Collective behaviour of cognitive agents

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Animal behaviour is driven by evolutionary adaptations that maximise fitness, through goals such as food acquisition, mate selection and predator avoidance. In many species cognition plays a key role in allowing animals to perceive the world, process information and plan actions. This fellowship will develop a comprehensive theory of cognitively-driven behaviour by agents responding to information from their physical and social environment, based on models of rational decision-making, Bayesian inference and artificial intelligence. Recent technological developments have dramatically increased the data available to researchers of animal behaviour. Researchers are using these data to understand how animals interact with their environment and with each other. However, a mechanistic, data-driven approach that focuses on predicting behaviour from the immediate stimuli ignores the cognitive ability of animals to learn about their world, strategise and execute patterns of behaviour to reach their goals. This in turn can lead to misleading conclusions about behaviour that do not generalise beyond the specific experimental scenario under which they were studied.Only by developing a theory of behaviour that explicitly includes cognitive processes and the end goals that animals are trying to accomplish can we understand why animals behave in the ways that we observe, why they respond more or less strongly to certain stimuli, and how behaviour may change when animals are confronted by a different environment. Such a theory must build on principles as well as observations. In this fellowship I will establish assume that agents are rational fitness-maximisers, capable of performing inference about the world and rationally updating their beliefs on the basis of experience, and able to make strategic decisions through the use of predictions about the likely consequences of their actions.Building on developments in artificial intelligence known as active-learning, I will model how an agent takes decisions to optimise long-term goals, such as fitness maximisation, while operating in a world of uncertainty. In this framework, agents must consider the consequences of their actions in terms of what they learn about the world. Actions with lower immediate reward may open up new possibilities later through knowledge gained. For example, a foraging animal may choose to explore new territory rather than exploit a known resource, hoping to find more productive areas. Because the outcomes of all actions are uncertain, an agent must weigh up what can be gained and learnt for all possible results, and decide how to balance these possibilities to optimise its long-term objectives. Coupled with this model of individual strategic behaviour, I will also develop models of agents interacting, cooperating and competing. Here, agents must pursue their individual objectives in the context of others who are pursuing their own goals. These individuals may have similar or differing characteristics, and their goals may be aligned or divergent. In each case, the best action for an agent to take must be based on a model of the actions others have taken, what information this conveys, and what actions they are likely to take in the future. This theoretical framework will reveal the observations that are required in order to verify or falsify the underlying assumptions on which the theory is built. Thus this fellowship will drive the next generation of empirical studies by identifying principles of experimental design to maximise the usefulness of data, and move towards an era of precision-targeted data collection. Successful empirical validation of the models will also allow their use for predicting the outcome of exogenous changes by in terms of changes to individual and group behaviours.