Capitalists vs. Entrepreneurs

This post was catalyzed by a post at The Technology Liberation Front. Thanks to those who debated me in those comments, you helped to clarify my points.

I was responding to this key point:

[P]eer production isn’t an assault on the principles of a free society, but an extension of those principles to aspects of human life that don’t directly involve money. ….

[A] lot of the intellectual tools that libertarians use to analyze markets apply equally well to other, non-monetary forms of decentralized coordination. It’s a shame that some libertarians see open source software, Wikipedia, and other peer-produced wealth as a threat to the free market rather than a natural complement.

Since peer production is an entirely voluntary activity it seems strange to view it as a threat to the free market. (My interlocutors in the comments demonstrated that this view of peer production is alive and well, at least in some minds.) So how could this opinion arise? And does it indicate some deeper issue?

I think viewing peer production as a threat is a symptom of an underlying issue with huge long-term consequences: In peer production, the interests of capitalists and entrepreneurs are no longer aligned.

I want to explore this in considerably more detail, but first let’s get rid of a distracting side issue.

It’s not about whether people can make money

The discussion in the original post got dragged into a debate about whether people contribute to open source software (and presumably peer production in general) because it “is a business” for them. This belief is easy to rebut with data.

But this is a side issue. I’m not arguing that people can’t increase their incomes directly or indirectly by participating in peer production. Sometimes of course they can. My point is that the incentives of entrepreneurs (whether they work for free, get consulting fees, or go public and become billionaires) and capitalists (who want to get a return on something they own) diverge in situations that are mainly coordinated through non-monetary incentives.

Examples and definitions

Let’s try to follow out this distinction between entrepreneurs and capitalists.

For example, Linus Torvalds is a great entrepreneur, and his management of the Linux community has been a key factor in the success of Linux. Success to an entrepreneur is coordinating social activity to create a new, self-sustaining social process. Entrepreneurship is essential to peer production, and successful entrepreneurs become “rock stars” in the peer production world.

A capitalist, by contrast, wants to get a return on something they own, such as money, a domain name, a patent, or a catalog of copyrighted works. A pure capitalist wants to maximize their return while minimizing the complexity of their actual business; in a pure capitalist scenario, coordination, production and thus entrepreneurship is overhead. Ideally, as a pure capitalist you just get income on an asset without having to manage a business.

The problem for capitalists in peer production is that typically there is no way to get a return on ownership. Linus Torvalds doesn’t own the Linux source code, Jimmy Wales doesn’t own the text of Wikipedia, etc. These are not just an incidental facts, they are at the core of the social phenomenon of peer production. A capitalist may benefit indirectly, for a while, from peer production, but the whole trend of the process is against returns on ownership per se.

Profit

Historically, entrepreneurship is associated with creating a profitable enterprise. In peer production, the idea of profit also splits into two concepts that are fairly independent, and are sometimes opposed to each other.

The classical idea of profit is monetary and is closely associated with the rate of (monetary) return on assets. This is obviously very much aligned with capitalist incentives. Entrepreneurs operating within this scenario create something valuable (typically a new business), own at least a large share of it, and profit from their return on the business as an asset.

The peer production equivalent of profit is creating a self-sustaining social entity that delivers value to participants. Typically the means are the same as those used by any classical entrepreneur: creating a product, publicizing the product, recruiting contributors, acquiring resources, generating support from larger organizations (legal, political, and sometimes financial), etc.

Before widespread peer production, the entrepreneur’s and capitalist’s definitions of success were typically congruent, because growing a business required capital, and gaining access to capital required providing a competitive return. So classical profit was usually required to build a self-sustaining business entity.

The change that enables widespread peer production is that today, an entity can become self-sustaining, and even grow explosively, with very small amounts of capital. As a result it doesn’t need to trade ownership for capital, and so it doesn’t need to provide any return on investment.

As others have noted, peer production is not new. The people who created educational institutions, social movements, scientific societies, etc. in the past were often entrepreneurs in the sense that I’m using here, and in their case as well, the definition of success was to create a self-sustaining entity, even though it often had no owners, and usually produced no “profit” in the classical sense.

These concepts of “profitability” can become opposed when obligations to provide classical profits to investors prevent an entity from becoming self-sustaining. In my experience, many startups die because of the barriers to participation that they create while trying to generate revenue. Of course if they are venture funded, they typically are compelled to do this by their investors. Unfortunately I don’t know of any way to get hard numbers on this phenomenon.

Conversely, there are examples where a dying business becomes a successful peer-production entity. The transformation of Netscape’s dying browser business into the successful Mozilla open source project is perhaps the clearest case. Note that while Netscape could not make enough profit from its browser to satisfy its owners, the Mozilla foundation is able to generate more than enough income to sustain its work and even fund other projects. However this income could not make Mozilla a (classically) profitable business, because wouldn’t come close to paying for all the contributions made by volunteers and other companies.

Current pathologies of capitalism

The conflicting incentives of entrepreneurs and capitalists come into sharp focus around questions of “intellectual property”. One commenter complained about open source advocates’ attacks on “software patents, … the DMCA and … IP firms”. These are all great examples of the divergence between ownership and entrepreneurship.

The DMCA was drafted and lobbied into existence by companies who wanted the government to help them extract money from consumers, with essentially no innovation on their part, and probably negative net social value. In almost every case, the DMCA advocates are not the people who created the copyrighted works that generate the revenue; instead they own the distribution systems that got those works to consumers, and they want to control any future distribution networks.

The DMCA hurts people who want to create new, more efficient modes of distribution, new artistic genres, new delivery devices, etc. In general it hurts entrepreneurs. However it helps some copyright owners get a return on their assets.

The consequences of patents and other IP protection are more mixed, but in many cases they inhibit innovation and entrepreneurship. Certainly patent trolls are an extremely clear example of the conflict — they buy patents not to produce anything, but to sue others who do produce something. Submarine patents (like the claimed patents on MP3 that just surfaced) are another example—a patent owner waits until a technology has been widely adopted (due to the work of others) and then asserts the right to skim revenue from ongoing use.

Intellectual property fragmentation is also a big problem. In many domains, especially biomedical, valuable innovations potentially require the right to practice dozens or even hundreds of patents, held by many different entities. Entrepreneurs often can’t get a new idea to market because the owners of these patents can’t all be brought to an agreement. Each owner has a perverse incentive to be the last to agree, so they can get any “excess” value. Owners also often overestimate the potential returns, and demand a higher “rent” than can actually be sustained. This phenomenon is called the “tragedy of the anti-commons“.

All of these issues, and other similar ones, make it harder for small companies, individuals and peer production projects to contribute innovation and entrepreneurship. Large companies with lawyers, lobbyists, and defensive patent portfolios can fight their way through the thickets of “intellectual property”. Small entrepreneurs are limited to clearings where they can hope to avoid IP problems.

Conclusion

Historically many benefits of entrepreneurship have been used to justify capitalism. However, we are beginning to see that in some cases we can have the benefits of a free market and entrepreneurship, while avoiding the social costs imposed by ensuring returns to property owners. The current battles over intellectual property rights are just the beginning of a much larger conflict about how to handle a broad shift from centralized, high capital production to decentralized, low capital production.

Networks of knowledge

My recent metaphysics post touches on a question I’ve been thinking about for some time: How can we judge whether a given domain of inquiry or a theoretical proposal is credible or not? Of course this is a very hard question, but I think we should pay more attention to an aspect of it that can give us at least retrospective insight.

Some domains were once very important, but have completely lost any credibility — for example astrology. Some domains have been losing credibility for a long time but haven’t been completely written off — for example Freudian psychological theory. Some domains seem quite credible but are being vigorously attacked by investigators who are themselves credible — for example string theory. Also, in some cases, proposals that were broadly rejected were later largely adopted, sometimes after many decades — for example continental drift, reborn as plate tectonics.

Philosophers of science, and many epistemologists, mine these historical trajectories for insight into the broader question. There are diverse approaches to explaining the success or failure of various theories and research programs. However I think it is fair to say that the vast majority of these attempts are “internalist”, in the sense that they focus on the internal state of a research program over time. Different approaches focus on formal characteristics of a sequence of theories, social and historical factors, methodological factors, etc. but almost all accounts assume that the answer is to be found within the research program itself.

I’d like to propose a different perspective: We can judge the health of a research program by its interactions with other research programs. As long as a research program is actively using and responding to results from other domains, and as long as other domains are using its results, or working on problems it proposes, it is healthy and will remain credible. If it proves to be incapable of producing results that other domains can use, or even worse, if it stops responding to new ideas and challenges from external research, it is on its way to becoming moribund.

Looking back at the historical trajectories of many research programs, this criterion works quite well. It is not very hard to see why this could be the case. Investigators in any given domain are constantly making practical judgments about where to spend their effort, what ideas proposed by others they should trust, etc. (Kitcher discusses this point in detail in The Advancement of Science.) Investigators who might take advantage of results from a given external domain have a strong incentive to make accurate assessments of whether those results can actually contribute to their work. Furthermore, they have a lot of information about how reliable, relevant, and easy to use a given result is likely to be (compared, for example, with an historian or philosopher). So if a research program isn’t generating useful results, its neighbors will sense that, and will have strong incentives to accurately reflect their judgment in their research practices.

However I think the implications are actually much deeper than these obvious (and probably valid) factors. For example, the trajectories of research programs are often dramatically shifted by new techniques that depend on external results. Plate tectonics became became dominant through a dramatic shift in opinion in 1966 largely as a result of improved measurements of magnetic orientation in sea floor rocks. Paleontology and archeology have been dramatically affected multiple times by improvements in dating based on physics. Evolutionary biology has been hugely reshaped by tools for analyzing genetic similarity between species. Etc.

Such shifts open up major new interesting questions and opportunities for progress. But they are much less likely to occur in domains that, for whatever reason, are cut off from active interchange with other research programs. Also, some reasons why a domain may be cut off — the desire to protect some theoretical positions, for example — will also tend to cause internal degeneration and ultimately loss of credibility.

More generally, my criterion reflects the fact that all research programs exist within a network of related activities — technical, intellectual, educational, etc. — without which they would wither and die. In essence, I’m advocating taking this network, and its changes over time, more seriously.

This criterion doesn’t engage in any obvious way with the usual question “Are these theories true?” (or at least, becoming more true, if we can figure out what that means). I’m not even sure that I can show that there is a strong connection.

Possibly this indicates that my criterion is fatally flawed. Or possibly it means I should look harder for a connection. But I suspect that this actually means that the idea of “truth” does not work very well at the scale of research programs. If a scientist is reporting experimental results, “truth” may be a very appropriate criterion, especially if we are concerned about fraud or sloppiness. But in these larger issues we should probably try to sharpen our criteria for pragmatic usefulness, and not waste time arguing about truth.

Dancing toward the singularity

Vernor Vinge gave a talk in the Long Now Foundation seminar series last week (which is great, by the way, you should go if you can). Stewart Brand sent out an email summary but it isn’t on the web site yet.

As Brand says, “Vinge began by declaring that he still believes that a Singularity event in the next few decades is the most likely outcome — meaning that self-accelerating technologies will speed up to the point of so profound a transformation that the other side of it is unknowable. And this transformation will be driven by Artificial Intelligences (AIs) that, once they become self-educating and self-empowering, soar beyond human capacity with shocking suddeness.”

At Stewart’s request, Vinge’s talk was about knowable futures – which by definition mean that the singularity doesn’t happen. But the follow up questions and discussion after the talk were mostly about the singularity.

All of this has crystallized my view of the singularity. The path isn’t all that strange, but I now have a much better sense of the details, and see aspects that haven’t been covered in any essays or science fiction stories I know.

One point that came out at Vinge’s talk is important for context. Vinge (and I) aren’t imagining a singularity as an absolute point in time, and in that sense the term “singularity” is quite misleading. The sense of singularity arises because looking further up the curve from a given point, we see “so profound a transformation that the other side of it is unknowable.” However as we’re moving along the curve we won’t perceive a discontinuity; the future will remain comprehensible to some extent, though our horizon may get closer. However, we also won’t be the same people we are now. Among other things we’ll only be able to understand what’s going on because we are gradually integrated into hybrid human / machine systems. I’ll discuss how that will happen as we go along.

What’s missing from current discussions of the singularity?

Above I claim that the path I imagine has “some features that haven’t been a part of any essays or science fiction stories about approaching the singularity”. So I’ll elaborate on that first.

A major enabler of progress for systems (computer or human / computer hybrid) is their ability to quickly and accurately learn effective models of parts of their environment.

Examples of current model learning technology:

  • model road conditions by tracking roads (Thrun);
  • model safe driving by watching a driver (Thrun);
  • model the immediate visual indicators of a “drivable road” by watching the road right in front of the vehicle (Thrun);
  • model the syntactic and semantic mapping between two languages by analyzing parallel texts (many);
  • model the motion style of dancers (Brand);
  • model the painting style of various painters (Lyu)
  • model users’ choices of topics, tags etc. for text (lots of people);
  • etc. etc. etc.

A few observations about this list:

  • Often model learning delivers performance comparable to humans. Thrun’s models performed at near human levels in adverse real-world conditions (130 miles of driving unpaved, unmarked roads with lots of obstacles). The best parallel text language learning is near mediocre human translation ability. Model learning performs far better for spam filtering than hand built filters. Statistical modeling of painting style can identify artists as well as human experts. Etc.
  • Model learning is quickly getting easier. Thrun created three quite robust modeling capabilities in less than a year. A basic parallel text translation system can be created with off the shelf components. Fairly good text classification can be done with simple open source software. Etc.
  • None of these models is part of a reflexive loop, except in the extremely weak sense that e.g. spam filtering contributes to the productivity of developers who work on spam filtering.

Generally, discussions of and stories about AI, the singularity etc. don’t emphasize this sort of modeling. For example, when have we seen a story in which robots could quickly and accurately imitate the body language, intonations, verbal style etc. of the people they are dealing with? But this is a basic human ability, and it probably is essential to human level interaction.

The larger story

The modeling I discuss above is just one of a set of trends that taken together will lead to the singularity (unless something stops us, like nuclear war, pandemic virus, or some as yet unexplained metaphysical impossibility). If all the following trends continue, I think we’ll get to the singularity within, as Vinge says, “a few decades”.

Increasing processing power, storage, and bandwidth per $

It seems like Moore’s law and its various relatives will continue to smile upon us for a while yet, so I don’t think we need to worry about this.

Also my intuition (for which I have very little evidence) is that we can do the vast majority of what we need with the processing power, storage and bandwidth we already have. Of course, moving up the curve will make things easier, and perhaps most importantly, economically accessible to many more people.

Huge amounts of real world data easily accessible

This is the sine qua non of effective model learning; any area where we don’t have a lot of raw data can’t be modeled effectively. Conversely, if we do have lots of data, even simple approaches are likely to produce fairly good models.

This is taking care of itself very nicely, with immense amounts of online text, click streams, video chat, surveillance cameras, etc. etc. As the model acquisition ability is built, the data will be available for it.

General purpose, high performance population-based computing

All model learning is population-based – that is, it works with distributions, not crisp values. Continued progress in modeling will lead to more and more general grasp of population-based computing. Conversely, general population-based computing will make writing new modeling tools much easier and faster. Also, population-based computing makes it immensely easier to scale up using massively parallel, distributed systems.

Right now we have piecemeal approaches to population-based computing, but we don’t have a family of general mechanisms, equivalent to the “instruction stream + RAM” model of computing that we know so well. I think a better conceptual synthesis is required here. The synthesis may or may not be a breakthrough in the sense of requiring big conceptual changes, and/or providing big new insights.

Tightly coupled hybrid human / machine systems

To make hybrid systems more powerful, we need high bandwidth interaction mechanisms, and UI support to maximize coupling. Multi-touch displays, for example, allow much higher bandwidth human input. Good speech understanding would also help. Etc. In the opposite direction, visual and auditory output needs to make good use of human ecological perception (e.g. our ability to notice potential predators or prey during a jungle walk without consciously looking for them, our ability to unconsciously read subtle shades of feeling on people’s faces, our ability to unconsciously draw on context to interpret what people say, etc.).

Lots of people are working on the underlying technology for this. I don’t know of any project that is explicitly working on “ecological” machine – human communication, but with better modeling of humans by machines, it will probably come about fairly naturally. I don’t see a need for any big synthesis or breakthrough to make this all work, just a lot of incremental improvement.

As an aside, I bet that the best ideas on full engagement of people’s ecological abilities for perception and action will come from gaming. The Wii is already a good example. Imagine a gaming system that could use all joint angles in a player’s arms and hands as input without requiring physical contact. This would certainly require modeling the player at several levels (physical, motor control, gesture interpretation). Such a UI will only be highly usable and natural if it supports rapid evolution of conventions between the machine and the human, largely without conscious human choice.

General, powerful model learning mechanisms

This is a huge topic and is getting lots of attention from lots of different disciplines: statistics, computer science, computational neuroscience, biological neuroscience, differential topology, etc.

Again here I doubt that we need any big synthesis or breakthrough (beyond the underlying general model of population computing above). However this is such a huge area that I have trouble guessing how it will evolve. There is a significant chance that along the way, we’ll get insights into model learning that will cause a lot of current complexity to collapse down into a much simpler and more powerful approach. This isn’t essential, but obviously it would accelerate progress.

Reflexive improvement of hybrid systems

This is where the big speedup comes. Existing hybrid systems, such as development teams using intensive computer support, are already reflexively improving themselves by writing better tools, integrating their social processes more deeply into their networks, etc. Once the machines in a hybrid system can model their users, this reflexive improvement should accelerate.

I don’t see any serious obstacles to user modeling in development systems, but I also don’t see any significant examples of it, which is somewhat puzzling. Right now, as far as I’m aware, there are no systems that apply user modeling to accelerating their own development.

Such systems are technically possible today; in fact, I can imagine feasible examples fairly easily. Here’s one: A machine could “watch” a developer identifying interesting events in trouble logs or performance logs, learn a model that can predict the person’s choices, and use the model to filter a much larger collection of log data. Even if the filter wasn’t all that good, it would probably help the human to see patterns they’d otherwise miss, or would take much longer to find. The human and the machine could continue to refine and extend the filter together. This kind of model learning isn’t terribly challenging any more.

We may be stuck in a conceptual blind spot here, as we were with hypertext prior to Berners-Lee. If so, the Christiansen disruptive innovation scenario will probably apply: A new “adaptive” development environment will be created by someone outside the mainstream. It will be much less functional than existing development environments, and will be denounced as a total pile of crap by existing development gurus. However it will be adopted rapidly because it fills a huge unmet need for much easier, more tightly coupled human / machine development.

At the risk of dangerous hubris, I think these ingredients are all we need to produce the singularity. Specifically, I believe that no conceptual breakthroughs are required beyond those listed above. (Of course unexpected breakthroughs might occur and accelerate things.) None of the ingredients seems particularly risky, although most of us (probably including me) would have been astounded by current model learning if it was demonstrated five years ago. (Bernie Widrow and a few other people knew we could do this even thirty years ago.)

How long will it be before things get really weird?

Systems will be hybrid human / machine up to the singularity, and maybe far beyond (allowing for some equivocation about what counts as “human”). I don’t expect the banner of progress to be carried by systems with no humans in them any time soon.

The rate of improvement of reflexive hybrid human / machine systems determines the rate of progress toward the singularity. These are systems that can monitor their own performance and needs, and adapt their own structure, using the same kinds of processes they use for anything else. This kind of hybrid system exists today – online software development communities are examples, since they can build their own tools, reconfigure their own processes, etc.

The power of these systems is a function of the depth of integration of the humans and machines, and the breadth of the tasks they can work on (flexibility). Since they can apply their power to improving themselves, increasing their power will generally accelerate their improvement.

We already learn models of our machines. Good UIs and programming environments are good because they help us build effective models. To really accelerate progress, our machines will have to model us, and use those models to improve the coupling between us and them. The depth of integration, and thus the power, and thus the rate of improvement of human / machine hybrids will depend on how accurately, deeply, and quickly machines can model humans.

The big unknowns in determing how fast we’ll move are

  • how quickly model learning will evolve,
  • how effectively we’ll apply model learning to enhancing reflexive hybrid human / machine development environments and
  • how quickly we’ll transition to population-based computing.

Probably I can do a worst case / best case / expected case trend projection for these three unknowns, but this will take some further research and thought.

Some scenarios

Vinge talked a bit about different trajectories approaching the singularity – in particular, he distinguished between “hard takeoffs” in which the necessary technology is developed in one place and then spreads explosively, and “soft takeoffs” in which the technology development is more open and a large fraction of the population participates in the takeoff.

My analysis has some strong implications for those scenarios, and introduces a new, intermediate one:

A hard takeoff

For example due to a large secret military project. This is possible, but unlikely. Such a takeoff would have to create human / machine hybrids far more powerful than any generally available, and at the same time, prevent the techniques involved from diffusing out into the world.

Over and over, we’ve seen that most closed research projects fail, because they lack the variety and external correctives that come from being embedded in a larger network of inquiry. Also, the very institutional mechanisms that can create a big project and keep it secret tend to prevent creative exploration of the marginal ideas that are probably needed for success.

Note: This point is about research projects, not engineering projects that operate within good existing theories. Thus e.g. the Manhattan project is not a counter-example.

A soft takeoff

This is very likely as long as progress depends on combining innovations across a wide range of groups, and I think this is likely to be essential throughout the process. As things speed up, the dynamics are increasingly sensitive to the ratio between the rate of local change and the rate of diffusion of innovations. Aside from institutional barriers, software innovations in a networked world are likely to diffuse extremely quickly, so the network is likely to approach takeoff as a whole.

Local disruptions

As we get closer to the singularity, a third possibillity emerges. Powerful reflexive systems may enable local, disruptive innovation, for example by relatively small groups that keep their activities secret. These groups would not be able to “outrun” the network as a whole, so they would not have a sustainable lead, but they could temporarily be very disruptive.

In Rainbow’s End Vinge describes a group like this, who are the villains, as it happens. Another example would be a horribly lethal disease created in a future rogue high school biology lab.

These localized disruptive innovations could make the world a very uncomfortable place during the transition. However, a much larger population of networked hybrid systems with local computing power and high bandwidth connections will be able to move faster and farther than such a small group. Furthermore, as we’ve increasingly been seeing, in a highly networked world, it gets very hard to hide anything substantial, and our emergency response keeps getting faster.

How will normal humans experience the singularity?

Humans who are not part of a hybrid system (and probably there will be lots of them) will gradually lose their ability to understand what’s going on. It will be as though their horizon shrinks, or perhaps as though their conceptual blind spots expand to cover more of their world. They will find larger and larger parts of the world just incomprehensible. This could produce a lot of social stress. Maybe it already does – for example, this could be a contributing factor to the anxiety that gets exploited by “stop the world, I want to get off” movements.

We don’t experience our blind spot as a hole in our visual field because our brain fills it in. I think this is a good analogy for how we deal with conceptual blind spots – we fill them in with myths, and we can’t tell (unless we do careful analysis) where our understanding ends and the myths begin. So non-hybridized humans mostly won’t be aware that a lot of the world is disappearing into their blind spots, they will spontaneously generate myths to “wrap” the incomprehensible parts of their world. Some will defend the validity of those myths very aggressively, others will accept them as “just stories”. Again this is pretty consistent with the rise of retrograde movements like intelligent design and modern geocentrism.

This phenomenon is closely related to Clark’s law: “Any sufficiently advanced technology is indistinguishable from magic.” “Sufficiently advanced” just means “beyond my understanding”.

A phase transition

Putting this all together, I now think that we actually could end up with a fairly sharp discontinuity that plays the role of the singularity, but is spread out in time and space.

This could happen if the transition through the singularity is effectively a phase transition. A metaphor may help. If we put a pot of water on the stove and turn on the heat, for a while all the water heats up, but not uniformly – we get all sorts of inhomogeneity and interesting dynamics. At some point, local phase transitions occur – little bubbles of water vapor start forming and then collapsing. As the water continues to heat up, the bubbles become more persistent, until we’ve reached a rolling boil. After a while, all the water has turned into vapor, and there’s no more liquid in the pot.

We’re now at the point where bubbles of hybrid systems (such as “gelled” development teams) can form, but they aren’t all that stable or powerful yet, and so they aren’t dramatically different from their social environment. Their phase boundary isn’t very sharp.

As we go forward and these bubbles get easier to form, more powerful and more stable, the overall social environment will be increasingly roiled up by their activities. As the bubbles merge to form a large hybrid network, the contrast between people who are part of hybrid systems and normal people will become starker.

Unlike the pot that boils dry, I’d expect the two phases—normal people and hybrid systems—to come to an approximate equilibrium, in which parts of the population choose to stay normal indefinitely. The Amish today are a good example of how a group can make that choice. Note that members of both populations will cross the phase boundary, just as water molecules are constantly in flux across phase boundaries. Amish children are expected to go out and explore the larger culture, and decide whether to return. I presume that in some cases, members of the outside culture also decide to join the Amish, perhaps through marriage.

If this is correct, as the singularity approaches, the “veil” between us and the future will become more opaque for normal people, and at the same time will shift from a “time-like” to a “space-like” boundary. In other words, the singularity currently falls between our present and our future, but will increasingly fall between normal humans and hybrid systems living at the same time. Hybrid systems will be able to “see into” normal human communities – in fact they’ll be able to understand them far more accurately than we can now understand ourselves – but normal humans will find hybrid communities opaque. Of course polite hybrids will present a quasi-normal surface to normal humans except in times of great stress.

By analogy with other kinds of phase changes, the distance we can see into the future will shrink as we go through the transition, but once we start to move toward a new equilibrium, our horizons will expand again, and we (that is hybrid systems) may even be able to see much further ahead than we can are today. Even normal people may be able to see further ahead (within their bubbles), as long as the equilibrium is stable. The Amish can see further ahead in their own world than we can in ours, because they have decided that their way of life will change slowly.

I’d love to read stories set in this kind of future world. I think the view of a normal human, watching their child go off into the hybrid world, and wondering if they will return, would be a great topic. The few stories that touch on this kind of transition are very poignant: for example Tunç Blumenthal’s brief story in Vinge’s Across Realtime, and the story of Glenn Tropile’s merger into the Snowflake, and then severance from it, in Pohl and Kornbluth’s Wolfbane.

Metaphysics that matters

I find questions about supervenience, the disjunction problem, etc. fascinating. I think at least some of these questions are very important.

But non-philosophers I know find these questions supremely boring — typically just pointless. These are people who find current “hard problems” in cosmology, quantum physics, mathematics, neuroscience, etc. interesting, even though they aren’t professionally involved in those fields. So why not philosophy?

Esoteric questions in other disciplines always seem to be connected to issues that make sense to non-experts. The dynamics of a probe D3-(anti-)brane propagating in a warped string compactification bear on whether there’s life after the big crunch. But technical problems in philosophy often seem disconnected from issues that matter to non-philosophers.

For example, typical arguments for property dualism assign the non-physical properties such a thin, peripheral, technical role that no one outside of philosophy has a reason to care if philosophers decide that property dualism is true or false. Zombies in some metaphysically possible (but nomologically impossible) world might be a little more or less unreal, and that’s about it. Similar disconnects exist for many other hot topics.

A constructive response

Enough complaining. Here is a list of metaphysical questions framed to emphasize their major implications outside of philosophy. I briefly connect each question with the existing philosophical debate and with some examples of non-philosophical implications, but I don’t provide enough background to make this very accessible to people who don’t already know the philosophical issues. If you want more context, ask!

  • How should we think about the relationship of a coarser grained entity to its finer grained components?

    This is my version of the question of how mind supervenes on the brain, how macroscopic entities supervene on micro-physics, etc. To connect with any field outside of philosophy, we have to accept that coarser grained entities “exist” in some useful sense; the question is what sense.

    This issue is very important in many disciplines:

    • How do individuals make up institutions?
    • How do modular brain sub-systems interact in complex cognitive skills?
    • How do molecular level biological processes coordinate to maintain and reproduce cellular level structure?

    Every discipline addresses these questions in limited, specific ways. However I think most disciplines avoid dealing with them fully and explicitly, because we currently lack the conceptual framework we need to talk about them clearly, or even to know what should count as a general answer. If philosophy can shed any light on the general question, it will help people better come to grips with the specific issues on their home turf.

  • How does a coarser grained entity affect the behavior of its finer grained components?

    This is the question of downward causation, an important issue in the context of supervenience. Again, to engage other disciplines, we need philosophical discussions that accept that disciplines need to think about how coarser grained entities do somehow affect the activities of their components,. Philosophy can potentially provide a schema for handling specific cases.

    Real examples, parallel to the questions above:

    • How do institutions influence the behavior of the people who make them up?
    • How do skills or habits organize the behavior of brain modules?
    • How do cells regulate the molecular processes that maintain them?
  • How can we tell whether a proposed concept picks out a meaningful aspect of the world, or not?

    This is typically discussed as the disjunction problem in philosophy. A recent example was the debate in the both the astronomy community and the public sphere over whether Pluto was “really” a planet.

    The deeper questions behind any specific disciplinary debate are:

    • Is this choice of terms arbitrary (perhaps socially determined), or do some terms actually “carve nature at the joints” better than others?
    • Assuming there are terms that better fit the structure of the world, what criteria tell us that we’ve found them?

    These are hard questions, debated by most disciplines from time to time, as new terms are needed or old ones become questionable. But currently, there is no bridge between the related debates in philosophy over the disjunction problem and more generally the relationship between propositions and the structure of the world, and the needs of practitioners in the disciplines.

  • How should we handle dubious references?

    There are a number of ongoing struggles within philosophy about how to handle problematic references — for example, to Sherlock Holmes’ hat (I’m sure you remember what it looks like). The problem of course is that Holmes never existed so we can’t even say he had no hat. But in various ways similar problems arise for the entities referenced in counterfactuals (“If a large spider had been here, James would have run away”), theoretical entities of uncertain status (the very D3-(anti-)brane referenced above), and even perfectly normal mathematical entities (3).

    Again, the status of hypothetical entities, and even how to debate that status, is an important issue from time to time in most disciplines. For example, the status of the entities posited by string theory (such as the brane above) is a matter of extremely heated debate. The debate is not just about whether these entities exist, but whether it even makes sense to treat them as hypothetical. More violent disagreements along these lines arise in fields such as literary theory, for example.

    Disciplines must answer questions similar to those above, when confronting any given cluster of dubious references:

    • How should we decide whether these references “work” well enough to be worth using?
    • What can we do to make them into respectable references, or alternatively discover that they should be rejected?

    And again, philosophy has an opportunity, if it chooses, to help disciplines make these judgments by finding ways to translate whatever insights can be derived from its internal debates.

So what?

Questions like these now fall into a no-mans land. The specific disciplines where they arise aren’t professionally concerned with the broad questions — they just want to resolve a specific problem and move on. Philosophy, which seems to be the natural home for these broad questions, appears to largely ignore connections to examples like those that arise in other disciplines.

So I would argue that philosophy is missing a major opportunity here, and failing to contribute in ways that would make it a much more credible and important discipline. Whether or not the discipline of philosophy as a whole addresses these questions, I think they deserve attention, and I plan to work on them.