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7. Computational legal systems and markets

Like human markets, direct computational markets will have many problems. Computational markets are enough like human societies that it is worth examining mechanisms-such as law and regulation-used by human societies to try to deal with these problems. However, these analogies must be used with care-there are also many differences between human and computational markets. For example, in proposed computational markets:

  • No negative externalities exist in basic resources (processor, memory, communications), hence there are no problems analogous to pollution.

  • Replicators directly own resources, making the evolutionary process more like "social darwinism" than like the actual evolution of human societies.

  • Participants are not people (or even animate), hence they are not hurt when they go broke.

  • Object encapsulation prevents force absolutely, hence there is no "who will watch the watchers" problem.

  • Only information services are sold, hence there are no depletable inventories of manufactured goods.

Given all these differences, one should not demand that government-like systems proposed for computation be closely patterned on those evolved by human societies. A more appropriate use of the social model is as a source of ideas and analogies, and ideas need not be workable in society to be worth considering in computation. Since computer science has already examined centralized forms of organizations, a promising direction to explore is the other extreme, that of highly decentralized models.

7.1. Remaining problems of direct computational markets Direct computational markets can be built so as to exclude theft and negative externalities; this leaves problems such as fraud, overall fluctuations (such as business cycles and depressions), monopolies, and provision of public goods (goods whose provision will benefit entities that do not purchase them, breaking the usual link between public benefit and producer reward). These problems are fundamentally different from the problem of theft, which can be eliminated in computation via simple local rules such as encapsulation. Eliminating any of the problems just mentioned requires the recognition of complex emergent phenomena:

  • Fraud involves the non-delivery of promised value; its elimination would require (at least) understanding all possible representation languages in which these promises can be expressed, and recognition of the conditions defining the fulfillment and non-fulfillment of all promises.

  • Overall fluctuations are typically measured in terms of economic aggregates such as GNP, which involves collecting considerable information about transactions and determining rules for aggregating them.

  • Monopolies can only be recognized by first determining both what constitutes an industry and what constitutes an "anti-competitive" practice.

  • Public goods situations can be recognized only if benefits can be recognized, along with the absence of ways to reward producers for them through normal market mechanisms.

An official fraud-deterring function must at least understand the "advertising" in the system, which is difficult in computation-every inter-object protocol constitutes a different sub-language, and in general, none is globally understood. As explained in Section 4.3, however, and in Section 5.3.3 of [I], computational markets can themselves deter types of fraud which local entities can recognize, just as they routinely judge the comparative values of different opportunities. For a workable system, it seems that the one essential anti-fraud law is also implementable-to prevent fraudulent use of another's trademark.

In human markets, overall fluctuations of an economy (such as business cycles and depressions) have stimulated the creation of governmental institutions such as federal reserve banks and deficit spending; the rise of monopolies in certain industries has stimulated the creation of anti-trust laws. In both cases, there is controversy regarding whether the problems themselves are intrinsic to human markets or whether they result from political intervention [16,29,30,31,32]. Computational markets will be different enough from human markets that these problems may either be severe, or not occur at all. The game theory of these phenomena is so complex that a priori argument may be fruitless; an experimental methodology seems more promising. The simplest approach may be to build computational markets that lack any foundational mechanisms to prevent these problems, and then observe the results.

A public good is one that, if provided, benefits members of a group independently of whether those members have paid for it. Examples include national defense and residential streets. Consider the incentives of a potential contributor: "If I refuse to pay, the good may be provided anyway, and I will have gotten it for free. If I do pay, my contribution will be such a small fraction of the total that I will not significantly increase the amount of the good being provided, so I will refuse to pay." (This is also known as the free-rider problem, because some entities can get a free ride on the efforts of others.) Thus, if left to the market, public goods will be underproduced, from the standpoint of overall benefit.

Many public goods problems can be avoided in a computational setting. Ingenuity can convert many seemingly public goods into marketable goods, thereby enabling market incentives to reward appropriate production. Examples include the "Dividend Algorithm" presented in [II], and the difference between selling information and selling information-based services explained in Section 6.2 of [I]. Nevertheless, for true public goods, the problem is intractable in a market framework. In the case of human society, a legal principle has been proposed to deal with this issue.

7.2. Public goods and "takings" Richard Epstein has proposed [33] a legal principle derived from the "takings" clause of the U.S. Constitution, which grants to the federal government the power of eminent domain over private property. The takings clause limits forcible seizure of property, stating ". . .nor shall private property be taken for public use, without just compensation." Epstein argues for the principle that any taking from an entity, including taxation, must be compensated by the return of benefits of equal or greater value. It may readily be seen that, where the taking is indeed of net benefit, full compensation (whether in money, goods, or services) will be possible. Where full compensation is quantitatively impossible, the net cost must exceed the net benefit-and the taking itself is therefore undesirable.

To apply this principle requires a complex evaluation of costs and benefits on a case-by-case basis. Epstein, as a legal scholar writing about human society, proposes the use of legal mechanisms (courts and evolved systems of law) presently unavailable in a computational setting. The closest equivalent of such mechanisms would comprise a complex set of heuristics-so complex that it would have to evolve, rather than be built into the computational foundations as a frozen set of rules. How might complex laws evolve in computation?

7.3. Political ecosystems In human societies, legal systems and governmental activities provide a framework for the market. They can be seen as a meta-level with respect to market activity, but they are not protected from evolutionary forces; instead, they evolve within a political ecosystem with its own mechanisms for the variation and selection of laws and interventions.

In recent years, the tools of economic analysis have been applied to the evolution of policies within democratic political ecosystems [34,35]. This work defines vote and growth motives that parallel the well-known profit motive. All these motives have some claim to be motives to act in the public interest; all, in practice, have their flaws:

  • The profit motive: do what people want, as shown by their willingness to pay for the result (but cheat customers, if your reputation won't catch up with you).

  • The vote motive: do what people want, as shown by their willingness to vote for you and your platform (but lie and sell out to special interests, if it will win votes).

  • The growth motive: do what people want, as shown by their elected leaders' willingness to expand your agency (but do not achieve goals too economically, or your budget will be cut).

In each case, evolutionary forces-not psychology-virtually guarantee behavior in accord with these motives: profitable businesses, vote-winning politicians, and growing agencies will (almost by definition) dominate their fields. Evolution selects for those that act in accord with these motives, whether or not they feel these motives, just as evolution selects for genes that act in accord with the reproduction motive, though genes have no minds or motives at all. And when genes build organisms with minds, those minds may feel a sex motive rather than a reproduction motive. In a like fashion, selection of individuals and ideas could, in a hypothetical world, evolve institutions led by public-spirited executives, politicians, and bureaucrats, all subjectively selfless, but all acting in accord with the profit, vote, and growth motives (if only to keep their positions of influence, to enable future good works).

Analysis of the vote motive shows how socially destructive policies can win elections [34,35], hence the idea of correcting computational markets with computational democracies should be approached warily, at best. Further, it is not immediately obvious how a computational democracy would work. If one were to substitute "one object, one vote" for "one person, one vote", the result would be the immediate creation of vast numbers of otherwise useless voting-objects. One would prefer a system with a better incentive structure.

7.4. Meta-market ecosystems Imagine a system in which computational objects can choose to operate in any one of a number of legal environments, and in which new legal environments can be created at any time. Since environments could be copies of other environments, they can replicate; since they can also vary and be selected, they can evolve. A measure of the evolutionary success of a legal environment is its level of use (objects can vote with their nonexistent feet); one should expect the behavior of evolved systems of this sort to be describable in terms of an "attractiveness motive".

Something of this sort is seen in the human world. There are many human markets, each with its own set of rules, and each interacting and competing with the others. Stock and commodity exchanges, diversified corporations, and nations each employ a different set of rules governing their internal markets. In each case, entities with different internal markets are able to trade and compete with each other. Factoring out the other dimensions, these amount to a system of competing legal systems.

Each of these legal systems would have an incentive to approximate Epstein's system, which allows any action that will benefit all participants. When a public goods situation occurs which involves subscribers of several different systems, it would be settled according to prior treaties-when these have been negotiated (for a discussion of similar notions, see [30]). When such treaties have not been negotiated, the public goods problem may go unsolved, and the participants are left with only simple market rules. The penalty for leaving some public goods unprovided may be minor in a computational market ecosystem; no strong example of a public goods problem has so far been proposed.

Even under the selective pressure of competition, it may not be possible to establish a computational legal system that can enforce Epstein's system well. If so, then the simple, stable, low-overhead rules of the computational market ecosystem will be the system of choice. This system is a simple starting point and enables experimentation with alternatives; experience can show whether any are better.

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