2,515 research outputs found
Development of application-specific adjacency models using fuzzy cognitive map
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology
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An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms
This is the post-print version of the final paper published in Industrial Marketing Management. 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 @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea
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
A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems
We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake
ecosystem model by augmenting the individual cognitive maps drawn by 8
scientists working in the area of shallow lake ecology. We calculated graph
theoretical indices of the individual cognitive maps and the collective
cognitive map produced by augmentation. The graph theoretical indices revealed
internal cycles showing non-linear dynamics in the shallow lake ecosystem. The
ecological processes were organized democratically without a top-down
hierarchical structure. The steady state condition of the generic model was a
characteristic turbid shallow lake ecosystem since there were no dynamic
environmental changes that could cause shifts between a turbid and a clearwater
state, and the generic model indicated that only a dynamic disturbance regime
could maintain the clearwater state. The model developed herein captured the
empirical behavior of shallow lakes, and contained the basic model of the
Alternative Stable States Theory. In addition, our model expanded the basic
model by quantifying the relative effects of connections and by extending it.
In our expanded model we ran 4 simulations: harvesting submerged plants,
nutrient reduction, fish removal without nutrient reduction, and
biomanipulation. Only biomanipulation, which included fish removal and nutrient
reduction, had the potential to shift the turbid state into clearwater state.
The structure and relationships in the generic model as well as the outcomes of
the management simulations were supported by actual field studies in shallow
lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to
understand the complex structure of shallow lake ecosystems as a whole and
obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure
Participatory Ecosystem Management Planning at Tuzla Lake (Turkey) Using Fuzzy Cognitive Mapping
A participatory environmental management plan was prepared for Tuzla Lake,
Turkey. Fuzzy cognitive mapping approach was used to obtain stakeholder views
and desires. Cognitive maps were prepared with 44 stakeholders (villagers,
local decisionmakers, government and non-government organization (NGO)
officials). Graph theory indices, statistical methods and "What-if" simulations
were used in the analysis. The most mentioned variables were livelihood,
agriculture and animal husbandry. The most central variable was agriculture for
local people (villagers and local decisionmakers) and education for NGO &
Government officials. All the stakeholders agreed that livelihood was increased
by agriculture and animal husbandry while hunting decreased birds and wildlife.
Although local people focused on their livelihoods, NGO & Government officials
focused on conservation of Tuzla Lake and education of local people.
Stakeholders indicated that the conservation status of Tuzla Lake should be
strengthened to conserve the ecosystem and biodiversity, which may be
negatively impacted by agriculture and irrigation. Stakeholders mentioned salt
extraction, ecotourism, and carpet weaving as alternative economic activities.
Cognitive mapping provided an effective tool for the inclusion of the
stakeholders' views and ensured initial participation in environmental planning
and policy making.Comment: 43 pages, 4 figure
Fuzzy cognitive mapping to support multi-agent decisions in development of urban policymaking
The awareness about environmental complexity involves real-time knowledge and demands urban planning initiatives. Knowledge is multiform, multi-agent and mirrors environmental complexity. Problems characterizing urban sustainability particularly claim non-expert knowledge, being informal, puzzling, uncertain, incomplete, hard to be handled, formalized, modelled. This study utilizes Fuzzy cognitive maps to explore such complexity and support multiagent decisions. It concerns the scenario-building process of the new plan of Taranto (Italy), a paradigmatic example of decaying industrial area, heavily characterized by social fragmentation and environment degradation. This approach aims at structuring environmental problems, modelling future strategies and contributing to build a multi-agent decision support system for complex urban planning contexts
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