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

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks

Incentive Systems in Multi-Level Markets for Virtual Goods

As an alternative to rigid DRM measures, ways of marketing virtual goods through multi-level or networked marketing have raised some interest. This report is a first approach to multi-level markets for virtual goods from the viewpoint of theoretical economy. A generic, kinematic model for the monetary flow in multi-level markets, which quantitatively describes the incentives that buyers receive through resales revenues, is devised. Building on it, the competition of goods is examined in a dynamical, utility-theoretic model enabling, in particular, a treatment of the free-rider problem. The most important implications for the design of multi-level market mechanisms for virtual goods, or multi-level incentive management systems, are outlined.Comment: 18 pages, 5 figures; graphics with reduced resolution. Full resolution available on author's homepage. Accepted contribution to the Workshop 'Virtual Goods' at the Conference AXMEDIS 2005, 30. November - 2. December, Florence, Ital

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks.Simulation; Stochastic processes; Cellular automata; Tourism; Business; Public Policy Issues; Management techniques; Marketing; Market segmentation; Customer behaviour model

Market-based Recommendation: Agents that Compete for Consumer Attention

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains