Modeling multidirectional, dynamic social influences in social networks

The study of social influence seems to have developed along two parallel, but largely independent lines of research. On the one hand, research in sociology and physics has focused on the macro-level, by studying dynamics of opinion flow within extended social influence networks and using aggregate-level variables (i.e., the proportion of a population in a particular state), with little regard for individual psychological processes working at the micro-level. On the other hand, social psychological research has focussed on individual psychological processes that underlie people's judgements and behaviours in carefully crafted laboratory experiments, without much consideration of the social contexts or networks in which these processes operate. However, it is clear that group-level outcomes of theoretical assumptions about intra-individual and inter-individual processes are rarely obvious, and also that individual processes often interact over time to create complex systems with non-intuitive, emergent properties (e.g. Resnick, 1994; Wolfram, 2002). A number of authors (Smith and Conrey, 2007) have therefore argued that in order to develop a full understanding of the nature of social influence, theories or models need to be constructed that take into account variables on both the individual and aggregate level of social systems. This paper introduces an attempt at such a model, by describing a connectionist Agent-based model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes (Van Rooy, Van Overwalle, Vanhoomissen, Labiouse & French, 2003). At the macro-level, these individual networks are combined into a “community of networks” so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them (Hutchins & Hazlehurst, 1995; Van Overwalle & Heylighen, 2006). The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. Additionally, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes.