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We show that any communication finding a Pareto efficient allocation in a private-information economy must also discover supporting Lindahl prices. In particular, efficient allocation of L indivisible objects requires naming a price for each of the 2 L ¡1 bundles. Furthermore, exponential communication in L is needed just to ensure a higher share of surplus than that realized by auctioning all items as a bundle, or even a higher expected surplus (for some probability distribution over valuations). When the valuations are submodular, efficiency still requires exponential communication (and fully polynomial approximation is impossible). When the objects are homogeneous, arbitrarily good approximation is obtained using exponentially less communication than that needed for exact efficiency.

We analyze the communication complexity of surplus-maximizing allocations. We study both the continuous and discrete models of communication, measuring its complexity with the dimensionality of the message space and the number of transmitted bits, respectively. In both cases, we offer a lower bound on the amount of communication. This bound is applied to the problem of allocating L heterogeneous objects among N agents, whose valuations are (i) unrestricted, (ii) submodular, or (iii) homogeneous in objects. In cases (i) and (ii), efficiency requires exponential communication in L. Furthermore, in case (i), polynomial communication in L cannot achieve a higher surplus than selling all objects as a bundle. On the other hand, in case (iii), exact efficiency requires the transmission of L numbers, but arbitrarily close approximation is achieved with only O(log L) bits. When a Walrasian equilibrium with per-item prices exists, efficiency is achieved with deterministic communication that is polynomial in L.

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This paper studies the properties of real-time decentralized information processing as a model of human information processing in organizations. Real-time decentralized processing---which models the computation of decision rules in a temporal decision pro blem by members of an organization---captures both the cost of computation in terms of the members' time and the constraints imposed by computational delay on the use of recent information. Unlike a batch processing model, it has no single measure of del ay because decisions are computed from data of heterogeneous lags. Furthermore, decentralization does not unambiguously reduce delay, because processing a message precludes processing current data. JEL Classifications: D83, D23 Keywords: real-time computation, decentralized information processing, organizations, bounded rationality Author's address: Department of Economics Princeton University Princeton, NJ 08544-1021 USA Voice: (609) 258-4050 Fax: (609) 258-6419 Email: tvz@Princeton....

Second generation theories of federalism extend traditional approaches by systematically studying the role of government incentives in economic performance. Providing government with the incentive to promote markets is especially acute for developing economies or those in transition from central planning. In these countries, governments have often been the central barrier to economic development. In this paper, we investigate empirically decentralization and fiscal incentives in the central-provincial relationship during China's reform. We find strong correlations between local government revenue collection and local government expenditure. Further, we show that China's fiscal contracting system provides local governments with strong (marginal) fiscal incentives and at the same time improves horizontal distribution across provinces in budgetary spending. We also find that stronger fiscal incentives — measured in terms of higher marginal revenue retention rate — implies faster development of non-state enterprises and more reform in state-owned enterprises. Finally, we compare federalism, Chinese style, with federalism, Russian style. 1

A key trend in the world—especially in electronic commerce—is a demand for higher levels of expressiveness in the mechanisms that mediate interactions, such as the allocation of resources, matching of peers, and elicitation of opinions from large and diverse communities. Intuitively, one would think that this increase in expressiveness would lead to more efficient mechanisms (e.g., due to better matching of supply and demand). However, until now we have lacked a general way of characterizing the expressiveness of these mechanisms, analyzing how it impacts the actions taken by rational agents—and ultimately the outcome of the mechanism. In this technical report we introduce a general model of expressiveness for mechanisms. Our model is based on a new measure which we refer to as the maximum impact dimension. The measure captures the number of different ways that an agent can impact the outcome of a mechanism. We proceed to uncover a fundamental connection between this measure and the concept of shattering from computational learning theory. We also provide a way to determine an upper bound on the expected efficiency of any mechanism under its most efficient Nash equilibrium which, remarkably, depends only on the mechanism’s expressiveness. We show that for any setting and any prior over agent preferences, the

Junk e-mail or spam is rapidly choking off e-mail as a reliable and efficient means of communication over the Internet. Although the demand for human attention increases rapidly with the volume of information and communication, the

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