Authors:FLORIANA GARGIULO, YERALI GANDICA, TIMOTEO CARLETTI Abstract: Advances in Complex Systems, Volume 20, Issue 01, February 2017. The Schelling model describes the formation of spatially segregated clusters starting from individual preferences based on tolerance. To adapt this framework to an urban scenario, characterized by several individuals sharing very close physical spaces, we propose a metapopulation version of the Schelling model defined on the top of a regular lattice whose cells can be interpreted as a bunch of buildings or a district containing several agents. We assume the model to contain two kinds of agents relocating themselves if their individual utility is smaller than a tolerance threshold. While the results for large values of the tolerances respect the common sense, namely coexistence is the rule, for small values of the latter we obtain two non-trivial results: first we observe complete segregation inside the cells, second the population redistributes highly heterogeneously among the available places, despite the initial uniform distribution. The system thus converges toward a complex heterogeneous configuration after a long quasi-stationary transient period, during which the population remains in a well mixed phase. We identify three possible global spatial regimes according to the tolerance value: microscopic clusters with local coexistence of both kinds of agents, macroscopic clusters with local coexistence (hereafter called soft segregation) and macroscopic clusters with local segregation but homogeneous densities (hereafter called hard segregation). Citation: Advances in Complex Systems PubDate: 2017-05-29T10:35:26Z DOI: 10.1142/S0219525917500011

Authors:FRIEDERIKE WALL Abstract: Advances in Complex Systems, Ahead of Print. The paper studies which incentive systems emerge in organizations when self-interested managers collaboratively search for higher levels of organizational performance and the headquarters learn about the success of the incentive systems employed. The study uses an agent-based simulation and, in particular, controls for different levels of intra-organizational complexity and modes of coordination, i.e., the way how preferences on the departmental site are aligned with each other in respect to the overall organizational objective. The results indicate that for different levels of intra-organizational complexity different incentive systems emerge: With lower intra-organizational complexity, in tendency, the less focus is put on firm performance and vice versa. However, results also suggest that the mode of coordination may considerably affect the emergence of the incentive structure. This provides support for the idea that multiple management controls, like the incentive system and the mode of coordination, should be regarded and designed as a system with interrelations among its components and not just as a collection of several control practices. Citation: Advances in Complex Systems PubDate: 2017-07-24T09:01:56Z DOI: 10.1142/S0219525917500035

Authors:PIERFRANCESCO DOTTA, MARCO TOLOTTI, JORGE YEPEZ Abstract: Advances in Complex Systems, Ahead of Print. Brand awareness is recognized to be an important determinant in shaping the success of durables [13, 16], yet it is very difficult to be quantified. This is exactly the main goal of this paper: propose a suitable model where brand awareness of two competing firms is modeled and, eventually, estimated. To this aim, we build a random utility model for a duopoly where each competitor is characterized by different pricing strategies and brand awareness. As a result, different levels of market shares will emerge at the equilibrium. As a case study, we calibrate the model with real data from the smartphone industry obtaining an estimate of the value of the brand awareness of two leading brands. Citation: Advances in Complex Systems PubDate: 2017-07-12T07:34:48Z DOI: 10.1142/S0219525917500047

Authors:KAZUTO SASAI, YUKIO-PEGIO GUNJI, TETSUO KINOSHITA Abstract: Advances in Complex Systems, Ahead of Print. Continuous asynchronous trading activity is a key to understanding real-world market behavior. However, it is not easy to implement an agent-based computational market model because of the ambiguity between time and space. In this study, we use a model of asynchrony in a continuous double auction market in the form of noise and order restrictions to link inside- and outside- uncertainties in the economic system. Our model shows intermittent behavior with a small parameter value, which leads to the misapplication of the price-update rule, and consequently drives burst behavior. The statistical property of time development shows a similar tendency to that in previous empirical studies. Thus, it demonstrates the relationship between the asynchronous property and the complexity of economic systems. Citation: Advances in Complex Systems PubDate: 2017-07-11T06:29:28Z DOI: 10.1142/S0219525917500059

Authors:FUQIANG ZHAO, LICHAO ZHANG, GUIJUN YANG, LI HE, FENGYU YAN Abstract: Advances in Complex Systems, Ahead of Print. In the graph of a complex network, the algebraic connectivity is the second smallest eigenvalue of a Laplacian matrix. In this paper, we present a cut algorithm based on edge centrality by minimizing the algebraic connectivity of graph. The edge centrality cut algorithm (ECCA) cuts [math] edges at a time in order to reduce temporal complexity, the algebraic connectivity of which experiences the fastest decline. To prevent nodes from overcutting, each edge sets the weight. We use the advanced ECCA (AECCA) to detect overlapping communities by calculating the correlation coefficients of the nodes. This paper also proposes upper, lower and weaker lower bounds of algebraic connectivity. We demonstrate that our algorithms are effective and accurate at discovering community structure in both artificial and real-world network data and that the algebraic connectivity of the cut algorithm lies between the upper and lower bounds. Our algorithms offer new insights into community detection by calculating the edge centrality. Citation: Advances in Complex Systems PubDate: 2017-05-15T07:59:19Z DOI: 10.1142/S0219525917500023