Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions

Cooperation in the evolutionary snowdrift game with a self-questioning updating mechanism is studied on annealed and quenched small-world networks with directed couplings. Around the payoff parameter value $r=0.5$, we find a size-invariant symmetrical cooperation effect. While generally suppressing cooperation for $r>0.5$ payoffs, rewired networks facilitated cooperative behavior for $r<0.5$. Fair amounts of noise were found to break the observed symmetry and further weaken cooperation at relatively large values of $r$. However, in the absence of noise, the self-questioning mechanism recovers symmetrical behavior and elevates altruism even under large-reward conditions. Our results suggest that an updating mechanism of this type is necessary to stabilize cooperation in a spatially structured environment which is otherwise detrimental to cooperative behavior, especially at high cost-to-benefit ratios. Additionally, we employ component and local stability analyses to better understand the nature of the manifested dynamics.Comment: 7 pages, 6 figures, 1 tabl

Learning, endogenous indexation and disinflation in the New-Keynesian Model

This paper introduces adaptive learning and endogenous indexation in the New-Keynesian Phillips curve and studies disinflation under inflation targeting policies. The analysis is motivated by the disinflation performance of many inflation-targeting countries, in particular the gradual Chilean disinflation with temporary annual targets. At the start of the disinflation episode price-setting firms’ expect inflation to be highly persistent and opt for backward-looking indexation. As the central bank acts to bring inflation under control, price-setting firms revise their estimates of the degree of persistence. Such adaptive learning lowers the cost of disinflation. This reduction can be exploited by a gradual approach to disinflation. Firms that choose the rate for indexation also re-assess the likelihood that announced inflation targets determine steady-state inflation and adjust indexation of contracts accordingly. A strategy of announcing and pursuing short-term targets for inflation is found to influence the likelihood that firms switch from backward-looking indexation to the central bank’s targets. As firms abandon backward-looking indexation the costs of disinflation decline further. We show that an inflation targeting strategy that employs temporary targets can benefit from lower disinflation costs due to the reduction in backward-looking indexation

Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201