Abstract :
Accounting for social, cultural, and political factors must form the basis for understanding decision-making, actions, and reactions of individuals, thus driving their behaviors and intentions. Clearly, the individual is not wholly defined by just personal social, cultural, and political beliefs but also functions within a group of individuals. Within these groups (or organizations), they assimilate a potentially wide variety of different social factors, which may or may not differ from their own. Also, the group itself can vary in degrees of complexity, styles of interaction, and so forth, resulting in highly dynamic and emergent modes of behaviors. Even more difficult, this also includes taking into account the values, attitudes, and beliefs of the local population/environment that the individual/group is situated within. Without all these factors, we cannot expect to effectively understand, analyze, or predict the behaviors and intentions of others which grows ever more critical as our society continues to globalize and especially in today´s conflicts and catastrophes. Thus, the need for a comprehensive modeling framework is evident as our only real hope of addressing such complexity. However, to date, only small isolated groups of pertinent behavioral factors have been studied, while there is little or no work towards developing a general unified and comprehensive approach that is also computational. The major challenges we face can be summed up in the following questions: 1. For prediction and explanation of intent and behavior, how does one computationally model individual or organizations and their emergent interactions with others, in various situations? 2. How does one organize and build the necessary social, cultural, political, behavioral, etc. knowledge- base? 3. How do you avoid brittleness and overspecialization? How do you construct these models efficiently and effectively, and dynamically evolve such models over time based on changing cultural and s- ocial factors? 4. How do you validate your models? In this talk, we will explore these challenges and focus on addressing pragmatic and computational issues in such modeling and examine some existing real-world efforts, current solutions, and openquestions.
