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Considering several main types of dynamic contests (the race, the tug-of-war, elimination contests and iterated incumbency fights) we identify a common pattern: the discouragement effect. This effect explains why the sum of rentseeking efforts often falls considerably short of the prize that is at stake. It may cause violent conflict in early rounds, but may also lead to long periods of peaceful interaction.

"Considering several main types of dynamic contests (the race, the tug-of-war,
elimination contests and iterated incumbency fights) we identify a common
pattern: the discouragement effect. This effect explains why the sum of rentseeking
efforts often falls considerably short of the prize that is at stake. It may
cause violent conflict in early rounds, but may also lead to long periods of
peaceful interaction." (author's abstract)

International audience The standard forms of bootstrap iteration are very computationally demanding. As a result, there have been several attempts to alleviate the computational burden by use of approximations. In this paper, we extend the fast double bootstrap of Davidson and MacKinnon (2007) to higher orders of iteration, and provide algorithms for their implementation. The new methods make computational demands that increase only linearly with the level of iteration, unlike standard procedures, whose demands increase exponentially. In a series of simulation experiments, we show that the fast triple bootstrap improves on both the standard and fast double bootstraps, in the sense that it suffers from less size distortion under the null with no accompanying loss of power.

Abstract
This paper introduces a family of domains of bargaining problems allowing for non-convexity. For each domain in this family, single-valued bargaining solutions satisfying the Nash axioms are explicitly characterized as solutions of the iterated maximization of Nash products weighted by the row vectors of the associated bargaining weight matrices. This paper also introduces a simple procedure to standardize bargaining weight matrices for each solution into an equivalent triangular bargaining weight matrix, which is simplified and easy to use for applications. Furthermore, the standardized bargaining weight matrix can be recovered from bargaining solutions of simple problems. This recovering result provides an empirical framework for determining the bargaining weights.

There is a remarkable consensus among scholars of international relations that bilateral cooperation is easier to achieve than multilateral cooperation. This essay proposes a formal model to show that this is incorrect, because a multilateral agreement may achieve what an equivalent series of bilateral agreements cannot. The author explores formally several different enforcement mechanisms, suggesting that the argument is robust. Throughout the essay, the author uses examples from the Marshall Plan to illustrate the logic of this result. The argument has implications for other substantive topics, such as most-favored-nation clauses in trade agreements, the theory of hegemonic stability, analysis of the European Economic Community, the politics of linkage, and the study of multilateralist norms.

We show that the line digraph technique, when iterated, provides dense digraphs, that is, with asymptotically large order for a given diameter (or with small diameter for a given order). This is a well-known result for regular digraphs. In this note we prove that this is also true for non-regular digraphs. ; This research is supported by projects MINECO MTM2014-60127-P, and 2014SGR1147 of the Catalan Government. This research has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 734922.

"This paper introduces a simple model of interacting agents that learn to
predict each other. For learning to predict the other's intended action we
apply genetic programming. The strategy of an agent is rational and fixed. It
does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an agent can choose from is infinite. Preliminary
simulation results are presented. They show that by varying the population
size of genetic programming, different learning characteristics can easily be
achieved, which lead to quite different communication patterns." (author's abstract)