Rationality is only an optimization

I’m reading a lovely little book by H. Peyton Young, Individual Strategy and Social Structure, very dense and tasty. I checked out what he had done recently, and found “Individual Learning and Social Rationality” in which, as he says, “[w]e show how high-rationality solutions can emerge in low-rationality environments provided the evolutionary process has sufficient time to unfold.”

This reminded me of work by Duncan Foley on (what might be called) low-rationality economics, beginning with “Maximum Entropy Exchange Equilibrium” and moving to a more general treatment in “Classical thermodynamics and economic general equilibrium theory“. Foley shows that the equilibria of neoclassical economics, typically derived assuming unbounded rationality, can in fact be approximated by repeated interactions between thoughtless agents with simple constraints. These results don’t even depend on agents changing due to experience.

So from the careful, well grounded results by these two scholars, I’d like to take an alarmingly speculative leap: I conjecture that all rationality is an optimization, which lets us get much faster to the same place we’d end up after sufficiently extended thoughtless wandering of the right sort. This hardly makes rationality unimportant, but it does tie it to something less magical sounding.

I like this way of thinking about rationality, because it suggests useful questions like “What thoughtless equilibrium does this rational rule summarize?” and “How much rationality do we need to get close to optimal results here?” In solving problems a little rationality is often enough, trying to add more just may just produce gratuitous formality and obscurity.

At least in economics and philosophy, rationality is often treated as a high value, sometimes even an ultimate value. If it is indeed an optimization of the path to thoughtless equilibria, it is certainly useful but probably not worthy of such high praise. Value is more to be found through comparing the quality of the equilibria and understanding the conditions that produce them, than by getting to them faster.

Capital is just another factor

Wow! Lots of people came to see Capitalists vs. Entrepreneurs, via great responses by Tim Lee, Jesse Walker, Tech and Science News Updates, and Logan Ferree (scroll down) and maybe others I didn’t see. Thanks! Reading over those posts and comments, I think perhaps the issue is simpler than I realized, although the implications certainly aren’t.

Really we are talking about a very basic idea: Capital is just another factor in production, like labor or material resources.

Since capital is just a factor, its importance in production will change over time. Specifically now, the importance of capital is falling. As we get richer and industry gets more productive,any given capital item gets cheaper. Things like a fast computer, a slice of network bandwidth, etc. are so cheap that any professional in a developed economy can do their own production of information goods with no outside capital.

It seems that we’ve confused free markets with “capitalism”. This only makes sense as long as the key issue in markets is the availability of capital. From a long term perspective, naming our economic system after one factor of production is just silly.

On the other hand, free markets depend essentially on individual judgment, choice, creativity, and on people’s ability to sustain a network of social relationships. These make free markets possible, and taken together they constitute entrepreneurship.

So unlike capital, entrepreneurship is central to any possible free market system.

The inevitability of peer production

In this context, rather than being strange or subversive, or even needing to be explained, peer production is viable when:

  1. capital costs (needed for production) fall far enough and
  2. coordination costs fall far enough.

Cheap computing and communication reduce both of these exponentially, so peer production becomes inevitable.

This was not apparent until recently, and even now is hard for many people to believe. People are still looking for an “economic justification” for peer production. “How does it help people make money?” they ask. But this confuses the means with the end. Money is a means of resource allocation and coordination. If we have other means that cost less or work better, economics dictates that we will use them instead of money.

A digression on coordination

Economists typically talk about “transaction costs” but I’m deliberately using the term “coordination costs”. Transactions (a la Coase) typically involve money, and certainly require at least contractual obligations. Coordination by contrast only depends on voluntary cooperation. Transaction costs will always be higher than coordination costs, because transactions require the ability to enforce the terms of the transaction. This imposes additional costs — often enormously larger costs.

As I point out in “The cost of money” introducing money into a relationship creates a floor for costs. I didn’t say it there, but it is equally true that contractual obligations introduce the same kind of floor for costs. Only when a relationship is freely maintained by the parties involved, with no requirement to monitor and enforce obligations, can these costs be entirely avoided.

Not surprisingly, peer production succeeds in domains where people can coordinate without any requirement to enforce prior obligations. Even the most limited enforcement costs typically kill it. Clay Shirky develops this argument in the specific case of Citizendum (a replacement for Wikipedia that attempts to validate the credentials of its contributors).

A shift of perspective

I’m only beginning to see the implications of this way of thinking about capital, but it has already brought to mind one entertaining analogy.

In the late middle ages, feudalism was being undermined by (among other things) the rise of trade. Merchants, previously beneath notice, began to get rich enough so that they could buy clothes, furniture and houses that were comparable to those of the nobility.

One response of the “establishment” was to institute sumptuary laws, strictly limiting the kinds of clothes, furniture, houses, etc. merchants could own. There was a period where rich merchants found ways to “hack” the laws with very expensive plain black cloth and so forth, and then the outraged nobility would try to extend the laws to prohibit the hack. Of course this attempt to hold back the tide failed.

I think that in the current bizarre and often self-damaging excesses of copyright and patent owners, we’re seeing something very like these sumptuary laws. Once again, the organization of economic activity is changing, and those who’ve benefited from the old regime aren’t happy about that at all. They are frantically throwing up any legal barriers they can to keep out the upstarts. But once again, attempts to hold back the tide will fail.

The path to a synthesis in statistical modeling

As I discuss in Dancing toward the singularity, progress in statistical modeling is a key step in achieving strongly reflexive netminds. However a very useful post by John Langford makes me think that this is a bigger leap than I hoped. Langford writes:

Attempts to abstract and study machine learning are within some given framework or mathematical model. It turns out that all of these models are significantly flawed ….

Langford lists fourteen frameworks:

  • Bayesian Learning
  • Graphical/generative Models
  • Convex Loss Optimization
  • Gradient Descent
  • Kernel-based learning
  • Boosting
  • Online Learning with Experts
  • Learning Reductions
  • PAC Learning
  • Statistical Learning Theory
  • Decision tree learning
  • Algorithmic complexity
  • RL, MDP learning
  • RL, POMDP learning

Within each framework there are often several significantly different techniques, which further divide statistical modeling practitioners into camps that have trouble sharing results.

In response, Andrew Gelman points out that many of these approaches use Bayesian statistics, which provides a unifying set of ideas and to some extent formal techniques.

I agree that Bayesian methods are helping to unify the field, but statistical modeling still seems quite fragmented.

So in “dancing” I was too optimistic to “doubt that we need any big synthesis or breakthrough” in statistical modeling to create strongly reflexive netminds. Langford’s mini-taxonomy, even with Gelman’s caveats, suggests that we won’t get a unified conceptual framework, applicable to actual engineering practice, across most kinds of statistical models until we have a conceptual breakthrough.

If this is true, of course we’d like to know: How big is the leap to a unified view, and how long before we get there?

Summary of my argument

The current state of statistical modeling seems pretty clearly “pre-synthesis” — somewhat heterogeneous, with different formal systems, computational techniques, and conceptual frameworks being used for different problems.

Looking at the trajectories of other more or less similar domains, we can see pretty clear points where a conceptual synthesis emerged, transforming the field from a welter of techniques to a single coherent domain that is then improved and expanded.

The necessary conditions for a synthesis are probably already in place, so it could occur at any time. Unfortunately, these syntheses seem to depend on (or at least involve) unique individuals who make the conceptual breakthrough. This makes the timing and form of the synthesis hard to predict.

When a synthesis has been achieved, it will probably already be embodied in software, and this will allow it to spread extremely quickly. However it will still need to be locally adapted and integrated, and this will slow down its impact to a more normal human scale.

The big exception to this scenario is that the synthesis could possibly arise through reflexive use of statistical modeling, and this reflexive use could be embodied in the software. In this case the new software could help with its own adoption, and all bets would be off.

Historical parallels

I’m inclined to compare our trajectory to the historical process that led to the differential and integral calculus. First we had a long tradition of paradoxes and special case solutions, from Zeno (about 450 BC) to the many specific methods based on infinitesimals up through the mid 1600s. Then in succession we got Barrow, Newton and Leibnitz. Newton was amazing but it seems pretty clear that the necessary synthesis would have taken place without him.

But at that point we were nowhere near done. Barrow, Newton and Leibnitz had found a general formalism for problems of change, but it still wasn’t on a sound mathematical footing, and we had to figure out how to apply it to specific situations case by case. I think it’s reasonable to say that it wasn’t until Hamilton’s work published in 1835 that we had a full synthesis for classical physics (which proved extensible to quantum mechanics and relativity).

So depending on how you count, the development of the calculus took around 250 years. We now seem to be at the point in our trajectory just prior to Barrow: lots of examples and some decent formal techniques, but no unified conceptual framework. Luckily, we seem to be moving considerably faster.

One problem for this analogy is that I can’t see any deep history for statistical modeling comparable to the deep history of the calculus beginning with Zeno’s paradox.

Perhaps a better historical parallel in some ways is population biology, which seems to have crystallized rather abruptly, with very few if any roots prior to about 1800. Darwin’s ideas were conceptually clear but mathematically informal, and the current formal treatment was established by Fisher in about 1920, and has been developed more or less incrementally since. So in this case, it took about 55 years for a synthesis to emerge after the basic issues were widely appreciated due to Darwin’s work.

Similarly, statistical modeling as a rough conceptual framework crystallized fairly abruptly with the work of the PDP Research Group in the 1980s. There were of course many prior examples of specific statistical learning or computing mechanisms, going back at least to the early 1960s, but as far as I know there was no research program attempting use statistical methods for general learning and cognition. The papers of the PDP Group provided excellent motivation for the new direction, and specific techniques for some interesting problems, but they fell far short of a general characterization of the whole range of statistical modeling problems, much less a comprehensive framework for solving such problems.

Fisher obviously benefited from the advances in mathematical technique, compared with the founders of calculus. We are benefiting from further advances in mathematics, but even more important, statistical modeling depends on computer support, to the point where we can’t study it without computer experiments. Quite likely the rapid crystallization of the basic ideas depended on rapid growth in the availability and power of computers.

So it is reasonable to hope that we can move from problems to synthesis in statistical modeling more quickly than in previous examples. If we take the PDP Group as the beginning of the process, we have already been working on the problems for twenty years.

The good news is that we do seem to be ready for a synthesis. We have a vast array of statistical modeling methods that work more or less well in different domains. Computer power is more than adequate to support huge amounts of experimentation. Sources of almost unlimited amounts of data are available and are growing rapidly.

On the other hand, an unfortunate implication of these historical parallels is that our synthesis may well depend on one or more unique individuals. Newton, Hamilton and Fisher were prodigies. The ability to move from a mass of overlapping problems and partial solutions to a unified conceptual system that meets both formal and practical goals seems to involve much more than incremental improvement.

Adoption of the synthesis

Once a synthesis is created, how quickly will it affect us? Historically it has taken decades for a radical synthesis to percolate into broad use. Dissemination of innovations requires reproducing the innovation, and it is hard to “copy” new ideas from mind to mind. They can easily be reproduced in print, but abstract and unfamiliar ideas are very hard for most readers to absorb from a printed page.

However, the situation for a statistical modeling synthesis is probably very different from our historical examples. Ideas in science and technology are often reproduced by “black boxing” them — building equipment that embodies them and then manufacturing that equipment. Depending on how quickly and cheaply the equipment can be manufactured, the ideas can diffuse quite rapidly.

Development of new ideas in statistical modeling depends on computer experiments. Thus when a synthesis is developed, it will exist at least partly in the form of software tools — already “black boxed” in other words. These tools can be replicated and distributed at almost zero cost and infinite speed.

So there is a good chance that when we do achieve a statistical modeling synthesis, “black boxes” that embody it will become available everywhere almost immediately. Initially these will only be useful to current statistical modeling researchers and software developers in related areas. The rate of adoption of the synthesis will be limited by the rate at which these black boxes can be adapted to local circumstances, integrated with existing software, and extended to new problems. This make adoption of the synthesis comparable to the spread of other innovations through the internet. However the increase in capability of systems will be far more dramatic than with prior innovations, and the size of subsequent innovations will be increased by the synthesis.

There is another, more radical possibility. A statistical modeling synthesis could be developed reflexively — that is, statistical modeling could be an essential tool in developing the synthesis itself. In that case the black boxes would potentially be able to support or guide their own adaptation, integration and extension, and the synthesis would change our world much more abruptly. I think this scenario currently is quite unlikely because none of the existing applications of statistical modeling lends themselves to this sort of reflexive use. It gets more likely the more we use statistical modeling in our development environments.

A reflexive synthesis has such major implications that it deserves careful consideration even if it seems unlikely.

Leaving knowledge on the table

Yesterday I had a very interesting conversation with an epidemiologist while I was buying a cup of coffee (it’s great to live in a university town).

She confirmed a dark suspicion I’ve had for some time — large population studies do a terrible job of extracting knowledge from their data. They use basic statistical methods, constrained by the traditions of the discipline, and by peer review that has an extremely narrow and wasteful view of what count as valid statistical tools. She also said that even if they had the freedom to use other methods, they don’t know how to find people who understand better tools and can still talk their language.

The sophisticated modeling methods that have been developed in fields like statistical learning aren’t being applied (as far as either of us know) to the very large, rich, expensive and extremely important datasets collected by these large population studies. As a result, we both suspect a lot of important knowledge remains locked up in the data.

For example, her datasets include information about family relationships between subjects, so the right kind of analysis could potentially show how specific aspects of diet interact with different genotypes. But the tools they are using can’t do that.

We’d all be a lot better off if some combinations of funding agencies and researchers could bridge this gap.

Netminds–present and future

Shel Kaphan commented on “Dancing toward the singularity“:

Personally, I am comfortable with the idea of participating in… groupings that may involve machines and other humans and have their own entity-hood, and I’m comfortable with the idea that my brain probably has already adapted and become dependent on substantial immersion in computing environments and other technology, and I know what it is like to be part of an early generation, fairly tightly coupled, computing-enhanced group with a focus. I’m just saying the name “hybrid system” as such doesn’t sound either desirable or healthy.

And of course he’s right, who wants to be “hybridized” or “part of a hybrid system”? Ugh, terrible marketing.

So from now on, I’ll call them “netminds”: groups of people and machines working together so closely that they form a thinking entity.

People who become part of a netmind don’t lose their own identity, but they adapt. A moderately good analogy is a dance troupe, a repertory theater company or a band. Each individual retains their own identity but they also adapt to the group. At the same time, the troupe or band selects people who fit its identity (maybe unconsciously). And over time the group identity, the set of members and to some extent the individuals (and brains) co-evolve. So the individual and group identities are in a complex interplay.

This interplay will get much more intense as humans and machines get more tightly coupled. The tightest groups could be much closer than any today, with individuals interacting through machine interpretation of details of muscle tension, micro-gestures, brain state, etc. etc. In such a group people would be “inside each others heads” and would need to give up most personal boundaries between group members. The boundary would fall between the netmind and the outside world.

The fullest exploration of such a merger (without machines) is Vernor Vinge’s Tines in A Fire Upon the Deep. But even Vinge can only sustain the dual point of view (individual and netmind) in places, and elsewhere falls back into treating the netmind as a monolithic entity. This may be necessary in a narrative that appeals to normal humans. Joan Vinge explores the emotional side of netminds in Catspaw.

Netminds today

Does it make sense to talk about netminds as existing today? I think it does, although today’s netminds are relatively weakly coupled.

Gelled development teams, working closely together in a shared online environment, are netminds. The level of coupling we can attain through a keyboard is pathetically low, but as anyone who has been part of such a team can attest, the experience is intense and the sense that one is part of a larger entity is strong.

Quite likely a guild in an online game is a netmind, especially when they are engaged in a raid. I don’t personally have any experience with this, but since it is a more or less real-time experience, it probably has some interesting attributes that are mostly lacking in the software development case.

At the other end of the spectrum, we might want to call some very large, diffuse systems netminds. An appealing example is the Wikipedia editors plus the Wikipedia servers (note that I’m not including readers who don’t contribute). Here the coupling is fairly weak, but arguably the resulting system is still a thinking entity. It forms opinions, makes decisions (albeit with internal conflicts), gets distracted, etc. We can also see the dynamics that I describe above: individuals adapt, some individuals are expelled, the netmind develops new processes to maintain its integrity, and so forth. Human groups without network support do the same kinds of things, but a non-networked group the size of Wikipedia would “think” hundreds or thousands of times more slowly, and probably couldn’t even remain a coherent entity.

I suppose we could even call the whole web plus the Google servers a netmind, in the weakest possible sense. (Probably it is only fair to include all the other search and ranking systems as well.) Because the coupling is so weak, the effect on individual identity is minimal, but people (and certainly websites) do adapt to Google, and Google does exclude websites that participate in (what it considers) inappropriate ways. Furthermore Google works fairly hard to retain its integrity in the face of challenges from link farms, click fraud, etc. But this case is so large and diffuse that it stretches my intuition about netminds past its limits.

Netminds tomorrow

Let’s return to the more tightly coupled cases. Humans seem to naturally get caught up in intense group activities. Usually immersion in the group identity is fleeting — think of rock concerts, sports events, and riots. But intense creative group activity can generate a prolonged emotional high. Many physicists who worked on development of the atomic bomb at Los Alamos remembered it as the peak experience of their life. Engineers can get almost addicted to intense team development.

We already have the technology to make gaming environments fairly addictive, even without intense human interaction; there’s a reason Everquest is called Evercrack.

It’s easy to imagine that tightly coupled netminds could exert a very powerful emotional hold over their participants. Netminds will tend to move in the direction of tighter, more intense bonds on their own, since they feel so good. As our technology for coupling individuals into netminds gets better, we’ll have to be careful to manage this tendency, and there are certain to be some major, highly publicized failures.

A related problem is exemplified by cults. Cults don’t provide the emotional high of intense creative effort; they seem to retain people by increasing their dependency and fear of the outside world. Probably technology for tight coupling could be exploited to produce cult-like bonds. Net cults are likely to to be created by exploitative people, rather than arising spontaneously, and such phenomena as cult-like info-sweatshops are disturbingly likely — in fact they arguably already exist in some online games.

Whether creative or cult-like, tightly coupled netminds are also likely to shape their participants brains quite strongly. The persistent personality changes in cult members, long term hostages, etc. are probably due to corresponding changes in their brains — typically reversible, but only with difficulty. Participants in tightly coupled creative groups probably undergo brain changes just as large, but these changes they tend to enhance the individual rather than disabling them, so they produce less concern. Nobody tries to deprogram graduate students who are too involved with their lab.

We already know enough to build netminds that would deliberately induce changes in participants’ brains. We’re already building systems that produce outcomes similar to very disciplined practice. But tightly coupled systems could probably go far beyond this, reshaping brains in ways that ordinary practice could never achieve. As with most of these scenarios, such reshaping could have major beneficial or even therapeutic effects or could go horribly wrong.

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.

What’s missing in Second Life?

Most of the features of Second Life enumerated in my previous post appear to be implemented adequately, if not brilliantly. However SL has three fairly glaring deficiencies, which are probably related.

  • Lack of external connections
    This one is simple. SL currently has no way to embed live web content or other connections to internet resources.

    The UI challenges could be finessed by starting with script-controlled web embedding. This alone would be enough to enable all kinds of neat uses. Full interactive web support may or may not be really necessary.

    More generally, SL should provide a general way to embed bi-directional external data connections, again via scripting. This would make possible “widgets” that could do all kinds of useful things.

    This change would bring SL much closer to a general platform for online activities.

  • Lack of reflexive abilities
    This requires bigger changes to the current SL model. Right now no one outside of Linden Labs can build higher level tools for creating and modifying prims. Thus a huge range of potential enhancements to the environment are impossible. For SL to become a true platform this is an essential change.

    The existing script-level data structures aren’t a good fit for higher level manipulation. However clean ways of describing and manipulating 3D content are available in open source implementations with friendly licenses. Similarly the existing scripting language is limited but cleaner, more powerful languages are easily available.

    My guess is that the big issue is that being able to create and modify prims opens the door to huge security and gray goo problems. Note that if this is a fundamental limitation, it implies that an SL type environment can never become a general online platform. In any case, these problems are the focus of my third bullet below.

  • Lack of a robust model of behavioral constraints
    Right now, SL has a variety of behavioral constraints, based on ownership, scripting limitations, administrator controls, etc. These make possible property (in some sense), a degree of privacy, tolerable freedom from griefing in most cases, rather porous defenses against gray goo, etc.

    On the other hand, these behavioral constraints seem ad hoc, require continuing and often urgent interventions by Linden staff, and frequently fail in inconvenient or damaging ways.

    If SL is going to become a general platform, this is one thing it needs that can’t be copied from some existing good example: a unified model of behavioral constraint that enables a socially viable world, and that doesn’t require continual tweaks and/or expensive staff support.

Because the need for a good model of behavioral constraints is so central, and so difficult, it deserves some further elaboration.

To enable a socially viable world, such a model has to support something close to the kind of ownership and control we currently expect of private property, and the level of personal security we normally have in our daily lives. Note that neither of these is perfect or seamless, nor are they free — we not only pay taxes for police and military security, we pay for locksmiths, title insurance, car registrations, etc. On the other hand, we don’t have to continually fiddle with security mechanisms or install security upgrades.

I can think of three further design requirements on any such model:

  • Manageable by users and developers
    Typical computer security models are very easy to screw up. Putting the wrong permissions on some file, forgetting to update some configuration information, etc. can open up a world of hurt. Most users and casual developers won’t accept this level of fragility. So any viable model has to be relatively easy to understand and to manage reliably, at least for the things ordinary users want to do.
  • Enforceable by servers alone
    Clients can and will be hacked. The behavior constrained by the security model has to be mediated by the server. This is in tension with the desire to off-load as much to the client as possible, to reduce latency and make the whole system more scalable.
  • (Almost) fully automated
    Human maintenance and enforcement will always be required, but the amount and level of administrative support must be very low relative to the level of user activity. This is essential if we want to create a platform that can move toward the ubiquity and low cost of the web.

Obviously, creating a new behavioral constraint model that meets all these requirements will be very difficult. I do not think it will be impossible, in fact I think we’ll have one within ten years, maybe less.

However I think it behooves us to recognize that the lack of such a model, and the difficulty of creating one, imposes very severe limitations on the potential of virtual worlds for the time being.

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