Information Sharing and Cooperative Search in Fisheries

We present a dynamic game of search and learning about the productivity of com-peting fishing locations. Perfect Bayesian Nash equilibrium search patterns for non-cooperating fishermen and members of an information sharing cooperative are com-pared with first-best outcomes. Independent fishermen do not internalize the full valueof information, and do not replicate first-best search. A fishing cooperative faces afree-riding problem, as each coop member prefers that other members undertake costlysearch for information. Pooling contracts among coop members may mitigate, butare not likely to eliminate free riding. Our results explain the paucity of informationsharing in fisheries and suggest regulators use caution in advocating cooperatives as asolution to common pool ineffciencies in fisheries.�search; Information sharing; Dynamic Bayesian game; Fishing cooperative

Optimal monetary policy under discretion with a zero bound on nominal interest rates

We determine optimal discretionary monetary policy in a New-Keynesian model when nominal interest rates are bounded below by zero. Nominal interest rates should be lowered faster in response to adverse shocks than in the case without bound. Such ‘preemptive easing’ is optimal because expectations of a possibly binding bound in the future amplify the effects of adverse shocks. Calibrating the model to the U.S. economy we ?nd the easing effect to be quantitatively important. Moreover, signi?cant welfare losses. Losses increase further when in?ation is partly determined by lagged in?ation in the Phillips curve. Targeting positive in?ation rates reduces the frequency of a binding lower bound, but tends to reduce welfare compared to a target rate of zero. The welfare gains from policy commitment, however, appear signi?cant and are much larger than in the case without lower bound. JEL Classification: C63 , E31 , E52liquidity trap, nonlinear policy, zero lower bound

Inflation scares and forecast-based monetary policy

Central banks pay close attention to inflation expectations. In standard models, however, inflation expectations are tied down by the assumption of rational expectations and should be of little independent interest to policy makers. In this paper, the authors relax the assumption of rational expectations with perfect knowledge and reexamine the role of inflation expectations in the economy and in the conduct of monetary policy. Agents are assumed to have imperfect knowledge of the precise structure of the economy and the policymakers' preferences. Expectations are governed by a perpetual learning technology. With learning, disturbances can give rise to endogenous inflation scares, that is, significant and persistent deviations of inflation expectations from those implied by rational expectations. The presence of learning increases the sensitivity of inflation expectations and the term structure of interest rates to economic shocks, in line with the empirical evidence. The authors also explore the role of private inflation expectations for the conduct of efficient monetary policy. Under rational expectations, inflation expectations equal a linear combination of macroeconomic variables and as such provide no additional information to the policy maker. In contrast, under learning, private inflation expectations follow a time-varying process and provide useful information for the conduct of monetary policy.Equilibrium (Economics) ; Monetary policy ; Macroeconomics ; Inflation (Finance) ; Forecasting

A generalised significance test for individual communities in networks

Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.Comment: 20 pages, 4 figures and 4 table

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems