Showing papers in "Complex Adaptive Systems Modeling in 2016"
TL;DR: Pajek is a program package for analysis and visualization of large networks (networks containing up to one billion of vertices, there is no limit—except the memory size—on the number of lines).
Abstract: Pajek is a program package for analysis and visualization of large networks (networks containing up to one billion of vertices, there is no limit—except the memory size—on the number of lines). It has been available for 20 years. The program, documentation and supporting material can be downloaded and used for free for noncommercial use from its web page: http://mrvar.fdv.uni-lj.si/pajek/
149 citations
TL;DR: This paper reviews sentiment analysis techniques and highlights the need to address natural language processing (NLP) specific open challenges, stressing on the need of standard datasets and evaluation methodology.
Abstract: There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.
61 citations
TL;DR: This paper presents a comprehensive multidisciplinary state-of-the-art review and taxonomy of game theory models of complex interactions between agents.
Abstract: In the real world, agents or entities are in a continuous state of interactions. These interactions lead to various types of complexity dynamics. One key difficulty in the study of complex agent interactions is the difficulty of modeling agent communication on the basis of rewards. Game theory offers a perspective of analysis and modeling these interactions. Previously, while a large amount of literature is available on game theory, most of it is from specific domains and does not cater for the concepts from an agent-based perspective. Here in this paper, we present a comprehensive multidisciplinary state-of-the-art review and taxonomy of game theory models of complex interactions between agents.
31 citations
TL;DR: The paper concludes that employing mobile ferries as gateways is better than deploying gateways based on geographical area when the sub networks interaction is facilitated in IoT network.
Abstract: Internet of things (IoT) is an interaction between more than one network to facilitate communication. These networks by themselves are complex networks. Therefore, IoT network is expected to grow at unprecedented scale involving other networks such as Mobile, VANET, and Wireless Sensor Networks (WSNs). In fact, modeling each network by itself is a complicated process. In addition, on a large scale, the communication among these networks increases the modeling complexity in which energy consumption could be critical due to large number of dropped messages. Therefore, this paper is a step forward towards modeling IoT complex network through gateway deployment. The paper answers the question of how to deploy these gateways in a way that guarantees an efficient and adaptive communication. Two models/methods are proposed and examined which are geographical based and mobile ferry based models. Due to the complexity of the deployment problem in reality, the deployment problem is treated as a complex adaptive problem and simulated through different sets of experiments and settings. The two methods have been compared through set of experiments using ONE simulator with the same number of employed gateways in the two methods. The experiments shows that ferry based model outperforms geographical based model with 29% improvement in messages delivery probability. Additionally, when the number of mobile ferries are reduced by 34% compared to gateways that are distributed based on geographical area, the mobile ferries approach still outperform geographical area based approach when it comes to messages delivery probability. The paper presents the two methods to model the complex internet of things environment and its sub networks interaction. The paper concludes that employing mobile ferries as gateways is better than deploying gateways based on geographical area when the sub networks interaction is facilitated in IoT network.
28 citations
TL;DR: A comprehensive literature review of VCPS from the collision-avoidance perspective is presented and includes a careful selection of articles from highly cited sources presented in the form of taxonomy.
Abstract: Road traffic is known to have its own complex dynamics. One implication of complexity is that road traffic collisions have become an unwelcome but unavoidable part of human life. One of the major causes of collisions is the human factor. While car manufacturers have been focusing on developing feasible solutions for autonomous and semi-autonomous vehicles to replace or assist human drivers, the proposed solutions have been designed only for individual vehicles. The road traffic, however, is an interaction-oriented system including complex flows. Such a system requires a complex systems approach to solving this problem as it involves considering not only pedestrians, road environment, but also road traffic which can include multiple vehicles. Recent research has demonstrated that large-scale autonomous vehicular traffic can be better modeled using a collective approach as proposed in the form of vehicular cyber-physical systems (VCPS) such as given by Li et al. (IEEE Trans Parallel Distrib Syst 23(9):1775–1789, 2012) or Work et al. (Automotive cyber physical systems in the context of human mobility. In: National workshop on high-confidence automotive cyber-physical systems, Troy, MI, 2008). To the best of our knowledge, there is currently no comprehensive review of collision avoidance in the VCPS. In this paper, we present a comprehensive literature review of VCPS from the collision-avoidance perspective. The review includes a careful selection of articles from highly cited sources presented in the form of taxonomy. We also highlight open research problems in this domain.
25 citations
TL;DR: The hybrid model reveals various viability constraints that characterize the conditional existence of cyclic states (homeostasis) that suggest larger cycle period of HIV-1 than the cycle periods of the other two entities to maintain a homeostatic expressions of these entities.
Abstract: The complex interactions between genetic machinery of HIV-1 and host immune cells mediate dynamic adaptive responses leading to Autoimmune Deficiency Syndrome. These interactions are captured as Biological Regulatory Network (BRN) which acts to maintain the viability of host cell machinery through feedback control mechanism which is a characteristic of complex adaptive systems. In this study, the BRN of immune response against HIV-1 infection is modeled to investigate the role of NF-κB and TNF-α in disease transmission using qualitative (discrete) and hybrid modeling formalisms. Qualitative and Hybrid modeling approaches are used to model the BRN for the dynamic analysis. The qualitative model is based on the logical parameters while the hybrid model is based on the time delay parameters. The qualitative model gives useful insights about the physiological condition observed as the homeostasis of all the entities of the BRN as well as pathophysiological behaviors representing high expression levels of NF-κB, TNF-α and HIV. Since the qualitative model is time abstracted, so a hybrid model is developed to analyze the behavior of the BRN by associating activation and inhibition time delays with each entity. HyTech tool synthesizes time delay constraints for the existence of homeostasis.
Hybrid model reveals various viability constraints that characterize the conditional existence of cyclic states (homeostasis). The resultant relations suggest larger cycle period of HIV-1 than the cycle periods of the other two entities (NF-κB and TNF-α) to maintain a homeostatic expressions of these entities.
18 citations
TL;DR: An efficient subnet-based failure recovery algorithm (SFRA) is proposed in this work which assumes the partitioning of WSAN into subnets which localizes the failure recovery procedure at subnet level achieving objective of efficiency.
Abstract: Wireless sensor and actor networks (WSANs) have various applications in safety and mission critical systems. Sensors are used for sensing the information whereas actors for taking intelligent decisions. Developing and modeling algorithms for WSANs have raised several research issues which have captured attention of the research community. Maintaining inter-actor connectivity or failure recovery is a critical issue in WSANs because these are deployed in harsh and inhospitable environment which may result into physical damage to actors loosing inter-actor connectivity. In case of failure of inter-actor connectivity, the topology of the network may be affected that might be inefficient to recover. Therefore an efficient subnet-based failure recovery algorithm (SFRA) is proposed in this work. It is assumed the partitioning of WSAN into subnets which localizes the failure recovery procedure at subnet level achieving objective of efficiency. Moreover, algorithm is hybrid as it assumes pre-failure planning and post-failure recovery. The proposed model is presented as a graph-based model to represent static part of the network topology. The graph model is transformed into a formal model using Vienna development method-specification language (VDM-SL). The static model is described by defining formal specification of subnets, network topology, sensors, actors and gateways as composite objects. The state space of the WSANs is described in the form of functions and operations as dynamic part of the model. Invariants are defined over the data types in static model for ensuring safety criteria and pre/post conditions are defined in functions and operations for changing state space of the system. The proposed model is validated and verified using VDM-SL Toolbox.
18 citations
TL;DR: An effective cardiovascular decision support mechanism for handling inaccuracies in the clinical risk assessment ofchest pain patients and help clinicians effectively distinguish acute angina/cardiac chest pain patients from those with other causes of chest pain is demonstrated.
Abstract: This multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data to facilitate cardiovascular preventative care. The proposed cardiovascular clinical decision support framework comprises of two novel key components:
(1) Ontology driven clinical risk assessment and recommendation system (ODCRARS) (2) Machine learning driven prognostic system (MLDPS). State of the art machine learning and feature selection methods are utilised for the prognostic modelling purposes. The ODCRARS is a knowledge-based system which is based on clinical expert’s knowledge, encoded in the form of clinical rules engine to carry out cardiac risk assessment for various cardiovascular diseases. The MLDPS is a non knowledge-based/data driven system which is developed using state of the art machine learning and feature selection techniques applied on real patient datasets. Clinical case studies in the RACPC, heart disease and breast cancer domains are considered for the development and clinical validation purposes. For the purpose of this paper, clinical case study in the RACPC/chest pain domain will be discussed in detail from the development and validation perspective. The proposed clinical decision support framework is validated through clinical case studies in the cardiovascular domain. This paper demonstrates an effective cardiovascular decision support mechanism for handling inaccuracies in the clinical risk assessment of chest pain patients and help clinicians effectively distinguish acute angina/cardiac chest pain patients from those with other causes of chest pain. The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. Various chest pain risk assessment prototypes have been developed and deployed online for further clinical trials.
16 citations
TL;DR: This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.
Abstract: Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.
14 citations
TL;DR: A bag of word representation method with hierarchical clustering has been proposed to assess the performance of a building energy system and it has been shown that the BoWR has produced better results as compared to DTW.
Abstract: Due to the large quantity of data that are recorded in energy efficient buildings, understanding the behavior of various underlying operations has become a complex and challenging task. This paper proposes a method to support analysis of energy systems and validates it using operational data from a cold water chiller. The method automatically detects various operation patterns in the energy system. The use of k-means clustering is being proposed to automatically identify the On (operational) cycles of a system operating with a duty cycle. The latter’s data is subsequently transformed to symbolic representations by using the symbolic aggregate approximation method. Afterward, the symbols are converted to bag of words representation (BoWR) for hierarchical clustering. A gap statistics method is used to find the best number of clusters in the data. Finally, operation patterns of the energy system are grouped together in each cluster. An adsorption chiller, operating under real life conditions, supplies the reference data for validation. The proposed method has been compared with dynamic time warping (DTW) method using cophenetic coefficients and it has been shown that the BoWR has produced better results as compared to DTW. The results of BoWR are further investigated and for finding the optimal number of clusters, gap statistics have been used. At the end, interesting patterns of each cluster are discussed in detail. The main goal of this research work is to provide analysis algorithms that automatically find the various patterns in the energy system of a building using as little configuration or field knowledge as possible. A bag of word representation method with hierarchical clustering has been proposed to assess the performance of a building energy system.
13 citations
TL;DR: The study proved the effectiveness of the proposed novel methodology for the design of online social network software by improving upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilientOnline social network sites.
Abstract: Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.
TL;DR: The delays at airport surface require effective safety and guidance protocols to control air traffic at the airport, and the proposed study is focussed more on the safety component.
Abstract: Air traffic management system is a complex adaptive and safety critical system which requires considerable attention for its modelling and verification. Currently Air traffic control (ATC) systems are heavily dependent upon human intervention at airport causing accidents and delays because of failure of communication. The purpose of this study is to develop, plan, manage and verify aircrafts movement procedures at the airport surface that prevent delays and collisions. The airport surface is decomposed into blocks and represented by the graph relation. The state space of the system is described by identifying all the possible components of the system. The ground and local controls monitor queues of the aircrafts moving from taxiway to take-off. It is insured that once an aircraft is inserted into a queue, it is eventually removed from it after the next queue has become available. The take-off procedure is provided using graph theory and Vienna Development Method Specification Language (VDM-SL) and analyzed using VDM-SL toolbox. Formal specification of graph-based model, taxiways, aircrafts, runways and controllers is provided in static part of the model. The state space analysis describing take-off algorithms is provided by defining optimal paths and possible operations in dynamic model expediting the departure procedure. The model is developed by a series of refinements following the stepwise development approach. The delays at airport surface require effective safety and guidance protocols to control air traffic at the airport. In static model, the safety criteria are described in terms of invariants over the data types carrying critical information. The safety is insured by defining pre/post conditions in description of operations for changing state space of the system. Although the proposed study is focussed more on the safety component, however, the efficiency is not ignored.
TL;DR: CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics, making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.
Abstract: Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process. This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail. CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model. CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.
TL;DR: This study takes a first step in providing an indigenous solution to an indigenous problem of designing an upgraded and verified signaling infrastructure for Pakistan Railway’s Rawalpindi Cantt train station by constructing a real-time model of railyard interlocking system using timed automata.
Abstract: Recent advancements in technology have enabled railway organizations to shift from manual to computer based automated interlocking systems for increasing their efficiency and profits. Since automated systems are complex and interlocking systems are safety critical systems, these systems should be modeled and verified against safety requirements to weed out any design bugs which might lead to catastrophes during their system life cycles. In this study, we model software based automated interlocking control system of a train station, located at Rawalpindi Cantt (Pakistan). We have modeled software based automated interlocking control system using timed automata and verified its correctness using UPPAAL model checking software. Timed automata have successfully been used for the modeling and verification of real-time systems. We constructed a real-time model of railyard interlocking system by employing a model-checking approach to determine behavior of the model under various conditions. The model checker ascertains the absence of errors in a system by inspecting all the possible states or scenarios of the modeled system. The results show that important properties related to the safety of the designed interlocking system of the railyard management system can be verified using our presented approach. These properties ranged from collision and de-railment avoidance to checking the correct error handling functionality of the timed automata models. The final modular design can easily adapt to the route upgrades and changes within the station by simple variable adjustments. Based on the laid down methodology and verification techniques, this study can be further built upon, extended and linked to cover the shunting aspect of the train station operations, run through operations, introducing automatic train stop (ATS) functionality and recommend three to four aspect traffic signaling for the train station. This study takes a first step in providing an indigenous solution to an indigenous problem of designing an upgraded and verified signaling infrastructure for Pakistan Railway’s Rawalpindi Cantt train station.
TL;DR: Sayama, H Introduction to the Modeling and Analysis of Complex Systems Open SUNY textbooks, Milne Library, State University of New York at Geneseo (2015).
Abstract: Sayama, H Introduction to the Modeling and Analysis of Complex Systems Open SUNY textbooks, Milne Library, State University of New York at Geneseo (2015). 485 pages, Print ISBN: 1942341083.
TL;DR: An agent-based model is used to explore how reciprocated dyadic and triadic patterns emerge from self-reinforced appreciation between peers in a small group, suggesting reciprocation based on appreciation is a strong candidate for explaining the formation of such patterns.
Abstract: In small cooperative and collaborative groups, patterns of interaction, discourse and dialogue are often strongly bidirectional; ties are reciprocal and reciprocated. This reciprocation of ties leads to the formation of interaction patterns that are reciprocated dyads (two individuals connected reciprocally) and triads (three individuals connected reciprocally). In this study, we use an agent-based model to explore how such reciprocated dyadic and triadic patterns emerge from self-reinforced appreciation between peers in a small group. The model assumes that the agents’ decisions to interact depend on how their self-appreciation compares to their appreciations of their peers (peer-appreciation). These comparisons are competitive in that an agent seek to increase its appreciation in relation to its peers. As a consequence, agents change their self-appreciation and appreciation towards their peers depending on their sensitivity to the competitive comparison. When agents’ sensitivity to competitive comparisons is low, the most common patterns of appreciation are egalitarian triads (all three agents appreciate each other), while for moderate sensitivity, leadership-type patterns emerge (one agent connected strongly to two other unconnected agents). When sensitivity is high, strong reciprocally connected dyads emerge. The model thus predicts thus a transition from egalitarian triads to strong dyads as agents’ sensitivity to competitive comparisons increases. The structural similarity between patterns emerging as model results and patterns reported in empirical research suggests that: (1) reciprocation based on appreciation is a strong candidate for explaining the formation of such patterns, and (2) individual sensitivity to competitive comparisons of appreciation may be a key factor that can be used to the tune dynamics of interaction in small groups.
TL;DR: Simulation results validate a critical proportion of committed individuals as a plausible basis for ideological shifts in societies and delineate the role of evangelism through social and non-social methods in propagating views.
Abstract: Opinions continuously evolve in society. While conservative ideas may get replaced by a new one, some views remain immutable. Opinion formation and innovation diffusion have witnessed lots of attention in the last decade due to its widespread applicability in the diverse domain of science and technology. We analyse these scenarios in which interactions at the micro level results in the changes in opinions at the macro level in a population of predefined ideological groups. We use the Bass model, otherwise well known for understanding innovation diffusion phenomena, to compute adoption probabilities of three opinion states-zealot, extremists and moderates. Thereafter, we employ cellular automata to explore the emergence of opinions through local and overlapped interactions between agents (people). NetLogo environment has been used to develop an agent-based model, simulating different ideological scenarios. Simulation results validate a critical proportion of committed individuals as a plausible basis for ideological shifts in societies. The analysis elucidates upon the role of moderates in the population and emergence of varying opinions. The results further delineate the role of evangelism through social and non-social methods in propagating views. The results obtained from these simulations endorse the conclusions reported in previous studies regarding the role of a critical zealot population, and the preponderance of non-social influence. We, however, use two-phase opinion model with different experimental settings. Additionally, we examine global observable, such as entropy of the system to reveal common patterns of adoption in the views and evenness of population after reaching a consensus.
TL;DR: This book discusses CiteSpace, a Practical Guide for Mapping Scientific Literature, and its applications in education, research, and teaching.
Abstract: Chen, C CiteSpace: A Practical Guide for Mapping Scientific Literature
Hauppauge, NY: Nova Science Publishers; 2016. 169 pages; ISBN print: 978-1-53610-280-2; eBook: 978-1-53610-295-6 Prices for both editions Softcover price: $73.80 eBook price: $82.00 Book page: http://cluster.ischool.drexel.edu/~cchen/citespace/books/
TL;DR: An introduction to Agent-Based Modeling; Modeling Natural, Social, and Engineered Complex Systems with NetLogo with Uri Wilensky and William Rand.
Abstract: Uri Wilensky and William Rand An Introduction to Agent-Based Modeling; Modeling Natural, Social, and Engineered Complex Systems with NetLogo; The MIT Press, Cambridge, Massachusetts London, England (2015), 504 pages, E-book ISBN: 9780262328111, Hard-cover ISBN: 9780262731898.
TL;DR: This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks and group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols.
Abstract: Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided.
TL;DR: Neittaanmaki et al. as discussed by the authors presented Mathematical Modeling and Optimization of Complex Structures; Series: Computational Methods in Applied Sciences, Springer International Publishing AG, Switzerland; 2016.
Abstract: Pekka Neittaanmaki, Sergey Repin and Tero Tuovinen (Eds.). Mathematical Modeling and Optimization of Complex Structures; Series: Computational Methods in Applied Sciences.
Springer International Publishing AG, Switzerland; 2016. E-book, XXI, 328 pages, ISBN: 9783319235646, Hard-cover ISBN: 9783319235639, Library of Congress Control Number: 2015947948, DOI 10.1007/978-3-319-23564-6
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TL;DR: There is a positive correlation between the extent of information being shared in a socially-inspired information eco-systems and the level of collective awareness in an urban mobility scenario.
Abstract: This paper studies the influence of information and communication technologies on human reasoning and decision making. It investigates the potential impact of ambient intelligence on change in pedestrian mobility behavior, using a large city-scale scenario. This work establishes an interplay between social and technological aspects of awareness by augmenting a model-driven framework of a realistic information eco-system. A distributed multi-agent system is developed to model a real-life urban mobility environment. The model is then simulated using a large scale parallel computing platform. Evaluation results revealed that the quality of information in ambient-assisted environments increases when compared with those without ambient intelligence. We conclude that there is a positive correlation between the extent of information being shared in a socially-inspired information eco-systems and the level of collective awareness in an urban mobility scenario.
TL;DR: In this paper, an agent-based model is developed to test the two competing hypotheses in the theory of self-enforcing agreement in cooperative teams, and the model takes heterogeneity of team members (e.g., their laziness, work ability and patience to future well-being) into consideration, which allows to better understand the divergence of these two arguments.
Abstract: In cooperative teams (such as agricultural cooperatives), self-enforcing agreement plays a critical role in guaranteeing members’ work incentives when the monitoring from a third party is absent. In order to provide an effective sanction to the violators so as to maintain the agreement, two seemingly conflicting strategies are proposed. One is allowing the members to exit the team freely. The other is imposing a high exit cost to restrict members from leaving the team. The arguments behind each strategy are elaborated in Lin (J Comp Econ 17:504–20, 1993) and Dong and Dow (J Comp Econ 17:472–84, 1993), respectively. However, these strategies have never been tested in the same model. In fact, no formal model is presented for one of the arguments. To fill this gap, we develop a model that incorporates the two arguments as two scenarios in a shared framework. An agent-based model is developed to test the two competing hypotheses in the theory of self-enforcing agreement. The model takes heterogeneity of team members (e.g., their laziness, work ability and patience to future well-being) into consideration, which allows us to better understand the divergence of these two arguments. Using the agent-based model, we conduct computational experiments for testing the two hypotheses. Estimation on the experiment outputs show that (1) The sustained discount rate is lower in exit-free cooperative teams than exit-restricted ones when shirking members exist, which confirms the argument of Lin (J Comp Econ 17:504–20, 1993), and (2) The sustained discount rate is lower in exit-restricted teams than exit-free ones when members’ leisure preferences are not too diverse and the economics of scale are not too large, or when the sizes of the teams are large enough, which verifies the argument of Dong and Dow (J Comp Econ 17:472–84, 1993). We find the two arguments essentially claim different consequences under different conditions of members’ characteristics and team size. Our study demonstrates agent-based simulation can be an effective approach of testing game theoretical arguments and exploring game theoretical ideas.
TL;DR: The GESR-LR method makes full use of the supervised learning information in the construction of the affinity matrix, and the affinity construction is combined with graph embedding in a single step to guarantee the global optimal solution.
Abstract: In this paper, we propose a novel method for semi-supervised learning by combining graph embedding and sparse regression, termed as graph embedding and sparse regression with structure low rank representation (GESR-LR), in which the embedding learning and the sparse regression are performed in a combined approach. Most of the graph based semi-supervised learning methods take into account the local neighborhood information while ignoring the global structure of the data. The proposed GESR-LR method learns a low-rank weight matrix by projecting the data onto a low-dimensional subspace. The GESR-LR makes full use of the supervised learning information in the construction of the affinity matrix, and the affinity construction is combined with graph embedding in a single step to guarantee the global optimal solution. In the dimensionality reduction procedure, the proposed GESR-LR can preserve the global structure of the data, and the learned low-rank weight matrix can effectively reduce the influence of the noise. An effective novel algorithm to solve the corresponding optimization problem was designed and is presented in this paper. Extensive experimental results demonstrate that the GESR-LR method can obtain a higher classification accuracy than other state-of-the-art methods.
TL;DR: Book detailsZonnenshain A, Stauber S. managing and Engineering Complex Technological Systems.
Abstract: Zonnenshain A, Stauber S. Managing and Engineering Complex Technological Systems. John Wiley & Sons, Inc., Hoboken, New Jersey, USA; 2015. 240 pages, ISBN: 978-1-119-06859-4.
TL;DR: This model helps clarify the role of reinforcement learning in the reinforcement learning process and provides a framework for future generations of interpreters to better understand and model the impact of abuse.
Abstract: © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ERRATUM Shah et al. Complex Adapt Syst Model (2016) 4:26 DOI 10.1186/s40294‐016‐0039‐2