Link, die in der anstrengenden Zeit des Schreibens immer verständnisvoll

We study the emergence of cooperation in dynamic, anonymous social networks, such as in online communities. We examine prisoner’s dilemma played under a social matching protocol, where individuals form random links to partners with whom they can interact. Cooperation results in mutual benefits, whereas defection results in a high short-term gain. Moreover, an agent that defects can escape reciprocity by virtue of anonymity: it is always possible for an agent to abandon his history and re-enter the network as a new user. We find that cooperation is sustainable at equilibrium in such a model. Indeed, cooperation allows an individual to interact with an increasing number of other cooperators, resulting in the formation of a type of social capital. This process arises endogenously, without the need for potentially harmful social enforcement rules. Additionally, for a rich class of parameter settings, our model predicts a stable coexistence of cooperating and defecting agents at equilibrium.

We observe that ranking systems—a theoretical framework for web page ranking and collaborative filtering introduced by Altman and Tennenholtz—and tournament solutions—a well-studied area of social choice theory—are strongly related. This relationship permits a mutual transfer of axioms and solution concepts. As a first step, we formally analyze a tournament solution that is based on Google’s PageRank algorithm and study its interrelationships with common tournament solutions. It turns out that the PageRank set is always contained in both the Schwartz set and the uncovered set, but may be disjoint from most other tournament solutions. While PageRank does not satisfy various standard properties from the tournament literature, it can be much more discriminatory than established tournament solutions.

Reasoning about agent preferences on a set of alternatives, and the aggregation of such preferences into some social ranking is a fundamental issue in reasoning about uncertainty and multiagent systems. When the set of agents and the set of alternatives coincide, we get the ranking systems setting. A famous type of ranking systems are page ranking systems in the context of search engines. In this paper we present an extensive axiomatic study of ranking systems. In particular, we consider two fundamental axioms: Transitivity, and Ranked Independence of Irrelevant Alternatives. Surprisingly, we find that there is no general social ranking rule that satisfies both requirements. Furthermore, we show that our impossibility result holds under various restrictions on the class of ranking problems considered.

Personalized ranking systems and trust systems are an essential tool for collaboration in a multi-agent environment. In these systems, trust relations between many agents are aggregated to produce a personalized trust rating of the agents. In this paper we introduce the first extensive axiomatic study of this setting, and explore a wide array of well-known and new personalized ranking systems. We adapt several axioms (basic criteria) from the literature on global ranking systems to the context of personalized ranking systems, and prove strong properties implied by the combination of these axioms.

Ranking systems are central to many internet applications including, notably, Google’s PageRank algorithm for ranking web pages. Ranking systems are a special case of a social choice problem in which the set of agents and the set of outcomes coincide. In this paper we consider PageRank as a particular ranking system and we present two axiomatizations that allow PageRank to be studied from a social choice perspective. The first axiomatization is normative and leads to the theory that no ranking system can simultaneously satisfy two desirable properties. Despite this discouraging result, PageRank is nonetheless highly successful and the second, descriptive, axiomatization characterizes PageRank uniquely among ranking systems. We conclude by suggesting areas for further research, including axioms describing vulnerability to manipulation and preferences submitted directly by human users. 1

Mechanism design has traditionally focused almost exclusively on the design of truthful mechanisms. There are several drawbacks to this: 1. in certain settings (e.g. voting settings), no desirable strategy-proof mechanisms exist; 2. truthful mechanisms are unable to take advantage of the fact that computationally bounded agents may not be able to find the best manipulation, and 3. when designing mechanisms automatically, this approach leads to constrained optimization problems for which current techniques do not scale to very large instances. In this paper, we suggest an entirely different approach: we start with a na ve (manipulable) mechanism, and incrementally make it more strategy-proof over a sequence of iterations. We give

In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. We show that with a reset probability of, the expected total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank n ln m of every node at all times is O ( 2). This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs

approach to model reduction of logistic

In this paper, we establish a connection between ranking theory and general equilibrium theory. First of all, we show that the ranking vector of PageRank or Invariant method is precisely the equilibrium of a special Cobb-Douglas market. This gives a natural economic interpretation for the PageRank or Invariant method. Furthermore, we propose a new ranking method, the CES ranking, which is minimally fair, strictly monotone and invariant to reference intensity, but not uniform or weakly additive. 1