IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans - new TOC TOC Alert for Publication# 3468
- 2012 Index IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans Vol. 42el enero 2, 2013 a las 8:45 pm
This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
- Modeling Human Recursive Reasoning Using Empirically Informed Interactive Partially Observable Markov Decision Processesel octubre 12, 2012 a las 8:45 pm
Recursive reasoning of the form what do I think that you think that I think (and so on) arises often while acting in multiagent settings. Previously, multiple experiments studied the level of recursive reasoning generally displayed by humans while playing sequential general-sum and fixed-sum, two-player games. The results show that subjects experiencing a general-sum strategic game display first or second level of recursive thinking with the first level being more prominent. However, if the game is made simpler and more competitive with fixed-sum payoffs, subjects predominantly attributed first-level recursive thinking to opponents thereby acting using second level. In this article, we model the behavioral data obtained from the studies using the interactive partially observable Markov decision process, appropriately simplified and augmented with well-known models simulating human learning and decision. We experiment with data collected at different points in the study to learn the models parameters. Accuracy of the predictions by our models is evaluated by comparing them with the observed study data, and the significance of the fit is demonstrated by comparing the mean squared error of our model predictions with those of a random hypothesis. Accuracy of the predictions by the models suggest that these could be viable ways for computationally modeling strategic behavioral data in a general way. While we do not claim the cognitive plausibility of the models in the absence of more evidence, they represent promising steps toward understanding and computationally simulating strategic human behavior.
- TAIEX Forecasting Using Fuzzy Time Series and Automatically Generated Weights of Multiple Factorsel octubre 12, 2012 a las 8:45 pm
In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) using fuzzy time series and automatically generated weights of multiple factors. The proposed method uses the variation magnitudes of adjacent historical data to generate fuzzy variation groups of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and the M1B), respectively. Based on the variation magnitudes of the main factor TAIEX and the elementary secondary factors of a particular trading day, it constructs the occurrence vector of the main factor and the occurrence vectors of the elementary secondary factors on the trading day, respectively. By calculating the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor, respectively, it calculates the relevance degree between the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor. Based on the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor on a trading day, it automatically generates the weights of the occurrence vector of the main factor and the occurrence vector of each elementary secondary factor on the trading day, respectively. Then, it calculates the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor on the trading day, respectively, to obtain the final forecasted variation on the trading day. Finally, based on the closing index of the TAIEX on the trading day and the final forecasted variation on the trading day, it generates the forecasted value of the next trading day. The experimental results show that the proposed method outperforms the existing methods.
- Phase Constancy in a Ladder Model of Neural Dynamicsel octubre 12, 2012 a las 8:45 pm
This paper presents a novel concept of modeling biological systems by means of preserving the natural rules governing the system's dynamics, i.e., their intrinsic fractal (recurrent) structure. The purpose of this paper is to illustrate the capability of recurrent ladder networks to capture the intrinsic recurrent anatomy of neural networks and to provide a dynamic model which shows typical neuronal phenomena, such as the phase constancy. As an illustrating example, the simplified model for a neural network consisting of motor neurons is used in simulation of a recurrent ladder network. Starting from a generalized approach, it is shown that, in the steady state, the result converges to a constant-phase behavior. The outcome of this paper indicates that the proposed model is a suitable tool for specific neural models in various neuroscience applications, being able to capture their fractal structure and the corresponding fractal dynamic behavior. A link to the dynamics of EEG activity is suggested. By studying specific neural populations by means of the ladder network model presented in this paper, one might be able to understand the changes observed in the EEG with normal aging or with neurodegenerative disorders.
- The Development of an Agent-Based Modeling Framework for Simulating Engineering Team Workel octubre 12, 2012 a las 8:45 pm
Team working is becoming increasingly important in modern organizations due to its beneficial outcomes. A team's performance levels are determined by complex interactions between the attributes of its individual members, the communication and dynamics between members, the working environment, and the team's work tasks. As organizations evolve, so too does the nature of team working. During the past two decades, product development in engineering organizations has increasingly been undertaken by multidisciplinary integrated product teams. Such increasing complexity means that the nature of research methods for studying teams must also evolve. Accordingly, this paper proposes an agent-based modeling approach for simulating team working within an engineering environment, informed by research conducted in two engineering organizations. The model includes a number of variables at an individual level (competency, motivation, availability, response rate), team level (communication, shared mental models, trust), and task level (difficulty, workflow), which jointly determine team performance (quality, time to complete the task, time spent working on the task). In addition to describing the model's development, the paper also reports the results of various simulation runs that were conducted in response to realistic team working scenarios, together with its validation. Finally, the paper discusses the model's practical applications as a tool for facilitating organizational decision making with respect to optimizing team working.