Modeller 87 Software environment for continuous system simulation

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An emotion understanding framework for intelligent agents based on episodic and semantic memories

10.1007/s10458-012-9214-9Autonomous Agents and Multi-Agent Systems281126-15

Computational Modeling of Uncertainty Avoidance in Consumer Behavior

Human purchasing behavior is affected by many influential factors. Culture at macro-level and personality at micro-level influence consumer purchasing behavior. People of different cultures tend to accept the values of their own group and consequently have different purchasing behavior. Also, people in the same culture have some differences in their purchases which can be described by their personal characteristics. Therefore, this paper studies Uncertainty Avoidance dimension of Hofstede culture model in consumer behavior as well as four personality traits. The consumer model includes three important module including perception, evaluation of the alternatives and post-purchase. Our experimental results show that people of high uncertainty avoidance tend to purchase the high quality products as well as famous brands to reduce the risk of their purchases. On the other hand, people in high uncertainty tolerant culture tend to purchase the new products. The paper discusses about the validity of the proposed model based on empirical dat

Agent-Based Modeling of Consumer Decision making Process Based on Power Distance and Personality

Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede’s model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae’s five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries

Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

Regulation of social exchanges refers to controlling social exchanges between agents so that the balance of exchange values involved in the exchanges are continuously kept—as far as possible—near to equilibrium. Previous work modeled the social exchange regulation problem as a POMDP (Partially Observable Markov Decision Process), and defined the policyToBDIplans algorithm to extract BDI (Beliefs, Desires, Intentions) plans from POMDP models, so that the derived BDI plans can be applied to keep in equilibrium social exchanges performed by BDI agents. The aim of the present paper is to extend that BDI-POMDP agent model for self-regulation of social exchanges with a module, based on HMM (Hidden Markov Model), for recognizing and learning partner agents’ social exchange strategies, thus extending its applicability to open societies, where new partner agents can freely appear at any time. For the recognition problem, patterns of refusals of exchange pro- posals are analyzed, as such refusals are produced by the partner agents. For the learning problem, HMMs are used to capture probabilistic state transition and observation functions that model the social exchange strategy of the partner agent, in order to translate them into POMDP’s actionbased state transition and observation functions. The paper formally addresses the problem of translating HMMs into POMDP models and vice versa, introducing the translation algorithms and some examples. A discussion on the results of simulations of strategy-based social exchanges is presented, together with an analysis about related work on social exchanges in multiagent systems