Coupled and complex: Human-environment interaction in the Greater Yellowstone Ecosystem, USA

Complexity theory has received considerable attention over the past decade from a wide variety of disciplines. Some who write on this topic suggest that complexity theory will lead to a unifying understanding of complex phenomena; others dismiss it as a passing and disruptive fad. We suggest that for the analysis of coupled natural/human systems, the truth emerges from the middle ground. As an approach focused as much on the connections among system elements as the elements themselves, we argue that complexity theory provides a useful conceptual framework for the study of coupled natural/human systems. It is, if nothing else, a framework that leads us to ask interesting questions about, for example, sustainability, resilience, threshold events, and predictability.In this paper we attempt to demystify the ongoing discussions on complexity theory by linking its evocative and overloaded terminology to real-world processes. We illustrate how a shift in focus from system elements to connections among elements can lead to meaningful insight into human-environment interactions that might otherwise be overlooked. We ground our discussion in ongoing interdisciplinary research surrounding Yellowstone National Park’s northern elk winter range; a tightly coupled natural/human system that has been the center of debate, conflict, and compromise for more than 135 years.     

新疆地震预报研究

新疆的地震预报是1970年开始的。二十多年来在“边观测、边研究、边预报”、“多路探索、多兵种联合作战”、“走综合预报之路”的方针指导下取得了长足的进展。建设了43个地震台站,投入189套仪器,建成了遍布全疆的地震无线通讯网,开展了历史地震调查和地震烈度区划工作,建立健全了地震会商预报制度,进行了多方面的地震预报研究。实际地震预报统计分析表明,扣除自然发震概率之后,趋势预报的成功率约0.3,短临预报的成功率约0.1。在前兆台网控制范围内取得了一些震例,说明地震确实是有前兆的,但又是非常复杂的。地震预报作为科学难题还有漫长的路要走。作者简要地讨论了地震观测的间接性和地震异常的离散性、难以区别的地震异常和地壳变动异常、建立在复杂现象基础上的地震前兆以及地震的混沌性对地震预报的影响等科学问题。     

岩石断裂作用的复杂性和混沌动力学

断裂是一个复杂的动力学体系,受到岩石结构、反应、流体迁移、应力、岩石变形和力学等多种地质因素和过程的耦合控制。本文建立了断裂体系的反应-输运-力学耦合动力学模型并编制了模拟程序。以湖南水口山矿区为例,通过动力学模拟表明不同地层岩性的断裂渗透率大小和演化特征存在显著差异,断裂作用促使岩石渗透率的空间非均匀性增强,从而有利于流体的局部汇聚和矿体的形成。断裂中压力随时间呈现出非周期振荡变化,反映了断裂演化的混沌特征。     

Epistemological possibilities and imperatives of complexity research: a reply to Reitsma

Reitsma (A response to ‘simplifying complexity’. Geoforum 34 (1) (2003) 13), in response to the article ‘Simplifying Complexity’ (Manson, S.M., Simplifying complexity: a review of complexity theory. Geoforum 32 (3) (2001) 405), highlights a number of interesting and important aspects of complexity theory that invite further discussion. In particular, there are three areas of complexity research that are open to deeper exploration: (1) the width and breadth of ‘complexity’ defined as a scientific endeavor; (2) the role of theory relative to practice and their relationship with pattern and process in complex systems; and (3) the need for greater discussion and exploration in order to define the conceptual bounds of complexity theory.     

Modelling complex systems of heterogeneous agents to better design sustainability transitions policy

This article proposes a fundamental methodological shift in the modelling of policy interventions for sustainability transitions in order to account for complexity (e.g. self-reinforcing mechanisms, such as technology lock-ins, arising from multi-agent interactions) and agent heterogeneity (e.g. differences in consumer and investment behaviour arising from income stratification). We first characterise the uncertainty faced by climate policy-makers and its implications for investment decision-makers. We then identify five shortcomings in the equilibrium and optimisation-based approaches most frequently used to inform sustainability policy: (i) their normative, optimisation-based nature, (ii) their unrealistic reliance on the full-rationality of agents, (iii) their inability to account for mutual influences among agents (multi-agent interactions) and capture related self-reinforcing (positive feedback) processes, (iv) their inability to represent multiple solutions and path-dependency, and (v) their inability to properly account for agent heterogeneity. The aim of this article is to introduce an alternative modelling approach based on complexity dynamics and agent heterogeneity, and explore its use in four key areas of sustainability policy, namely (1) technology adoption and diffusion, (2) macroeconomic impacts of low-carbon policies, (3) interactions between the socio-economic system and the natural environment, and (4) the anticipation of policy outcomes. The practical relevance of the proposed methodology is subsequently discussed by reference to four specific applications relating to each of the above areas: the diffusion of transport technology, the impact of low-carbon investment on income and employment, the management of cascading uncertainties, and the cross-sectoral impact of biofuels policies. In conclusion, the article calls for a fundamental methodological shift aligning the modelling of the socio-economic system with that of the climatic system, for a combined and realistic understanding of the impact of sustainability policies.     

Reducing model complexity for explanation and prediction

Numerical models can be useful for explaining poorly understood phenomena or for reliable quantitative predictions. When modeling a multi-scale system, a ‘top-down’ approach—basing models on emergent variables and interactions, rather than explicitly on the much faster and smaller scale processes that give rise to them—facilitates both goals. Parameterizations representing emergent interactions range from highly simplified and abstracted to more quantitatively accurate. Empirically based large-scale parameterizations lead more reliably to accurate large-scale behavior than do parameterizations of much smaller scale processes. Conversely, purposefully simplified representations of model interactions can enhance a model's utility for explanation, clarifying the key feedbacks leading to an enigmatic behavior. For such potential insights to be relevant, the interactions in the model need to correspond to those in the ‘real’ system in some straightforward way. Such a correspondence usually holds for models constructed for predictive purposes, although this is not a requirement. The goals motivating a modeling endeavor help determine the most appropriate modeling strategies, as well as the most appropriate criteria for judging model usefulness.     

Glaciolacustrine varved sediment as an alpine hydroclimatic proxy

The physical significance of a negative correlation between a varve record from Mud Lake, British Columbia, and temperature is discussed in the context of a process-network. The process-network is defined as the system of temporally and spatially connected processes involved in the transfer of a signal from climate to varved glaciolacustrine sediment. The six systems defining the network include climate, glacier, fluvial, geomorphic, terrestrial biologic and lacustrine systems to which each process belongs. A literature review outlines significant variation in the strength and character of correlations between components of the process-network and highlights that more comprehensive interpretations of varves as a hydroclimatic proxy require an improved understanding of the process-network. Documenting each process in the network is integral to informing a more complete model of this system, identifying processes that constitute signal transfer and assessing hydroclimatic proxies based on linear correlation. Such documentation is of growing importance as varved lacustrine sediments are increasingly used as a hydroclimatic proxy. The complex nature of the process-network requires greater emphasis on interdisciplinary cooperation and alternative methods to the linear statistical model.     

自然灾害复杂性研究

从自然灾害系统的观点出发,阐述了自然灾害系统的特点,提出了自然灾害复杂性的概念。基于前人研究的一些成果,讨论了分形,混沌,人工神经网络等非线性理论以及定性与定量综合集成技术在自然灾复杂性研究中的应用。     

Disaster Risk Science Development and Disaster Risk Reduction Using Science and Technology

Based on a series of international conferences for establishing HFA2 framework, this paper analyzed key issues of frontier of disaster risk science, integrated disaster prevention and mitigation strategies and integrated disaster risk governance. The future direction of disaster risk science was comprehensively discussed according to the widely discussed Post 2015 Hyogo Framework for Action (HFA2). It was proposed to deepen the cognition of the complexity of disaster system in terms of recognizing the complexity of disaster system from the interaction among various elements of the system, recognizing the complexity of climate change risk from the regional characteristics and formation mechanism of the global climate change and recognizing the complexity of the catastrophe risk from the regional development levels and patterns. Furthermore, it was suggested to make integrated disaster risk reduction strategies and countermeasures from the perspective of the complexity of the disaster system, including the establishment of regional integrated disaster risk governance framework to face climate change, the establishment of integrated disaster risk governance system in multi-spatial scale, the establishment of disaster risk governance financial system integrating the insurance, bonds and lottery, the establishment of consilience mode in integrated risk governance considering multi-stakeholder and the establishment of modeling and simulation platform integrated disaster risk information services and disaster risk reduction strategies. Finally, it was pointed out that the assessment of disaster risk levels for different regions in multi spatial scale can provide robust scientific support for integrated disaster risk governance of the world and regions, industries and enterprises so as to improve response to global change and guarantee a global and regional sustainable development.     

A conceptual framework for analysing adaptive capacity and multi-level learning processes in resource governance regimes

Governance failures are at the origin of many resource management problems. In particular climate change and the concomitant increase of extreme weather events has exposed the inability of current governance regimes to deal with present and future challenges. Still our knowledge about resource governance regimes and how they change is quite limited. This paper develops a conceptual framework addressing the dynamics and adaptive capacity of resource governance regimes as multi-level learning processes. The influence of formal and informal institutions, the role of state and non-state actors, the nature of multi-level interactions and the relative importance of bureaucratic hierarchies, markets and networks are identified as major structural characteristics of governance regimes. Change is conceptualized as social and societal learning that proceeds in a stepwise fashion moving from single to double to triple loop learning. Informal networks are considered to play a crucial role in such learning processes. The framework supports flexible and context sensitive analysis without being case study specific.First empirical evidence from water governance supports the assumptions made on the dynamics of governance regimes and the usefulness of the chosen approach. More complex and diverse governance regimes have a higher adaptive capacity. However, it is still an open question how to overcome the state of single-loop learning that seem to characterize many attempts to adapt to climate change. Only further development and application of shared conceptual frameworks taking into account the real complexity of governance regimes can generate the knowledge base needed to advance current understanding to a state that allows giving meaningful policy advice.