old fiber links and CPUs.
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[1] Since I’m pre-occupied with mental models, I can’t resist pointing out how pervasive the “communications is stuff flowing in pipes” metaphor is. It’s not just poor Sen. Stevens; the term “data flow” occurs 1.2 million times on the web, according to Google. I also don’t think it’s a coincidence that a dominant, though increasingly discredited, folk theory of language meaning is the conduit metaphor.

Too complex for humans

Bob Stein has described some of the digital media he’s working on as “too complex for humans” (personal communication, 19 Jan 07). This wonderful expression beautifully captures the essence of what I’m struggling to understand in the hard intangibles project.

Bob speculated that two of the pioneering experiments being done by his Institute for the Future of the Book may fall in this category: GAM3R 7H30RY, a networked book by McKenzie Wark, and Operation Iraqi Quagmire, an annotated version of the Iraq Study Group Report. These “high dimensional” artifacts may become so complex that they are beyond our ability to grasp.

One can certainly think of each content type in these linked conversations as a dimension (e.g. base text, comments, commenters, time), and we clearly can’t think intuitively about spaces with more than three spatial dimensions. I suspect that there’s even more going on than high dimensionality, though: hyper-linking creates texts with topologies that are qualitatively different from most of the books we’ve known to date.

Topology refers to the interconnectedness of structures, regardless of their exact shape and size. It’s the difference between a cup and a glass: the one has handle you can hook your finger through, and the other doesn’t. In topological terms, any glass can be transformed into any other by squishing and stretching, but it can’t be turned into a cup without tearing. Topologically, a glass is equivalent to a plate, or a knife, or a paper clip. As for cups, there’s the old joke, “What is a topologist? Someone who cannot distinguish between a doughnut and a coffee cup.” (Renteln and Dundes 2005, cited in MathWorld.)

A traditional linear text is like a glass or a plate: one can condense it without losing its narrative essence. While some cases like Reader’s Digest or Cliff Notes are straightforward, others are anything but trivial. For example, Tribonian’s 5th Century achievement of boiling down a vast and contradictory corpus of centuries of Roman law into a usable legal code stood for centuries after his death, and was a pillar of the Byzantine Empire. (For a wonderful survey of the socio-political context, cf. Lars Brownworth’s first lecture (MP3) on the Emperor Justinian in his series 12 Byzantine Rulers.)

Sometimes the précis of a large body of law is presented as a commentary, as in the acclaimed work of 11th Century Jewish scholar Rashi. While much of his work was interpretation, and could be seen as a hyperlinked commentary, his explication of the Talmud and Tanach made it understandable to lay people and is used to this day.

Tribonian’s work condensed a large text into a shorter one. Rashi provided links from canonical texts to his explanations, which could be used in place of the original. Topologically both scholars were doing similar things. Condensing a text is just squeezing down its substance. If one imagines links from a text to a commentary as a nodule growing from a plane, then replacing source text with a comment is squishing the nodule into the plane without changing the topology.

Hyper-linked conversations, for example where a text comments on an earlier comment to the text itself, are different in kind. A series of links that eventually loops back to its starting point is like the handle on a cup; it cannot be removed without tearing the structure of the resulting text. A loop-back topology in texts is closely tied to change over time. An evolving text that takes references to earlier versions of itself into account leads to loops. A notable characteristic of digital texts is that they can change rapidly. For example, a wikipedia entry may change minute by minute, but one has to consult the “history” tab to see it. Bob Stein has observed that many people are disturbed by the lack of a fixed reference in digital texts. He’s wondering whether we may just have to give up a notion of permanence in these media (personal communication, 18 Jan 07).


While large volatile loop-back texts may be too complex for humans, one shouldn’t ignore the consequences of scale, even in topologically simple texts where each comment builds sequentially on earlier ones. James Elkins discusses “monstrous artworks”: works that have attracted so much attention that they have effectively outgrown the discipline of art history (Why are our Pictures Puzzles, 1999). Their literature can no longer be mastered by a single scholar, or judiciously discussed in a single volume, or taught in a year-long seminar. Examples include The Brancacci Chapel, the Mona Lisa, and Velasquez’s Las Meniñas). Bob Stein has wondered what the 50 page Communist Manifesto would look like today if it were published as a website with comments, and about how one might represent the gigantic hyper-text of the Manifesto plus all the commentaries that link to it, and among themselves.

The loop-back topology I’m describing is not new. It’s inherent in any conversation, and traditional linear texts contain complex forward and back and backward references that can make them difficult to grasp. What may be new, though, is the scale of the artifact (there is no limit on the number of pages in a digital text), explicitness of the links (which make a false promise of intelligibility), and the instability of reference when a digital text is constantly and invisibly updated.

Four Decades of Free

Peter Rinearson has pointed out two major milestones in computing: the mid-1980s when computing became free, and the mid-1990s when communications became free. (Not exactly free, of course, but so cheap that they were no longer limited to the elite.) He set me thinking about what happened since 1995, and what may be coming down the pike.

Arguably, the mid-2000’s were marked by data “becoming free.” Google’s web search is predicated on massive data stores, which would not be possible without very cheap data storage. The Bloggers, MySpaces and, especially, YouTubes of the web depend on being able to host data without charging users.

1980’s: Free Computing
1990’s: Free Communications
2000’s: Free Data

So far I’ve taken “free” to be “free as in beer, not free as in speech”. Both the Rinearson milestones also had non-commercially liberating consequences. The Open Source movement, for example, which was fuelled by cheap, fast global communications between people using powerful home computers.

The argument over digital copyright follows from Free Data: filling up the gigabytes of storage on an iPod is much easier when you don’t have to pay for all the songs. Similarly, the DIY culture of making your own video is enabled by YouTube offering free data hosting.

Prognosticating, I expect that the 2010s will be marked by Free Stuff: the ability to have any material thing you want produced instantly and at negligible cost. A teen will design their cell phone the way they create their MySpace page. (The phone might even by sponsored by MySpace...) Nanotechnology will at large emerge from the hype cycle. We’ll manipulate both inorganic and genetic material at will: mecha-nano and bio-nano.

2010’s: Free Stuff

The dark cloud looming behind Free Everything is its environmental cost. Materialism is part of human nature, and everybody on the planet naturally wants the surfeit of things the Developed World has pioneered. According to New Scientist, if the 5 billion-plus people in developing nations matched the consumption patterns of the 1.2 billion in the industrialized world, at least two more Earths would be needed to support everyone (Sidebar "Apocalypse Soon?" vol. 191, # 2571, 30 Sep 2006, p. 50).

The “dark under-belly of Moore’s Law” is that electrical consumption has increased rapidly, along with processing power. There is talk of skyrocketing energy bills in data centers. Free Stuff could make matters worse – but could also help if it’s designed from the start to be sustainable. Demanufacturing will be essential, as will the can-do attitude of groups like worldchanging.

Hard Scientific Problems

Much discussion of problems concerns what I defined as social and cognitive problems, e.g. wicked problems and bounded rationality. Writers like Nancy Roberts and Jerry Talley seem to dismiss analytical problems by categorizing them as “simple problems” or “basic problems” (see Kinds of Hard (ctd.) ).

To show the depth and range of analytical problems, I mined New Scientist's special anniversary feature in which “Brilliant minds forecast the next 50 years.” Some contributors explicitly posed questions, though most talked about hoped-for breakthroughs. In cases where I was able to do so, I have recast breakthroughs as questions.

The resulting list is great for staring at, and mulling over. A few questions were posed by multiple people: the nature of consciousness, life beyond the earth, details of the big bang, and dark matter. Looks like there’s most consensus among astrophysicists ... Counting the different categories of question shows a very operational/instrumental approach to science: Existence: 7; How: 19; What: 20; Why: 2. The most common preoccupations, even for big brains thinking 50 years out, were What and How questions. Very few were interested in Why...

Are the laws of physics unique and was our big band the only one? (Martin Rees)

Can we truly understand how we understand others, their intentions, desires and beliefs? (Michael Gazzaniga)

Do we mostly get along because we enjoy common reactions to similar challenges? (Michael Gazzaniga)

Does our species have a moral compass? (Michael Gazzaniga)

How are molecular aspects of memory are influenced by society and culture? (Daniel Schacter)

How are neural nets in the brain stitched together to produce mental activities that are familiar in cognitive psychology? (Dan Dennett)

How can children reliably name the class of an object? (That is, the generic object recognition problem.) (Rodney Brooks)

How can one efficiently and conveniently store a young woman's ovarian tissue or eggs to be used years later? (Carl Djerassi)

How can one visualise the connections between human organisations and technological objects? (Bruno Latour)

How can our currently fragmented theory of the physical world be synthesized into a coherent way of thinking? (Carlo Rovelli)

How can so few genes (relatively speaking) create so much complexity in the human brain? (Antonio Damasio)

How can we disentangle the feedback loop between brain development and the ancient primate tendencies that shape our societies? (Frans de Waal)

How did cooperative behavior evolve? (Robert May)

How did elementary particles acquire their mass? (Lisa Randall)

How did human spoken language evolve? (Irene Pepperberg)

How did our memory systems evolve? (Daniel Schacter)

How do human institutions work, in particular, what are the impediments to collective, cooperative activity in which all individuals pay small costs to reap large group benefits? (Robert May)

How does the brain create consciousness? (Igor Aleksander, Terry Sejnowski, Susan Greenfield)

How does evolution work in fine detail? (Bernard Wood)

How does the environment of early childhood shape how people interact with the world in which they grow up, live and work? (Michael Marmot)

How does the mind translate environmental threats into the body's stress reactions? (Michael Marmot)

How have biological and physical processes interacted over billions of years to bring us to our own evolutionary moment? (Andrew Knoll)

How long can humans live? (Francis Collins)

Is the entire universe very big but everywhere the same, or is our patch part of a “multiverse” with a great many environments, each with its own laws, particles and constants? (Leonard Susskind)

Is there life beyond earth? (Colin Pillinger, Carolyn Porco, Freeman Dyson, Monica Grady, Piet Hut, Steve Squyres)

Is searching for solutions harder than checking that the solutions are correct? (That is, the P=NP problem.) (Timothy Gowers)

To what degree are genetic and morphological change are correlated with speciation? (Niles Eldridge)

What are the causes of the major psychiatric disorders – schizophrenia, depression, bipolar disorder, and anxiety disorders – and how are they best treated? (Charles Nemeroff)

What are the equations describing the unified physical laws of the universe? (Max Tegmark)

What are the links between genes and morphology, between animals and behaviour, and between behaviour and life history strategies? (Alan Walker)

What are the molecular pathways that render cells from long-lived animals resistant to many forms of injury? (Richard Miller)

What are the several missing links on the march between the specifications of genes, and the neural structures and operations which support behavior and cognition? (Antonio Damasio)

What happened during the first second of the big bang, and before? (Sean Carroll, Kip Thorne)

What is a general theory of imagination, consciousness and self that will be powerful and illuminating, and applicable in principle to sentient species everywhere? (Oliver Sacks)

What is life? (Paul Nurse)

What is the connection between our understanding of the function of individual brain cells or pairs of cells and of the larger-scale cognitive functions processed by multiple brain areas? (Fred Gage)

What is the general theory of what, according to the laws of physics, can or cannot be built and with what resources? (That is, the quantum theory of construction) (David Deutsch)

What is the geographic distribution of biodiversity at the species level (Edward O Wilson)

What is the mysterious dark matter that appears to make up about 25 per cent of the mass of the universe? (Lawrence Krauss, Arthur McDonald)

What is the neural basis of individual differences in memory? (Daniel Schacter)

What is the role and importance of extinctions in shaping subsequent evolution? (Niles Eldridge)

What is the role of particular cells, circuits and genes in memory formation and retrieval? (Daniel Schacter)

What is the role of isolation in evolution? (Niles Eldridge)

What is the structure of the tree of life? (Michael Benton)

What is the template that describes the distribution of the prime numbers? (Marcus du Sautoy)

What is the underlying nature of matter? (Lisa Randall)

What is the unified description of the (superficially) different forces of nature, and (superficially) different building blocks of matter? (Frank Wilczek)

Why is the weak interaction weak? (Frank Wilczek)

Why is nature such that we have a description that is so enormously successful, yet so counterintuitive? (That is, resolving the paradoxes of quantum mechanics.) (Anton Zeilinger)

If you can't take the heat

It was as if I’d grown up in a hotel, always eating food set before me. One evening I dreamed up a meal, but there was no one to ask for it.

So I went in through the doors where the food came out, and found a shining kitchen with ranks of surfaces and impenetrable implements, and the pantries full of mysterious raw materials.

I didn’t know frying from baking, and couldn’t recognize a grill. I couldn’t connect an egg to an omelet, or a clove of garlic to its taste. Flour looked nothing like bread, and what did one do with an onion?

Suddenly the kitchen was crowded with frenzied cooks, no time to explain, and in any case, just learning one station took years, who was this naïf who wanted to walk in and cook a menu?

Then I realized that there wasn’t one kitchen, but many – if I’d known their names, I would’ve recognized Asian, French, Indian, Italian – each with its own staff, techniques, and secret ingredients. If I wanted to apprentice myself, I’d have to pick one; but which, if any, could make the food I wanted?

This is what it feels like right now, trying to figure out how to make progress on the Hard Intangibles project.

Disconnected understanding

An explanation for a very rare mental disorder has helped me think about how understanding works. A sufferer of Capgras’ Syndrome believes that people they know very well aren’t who they appear to be; they’re perfect doubles who are impersonating them. One explanation is that there’s a disconnect between two necessary elements of the visual recognition system. One system does the pattern matching, and the other provides the emotional texture associated with what’s recognized. In a Capgras patient, the recognition works but the emotional texture is missing. The patient rationalizes the combined experience by confabulating that the person seen must be a physically identital imposter. They know something doesn’t feel right, and this is the best available explanation.

My experience doing quantum chromodynamics (QCD) was faintly like this. I could do the math, but it was mechanical; I always felt that the theory had a better idea of the answer than I did. On the other hand, I sensed that people who could really do physics had a visceral feel for the subject. Not only could they handle the sums, but they intuited what the answer should be.

This may be part of what skill means. One has to master both the mechanics of the knowledge, and the link between the knowledge and the intuitions that allow one to find short cuts and anticipate results. Mechanical knowledge links subject matter and theory; metaphor links theory and intuition.

In the Capgras case, the visual recognition system links a person’s appearance with their identity, and the emotional affect system links that identity with the meanings that are important to the perceiver.

To sum up the three preceding examples:

Skill in general: subject matter – theory // theory – intuition
Physics ‘n’ me: experiment – QCD // QCD – intuition
Capgras syndrome: face – spouse’s name // spouse’s name – feelings for spouse

Stretching the analogy even further, perhaps this is why metaphors are so important in science and engineering. A software engineer doesn’t need metaphors like Classes-as-Containers or Programs-as-Language to get work done– the unadorned mathematics would do just fine – but analogies provide an intuitive basis that underpins thinking and provides comforting context. When a programmer tries to write about software without using metaphors, they become uncomfortably aware just how ingrained they are. The two-part model in this case:

mathematics – programming // programming – intuition

Kinds of Hard (ctd.)

Some follow-ups on the earlier post:

Nancy Roberts’ approach is essentially social, and based on a problem/solution dichotomy:

• simple problems: consensus on problem definition and solution
• complex problems: agreement on problem definition, but not solution
• wicked problems: no agreement on either definition or solution

In this approach, the amount of conflict increases as one moves from one type to another.

Jerry Talley defines eight problem types which he groups into three clusters (for more details, see intro, overview with issues, definitions with examples)

• basic problems: characterized by confidence in existence of solution, but lack time or key pieces of information to solve
• mysterious problems: characterized by confusion, complexity, and hidden dynamics
• dangerous problems: characterized by disagreement and conflict

Roberts and Talley both focus on organizational dynamics, and thus understate the difficulty of some scientific problems, which they would characterize as “simple” or “basic,” respectively.

Since we’re in a Rule of Three Mode, here’s my working model:

1. Analytical problems

These are problems which can be stated clearly, e.g. problems in science and mathematics. Answering them may relatively easy, as in puzzles, or hard in various degrees, such as solving equations in multi-dimensional quantum field theories. Roberts’ “simple problems” and Talley’s “Puzzles” (in the “basic problem” cluster) fall into this category.

2. Social problems

Interaction between people is the complicating factor here. Arguably, all the problems considered by Roberts and Talley are social problems. “Wicked problems” fall in this class.

3. Cognitive problems

These problems are also hard because of human nature, but in this case it’s a problem in the individual. They include the phenomena studied under the heading of cognitive biases, behavioral finance, neuroeconomics, etc.

These three are attribute classes, rather than categories; a problem won’t fit neatly into a class. Very hard problems will have aspects of all three. For example, conflicts in social problems are often exacerbated because each participant has their own confirmation bias. Scientific controversies often combine analytical questions with conflicts over what counts as a valid solution, a characteristic of social problems.