Indirect effects and informational entropy in natural and human networks

Advanced System Analysis (ASA) Program researchers develop methods and case-studies analyzing ecological, economic, energy, financial and other networked empirical systems. These methods often originate in the natural science disciplines (e.g., physics, ecology) and then transfer to social sciences disciplines (e.g., economics).

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Systems ecology attempts to study ecological systems in a holistic fashion. Its branch called network ecology in particular deals with ecological systems, notably food webs, as networks of nodes – species or functional groups – that interact with each other by means of predation. Methods of network analysis quantifying effects of both direct and indirect interactions (i.e., those involving intermediate nodes) were first suggested by systems ecologists [1]. These methods were based on analyses of the matrix of stationary systems flows and were applied to food webs to reveal ecological relationships among system components.

ASA is developing applications of network analysis to study food webs. In 2014 researchers [2] investigated the impact of the construction of a dam on the upper Mekong River on the river’s food web; they found the most valued fishery to be significantly impacted and potentially endangered because of the dam construction. Network-based methodology assessing the potential environmental impact of multiple stressors after damming (i.e., sedimentation, discharge change, and heavy metal pollution) provides a new way of highlighting which factors are dominant for the ecosystem and how the management strategies should be prioritized.

Inspired by the power of network ecology methodology ASA is exploring its potential to be transferred to various other applications. Results published in 2014 include: application of the network ecology paradigm to study embodied water consumption by [3]; and a case study of application of network ecology to explore the symbiosis of the plant system in Shandong Lubei eco-industrial park in China by analyzing the sulfur flows that occur in production [4].

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Using network ecology to study the functioning of the city also appears to be promising [5]. Starting from the input-output data and monetary flows between the city’s economic sectors, ASA researchers are studying urban energy metabolism by means of the network-based analysis. In [6][7][8] the time dynamics of embodied energy consumption and associated carbon footprints were investigated, as well as the ecological role of economic sectors in Beijing; this revealed the ecological hierarchy of the urban metabolic system. In [9] the transfer routes of major air pollutant, total suspended particulate matter (TSPM), were studied between the urban ecosystem and surrounding regions in the Beijing–Tianjin–Hebei area. The study identified major TSPM sources by analyzing the embodied TSPM flows through inter-regional trade. Tracking TSPM from origin to end via consumption activities provided a new approach and insights into developing air pollution mitigation strategies.

Another approach to studying networked human-made systems developed by ASA is based on the notion of informational entropy. It is known that physical systems consisting of large number of elements (especially statistically simple systems such as gases) are subject to the maximum entropy principle. In [10] the authors introduced a so-called generalized entropy and showed that the maximum (generalized) entropy principle also holds for more complex systems, whose elements interact strongly and have memory and path-dependence, thus suggesting a powerful theoretical tool for studying such systems.


[1] Borrett SR, Fath BD, Whipple SJ (2014). Introduction to the special issue "Systems Ecology: A Network Perspective and Retrospective." Ecological Modelling, 293:1-3

[2] Chen S, Chen B, Fath BD (2015). Assessing the cumulative environmental impact of hydropower construction on river systems based on energy network model. Renewable and Sustainable Energy Reviews, 42:78-92 (Published online 23 October 2014)

[3] Fang D, Fath BD, Chen B, Scharler UM (2014). Network environ analysis for socio-economic water system. Ecological Indicators, 47:80-88

[4] Zhang Y, Zheng H, Fath BD (2014a). Ecological network analysis of an industrial symbiosis system: A case study of the Shandong Lubei eco-industrial park. Ecological Modelling, Article in press (Published online 12 June 2014)

[5] Chen S, Chen B, Fath BD (2014). Urban ecosystem modeling and global change: Potential for rational urban management and emissions mitigation. Environmental Pollution, 190:139-149

[6] Zhang Y, Zheng H, Fath BD (2014). Analysis of the energy metabolism of urban socioeconomic sectors and the associated carbon footprints: Model development and a case study for Beijing. Energy Policy, 73:540-551

[7] Zhang Y, Liu H, Fath BD (2014). Synergism analysis of an urban metabolic system: Model development and a case study for Beijing, China. Ecological Modelling, 272:188-197

[8] Zhang Y, Zheng H, Fath BD, Liu H, Yang Z, Liu G, Su M (2014). Ecological network analysis of an urban metabolic system based on input-output tables: Model development and case study for Beijing. Science of the Total Environment, 468-469:642-653

[9] Yang S, Chen B, Fath BD (2014). Trans-boundary total suspended particulate matter (TSPM) in urban ecosystems. Ecological Modelling, Article in press (Published online 15 October 2014)

[10] Hanel R, Thurner S, Gell-Mann M (2014). How multiplicity determines entropy and the derivation of the maximum entropy principle for complex systems. PNAS, 111(19):6905-6910

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Last edited: 12 March 2015


Elena Rovenskaya

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Advanced Systems Analysis

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