Effects of uniform-allocation constraints in networked common-pool resource extraction games

Communities that share common-pool resources (CPRs) often coordinate their actions to sustain resource quality more effectively than if they were regulated by some centralized authority. Networked models of CPR extraction suggest that the flexibility of individual agents to selectively allocate extraction effort among multiple resources plays an important role in maximizing their payoffs. However, empirical evidence suggests that real-world CPR appropriators may often de-emphasize issues of allocation, for example by responding to the degradation of a single resource by reducing extraction from multiple resources, rather than by reallocating extraction effort away from the degraded resource. Here, we study the population-level consequences that emerge when individuals are constrained to apply an equal amount of extraction effort to all CPRs that are available to them within an affiliation network linking agents to resources. In systems where all resources have the same capacity, this uniform-allocation constraint leads to reduced collective wealth compared to unconstrained best-response extraction, but it can produce more egalitarian wealth distributions. The differences are more pronounced in networks that have higher degree heterogeneity among resources. In the case that the capacity of each CPR is proportional to its number of appropriators, the uniform-allocation constraint can lead to more efficient collective extraction since it serves to distribute the burden of over-extraction more evenly among the network’s CPRs. Our results reinforce the importance of adaptive allocation in self-regulation for populations who share linearly degrading CPRs; although uniform-allocation extraction habits can help to sustain higher resource quality than does unconstrained extraction, in general this does not improve collective benefits for a population in the long term.

An Attention-Based Meta Learning Network via Prior Meta-training Strategy for Intelligent Fault Detection of Shipboard Antenna under Small Samples Prerequisite

Fault detection of shipboard antenna is of great significance to ensure the safe operation and smooth completion of astronautic measurement ship. With the development of data-oriented technology, intelligent fault detection is desiderated to improve self-management of entire shipboard antenna system. However, insufficient fault data results in intelligent algorithms stagnation. In this paper, a meta-learning network is specially designed for fault identification of shipboard antenna under small samples prerequisite, which is named affiliation network (AN). The AN consists of a random sampler, a feature extractor, an auxiliary classifier and a discriminator. The former three are utilized to extract and concatenate the features from training and testing samples, while the latter trains an adaptive pseudo-distance to evaluate the affiliation degree between concatenation features for identifying unknown data. Besides, a prior sufficient meta-training strategy is specially designed to realize metric-based knowledge transfer for acquiring the more generic AN, thus avoiding reiterative training of the AN in different application scenarios. Effectiveness of proposed method are validated by three experimental cases. Results indicate that, comparing with conventional intelligent models, the prior trained AN only utilized few samples to effectively identify failure categories of shipboard antenna even with complex operating conditions.

Linking Fishing Behavior and Ecosystem Dynamics Using Social and Ecological Network Models

One goal of ecosystem-based management is studying an ecosystem and its people, the socio-ecological system, in a qualitative and quantitative modeling approach that can provide management agencies with possible outcomes of their actions using scenario forecasting. Ecosystem-based fisheries management strives to use the socio-ecological system approach, including direct and indirect impacts on multiple species including the behavioral responses of fishers after a regulatory change (a gillnet ban). Here, we link fisher behavioral networks with a mass-balanced food-web ECOPATH network model of an estuarine ecosystem and its commercial fisheries for an analysis of fishing impacts after a gillnet ban on multiple species using ECOSIM. We modeled fisher behavioral networks using reported catches of species from individual fishers along with the gear fished to create nodes in a gear/species affiliation network. Individual fishers with common gear/species use are indicative of common fishing behavior. When such fishers have high network centrality and are engaged in multiple gear/species fisheries, they can transition to other gear/species fisheries along “switching pathways” when facing a regulatory change. We used an index of joint gear participation to identify likely gear switching pathways, and we predicted changes in fishing effort after a gill net ban. We simulated the gill net ban in ECOSIM under two scenarios of fishing effort: Scenario 1, gill net fishing effort of 0%; Scenario 2, gill net fishing effort of 0% with increased effort in the alternative gear fisheries using the predicted switching pathways for the affiliation network. Scenario 1 predicted an increase in flounder (Paralichthys spp.) biomass over a decade. Under Scenario 2, fishers targeting flounders were predicted to switch from gill nets to pound nets. Scenario 2 predicted a 7% decline in flounder biomass over ten years, rather than an increase in flounders. The gillnet ban with increased effort due to switching is predicted to have the opposite effect on the conservation goal, which was to increase flounder stocks. Fishery management that incorporates a socio-ecological approach modeling both fisher behaviors and multi-species ecosystem responses can reveal single-species responses that are in the opposite direction of the anticipated management goals.

The small world of German CEOs: a multi-method analysis of the affiliation network structure

This paper seeks to understand the structure of corporate networks in the period following the dissolution of Deutschland AG (“Germany Inc.”). For this purpose, affiliation networks among chief executive officers (CEOs) that are based on common membership in various societal organizations will be examined. I apply an innovative mix of methods for studying a sample of CEOs from the 100 top companies in Germany in the 2010s. Based on social network analysis, I show that the overall affiliation network has all features of a small-world network, i.e., a high clustering coefficient and a short path length among the CEOs. The average degree of separation among German CEOs is only two steps. Another innovative contribution of this paper is its study of the linkage between affiliation network features and patterns of corporate recruitment. Using multiple correspondence analysis, I show that different subgroups of the overall affiliation network have their specific network characteristics and recruitment patterns. Within the network, managers from automotive and technical engineering often assume brokerage positions, while managers from the trade branch are largely isolated. This study shows that the affiliation networks and corporate recruitment patterns are interlinked; the transformation of corporate networks is a dynamic outcome of interrelations among different subgroups within the network, each with distinct educational, professional, and network characteristics.

Sistemas deliberativos em perspectiva meso: o subsistema dos conselhos nacionais de políticas públicas e seus conectores

RESUMO Introdução: O objetivo do artigo é descrever o subsistema dos conselhos nacionais de políticas públicas e identificar quais seriam os principais conectores dessas arenas. A seguir, pretende-se apresentar as similitudes e diferenças nos subsistemas de conselhos entre os diferentes níveis de governo (município, estado e união). Isto é, investigam-se os efeitos do problema da escala para a teoria deliberativa. Materiais e métodos: Foram realizadas pesquisas nos sítios eletrônicos dos conselhos nacionais de políticas públicas. Obteve-se informação para vinte e cinco conselhos relativos à composição dos conselhos, principalmente, os segmentos que o compõe. A seguir, realizou-se uma técnica do campo da Análise de Redes Socais denominada affiliation network analysis or two-mode network. As informações foram processadas no software Pajek 5.0. O propósito é verificar a hipótese de que a elevação da escala aumenta a centralidade dos atores da burocracia (atores governamentais) como conectores principais das redes. Resultados: Um dos resultados principais é o de que os conselhos nacionais apresentam baixo grau de conectividade, diferentemente dos achados relativos ao município e estado presente em pesquisas anteriores. Encontramos, ainda, um expressivo número de conectores híbridos inexistentes e/ou presentes com menor importância nos municípios e Estado previamente analisados. A hipótese foi confirmada, pois foi bastante expressivo o papel dos conselheiros governamentais como conectores das arenas deliberativas analisadas. Discussão: O artigo contribui para a literatura ao sugerir a utilização de técnicas do campo da Análise de Redes Sociais para identificar os conectores das arenas deliberativas. Indica, ainda, os avanços e os limites da perspectiva sistêmica, com foco nos seus conectores e nos mecanismos de transmissão. Apresenta-se abordagem original dos subsistemas e realiza-se a descrição dos aspectos metodológicos envolvidos. A terceira contribuição problematiza os resultados da análise e propõe estratégias de continuidade desta agenda no campo de pesquisas, sobretudo, a possibilidade de se investigar os sistemas de políticas públicas e as possíveis conexões entre arenas de diferentes entes federativos.

Who Joins the Franchise, Affiliation Model of Hospital Networks? An Analysis of Hospital and Market Characteristics of Members

Joining nonownership based, organization-driven networks and alliances is a common strategy for hospitals to pursue yet little is known about what types of hospitals join these collaborations, due in part to challenges in identifying members. One novel network form that has recently emerged, and made identification feasible, is franchise-like “affiliation networks” in which affiliate hospitals pay an annual membership fee that allows access to the clinical expertise and resources of high-status, nationally ranked sponsor hospitals. Affiliation networks and their members publicize affiliation. Using 2006-2015 data on United States’ hospitals, we find hospitals with higher patient acuity, teaching hospitals, and hospitals located in areas of higher utilization intensity were more likely to join an affiliation network. Joining affiliation networks does not appear to be in response to highly competitive markets because hospitals in less competitive environments are more likely to join and hospitals with higher net incomes are more likely to join.