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This tutorial summarizes the new approach to complex system theory that comes basically from physical information analogies. The information components and gates are defined in a similar way as components in electrical or mechanical engineering. Such approach enables the creation of complex networks through their serial, parallel or feedback orderi...
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... many practical applications of complex system's analyses there is a demand for modelling of hierarchical networks as it is shown in Fig. 6. We can assume that the first layer subsystems A1, A2, A3 and A4 play the key roles (system's genetic code [39]) represented by the probabilities P (A1), P (A2), P (A3) and P (A4). The second and third layer is responsible for co-ordination activities: B1 co-ordinates A1 and A2, B2 co-ordinates A3 and A4 and C1 is responsible for ...
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... They can promote the collaboration of energy flow, information flow and value flow and realize value intelligence creation. According to complex system theory [3], the photovoltaic-storage-use value chain is a complex network system composed of multi-agents, multi-nodes and multi-links, as well as a complex adaptive system formed by the interaction and coupling of energy flow, information flow and value flow [4]. According to the theory of synergy [5], in complex systems, there are nonlinear relationships in the internal composition in the system and in each subsystem. ...
... Unlike previous studies, this model considers a situation where the photovoltaic power generator, energy storage provider and user make collaborative decisions. (3) Considering the driving role of digital technology, the correlation coefficients of enabling benefit, driving cost and potential risk are introduced into the model. The influencing factors such as incentive compensation and information asymmetry penalty are also considered. ...
... Similarly, taking the derivative of F(z) yields d(F(z))/dz = (1 − 2z)(xs 3 ...
In the context of “carbon neutral”, distributed energy, including photovoltaic power generation and energy storage systems, is developing rapidly. Meanwhile, the new generation of information technology, such as “Cloud computing, Big data, the Internet of things, Mobile Internet, AI, Blockchain”, is driving the digital transformation of the energy industry. Under digital drive, how the agents in the photovoltaic–storage–use value chain collaborate and create value intelligently is a question worthy of deep consideration. Firstly, the value creation mechanism and collaborative process of the digital-driven photovoltaic–storage–use value chain are analyzed from a value intelligence creation perspective. Secondly, the tripartite evolutionary game model of photovoltaic power generator, energy storage provider and user is established. Finally, the influencing factors of digital- driven photovoltaic–storage–use value chain collaboration are explored through a numerical simulation, and management suggestions are put forward. The study finds the following: (1) The behavior choice of each agent in the value chain will affect the decision of other agents. In particular, the photovoltaic power generator has a great influence on the cooperative willingness of other agents. To promote value chain collaboration, the guiding role of the photovoltaic power generator should be fully realized. (2) Agents on the value chain can use a variety of digital technologies to improve enabling benefits, which is conducive to promoting value chain collaboration. (3) The driving costs and potential risks are obstacles for value chain collaboration. Cost reduction and risk prevention are effective ways to improve the willingness of collaboration. (4) Reasonable incentive compensation mechanisms and information asymmetry punishment measures are the keys to enhancing collective willingness. This research provides theoretical support for photovoltaic–storage–use value chain collaboration from a value intelligence creation perspective.
... The allocated SF area has not been optimally utilized by local communities living in surrounding forest areas. Several issues related to SF program include policy implementation, complicated licensing process, access to funding, forest products marketing, profit IOP Publishing doi: 10.1088/1755-1315/1115/1/012057 2 sharing, insufficient institutional capacity and variability of forest resource characteristics [3,4]. As a result, SF program attractiveness tends to be low [5,6]. ...
... Meanwhile, environment refers to forest areas allocated for SF programs. These elements are used to develop agent-based modelling, which enables the integration between information and knowledge [10]. Agent based modelling is useful to study behavior [11], even though the information is incomplete. ...
... Such information can be supported by knowledge related to the issue. Therefore, system analysis requires strong basic knowledge of particular topics [10]. In this study, the basic knowledge includes SF objectives and strategies. ...
Social forestry (SF) as a community involvement program in forest areas needs to be frequently evaluated, particularly on policy aspects considering the dynamics of program implementation. Policy issues related to SF program have not been well identified. As a result, the implementation of SF policies may not achieve the target and recurrently lead to new problems. This research aims to develop the best scenario to support the achievement of SF target but is limited to SF areas adjacent to oil palm plantations. The actor-based modelling was developed using Netlogo 6.2 analytical tool. The results showed that the implementation of SF program should include the economic welfare improvement of local communities as a main consideration. The best scenario to achieve SF target is providing full access for local communities to manage forest areas and developing a mechanism to market the commodities by considering domestic wood consumption. The model simulation indicates that this management approach would be able to maintain forest cover as well as increase the community’s income. Therefore, information on wood consumption level needs to be promoted as an investment opportunity in the forestry sector, which has not been well considered previously in SF program. This strategy can be used to increase the participation of local communities in SF program by providing sufficient economic benefit as well as improving the inclusion of local communities in sustainable forest management.
... But there is an important exception. Interestingly, there is an important case when, unexpectedly, equations that describe phenomena on one level are strangely successful in a completely different level: this is the case of quantum economics, a successful application of quantum physics to the description of economic (and, more generally, social) phenomena; see, e.g., [1,3,4,5,6,7,15,16,17,18,24,25,26,28,29] (see also [22,20,30]). ...
Somewhat surprisingly, several formulas of quantum physics—the physics of micro-world—provide a good first approximation to many social phenomena, in particular, to many economic phenomena, phenomena which are very far from micro-physics. In this paper, we provide three possible explanations for this surprising fact. First, we show that several formulas from quantum physics actually provide a good first-approximation description for many phenomena in general, not only to the phenomena of micro-physics. Second, we show that some quantum formulas represent the fastest way to compute nonlinear dependencies and thus, naturally appear when we look for easily computable models; in this aspect, there is a very strong similarity between quantum techniques and neural networks. Third, due to numerous practical applications of micro-phenomena, many problems related to quantum equations have been solved; so, when we use quantum techniques to describe social phenomena, we can utilize the numerous existing solutions—which would not have been the case if we use other nonlinear techniques for which not many solutions are known. All this provides an explanation of why quantum techniques work reasonably well in economics. However, of course, economics is different from quantum world, quantum equations only provide a first approximation to economic situations. In this paper, we use the ideas behind our explanations to speculate on what should be the next—not-exactly-quantum—approximation to social and economic phenomena.
... If no satisfactory result is achieved, then it is possible to change the value of parameter k, neuronal function, or even a number of network layers [14]. Then, as soon as infomartion power of a correctly configured neural network achieves a complex level [22] it is able to solve various tasks (as for example forecasting of time series). ...
... Satellite data was the primary source of data for this quantification and the sources of these datasets are listed in Table 1. Sagnik Bhattacharjee 1 , Jiri Tencar 2 , Tatsiana Danilchyk 3 , Miroslav Svitek 4 , Tomas Horak 5 and Yashar Naghdi 6 The framework adapted for this study is based on the framework used by [8]. Exposure and sensitivity combined together to represent impact of a hazard (in this case, extreme heat) on the city and adaptive capacity is supposed to decreasing this impact as shown in Fig. 1. ...
The paper presents the comparative assessment of urban heat vulnerability for El Paso, USA and Ciudad Juarez, Mexico as a part of smart resilient city index. First, the heat vulnerability framework is presented. Based on available data the urban heat vulnerability index for the both cities are calculated. The results of analyse are discussed as an illustrative example of smart border concept.
... To guarantee a resilient city, it is necessary to manage the flow of resources, paying special attention to their interrelationships, thus optimizing use [2]. Processes need to be planned in real-time within a complex system model [3], [4]. ...
... The Smart City Testbed and its components were reviewed as a complex system [4] from the system science point of view. We design for future developments, and for this reason every possible implementation. ...
... It is thus reasonable to try to use quantum techniques to describe social phenomena. This idea has been indeed successfully tried; see, e.g., [1,3,4,[10][11][12] and references therein. ...
In the last decades, several papers have shown that quantum techniques can be successful in describing not only events in the micro-scale physical world-for which they were originally invented-but also in describing social phenomena, e.g., different economic processes. In our previous paper, we provide an explanation for this somewhat surprising successes. In this paper, we extend this explanation and show that quantum (and more general) techniques can also be used to model research collaboration.
... The main goal of the paper is to extend a quantum model [2] with (2N − 1) free parameters in such a way that it could describe the system with more parameters. In this way, the curse of dimensionality can be overcome, at least in part, with the increasing dimensionality of the system, the number of required parameters for its full description rapidly increases. ...
... Such logical rules give us mathematical instrument for wave probabilistic interferences that yields to modelling of new multi-dimensional complex systems like quantum entanglement [2]. ...
... We can divide events' intersections into two groups: inner (microscopic) and outer (macroscopic). Inner (microscopic) intersections represent emergent feature of complex system and are modeled by phase differences of wave probabilistic functions [2]. Due to positive or negative sigs, they could go to either inner attraction or repelling of events. ...
The paper presents a new methodology how to extend the well-known quantum model with (2N-1) free parameters (moduli and phases) of wave probabilistic functions assigned into events A1,..,AN to N (N+1)/2 free parameters necessary for full N-dimensional representation of complex system. Our approach generally enables to include additional functions applied on events . In the paper, we will demonstrate this mathematical instrument on additional wave probabilistic functions connected with macroscopic events’ intersections.
... Quantum information science was introduced in [11] and extended to information circuits and networks in [12,13]. In [7] a wave information flow φ and wave information content I were introduced. The x-axis of complex domain is the realworld part. ...
... The typical representation of wave probabilistic flow ψ φ and content ψ I [7] is shown in Fig. 1. For other analyzes we will use only their real values easily marked as φ and I. Section 2 of the paper presents the explanation of distance between wave probabilistic functions. ...
... The typical representation of wave probabilistic flow and content [7] is shown in Fig. 1. For other analyzes we will use only their real values easily marked as and I. Fig. 1 Representation of wave probabilistic flow and wave probabilistic content Session 2 of the paper presents the explanation of distance between wave probabilistic functions. ...
This paper elaborates on the area of physical information analogies and introduces new features such as the distance between wave probabilistic functions or sets of new information quantities such as strength, strength moment, strength potential energy and generalized charge. New parameters are used to define the rules for a quantum node. The knowledge cycle, which is equivalent to the Otto thermodynamic cycle, is adopted for modeling of the soft systems together with its static and dynamic information stability. Looking at the closed knowledge cycle, the evolutionary field equivalent to a magnetic field is therefore determined.
... All sectors must contribute, and the transition must be appropriate and acceptable; particularly generation and distribution of energy, as well as transport and buildings, are among the main sectors for implementing CO 2 reduction. There are also three main pillars on which the structure of urban energy systems is based [1,2,30]. The energy sector has the greatest potential to limit emissions. ...
