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This paper explores the social dynamics of so-called intercomparison projects. An intercomparison project is a type of collaborative project that takes place in a number of simulation-based research areas such as astrophysics and climate modelling. Intercomparison projects can be seen as form of metrological practice in which the participants compare the results of numerical simulations of the ‘same’ scientific problem in order to ensure their reliability and validity. The paper is based on case studies of astrophysics, meteorology and oceanography, and the focus is on the organization and coordination of intercomparison projects. I argue that such projects have the effect of defining which scientists work on a particular problem and that they also serve as organizational vehicles for creating and presenting a dominant view of — and a standard result for — that problem. These types of projects are important for understanding numerical simulation-based research, because they show that expectations about desirable results are generated within the group. [ABSTRACT FROM PUBLISHER]

This paper presents an agent-based model to analyse behaviour produced under noisy and deceptive information conditions. A simple yet powerful simulation environment is developed where adaptive agents act and adapt to varying levels of information quality that they sense about their environment. The simulation environment consists of two types of agents moving in a bounded two-dimensional continuous plane: a neuro-evolutionary learning agent that adapts its manoeuvreing strategies to escape a pre-programmed deceptive agent; and a pre-programmed agent, whose goal is to capture the adaptive agent, that acts on noisy information about the adaptive agent’s manoeuvres that it senses from the environment. The pre-programmed agent is also able to produce deceptive actions to confuse the adaptive agent. The behaviour is represented in terms of the manoeuvreing strategies that the agents adopt as their actions to the environmental changes. A behaviour analysis methodology is developed to compare agent actions under different information conditions, that elicits interesting relationships between behaviour and the studied information conditions. The framework is easily extendable to analyse human behaviour in similar environments by replacing the adaptive agent with an interactive human–machine interface. [ABSTRACT FROM AUTHOR]