Authors:Carlos M. Fernández-Márquez; Francisco Fatas-Villafranca; Francisco J. Vázquez Pages: 319 - 346 Abstract: Abstract We present an agent-based market model in which social emulation by consumers and the adaptation of producers to demand play a significant role. Our theoretical approach considers boundedly-rational agents, heterogeneity of agents and product characteristics, and the co-evolution of consumers’ desires and firms’ adaptation efforts. The model reproduces, and allows us to interpret, statistical regularities which have been observed in the evolution of industrial sectors, and that seem to be also significant in the case of discretionary consumption activities. Thus, we suggest new determinants and explanations (from the consumer-side) for these stylized facts, and we obtain new theoretical patterns which may be of help to better understand the dynamics of discretionary goods markets. This model and results may contribute to guide future research on the field of consumer market. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9230-4 Issue No:Vol. 23, No. 3 (2017)

Authors:Andrés Mora-Valencia; Trino-Manuel Ñíguez; Javier Perote Pages: 347 - 361 Abstract: Abstract This article proposes a three-step procedure to estimate portfolio return distributions under the multivariate Gram–Charlier (MGC) distribution. The method combines quasi maximum likelihood (QML) estimation for conditional means and variances and the method of moments (MM) estimation for the rest of the density parameters, including the correlation coefficients. The procedure involves consistent estimates even under density misspecification and solves the so-called ‘curse of dimensionality’ of multivariate modelling. Furthermore, the use of a MGC distribution represents a flexible and general approximation to the true distribution of portfolio returns and accounts for all its empirical regularities. An application of such procedure is performed for a portfolio composed of three European indices as an illustration. The MM estimation of the MGC (MGC-MM) is compared with the traditional maximum likelihood of both the MGC and multivariate Student’s t (benchmark) densities. A simulation on Value-at-Risk (VaR) performance for an equally weighted portfolio at 1 and 5 % confidence indicates that the MGC-MM method provides reasonable approximations to the true empirical VaR. Therefore, the procedure seems to be a useful tool for risk managers and practitioners. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9231-3 Issue No:Vol. 23, No. 3 (2017)

Authors:Meysam Alizadeh; Claudio Cioffi-Revilla; Andrew Crooks Pages: 362 - 390 Abstract: Abstract In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9232-2 Issue No:Vol. 23, No. 3 (2017)

Authors:Víctor G. Alfaro-García; Anna M. Gil-Lafuente; Gerardo G. Alfaro Calderón Pages: 391 - 408 Abstract: Abstract In recent years, a trend for accelerating the economic, social and environmental development of cities through associations, organization and creation of synergies has been identified. Our investigation applies a grouping model in order to identify municipalities that could create optimal synergies towards the construction of competitive advantages. In order to achieve this task, we use tools of Fuzzy Logic to evaluate subjective and qualitative characteristic elements of different municipalities under Galois’ group theory. Results conclude on 32 different groups ordered in 7 different levels, relating 12 municipalities of a specific region according to 8 competitive variables. This work seeks to shed light in the conformation of groups under uncertain conditions and the deep examination of the characteristic competitive elements in a specific region for further policy and decision-making processes. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9233-1 Issue No:Vol. 23, No. 3 (2017)

Authors:Che-Jung Chang; Jan-Yan Lin; Peng Jin Pages: 409 - 422 Abstract: Abstract Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9234-0 Issue No:Vol. 23, No. 3 (2017)

Authors:Stefania Monica; Federico Bergenti Pages: 423 - 450 Abstract: Abstract In this paper opinion dynamics in multi-agent systems is investigated analytically using a kinetic approach. Interactions among agents are interpreted as collisions among molecules in gases and opinion dynamics is described according to the Boltzmann equation. Starting from a microscopic description of single interactions, global properties of the opinion distribution are derived analytically. The proposed analytic model is general enough to allow reproducing features of real societies of agents, such as positive and negative influences and bounded confidence, which are typically used to study opinion distribution models. Analytic results relative to emergent and global characteristics of considered multi-agent systems are verified by simulations obtained via direct implementation of the proposed microscopic interactions rules. Simulations confirm analytic results. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9235-z Issue No:Vol. 23, No. 3 (2017)

Authors:Feiqiong Chen; Qiaoshuang Meng; Fei Li Pages: 167 - 198 Abstract: Abstract Overseas mergers and acquisitions (M&A) proposed by companies from emerging economies have been aiming to secure outward technology sourcing from developed countries in order to improve their technology innovation abilities in recent years. This paper proposes a comprehensive analytical framework of post-merger integration’s influence on technology innovation by global game modeling. We show how different resource similarity and resource complementarity backgrounds of the acquirer and target companies can affect post-merger strategies and technology innovation output through multi-stage analysis with an asymmetrical payoff structure. We focus on two main dimensions of post-merger integration, which are integration degree and target autonomy. Equilibrium analysis that is based on potential innovation output signals show that resource similarity has a positive relation with integration and a negative relation with target autonomy in overseas M&A; however, resource complementarity has the opposite effects compared with resource similarity. The positive interaction between resource similarity and complementarity will trigger more M&A and increase the degrees of integration and autonomy; M&A integration has a positive impact on technology innovation output. The innovation growth of the acquiring company is affected by the effectiveness of the post-merger process and the interaction of substitution elasticity with resource potential difference. Our study provides insight into the factors driving post-merger decisions and contributes to a multi-stage resource-based understanding of technology innovation induced by overseas post-merger integration. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9222-4 Issue No:Vol. 23, No. 2 (2017)

Authors:Aida Isabel Tavares Pages: 199 - 220 Abstract: Abstract Generic substitution policy has been adopted in several countries in order to control health expenditures. Using a model based on incentives, this work aims to analyze the response of doctors and pharmaceutical companies to the implementation of this policy. It is shown that after the implementation of GSP, the effort of doctor’s convincing the patient to take generics increase or decrease depending on his level of concern for patient well-being; pharmaceutical companies decrease the amount of detailing and the market share of generics tends to increase. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9223-3 Issue No:Vol. 23, No. 2 (2017)

Authors:David Anzola; Peter Barbrook-Johnson; Juan I. Cano Pages: 221 - 257 Abstract: Abstract Complexity science and its methodological applications have increased in popularity in social science during the last two decades. One key concept within complexity science is that of self-organization. Self-organization is used to refer to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level. In spite of its potential relevance for the study of social dynamics, the articulation and use of the concept of self-organization has been kept within the boundaries of complexity science and links to and from mainstream social science are scarce. These links can be difficult to establish, even for researchers working in social complexity with a background in social science, because of the theoretical and conceptual diversity and fragmentation in traditional social science. This article is meant to serve as a first step in the process of overcoming this lack of cross-fertilization between complexity and mainstream social science. A systematic review of the concept of self-organization and a critical discussion of similar notions in mainstream social science is presented, in an effort to help practitioners within subareas of complexity science to identify literature from traditional social science that could potentially inform their research. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9224-2 Issue No:Vol. 23, No. 2 (2017)

Authors:Hasan Özyapıcı; İlhan Dalcı; Ali Özyapıcı Pages: 258 - 270 Abstract: Abstract Numerical interpolation methods are essential for the estimation of nonlinear functions and they have a wide range of applications in economics and accounting. In this regard, the idea of using interpolation methods based on multiplicative calculus for suitable accounting problems is self-evident. The purpose of this study, therefore, is to develop a way to better estimate the learning curve, which is an exponentially decreasing function, based on multiplicative Lagrange interpolation. The results of this study show that the proposed multiplicative method of learning curve provides more accurate estimates of labour costs when compared to the conventional methods. This is because the exponential functions are linear in multiplicative calculus. Furthermore, the results reveal that using the proposed method enables cost and managerial accountants to better calculate both cost of unused capacity and product cost in a cumulative production represented by a nonlinear function. The results of this study are also expected to help researchers, practitioners, economists, business managers, and cost and managerial accountants to understand how to construct a multiplicative based learning curve to improve such decisions as pricing, profit planning, capacity management, and budgeting. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9225-1 Issue No:Vol. 23, No. 2 (2017)

Authors:Duanxu Wang; Xin Pi; Yuhao Pan Pages: 271 - 292 Abstract: Abstract Previous studies examining the impact of the unethical behavior of a group of colleagues on an individuals unethical behavior have typically employed social learning theory as a theoretical foundation. In this research, we extend these rich yet defective examinations by addressing the largely ignored relationship perspective. Drawing on the social network perspective, we posit that the structure of relationships significantly influences the process of unethical behavior diffusion. Consistent with the theoretically derived hypotheses, our agent-based model simulation results provide general support for our theoretical model: colleagues unethical behaviors positively affect an individuals unethical behaviors, and this influence is positively moderated by group network density, group network closeness centrality and group size. This paper also discusses theoretical contributions, practical values, limitations and directions for future research. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9226-0 Issue No:Vol. 23, No. 2 (2017)

Authors:Thebeth Rufaro Mukwembi; Simon Mukwembi Pages: 293 - 300 Abstract: Abstract We consider a corruption network where agents, both internal or external to the network, use connections and bribes to obtain goods or services outside the formal procedures. We develop a graph-theoretic model for the system and present sufficient conditions for detectability of the corruption status of at least one agent. Where detectability is not possible, we determine the topology of the network and all the possible corruption statuses of the agents. Further we provide, if we have information on the corruption status of a single agent, an algorithm that identifies the corruption status of every other agent in the network. Our results provide tools for detecting corrupt agents in organizations such as revenue authorities, municipalities, police, vehicle inspection departments, financial institutions and firms, while allowing the system to operate in normal mode. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9227-z Issue No:Vol. 23, No. 2 (2017)

Authors:Terrill L. Frantz; Kathleen M. Carley Pages: 301 - 312 Abstract: Abstract This article introduces a confidence level (CL) statistic to accompany the identification of the most central actor in relational, social network data. CL is the likelihood that the most-central actor assertion is correct in light of imperfect network data. The CL value is derived from a frequency-based probability according to perturbed samples of feature-equivalent network data. Analysts often focus attention towards the most central, highest valued, top actor [or node] according to one of four traditional measures: degree, betweenness, closeness or eigenvector centrality. However, given that collected social network data often has missing relational links, the correctness of the top-actor claim becomes uncertain. This paper describes and illustrates a practical approach for estimating and applying a CL to the top-actor identification task. We provide a simple example of the technique used to derive a posterior probability, then apply the same approach to larger, more pragmatic random network by using the results of an extensive virtual experiment involving uniform random and scale-free topologies. This article has implications in organizational practice and theory; it is simple and lays groundwork for developing more intricate estimates of reliability for other network measures. PubDate: 2017-06-01 DOI: 10.1007/s10588-016-9229-x Issue No:Vol. 23, No. 2 (2017)

Abstract: Abstract The strategy of integration known as vendor-managed inventory (VMI), which allows the coordination of inventory policies between producers and buyers in supply chains, has long been considered a strategy for inventory cost reduction. Although the literature acknowledges the importance of understanding the dynamics of VMI implementation through evolutionary games, research in this topic still remains scarce. This paper studies the dynamics of strategic interaction of a producer–buyer supply chain under a newly developed VMI scheme, which makes use of a synchronization mechanism between the buyer and the producer replenishment cycles. By using this alternative VMI representation, we obtain the mathematical conditions that determine the degree of stability of evolutionarily stable strategies. As other evolutionary game theoretical approaches, we also find a lower bound for penalty costs that ensures a VMI contract, but most importantly, we also find how a VMI implementation might depend on the difference between production and demand rates, regardless of any penalty costs. PubDate: 2017-09-08 DOI: 10.1007/s10588-017-9259-z

Authors:Bàrbara Llacay; Gilbert Peffer Abstract: Abstract The use of agent-based models (ABMs) has increased in the last years to simulate social systems and, in particular, financial markets. ABMs of financial markets are usually validated by checking the ability of the model to reproduce a set of empirical stylised facts. However, other common-sense evidence is available which is often not taken into account, ending with models which are valid but not sensible. In this paper we present an ABM of a stock market which incorporates this type of common-sense evidence and implements realistic trading strategies based on practitioners literature. We next validate the model using a comprehensive approach consisting of four steps: assessment of face validity, sensitivity analysis, calibration and validation of model outputs. PubDate: 2017-08-29 DOI: 10.1007/s10588-017-9258-0

Authors:Debdatta Pal; Subrata K. Mitra Abstract: Abstract This article examines the question of whether the inclusion of problem loans leads to any variation in the technical efficiency of microfinance institutions (MFIs). This question has become pertinent as MFIs, which are well known for their excellent asset quality, have been vulnerable to a delinquency crisis worldwide. Traditionally, the efficiency of MFIs has been measured through non-parametric data envelopment analysis (DEA) or parametric stochastic frontier analysis. As both methods are not flexible enough to cover undesirable outputs, we have instead used the method of directional distance function (DDF) that accounts for the joint production of both desirable and undesirable outputs. Using data from 64 large MFIs, this study reveals corroborative evidence that, with the inclusion of at-risk portfolios as undesirable outputs in the efficiency analysis, the scores and rankings of sample MFIs differ significantly from the results of conventional DEA after the use of DDF. MFIs whose numbers of at-risk portfolios are comparatively high have exhibited lower efficiency scores and vice versa. It is therefore critical that MFIs also include problem loans in their efficiency assessment. This would help MFIs get a more accurate picture of their performance as compared to their peers. PubDate: 2017-08-14 DOI: 10.1007/s10588-017-9257-1

Authors:R. Krishankumar; K. S. Ravichandran Abstract: Abstract Social behaviors are an integral part of team building. In this context, we propose a novel classification model that chooses an optimal classifier from the pool of classifiers for predicting the overall performance (OP). Secondly, the chosen classifier is used to investigate the impact of trust and personality on OP. To achieve these goals a pilot study with real time data from 442 respondents are collected from cross functional teams (CFTs) in India using an E-Questionnaire system. The results indicate that the adaptive neuro fuzzy inference system (ANFIS) method is an optimal classifier (A = 89.14%) with respect to other classifiers. We also infer that the predictors, trust and personality are most suitable for predicting OP with a direct relationship to OP and play an indispensable role; as a catalyst; for boosting OP. PubDate: 2017-07-04 DOI: 10.1007/s10588-017-9256-2

Authors:Matthew Benigni; Kenneth Joseph; Kathleen M. Carley Abstract: Abstract The ability of OSNs to propagate civil unrest has been powerfully observed through the rise of the ISIS and the ongoing conflict in Crimea. As a result, the ability to understand and in some cases mitigate the effects of user communities promoting civil unrest online has become an important area of research. Although methods to detect large online extremist communities have emerged in literature, the ability to summarize community content in meaningful ways remains an open research question. We introduce novel applications of the following methods: ideological user clustering with bipartite spectral graph partitioning, narrative mining with hash tag co-occurrence graph clustering, and identifying radicalization with directed URL sharing networks. In each case we describe how the method can be applied to social media. We subsequently apply them to online Twitter communities interested in the Syrian revolution and ongoing Crimean conflict. PubDate: 2017-06-20 DOI: 10.1007/s10588-017-9255-3

Authors:Antônio Carlos da Rocha Costa Abstract: Abstract This paper presents elements for an operational approach to the formal modeling of the macro functional aspects of agent societies. The concept of agent society used in the paper is summarized. The exchange process-based concept of elementary social function is reviewed and a corresponding concept of elementary social mechanism is introduced. Together, these concepts allow for the recursive definition of the concept of functional system, with which one can account for the general functions performed by the core organizational structure of agent societies. Two case studies are developed to illustrate the type of functional modeling of agent societies that is enabled by the concepts introduced in the paper. The first case study concerns the functional analysis of a simple motivating thought experiment. The second concerns the use of agent societies as formal models for natural societies: it sketches the formalization of Pierre Bourdieu’s functional analysis of the reproduction process of contemporary human societies. PubDate: 2017-05-19 DOI: 10.1007/s10588-017-9254-4