Computation Models For Marketing & Business Management
Decision making in management is affected by complexity and emerging phenomena. Complexity science and artificial intelligence methods and technologies can help capture and represent the most important information to make critical decisions effectively and efficiently. A set of diverse techniques such as agent-based modeling, social network analysis, evolutionary game theory, evolutionary algorithms, fuzzy sets, and machine learning allows us to model, explain, and handle complex and emerging behavior patterns arising in marketing, economic systems, and business management.
Main research lines:
- Market Modeling. Using agent-based models, behavioral economics, and machine learning, we have created reliable digital twins of real markets. The considered micro-level, bottom-up approach recreates competitive markets for modeling and predicting sales, touchpoints, perceptions, awareness, and word-of-mouth.
- Model Calibration and Validation. We are working on the design of new multi-modal and multi-objective evolutionary methods for calibrating and validating agent-based models by improving both the accuracy and the modeler support.
- Evolutionary Game Theory and Consumer Behavior Modeling. We work on the design of evolutionary game models to answer high-level decisions and social dilemmas found in very diverse areas such as climate change, tourism, and management.
|Chica Serrano, Manuel||manuelchicaBimWw7@V9O@go.ugr.es||–|
|Cordón García, Óscaremail@example.comYYtRhcyap@zWr.es||–|
|Damas Arroyo, Sergio||sdamas@ugr.RgKpTLZ9es||–|
|Giráldez Crú, Jesúsfirstname.lastname@example.orgF7es||–|
|Zarco Fernández, Carmen||carmen.zarcoMImuiEFiIJ9m@ugr.es||–|