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Abstract — The problem is studied of achieving a specified formation among a group of mobile autonomous agents by distributed control. If convergence to a point is feasible, then more general formations are achievable too, so the focus is on convergence to a point (the agreement problem). Three formation strategies are studied and convergence is proved under certain conditions. Also, motivated by the question of whether collisions occur, formation evolution is studied. I.

A general probabilistic model of the gene structural and compositional properties of human genomic DNA is introduced and applied to the problem of identifying genes in unannotated human genomic sequences. The model uses a \Hidden semi-Markov" or semi-Markov source architecture which incorporates probabilistic descriptions of fundamental transcriptional, translational and splicing signals, as well as length distri-butions and compositional features of exons, introns and intergenic regions. Distinct sets of model parameters are derived which account for many of the substantial di er-ences in gene density and structure observed in distinct C+G compositional regions (\isochores") of the human genome. A novel model building procedure, termed Max-imal Dependence Decomposition, is introduced which captures potentially important dependencies between non-adjacent aswell as adjacent positions in a biological signal. Application of this model to the donor splice signal not only gives better discrimina-tion of potential donor sites than previous probabilistic models, but also reveals subtle properties of this signal which suggest aspects of its biochemical function. Acceptor

Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a welldefined set of system-level performance criteria, are called collectives. The fundamental problem in analyzing/designing such systems is in determining how the combined actions of a large number of agents leads to “coordinated ” behavior on the global scale. Examples of artificial systems which exhibit such behavior include packet routing across a data network, control of an array of communication satellites, coordination of multiple rovers, and dynamic job scheduling across a distributed computer grid. Examples of natural systems include ecosystems, economies, and the organelles within a living cell. No current scientific discipline provides a thorough understanding of the relation between the structure of collectives and how well they meet their overall performance criteria. Although still very young, research on collectives has resulted in successes both in understanding and designing such systems. It is expected that as it matures and draws upon other disciplines related to collectives, this field will greatly expand the range of computationally addressable tasks. Moreover, in addition to drawing on them, such a fully developed field of collective intelligence may provide insight into already established scientific fields, such as mechanism design, economics, game theory, and population biology. This chapter provides a survey to the emerging science of collectives.

This paper reviews a framework for numerically analyzing dynamic interactions in imperfectly competitive industries. The framework dates back to Ericson & Pakes (1995), but it is based on equilibrium notions that had been available for some time be-fore, and it has been extended in many ways by different authors since. The framework requires as input a set of primitives which describe the institutional structure in the industry to be analyzed. The framework outputs profits and policies for every incum-bent and potential entrant at each possible state of the industry. These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure. The sample paths generated by the model can be quite different depending on the primitives that are fed into it, and most of the extensions were designed to enable the framework to accommodate empirically relevant cases that required modification of the initial structure. The sample paths possess similar properties to those observed in (the recently made available) panel data sets on industries. These sample paths can be used either for an analysis of the likely response to a policy or an environmental change,

With applied work in mind, we define an equilibrium notion for dynamic games with assymmetric information which does not require a specification for players ’ beliefs about their opponents types. This enables us to define equilibrium conditions which, at least in principal, are testable and can be computed using a simple reinforcement learning algorithm. We conclude with an example that endogenizes the maintenance decisions for electricity generators in a dynamic game among electric utilities in which the costs states of the generators are private information.

A model of an incomplete market with the incorporation of a new notion of "inside information" is posed. The usual assumption that the stock price is Markovian is modified by adjoining a hidden Markov process to the Black-Scholes exponential Brownian motion model for stock fluctuations. The drift and volatility parameters take different values when the hidden Markov process is in different states. For example, it is 0 when there is no subset of the market which has or which believes it has, extra information. However, the hidden process is in state 1 when information is not equally shared by all, and then the behavior of the members in the subset causes increased fluctuations in the stock price. This model

This paper argues that incompleteness of intertemporal financial markets has little effect (on welfare, prices, or consumptions) in an economy with a single consumption good, provided that traders are long-lived and patient, a riskless bond is traded, shocks are transitory, and there is no aggregate risk. In an economy with aggregate risk, a similar conclusion holds, provided traders share the same CRRA utility function and the right assets are traded. Examples demonstrate that these conclusions need not hold if the wrong assets are traded or if the economy has multiple consumption goods.

We have available a fixed number of btfffer spaces to be allocated among the nodes of an open network of exponential servers with Bernoulli routing and Poisson arrivals. The goal is to optimize a perfor- mance criterion associated with the time to buffer overflow, such as maximizing its mean or maximizing the probabili that it exceeds .some value. We argue that for any such criterion the assignment should be done roughly in inverse proportion to the logarithms of the effective service rales at the nodes. Here, by effective service rate we mean the ratio of the service rate no the stationary arrival rate at the node in the network with infinite buffers.

Abstract. One of the important Internet challenges in coming years will be the introduction of intelligent services and the creation of a more personalized environment for users. A key prerequisite for such services is the modeling of user behavior and a natural starting place for this are Web logs. In this paper we propose a model for predicting sequences of user accesses which is distinguished by two elements: it is customizable and it reflects sequentiality. Customizable, in this context, means that the proposed model can be adapted to the characteristics of the server to more accurately capture its behavior. The concept of sequentiality in our model consists of three elements: (1) preservation of the sequence of the click stream in the antecedent, (2) preservation of the sequence of the click stream in the consequent and (3) a measure of the gap between the antecedent and the consequent in terms of the number of user clicks. 1