fraction of $1 required in trade for the asset “Pays $x” can be interpreted as a market estimate of E[x]. Similarly, the fraction of the asset “Pays $1 if C” traded for the asset “Pays $x if C” can be interpreted as a market estimate of E[x|C]. Since 1996, the Iowa Electronic Markets have actually had markets whose prices could be combined to represent estimates of each party’s expected vote fraction conditional on various possible nominees for the other party (Berg & Rietz, 2003). For example, 1996 prices indicated that Dole, the actual Republican nominee, was a weak candidate against Clinton; there was always another candidate who speculators said would have received a higher expected vote fraction than Dole. One can similarly create markets to estimate whether a new policy N is an improvement over the status quo Q in increasing national welfare W. Given a measure of national welfare W,normalized to be between zero and one, markets that trade assets “Pays $W if N” for some fraction of “Pays $1 if N” give a market price estimate of E[W|N]. Just as people considering what fraction of $1 to pay for “Pays $x” will estimate the average value of x across all plausible scenarios, people considering what fraction of “Pays $1 if N” to pay for “Pays $x if N” will average only over plausible scenarios consistent with the event N. When the market estimate of E[W|N]isclearly greater than E[W|Q], speculators are saying that this new policy is expected to increase national welfare. The Engineering of Institutions It is tempting to use the success of betting markets as information institutions to solve the problems of democracies as information institutions. But do we know enough about either type of institution to be proposing new forms of government based on this idea? It depends on whether one thinks like a scientist or like an engineer. A scientist (or at least a caricature of one) insists on saying “I do not know” about a theory until it has robust empirical support, or has clear theoretical support from some other well-supported theory. A scientist thus bases policy recommendations only on relatively direct data, or on well-supported theory, and so stays quiet about radical new forms of government, which can not possibly have direct empirical support, and which are too complex for our theories to make direct predictions. An engineer, on the other hand, is more interested in improving systems than theory. An engineer is happy to work on a concept with a five percent chance of success, if the payoff from success would be thirty times the cost of trying. An engineer uses theory-informed intuitions to consider a wide range of design issues, and if the concept still seems promising, moves on to increasingly realistic “proof of concept” prototypes, from simulations, to “wind tunnel” models, to field tests. Scientists have little use for prototypes and their tests, being neither theory nor data that tests theory, but prototypes are what make the engineers’ world go round. While academic study of social systems is now mostly dominated by a scientific style, this paper takes an engineering style to consider a new form of governance. The purpose of this paper is not to induce high confidence that this concept would work well, but merely to raise readers’ confidence up to a level that would justify further exploration via the next level of prototype. This paper thus, from this point forward, mainly takes on an engineering tone, qualitatively identifying and addressing a wide range of design issues. 10