Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems

Indigenous people of the Rupununi region of Amazonian Guyana interact with their natural environment through hunting and subsistence agriculture. To date the sustainability of indigenous livelihoods has been analyzed by modeling either hunting or forest clearing. Here we develop a holistic model framework with agent-based modeling to examine interactions between demographic growth, hunting, subsistence agriculture, land cover change, and animal population in the Rupununi. We use an extensive field dataset from social surveys, animal observation records and hunting kill locations along with satellite images. The model exhibits feedback loops between a growing human population and depletion of local natural resources. Our model can reproduce the population size of two different villages along with landscape patterns without further calibration. Our model can be used for understanding the conditions of sustainability for indigenous communities relying on subsistence agriculture and hunting, and for scenario analyses to examine the implications of external interventions.     

Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe

Social technologies are increasingly used in several organizational functions, including human resource management. The focus of this study is on social media adoption in employee recruitment and selection in Central and Eastern Europe (CEE). The theoretical lens used in this study is the Unified Theory of Acceptance and Use of Technology (UTAUT), extended to include the impact of the recruiters' managerial position and educational level on technology adoption. Results obtained from partial least squares path modeling, using cross-sectional data collected from 224 recruiters, showed that the core hypotheses of the UTAUT were supported, namely the positive impact of performance expectancy, effort expectancy and social influence on behavioral intention, as well as the positive impact of facilitating conditions and behavioral intention on usage behavior. As expected, the recruiters' characteristics triggered many interaction effects, but none of them impacted facilitating conditions. Managerial implications are discussed and future research suggestions are provided.     

The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation,Limited Information and Bounded Rationality

In the mainstream top-down approach, money is neutral except with special assumptions. Intending to make money “essential”, random-matching models introduced decentralisation by considering pair-wise transactions. Nevertheless, in both cases top-level equilibrium constrains agents’ behaviour. Instead, here we use a bottom-up approach. In a competitive market, decentralised autonomous agents meet and exchange a commodity for money. Their decisions use minimal information. They are triggered by simple rules founded on a “satisficing” procedure and on a random decision process that simulates bounded rationality. The conclusions are, first, that non-monetary costs are essential to avoid collapse of the economy. Second, mainly “price setters” who are adequately satisfied achieve equilibrium protecting themselves by evolving advantages to avoid competition that is too tough. Third, and the most important conclusion is that money ceases to be neutral as soon as competition arises between individual firms.     

Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems

Understanding post adoption behavior has emerged as an important issue in IS research. Organizations have invested in a plethora of Information Systems (IS) and the benefits that can be gained from these systems depend on their usage. Prior research has examined factors that impact users’ intention to continue using the IS. However, comprehending post adoption IS usage behavior has not received much attention. Building on TAM and IS success model, this study proposes that information quality and system integration influence perceived IS usefulness which will then drive post adoption usage of the IS. Post adoption IS usage is conceptualized as a broad concept that includes extended usage and exploratory usage. Data (N = 1032) was collected to test the model, in the context of a web-based student information system that students use to manage their academic work. The results show that at the post adoption stage, perceived IS usefulness is a good predictor of extended usage and exploratory usage. However, surprisingly IS usefulness was found to explain a much larger variance in exploratory usage. Information quality and system integration were found to influence IS usefulness. However, information quality also has a direct affect on extended usage, while system integration directly influenced exploratory usage. Assessment of the moderating role of gender and internet experience on model relationships reveals interesting insights. Implications are drawn for future research and practice.     

Exploring students' awareness and perceptions: Influencing factors and individual differences driving m-learning adoption

This study investigates students' awareness and perceptions of m-learning and examines the factors affecting students' behavioral intention to adopt m-learning, by using a modified research model that integrate technology acceptance model (perceived usefulness and perceived ease of use) and unified theory of acceptance and use of technology (social influence) along with other factors (m-learning services and mobile limitations). In addition, control (gender, field of study, study level) and moderator variables (mobile capabilities, level of mobile usage, and frequent use of m-services) were introduced to verify the individual differences between respondents on the key factors affecting the adoption and usage of m-learning. Structural equations modeling and path analysis were used to test the hypotheses and the proposed model. The results revealed that perceived usefulness and perceived ease of use were found to be the primary factors driving students' intentions to use m-learning. Both m-learning services and social influence have positive effects on the acceptance of m-learning, while mobile limitations were found to be the main obstacle restraining students' participation in a m-learning environment. Most of the control variables yield no significant differences between students, but all the moderator variables were found to be significant determinants that can influence students to adopt m-learning. Overall, students have great potential to engage and integrate mobile technology into their educational environment.     

基于Agent的建模与仿真概述

基于Agent的建模与仿真(ABMS)是研究复杂系统的有效途径和建模仿真方法学,足当前最具有活力、有所突破的仿真方法学,已经成为系统仿真领域的一个新的研究方向.全面总结和阐述了基于Agent的建模与仿真的相关理论基础和概念,包括CAS理论,ABMS中Agent的概念、结构;阐述了ABMS的原理(简单规则导致复杂的行为)与研究步骤;总结了ABMS的主要应用领域,包括经济领域、社会科学领域和军事领域;慨述了ABMS软件开发平台和工具包,给出了ABMS开发方法;给出了互联网上关于ABMS的的资源链接.因而有利于全面认识、应用和研究ABMS.     

复杂系统基于Agent的建模与仿真设计模式及软件框架

提出了一种基于Agent的建模与仿真设计模式——ABMS设计模式，阐述了ABMS设计模式的主要内容。该模式的提出有利于在建模与仿真领域以及软件开发界之间建立关于ABMS的对话联系与交流。然后设计了一种包含仿真服务模型ServerAgent和仿真模型结构SimAgent的ABMS分布仿真框架，该框架将最大限度实现仿真重用，并将支持大规模的基于Agent的复杂系统的仿真，包括军事对抗以及天战系统的仿真。该框架部分借鉴了HLA的思想，其中的ServerAgent提供底层的通用仿真服务。     

Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit

The factors influencing KMS usage are of major concern to the MIS community. Among the diverse theories employed to help understand this is task technology fit (TTF), which considers the needed technological characteristics of the task as a major factor determining usage. This theory, however, ignores the personal cognition dimension, which has been found to affect the use of an IS. By integrating TTF and social cognitive theory (SCT), we attempted to determine the key factors affecting KMS usage in IT, the organizational task, and personal cognition. Through a survey of 192 KMS users, task interdependence, perceived task technology fit, KMS self-efficacy, and personal outcome expectations were found to have substantial influences on KMS usage. Among the key factors, KMS self-efficacy was found to be especially important as it was substantially and positively correlated to perceived task technology fit, personal and performance-related outcome expectations, and KMS usage.     

Reciprocal Recommender Systems: Analysis of state-of-art literature,challenges and opportunities towards social recommendation

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behavior. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.     

Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits

Mobile payment is an emerging and important application of mobile commerce. The adoption and use of mobile payment services are critical for both service providers and investors to profit from such an innovation. The present study attempts to identify the determinants of pre-adoption of mobile payment services and explore the temporal evolution of these determinants across the pre-adoption and post-adoption stages from a holistic perspective including behavioral beliefs, social influences, and personal traits. A research model that reflects the characteristics and usage contexts of mobile payment services is developed and empirically tested by using structural equation modeling on datasets consisting of 483 potential adopters and 156 current users of a mobile payment service in China. Our findings show that behavioral beliefs in combination with social influences and personal traits are all important determinants for mobile payment services adoption and use, but their impacts on behavioral intention do vary across in different stages. Theoretical and practical implications of the findings are presented.     

The factors that affect the performance of open source software development – the perspective of social capital and expertise integration

Drawing on social capital theory, we develop a theoretical model aiming to explore how open source software (OSS) project effectiveness (in terms of team size, team effort and team's level of completion) is affected by expertise integration. This in turn is influenced by three types of social capital – relational capital, cognitive capital and structural capital. In addition, this study also examines two moderating effects – the impact of technical complexity on the relationship between cognitive capital and expertise integration, and of task interdependence on the relationship between expertise integration and task completion. Through a field survey of 160 OSS members from five Taiwanese communities, there is support for some of the proposed hypotheses. Both reciprocity and centrality affect expertise integration as expected, but the influence of commitment and cognitive capital (including expertise and tenure) on expertise integration is not significant. Finally, expertise integration affects both team size and team effort, which in turn jointly influence task completion. This research contributes to advancing theoretical understanding of the effectiveness of free OSS development as well as providing OSS practitioners with insight into how to leverage social capital for improving the performance of OSS development.     

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate,fast, and reliable approach

This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm² GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.     

Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT

The unified theory of acceptance and use of technology (UTAUT), a model of the user acceptance of IT, synthesizes elements from several prevailing user acceptance models. It has been credited with explaining a larger proportion of the variance of ‘intention to use’ and ‘usage behavior’ than do preceding models. However, it has not been validated in non-Western cultures. Using a survey sample collected from 722 knowledge workers using desktop computer applications on a voluntary basis in Saudi Arabia, we examined the relative power of a modified version of UTAUT in determining ‘intention to use’ and ‘usage behavior’. We found that the model explained 39.1% of intention to use variance, and 42.1% of usage variance. In addition, drawing on the theory of cultural dimensions, we hypothesized and tested the similarities and differences between the North American and Saudi validations of UTAUT in terms of cultural differences that affected the organizational acceptance of IT in the two societies.     

Infrastructure for Design, Deployment and Experimentation of Distributed Agent-based Systems: The Requirements, The Technologies, and An Example

This paper discusses infrastructure for design, development, and experimentation of multi-agent systems. Multi-agent system design requires determining (1) how domain requirements drive the use of agents and AI techniques, (2) what competencies agents need in a MAS, and (3) which techniques implement those competencies. Deployment requirements include code reuse, parallel development through formal standardized object specifications, multi-language and multi-platform support, simulation and experimentation facilities, and user interfaces to view internal module, agent, and system operations. We discuss how standard infrastructure technologies such as OMG IDL, OMG CORBA, Java, and VRML support these services. Empirical evaluation of complex software systems requires iteration through combinations of experimental parameters and recording desired data. Infrastructure software can ease the setup, running, and analysis of large-scale computational experiments. The development of the Sensible Agent Testbed and architecture over the past six years provides a concrete example. The design rationale for the Sensible Agent architecture emphasizes domain-independent requirements and rapid deployment to new application domains. The Sensible Agent Testbed is a suite of tools providing or assisting in setting up, running, visually monitoring, and chronicling empirical testing and operation of complex, distributed multi-agent systems. A thorough look at the various Sensible Agents infrastructure pieces illustrates the engineering principles essential for multi-agent infrastructure, while documenting the software for users.     

Efficiency analysis of information technology and online social networks management: An integrated DEA-model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business “social Media” activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model's robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company's ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.     

Herd behavior in social media: The role of Facebook likes,strength of ties,and expertise

When do social media users click on sponsored content or intend to visit the website at a later time? A qualitative comparative analysis (QCA) using arguments based on herd theory, strength of ties, and social distance shows that only “likes” from socially close and knowledgeable users can consistently generate click-through or view-through intentions. Considering social tie strength in a herd behavior context, the analysis of sufficient configurations for click- and view-through intentions provides a nuanced perspective on social media user behavior and social influence. For instance, click-through intention requires observing a “like” from a close person, while view-through intentions can also develop after observing “likes” from less close acquaintances, yet in the last case only if the user assumes the acquaintance is better informed regarding the sponsored content. In addition, a “like” from a close friend deemed better informed can even make a user click on a sponsored content that was not considered valuable before.     

Who puts the best “face” forward on Facebook?: Positive self-presentation in online social networking and the role of self-consciousness,actual-to-total Friends ratio,and culture

The present research investigated how individual, interpersonal, and cultural variables influence positive self-presentation in online social networking. In particular, we examined the role of self-consciousness, actual-to-total Friends ratio, and culture in positive self-presentation on Facebook. A cross-sectional survey was conducted with college-age participants in the United States (n = 183) and South Korea (n = 137). Results showed that self-consciousness (public vs. private) and actual-to-total Friends ratio were not significantly associated with positive self-presentation on Facebook; however, culture showed a statistically significant association with positive self-presentation on Facebook, with the U.S. participants engaging in positive self-presentation on Facebook to a greater extent than the South Korean participants. More interestingly, culture significantly moderated the relationship between public self-consciousness and positive self-presentation as well as the relationship between actual-to-total Friends ratio and positive self-presentation. Specifically, positive self-presentation showed a significant positive association with public self-consciousness and a significant negative association with actual-to-total Friends ratio only among the South Korean participants and not among the U.S. participants. Theoretical and practical implications for understanding cross-cultural differences in self-presentation behaviors on social network sites were discussed.     

Integrating scientific modeling and supporting dynamic hazard management with a GeoAgent-based representation of human–environment interactions: A drought example in Central Pennsylvania, USA

Recent natural disasters indicate that modern technologies for environmental monitoring, modeling, and forecasting are not well integrated with cross-level social responses in many hazard-management systems. This research addresses this problem through a Java-based multi-agent prototype system, GeoAgent-based Knowledge System (GeoAgentKS). This system allows: (1) computer representation of institutional regulations and behavioral rules used by multiple social institutions and individuals in cross-level human–environment interactions, (2) integration of this representation with scientific modeling of dynamic hazard development, and (3) application of automated reasoning that suggests to users the appropriate actions for supporting cooperative social responses. This paper demonstrates the software architecture of GeoAgentKS and presents such an integrated approach by modeling the drought management processes in Central Pennsylvania, USA. The results show that it is possible to use GeoAgentKS to represent multilevel human–environment interactions and to use those interactions as input to decision making in hazard management.