Emerging small-world referral networks in evolutionary labormarkets

We model a labor market that includes referral networks using an agent-based simulation. Agents maximize their employment satisfaction by allocating resources to build friendship networks and to adjust search intensity. We use a local selection evolutionary algorithm, which maintains a diverse population of strategies, to study the adaptive graph topologies resulting from the model. The evolved networks display mixtures of regularity and randomness, as in small-world networks. A second characteristic emerges in our model as time progresses: the population loses efficiency due to over competition for job referral contacts in a way similar to social dilemmas such as the tragedy of the commons. Analysis reveals that the loss of global fitness is driven by an increase in individual robustness, which allows agents to live longer by surviving job losses. The behavior of our model suggests predictions for a number of policies     

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.     

Approval network: a novel approach for sentiment analysis in social networks

The data-centric impetus and the development of online social networks has led to a significant amount of research that is nowadays more flexible in demonstrating several sociological hypotheses, such as the sentiment influence and transfer among users. Most of the works regarding sentiment classification usually consider text as unique source of information, do not taking into account that social networks are actually networked environments. To overcome this limitation, two main sociological theories should be accounted for addressing any sentiment analysis tasks: homophily and constructuralism. In this paper, we propose Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. To show the potentiality of the proposed representation, two novel sentiment analysis models have been proposed. The first one, related to user-level polarity classification, is approached by presenting a semi-supervised framework grounded on a Markov-based probabilistic model. The second task, aimed at simultaneously extracting aspects and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval Networks are able to outperform not only the traditional models where the relationships are disregarded, but also those computational approaches based on traditional friendship connections.     

On the engineering of agent-based simulations of social activities with social networks

ContextModels of how people move around cities play a role in making decisions about urban and land-use planning. Previous models have been based on space and time, and have neglected the social aspect of travel. Recent work on agent-based modelling shows promise as a new approach, especially for models with both social and spatial elements.ObjectiveThis paper demonstrates the design and implementation of an agent-based model of social activity generation and scheduling for experimental purposes to explore the effects of social space in addition to physical space. As a side-effect, the paper discusses the need for and requirements on structured design of agent-based models and simulations.MethodModel design was based on the MASQ meta-model and implemented in Python. The model was then tested against several hypotheses with several initial networks.ResultsThe model allowed us to investigate the effects of social networks. We found that the model was most sensitive to the pair attributes of the network, rather than the global or personal attributes.ConclusionAs demonstrated, a structured approach to model development is important in order to be able to understand and apply the results, and for the model to be extensible in the future. Agent-based modelling approaches allow for inclusion of social elements. For models incorporating social networks, testing the sensitivity to the initial network is important to ensure the model performs as expected.     

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.     

Two distribution methods for multiagent traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.     

Agent-based modeling of host-pathogen systems: The successes and challenges

Agent-based models have been employed to describe numerous processes in immunology. Simulations based on these types of models have been used to enhance our understanding of immunology and disease pathology. We review various agent-based models relevant to host-pathogen systems and discuss their contributions to our understanding of biological processes. We then point out some limitations and challenges of agent-based models and encourage efforts towards reproducibility and model validation.     

Simulation Analysis Using Multi-Agent Systems for Social Norms

With the existence of the social customs or norms, Naylor demonstrates a possibility of stable long-run equilibria of support for a strike in a labor market, and this implies that at least some individuals will behave cooperatively and hence the prisoners’ dilemma could be escaped. In this paper, using an agent-based simulation model in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms, we compare the results of our simulation analysis with that of the mathematical model by Naylor. In particular, while Naylor’s model is based on rationality as it relates to individual utility maximization, agents behave adaptively in our agent-based simulation model; agents make decisions by trial and error, and they learn from experiences to make better decisions.      

Data-driven agent-based modeling,with application to rooftop solar adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.     

Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.     

A COMPARISON OF SOME FUZZY RELATION-BASED LINGUISTIC PREFERENCE MODELS FOR MULTIPLE-FACTOR PROJECT ASSESSMENT

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach allows the representation of heterogenous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.     

A Discontinuity Model of Technological Change: Catastrophe Theory and Network Structure

Discontinuities as a crucial aspect of economic systems have been discussed both verbally—particularly in institutionality theory—and formally, chiefly using catastrophe theory. Catastrophe theory has, however, been criticized heavily for lacking micro-foundations and has mainly fallen out of use in economics and social sciences. The present paper proposes a simple catastrophe theory model of technological change with network externalities and reevaluates the value of such a model by adding an agent-based micro layer. To this end an agent-based variant of the model is proposed and investigated specifically with regard to the network structure among the agents. While the macro level of the model produces a classical cusp catastrophe—a result that is preserved in the agent-based form—it is found that the behavior of the model changes locally depending on the network structure, especially if networks with features that resemble social networks (low diameter, high clustering, power law distributed node degree) are considered. While the present work investigates merely an aspect out of a large possibility space, it encourages further research using agent-based catastrophe theory models especially of economic aspects to which catastrophe theory has previously successfully been applied; aspects such as technological and institutional change, economic crises, or industry structure.     

An agent-based distributed computational experiment framework for virtual supply chain network development

This paper focuses on research on virtual supply chain networks instead of real supply chain networks by making use of agent technology and computational experiment method. However, the recent research is inefficient in computational experiment modeling and lack of a related methodological framework. This paper proposes an agent-based distributed computational experiment framework with in-depth study of material flow, information flow and time flow modeling in supply chain networks. In this framework, a matrix-based formal representation method for material flow, a task-centered representation method for information flow and an agent-based time synchronization mechanism for time flow are proposed to aid building a high quality computational experiment model for a multi-layer supply chain network. In order to conduct the model, a computational experiment architecture for virtual supply chain networks is proposed. In this architecture, coordination mechanisms among agents based on material flow, information flow and time flow as well as consistency check methods for computational experiment models are discussed. Finally, an implementation architecture of the framework is given and a case of virtual supply chain network is developed to illustrate the application of the framework. The computational experiment results of the case show that the proposed framework, not only feasible but correct, has sound advantages in virtual supply chain network development, computational experiment modeling and implementation.     

Resisting structural re-identification in anonymized social networks

We identify privacy risks associated with releasing network datasets and provide an algorithm that mitigates those risks. A network dataset is a graph representing entities connected by edges representing relations such as friendship, communication or shared activity. Maintaining privacy when publishing a network dataset is uniquely challenging because an individual’s network context can be used to identify them even if other identifying information is removed. In this paper, we introduce a parameterized model of structural knowledge available to the adversary and quantify the success of attacks on individuals in anonymized networks. We show that the risks of these attacks vary based on network structure and size and provide theoretical results that explain the anonymity risk in random networks. We then propose a novel approach to anonymizing network data that models aggregate network structure and allows analysis to be performed by sampling from the model. The approach guarantees anonymity for entities in the network while allowing accurate estimates of a variety of network measures with relatively little bias.     

User authentication and undeniable billing support for agent-based roaming service in WLAN/cellular integrated mobile networks

In this paper, a framework of authentication and undeniable billing support for an agent-based roaming service in WLAN/cellular networks interworking networks is proposed. This framework circumvents the requirement of peer-to-peer roaming agreements to provide seamless roaming service between WLAN hotspots and cellular networks operated by independent wireless network service providers. Within the framework, an adaptive authentication and an event-tracking scheme have been developed, which allow the application of undeniable billing service to cellular network even when it still uses a traditional authentication scheme. The proposed modified dual directional hash chain (MDDHC) based billing support mechanism features mutual non-repudiation. Security analysis and overhead evaluation demonstrate that the proposed framework is secure and efficient.     

Contrarian Behavior,Information Networks and Heterogeneous Expectations in an Asset Pricing Model

This paper studies the emergence of contrarian behavior in information networks in an asset pricing model. Financial traders coordinate on similar behavior, but have heterogeneous price expectations and are influenced by friends. According to a popular belief, they are prone to herding. However, in laboratory experiments subjects use contrarian strategies. Theoretical literature on learning in networks is scarce and cannot explain this conundrum (Panchenko et al. in J Econ Dyn Control 37(12):2623–2642, 2013). The paper follows Anufriev et al. (CeNDEF Working paper 15–07, 2015) and investigates an agent-based model, in which agents forecast price with a simple general heuristic: adaptive and trend extrapolation expectations, with an additional term of (dis-)trust towards their friends’ mood. Agents independently use Genetic Algorithms to optimize the parameters of the heuristic. The paper considers friendship networks of symmetric (regular lattice, fully connected) and asymmetric architecture (random, rewired, star). The main finding is that the agents learn contrarian strategies, which amplifies market turn-overs and hence price oscillations. Nevertheless, agents learn similar behavior and their forecasts remain well coordinated. The model therefore offers a natural interpretation for the difference between the experimental stylized facts and market surveys.     

基于Agent计算金融的计算机仿真研究综述

传统的金融学研究方法为"由上及下",此类方法倚重于总体把握,而忽视个体行为,特别是缺乏个体以及个体与环境之间的互动.基于Agent的计算金融是一种新的研究金融市场行为的工具.首先阐述了金融市场本身的复杂性和传统金融学存在的一些困惑.在这基础上提出了基于Agent的计算金融方法.其次,概要介绍了目前基于Agent的人工金融市场的主要设计方法,并且提出了在设计过程中要注意的一些问题.最后,分析了这种方法相对于传统金融学研究方法的优点和存在的问题,并进一步提出了该领域的未来研究方向.     

Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining

We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified information-theory-based Bayesian network algorithm and a modified association rule algorithm. Simulation-based evaluation using six datasets indicated that both algorithms outperformed their unmodified counterparts, especially when analyzing large numbers of genes. Both algorithms learned about 20% (50% if directionality and relation type were not considered) of the relations in the actual models. In our empirical evaluation based on two real datasets, domain experts evaluated subsets of learned relations with high confidence and identified 20–30% to be “interesting” or “maybe interesting” as potential experiment hypotheses.     

大型结构CAD平台架构的研究

在分析了原有结构CAD系统的基础上，进一步提出了基于两类层次的系统设计方案，在底层使用基于复合文件的工程数据库，在设计时根据模块自身不同的特点，加以区别对待。在模块内部使用面向对象的方法设计，在主要模块之间使用数据流的分析方法，对附属模块采用COM技术实现插件机制。