Decision making in social neurobiological systems modeled as transitions in dynamic pattern formation

Extant models of decision making in social neurobiological systems have typically explained task dynamics as characterized by transitions between two attractors. In this paper, we model a three-attractor task exemplified in a team sport context. The model showed that an attacker–defender dyadic system can be described by the angle x between a vector connecting the participants and the try line. This variable was proposed as an order parameter of the system and could be dynamically expressed by integrating a potential function. Empirical evidence has revealed that this kind of system has three stable attractors, with a potential function of the form V(x)=−k1x+k2ax2/2−bx4/4+x6/6, where k1 and k2 are two control parameters. Random fluctuations were also observed in system behavior, modeled as white noise εt, leading to the motion equation dx/dt = −dV/dx+Q0.5εt, where Q is the noise variance. The model successfully mirrored the behavioral dynamics of agents in a social neurobiological system, exemplified by interactions of players in a team sport.

Reshaping housing : the role of prefabricated systems

Prefabricated housing innovations have the potential to reduce the environmental impact of construction through improving efficiency and quality. The current paper systematically summarises the published evidence since 1990 that describes the barriers and drivers affecting the uptake of prefabricated housing innovations. These are discussed in relation to a ‘Project-Based Product Framework’ which considers multiple stakeholders including builders and other intermediaries, suppliers, end-users, the broader policy context and technical issues. The framework facilitated identification of central issues such as the prevalent business and cultural resistance associated with process changes; the potential for efficiency and quality improvements and cost savings; the simultaneous risks and benefits of close supplier-builder relationships, and negative user perceptions towards prefabricated houses. Though there is a lack of evidence regarding the effects of regulations and government policies on prefabrication uptake, there are indications of the positive potential of financial and social incentives. Directions for further research include understanding how to: manage the industry’s transition to prefabricated houses; appropriately compare prefabricated housing to traditional housing on cost, efficiency and quality measures; reconcile the differing perspectives of various stakeholders; quantify and identify the perspectives of the potential end-user population, and manage the interface between the emerging industry and information technology improvements.