Organizational learning during policy implementation: lessons from U.S. forest planning

ABSTRACT

Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.     

Ants can sort their brood without a gaseous template

A fundamental issue of collective intelligence is whether the collective pattern or process is based on environmental information that explicitly codes for it or arises through self-organization of the individuals. Sometimes, these alternatives occur together. Adaptive systems may also be capable of utilizing different types of mechanism under different conditions. Sendova-Franks et al. (Anim Behav 68:1095–1106, 2004) demonstrated evidence for a self-organization mechanism of brood sorting in the ant Temnothorax albipennis, where the brood are sorted in a series of bands or concentric annuli that increase in size with distance from the colony centre. The work by Cox and Blanchard (J Theor Biol 204:223-238, 2000) suggests an alternative or complementary mechanism whereby the brood pattern is specified by the template of a CO₂ gradient. Here, we test for a gaseous template as a necessary condition for brood sorting. Under the experimental condition, we pumped the air out of the nest continuously to prevent the accumulation of any gaseous substances. We compared the brood pattern between the experimental and control conditions according to four characteristics: mean distance from centre, mean nearest-neighbour distance, shape and area. Under the experimental condition, the order of brood types according to the first two characteristics was the same as in the control. The area of the brood pattern was smaller, and its shape elongated under the experimental condition. As expected on the basis of these differences, mean distance from centre was greater and mean nearest-neighbour distance was smaller under the experimental condition (although not statistically significantly) and by the expected amount. We found evidence that ants avoid placing brood in the strongest airflow stream. This could explain the reduced area and elongated shape of the brood pattern under the experimental condition. We conclude that a gaseous template is not a necessary condition for brood sorting. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Collective decisions in ants when foraging under crowded conditions

In this paper we examine the effect of crowding on the selection of a path in the mass-recruiting ant Lasius niger. In our experiment, ants had to go from their nest to a food source by crossing a diamond-shaped bridge, giving the choice between two paths. Two types of bridges were used: the first had two branches of equal length but different width while the second had two branches of different length and width. Experiments at high traffic volume always ended up with the selection of the wider branch, even if it was longer. This result shows that overcrowding on the narrow branch plays an essential role in the mechanism underlying the choice of route in ants. A mathematical model was developed to evaluate the importance of two mechanisms that could account for this result. The first is based on the difference in travel duration between the two paths. The second is based on the repulsive interactions between workers making head-on encounters. The model shows that travel duration per se is not sufficient to explain path choice. Rather, it is the interplay between trail following behaviour and repulsive interactions that allows ants to choose the path that minimizes their travel time. When choosing a path ants thus prefer to trade time against energy. Our results demonstrate that any environmental constraint that alters the dynamics of trail recruitment can lead to the emergence of adaptive foraging decisions without any explicit coding of information by the foragers at the individual level.     

工作不安全感对安全绩效的影响 ——基于某大型电力公司的实证研究

由于安全问题备受关注,采用问卷研究法,通过构建调节—中介模型探讨工作不安全感影响员工安全绩效的机制。对某大型电力公司329位员工进行问卷调查后发现,员工的工作满意感中工作不安全感对员工安全绩效的消极影响,情绪智力通过调节工作不安全感与工作满意感的关系削弱了工作不安全感对安全绩效的消极作用,即当组织变革给员工带来工作不安全感时,高情绪智力的个体会通过有效的情绪管理减少工作不满意感,进而缓冲工作不安全感对安全绩效的影响,而低情绪智力的个体会过于情绪化,使得工作不安全感显著影响工作满意感,进而使员工的安全绩效受到影响。     

区块链与端边云技术赋能城市应急预警系统*

为解决现有城市应急预警系统（urban emergency early-warning system,UEES）存在的缺乏设备身份认证、数据共享性差、数据传输处理效率低、区域性智能自治能力不足等问题,根据区块链与端边云技术特点和现有系统的业务需求,提出区块链与端边云技术相融合的城市应急预警系统（UEES integrating blockchain and end-to-end cloud technology,UEES-BE）框架;对该框架存在的边缘节点认证与数据共享、区块链与边缘智能、边云协同分类分级分布式存储3个关键问题进行探讨;以城市内涝灾害预警为例,将区块链与端边云技术与城市内涝灾害预警系统业务相结合,提出4点优化建议。结果表明:区块链和端边云技术理论上可实现城市灾害安全、共享、实时、智能的预警,对构建城市应急预警系统具有指导意义。     

基于时序片段的油气管道运行工况识别方法*

为准确识别管道系统运行工况,提高对油气管道突发事故的响应速度,综合提升管网安全管理水平,提出1种基于时序片段的油气管道运行工况识别方法。首先,构建基于概率分布的状态变化识别模型,提取油气管道中不同运行状态点;其次,建立基于时间序列片段的工况识别模型,快速识别不同时间长度内油气管道运行工况;最后,以国内某成品油管道为例进行方法验证。研究结果表明:该方法可有效识别成品油管道阀门开关状态、泵异常停机和阀门内漏3种运行工况。对比传统的识别方法,该方法可降低状态变化点的漏报率,提升管道运行工况识别的准确率。研究结果可为油气管道系统运行工况识别提供新的借鉴方法。     

建筑火灾中值得深入研究的几个问题

提出了一些建筑火灾中值得进一步研究的,流动、传热、热解、燃烧、污染物生成等方面的基础问题。着重讨论了烟气扩散、流动中的混沌现象,提出了建立建筑火灾数据库、专家系统的设想,和建立非线性燃烧模型、开发智能型火灾自动报警系统的技术思路。     

How experienced individuals contribute to an improvement in collective performance in ants

Certain groups of organisms are capable of improving their collective performance with experience. In a recent study, we demonstrated that, over successive emigrations, colonies of the ant Temnothorax albipennis are able to improve their collective performance by reducing the time taken to complete an emigration (Langridge et al., Behav Ecol Sociobiol 56:523–529, 2004). In this paper, by recording the performance of individually marked workers during repeated emigrations, we were able to analyse some of the ways in which time gains are achieved. We found that: (1) those transporters that also transported in the preceding emigration began to transport earlier in the current emigration and, in the majority of emigrations, transported more items than those transporters that had not transported in the preceding emigration; (2) the time that elapsed before the first item was transported into the new nest reduced over successive emigrations, and this first item was, in the majority of emigrations, carried by a transporter that had also transported in the preceding emigration; (3) the number of adults that were transported reduced over successive emigrations. Our results strongly suggest that the behaviour of transporters that also transported in a preceding emigration may be modified as a result of their experience and that, consequently, their efforts in the next emigration make a major contribution to the improved performance of the colony as a whole.     

基于神经网络的东北地区秸秆焚烧预测

选取农作物秸秆露天燃烧严重的东北地区,采用人工神经网络的方法,结合卫星火点和气象数据,开展秸秆露天燃烧预测研究.结果表明：人工神经网络预测模型成功验证了松嫩平原地区2015年10月25日~11月15日的秸秆露天燃烧情况,其准确度为67.1%,经过多次试验,在神经网络建模与验证数据配比为80：20时,预测准确度最高,可达69.7%,同时该模型的稳定性较好.而对不同区域,不同时间段的预测研究表明,人工神经网络较适用于长时间序列的预测.就影响因素而言,相对湿度是影响秸秆露天燃烧的最重要因素.本研究结果可为空气质量模式提供火点预测数据,提高其预报预警能力,为区域联防联控政策的制定提供科技支持.