An evolutionary approach to solve a system of multiple interrelated agent problems

Deterministic approaches to simultaneously solve different interrelated optimisation problems lead to a general class of nonlinear complementarity problem (NCP). Due to differentiability and convexity requirements of the problems, sophisticated algorithms are introduced in literature. This paper develops an evolutionary algorithm to solve the NCPs. The proposed approach is a parallel search in which multiple populations representing different agents evolve simultaneously whilst in contact with each other. In this context, each agent autonomously solves its optimisation programme while sharing its decisions with the neighbouring agents and, hence, it affects their actions. The framework is applied to an environmental and an aerospace application where the obtained results are compared with those found in literature. The convergence and scalability of the approach is tested and its search algorithm performance is analysed. Results encourage the application of such an evolutionary based algorithm for complementarity problems and future work should investigate its development as well as its performance improvements

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Parallel ACO with a Ring Neighborhood for Dynamic TSP

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc

Understanding Leadership A Coordination Theory

Important aspects of leadership behavior can be rendered intelligible through a focus on coordination games. The concept of common knowledge is shown to be particularly important to understanding leadership. Thus, leaders may establish common knowledge conditions and assist the coordination of strategies in this way, or make decisions in situations where coordination problems persist in spite of common knowledge.Game theory, management, organization

The Sociology of Creativity: A Sociological Systems Framework to Identify and Explain Social Mechanisms of Creativity and Innovative Developments

Creativity is a universal activity, essential in an evolutionary perspective, to adaptation and sustainability. This manuscript on the sociology of creativity has three purposes: (1) to develop the argument that key factors in creative activity are socially based and developed; hence, sociology can contribute significantly to understanding and explaining human creativity; (2) to present a systems approach which enables us to link in a systematic and coherent way the disparate social factors and mechanisms that are involved in creative activity and to describe and explain creativity; (3) to illustrate a sociological systems theory’s (Actor-Systems-Dynamics) conceptualization of multiple interrelated institutional, cultural, and interaction factors and mechanisms and their role in creativity and innovative development with respect to diverse empirical bases. The approach shares with key psychological theory approaches in the area consideration of key concepts such as “persons”, “processes”, “products”, and “places “but extends these to include additional factors such as social structures and resources, social powers, selection mechanisms (acceptance or rejection), and institutionalization. Moreover, the complex of factors identified and analyzed are specified in this article in sociological terms. The resulting model enables one to address and answer key questions relating to creative actions and innovative developments such as “who” is involved, “why” are they driving these activities, “what” are they doing or trying to do concretely, “how”, “where”, and “when” in diverse instances/illustrations which illuminate human creativity. The general model enables us to distinguish between and analyze processes of creative origination/formation, on the one hand, and processes of institutional acceptance and realization, on the other hand. Innovation in these distinct phases is distinguished analytically. It formulates a phase structure model in which the phases of origination and innovation generally and the phases of acceptance and institutionalization are identified and analyzed. Finally, the work introduces and applies key concepts such as rules and rule regimes -- norms, roles, institutions, and cultural formations -- in general, social structure. Moreover, it identifies socially based creativity production functions and particular cognitive and action mechanisms as features of rule regimes that generate innovations. Applications and illustrations in the article are diverse ranging from, for instance: (i) “the lone coyote” who exercises creativity based on absorbing a field of knowledge, concepts, challenges, problems, solution strategies, creativity production functions or programs (and who is likely to be in contact with libraries, relevant journals and may be directly or indirectly in contact with a network of others); (ii) groups in their particular fields operating greenhouse driving problem-solving and creative activities – both self-organizing groups as well as groups established by external powers (whether a private company, a government, or a non-government organization or movement); (iii) or entire societies undergoing transformations and radical development as in the industrial and later revolutions. The article introduces and applies a model stressing the socio-cultural and political embeddedness of agents, either as individuals or groups, in their creative activities and innovative productions. The agents are socialized agents, carriers of socio-cultural knowledge, including some of the knowledge essential to engage in creative processes in a particular domain or field. In their creativity, agents manipulate symbols, rules, technologies, and materials that are socially derived and developed. Their motivation for doing what they do derives in part from their social roles and positions, in part in response to the incentives and opportunities – many socially constructed – shaping their interaction situations and domains. Their capabilities including their social powers derive from the culturally and institutional frameworks in which they are embedded. In carrying out their actions, agents mobilize resources through the institutions and networks of which they are a part. As social agents, they are carriers of constructed values and motives and culturally established ideas, strategies, and practices (e.g., “a cultural tool kit.”) Their creative actions are social actions, given meanings in cultural and institutional terms in the domains or fields in which they engage in their activities. Power considerations are part and parcel of the analyses, for instance the role of the state as well as private interests and social movements in facilitating and/or constraining innovations and creative developments in society. In the perspective presented here, generally speaking, creativity can be consistently and systematically considered to a great extent as social, cultural, institutional and material rather than largely psychological or biological

Modeling Routines and Organizational Learning. A Discussion of the State-of-the-Art

This paper presents a critical overview of some recent attempts at building formal models of organizations as information-processing and problem-solving entities. We distinguish between two classes of models according to the different objects of analysis. The first class includes models mainly addressing information processing and learning and analyzes the relations between the structure of information flows, learning patterns, and organizational performances. The second class focuses on the relationship between the division of cognitive labor and search processes in some problem-solving space, addressing more directly the notion of organizations as repositories of problem-solving knowledge. Here the objects of analysis are the problem-solving procedures which the organization embodies. The results begin to highlight important comparative properties regarding the impact on problem-solving efficiency and learning of different forms of hierarchical governance, the dangers of lock-in associated with specific forms of adaptive learning, the relative role of “online” vs. “offline” learning, the impact of the “cognitive maps” which organizations embody, the possible trade-offs between accuracy and speed of convergence associated with different “decomposition schemes”. We argue that these are important formal tools towards the development of a comparative institutional analysis addressing the distinct properties of different forms of organization and accumulation of knowledge.Division of labor, Mental models, Problem-solving, Problem decomposition.