Centre For The study of Complex Dynamics (CSDC)
Mind, computers and society: how the knowledge of psychology may be useful to computer science, and to simulations of societies.
Psychology is a composite science of mind and behaviour. In some sense, it may be considered the connection between neuroscience and sociology. Psychologists explore such concepts as perception, cognition, attention, emotion, motivation, brain functioning, personality, behaviour, and interpersonal relationships. When viewed in an evolutionary perspective, psychology acquires the the sense of the study of a finely optimized system (the human brain) for social interactions. Traditionally, psychology has a descriptive character for what concerns the motivations, and a quantitative approach to data analysis. The increasing knowledge of the working of mind and brain, and the study of social phenomena has opened the study of social modelling, that may represent a key topic in many disciplines, such as sociology, epidemics, politics, marketing. But an even more interesting application concerns information and computers (ICT world): not only because computer devices are designed for human interaction, but also for exploiting the knowledge about humans in the ICT field.
VirtHuLab: a new framework to investigate the Human Virtual Dynamics
Humans are daily asked to take decision based on incomplete and ambiguous information, yet they developed, through centuries of selection, efficient methods and mental structures to face with these tasks. Our goal is that of developing a new framework to studying and modeling this features.
Cognitive psychology is the name of the discipline that investigates how human beings face these problems. These studies do not start from the neural structures, but rather deal with the empirical relationship among "atomic" processes that can be identified into the human behavior.
Our challenge is to refine a small pool of cognitive theories able to describe those fundamental attributes of humans' cognition which could lead toward an artificial self-awareness. Since 1960, Social cognition and cognitive psychology has developed a coherent approach to the study of human cognition both with theories about its functioning and through the definition of a new taxonomy of concepts and grammars. After the cognitive revolution produced by the works of Neisser in the 1964, Social cognition started to describe mental processes using a new language, able to threat the mathematical structures of the mental processes. The basic concepts of this scaffholding are the cognitive processes and the mental schemes. This two label have inside the cognitive terminology an hard coded definition: while Mental scheme rely on the mental representation (storage/retrieval) of the information, the cognitive processes are those operations that the mind appear to execute on this schemes. Mental Schemes theory (Hastie, Kelley, Zadby, Gerard, Markus) can supply an accurate and implementable description for the information storage/retrieval, while Relevance theory can describe the communications processes among entities, the Social Learning theory (Bandura) can be used to structure the evolution of the cognitive processes, and the definition of data-driven and schema-driven processes (Fiske, Neuberg) can capture the great adaptability and optimization of the mind. Finally cognitive heuristics may inspire those mechanisms which act on the scheme and for the processes, while the Cognitive Economy Principles can be considered for the resource management.
Small Group Dynamics: A Preliminary Results
We present a research framework consisting of a standard chat environment and a set of analytical tools, able to detect some relevant characteristics of the group dynamics of interacting people. The analysis is independent of the semantic content of the exchanged messages, and the standardized interface avoids hard-to-detect non-verbal communications, still providing the expression of emotional contents.
This study proposes a quantitative approach to the investigation of the existing relationship between the individual dimensions, considering the personal cognition about the interaction with the others, and the group dimension, trough its dynamical evolution. The subsequent analysis, mixing social network theory and concepts from social and opinion dynamics, allows us to quantitatively investigate how people shape their social space in virtual interactions, exploring the role of topology and the structure of the group evolution. Finally, we present a regression model to explain how the virtual social space is represented by the individuals in interaction with a group.
Cognitive Complex Networks: from simple definitions to community detection
Detecting communities is a task of great importance in many disciplines, namely sociology, biology and computer science, where systems are often represented as graphs. Community detection is linked to clustering of data: many clustering methods establish links among representative points that are nearer than a given threshold, and then proceed in identifying communities on the resulting graphs. We want to explore the behavior of exploratory methods inspired by human heuristics, in the hope of exploiting the \social knowledge" of human mind and also for developing more \natural" human-computer interfaces.
Clearly, we do not pretend to simulate the real human behavior, but only to study the behavior of simpli.ed models inspired by it. In particular, we deal with the task of identifying communities in an existing graphs, using a local algorithm and not relying on global quantities like betweenness, centrality, etc. An individual is simply modeled as a memory and a set of connections to other individuals. We explore two di.erent approaches: in the first, information about neighboring nodes if propagated and elaborated locally, but the connections do not change. In the second approach, information is not elaborated while it is the wiring that is varied with the result of directly connecting to a \central node". Both processes can be considered implementations of the availability heuristic, which is simply the assumption that the most vivid or easily recallable information give an accurate estimate of the frequency of the related event in the population.