64 IEEE TRANSACTIONS ON CYBERNETICS, VOL. 43, NO. 1, FEBRUARY 2013 A Verification Method for MASOES N. Perozo, J. Aguilar, O. Terán, and H. Molina Abstract—MASOES is a 3agent architecture for designing and modeling self-organizing and emergent systems. This architecture describes the elements, relationships, and mechanisms, both at the individual and the collective levels, that favor the analysis of the self-organizing and emergent phenomenon without mathemat- ically modeling the system. In this paper, a method is proposed for verifying MASOES from the point of view of design in order to study the self-organizing and emergent behaviors of the modeled systems. The verification criteria are set according to what is proposed in MASOES for modeling self-organizing and emerging systems and the principles of the wisdom of crowd paradigm and the fuzzy cognitive map (FCM) theory. The verification method for MASOES has been implemented in a tool called FCM Designer and has been tested to model a community of free software devel- opers that works under the bazaar style as well as a Wikipedia community in order to study their behavior and determine their self-organizing and emergent capacities. Index Terms—Emergent systems, fuzzy cognitive maps (FCMs), multiagent systems, self-organization, wisdom of crowds. I. I NTRODUCTION N OWADAYS, the multiagent approach can be used to model systems with a high density of agents and inter- actions in very dynamic and/or unpredictable environments, where solutions are not known beforehand and/or change con- stantly and where agents could work in a decentralized manner based on their local interactions, with an emergent behavior that favors their adaptation in changing situations [10]. Research directions for using multiagent systems in diverse applications could be found in [41] and [42]. Moreover, how a multiagent system is used in real and complex environment is showed in [43]. There are other works [44]–[46] that indicate some requirements to be considered to develop a multiagent model or architecture for self-organizing and emergent systems in en- gineering projects. Considering this potential use of multiagent systems, we define MASOES as a multiagent architecture for designing, modeling, and studying self-organizing and emer- gent systems [1]. A methodology for modeling with MASOES Manuscript received February 23, 2011; revised June 29, 2011, September 14, 2011, January 13, 2012, and April 8, 2012; accepted April 10, 2012. Date of publication June 4, 2012; date of current version January 11, 2013. This work was supported in part by the Alban Program under Grant E06D101067VE. This paper was recommended by Editor E. Santos, Jr. N. Perozo is with the Unidad de Inteligencia Artificial, Decanato de Ciencias y Tecnología, Universidad Centroccidental “Lisandro Alvarado,” Barquisimeto 3001, Venezuela (e-mail: nperozo@ucla.edu.ve). J. Aguilar and H. Molina are with the Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Facultad de Ingeniería, Universidad de los Andes, Mérida 5101, Venezuela (e-mail: aguilar@ula.ve; heidym@ula.ve). O. Terán is with the Centro de Simulación y Modelos and Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Facultad de Ingeniería, Universidad de los Andes, Mérida 5101, Venezuela (e-mail: oteran@ula.ve). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMCB.2012.2199106 is specified in [25]. It explains how to describe the elements, relations, and mechanisms via individual and collective levels of the society of agents that favor the analysis of the self- organizing and emergent phenomenon without modeling the system mathematically. MASOES, with respect to other works [2]–[4], [12], [15], considers both the microscopic and the macroscopic aspects of a system; that is, it manages knowledge at the collective and individual level in a generic form. It is a generic architecture with a society of agents working in a de- centralized way. Agents may have different types of behavior: reactive, imitative, or cognitive. Indeed, MASOES allows mod- eling social systems of homogeneous or heterogeneous agents with complex behavior in a much more flexible and real way than a reactive behavior, as is frequently considered in these related works. Likewise, MASOES allows each agent to change its behavior, guided by its emotional states, providing the system designer with a tool that can be applied in the modeling of self-organizing and emergent systems in different contexts. In this paper, a verification method is proposed for sys- tems modeled through MASOES, i.e., the aim is to verify whether the MASOES-based models show emergent and/or self-organizing properties as those observed in the real systems. The verification criteria are set according to what is proposed in MASOES for modeling self-organizing and emerging systems and the principles of the wisdom of crowd paradigm (WoCP) and the fuzzy cognitive map (FCM) theory. Hence, when the MASOES verification method is instanced in a complex system, it should provide us with information, properties, and mechanisms about its components and facilitates to introduce changes such as the elimination of components or mechanisms and the modification of the behavior at the individual and collective levels in order to determine what happens with its self-organizing and emergent behavior. Moreover, this verifi- cation method is useful for testing through diverse scenarios and comparisons with previous works related to the modeled system, the design specifications, and the methodology of use established for MASOES in [25]. This will be done through the metamodel based on FCMs generated by our verification method. Other related works with respect to the verification of the existence of self-organizing and emergent properties in a system similar to the verification method proposed here were not found. Hence, this work represents a novel alternative to study, test, verify, or validate self-organization and emergence in complex systems and test the multiagent model since it is difficult to run tests in these systems directly, given the level of complexity that they manage. Finally, the rest of this paper is organized as follows. An overview of MASOES, FCM theory, and WoCP is introduced in Section II with some related works. Section III presents the proposed verification method for MASOES. Two case studies 2168-2267/$31.00 © 2012 IEEE