This survey characterizes an emerging research area, sometimes called coordination theory, that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology. A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints, and tank/subtask dependencies. Section 3 summarizes ways of applying a coordination perspective in three different domains: (1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research

: Reasoning about knowledge seems to play a fundamental role in distributed systems. Indeed, such reasoning is a central part of the informal intuitive arguments used in the design of distributed protocols. Communication in a distributed system can be viewed as the act of transforming the system's state of knowledge. This paper presents a general framework for formalizing and reasoning about knowledge in distributed systems. We argue that states of knowledge of groups of processors are useful concepts for the design and analysis of distributed protocols. In particular, distributed knowledge corresponds to knowledge that is "distributed" among the members of the group, while common knowledge corresponds to a fact being "publicly known". The relationship between common knowledge and a variety of desirable actions in a distributed system is illustrated. Furthermore, it is shown that, formally speaking, in practical systems common knowledge cannot be attained. A number of weaker variants...

According to conventional wisdom, Nash equilibrium in a game “involves” common knowl-edge of the payoff functions, of the rationality of the players, and of the strategies played. The basis for this wisdom is explored, and it turns out that considerably weaker conditions suffice. First, note that if each player is rational and knows his own payoff function, and the strategy choices of the players are mutually known, then these choices form a Nash equilibrium. The other two results treat the mixed strategies of a player not as conscious randomization of that player, but as conjectures of the other players about what he will do. When n = 2, mutual knowledge of the payoff functions, of rationality, and of the conjectures yields Nash equilibrium. When n ≥ 3, mutual knowledge of the payoff functions and of rationality, and common knowl-edge of the conjectures yield Nash equilibrium when there is a common prior. Examples are provided showing these results to be sharp.

: We present a formal model that captures the subtle interaction between knowledge and action in distributed systems. We view a distributed system as a set of runs, where a run is a function from time to global states and a global state is a tuple consisting of an environment state and a local state for each process in the system. This model is a generalization of those used in many previous papers. Actions in this model are associated with functions from global states to global states. A protocol is a function from local states to actions. We extend the standard notion of a protocol by defining knowledge-based protocols, ones in which a process' actions may depend explicitly on its knowledge. Knowledge-based protocols provide a natural way of describing how actions should take place in a distributed system. Finally, we show how the notion of one protocol implementing another can be captured in our model. Some material in this paper appeared in preliminary form in [HF85]. An abridge...

Abstract: What should it mean for an agent toknowor believe an assertion is true with probability:99? Di erent papers [FH88, FZ88a, HMT88] givedi erent answers, choosing to use quite di erent probability spaces when computing the probability that an agent assigns to an event. We showthat each choice can be understood in terms of a betting game. This betting game itself can be understood in terms of three types of adversaries in uencing three di erent aspects of the game. The rst selects the outcome of all nondeterministic choices in the system� the second represents the knowledge of the agent's opponent in the betting game (this is the key place the papers mentioned above di er) � the third is needed in asynchronous systems to choose the time the bet is placed. We illustrate the need for considering all three types of adversaries with a number of examples. Given a class of adversaries, we show howto assign probability spaces to agents in a way most appropriate for that class, where \most appropriate " is made precise in terms of this betting game. We conclude by showing how di erent assignments of probability spaces (corresponding to di erent opponents) yield di erent levels of guarantees in probabilistic coordinated attack.

Understanding knowledge is a fundamental issue in many disciplines. In computer science, knowledge arises not only in the obvious contexts (such as knowledgebased systems), but also in distributed systems (where the goal is to have each processor "know" something, as in agreement protocols). A general semantic model of knowledge is introduced, to allow reasoning about statements such as "He knows that I know whether or not she knows whether or not it is raining." This approach more naturally models a state of knowledge than previous proposals (including Kripke structures). Using this notion of model, a model theory for knowledge is developed. This theory enables one to interpret the notion of a "finite amount of information". A preliminary version of this paper appeared in Proc. 25th IEEE Symp. on Foundations of Computer Science, 1984, pp. 268--278. This version is essentially identical to the version that appears in Journal of the ACM 38:2, 1991, pp. 382--428. y Part of th...

In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals.

Abstract: In this overview paper, I will attempt to identify and describe some of the common threads that tie together work in reasoning about knowledge in such diverse fields as philosophy, economics, linguistics, artificial intelligence, and theoretical computer sciencce. I will briefly discuss some of the more recent work, particularly in computer science, and suggest some lines for future research.

OLIGOPOLISTIC pricing decisions—in which the choice variable is not dichotomous as in the simple Prisoner’s Dilemma but continuous—have been modeled as a Generalized Prisoner’s Dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoport’s Tit for Tat for the threeperson repeated game. Holland’s genetic algorithm and Axelrod’s representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions. The paper discusses how findings from two-person tournaments can be extended to the GPD, in particular how the author’s winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of “niche ” strategies, successful

In this paper we introduce a new type of knowledge operator, called evidencebased knowledge, intended to capture the constructive core of common knowledge. An evidence-based knowledge system is obtained by augmenting a multi-agent logic of knowledge with a system of evidence assertions t:ϕ (“t is an evidence for ϕ”) based on the following plausible assumptions: 1) each axiom has evidence; 3) evidence is checkable; 3) any evidence implies individual knowledge for each agent. Normally, the following monotonicity property is also assumed: 4) any piece of evidence is compatible with any other evidence. We show that the evidence-based knowledge operator is a stronger version of the common knowledge operator. Evidence-based knowledge is free of logical omniscience, model-independent, and has a natural motivation. Furthermore, evidence-based knowledge can be presented by normal multi-modal logics, which are in the scope of well-developed machinery applicable to modal logic: epistemic models, normalized proofs, automated proof search, etc. 1