Collective behaviour of cognitive agents

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.

This fellowship has potential applications in biological, social and engineering spheres.

Biological: The models developed in this fellowship will make predictions about how groups of social animals will respond to changes in their environment, such as changes in food abundance or variability. Accurate predictions of the consequences of such changes are important for several organisations. Conservation groups need to know how animal populations are likely to respond to anthropogenic changes to the environment such as climate-change driven habitat loss or food supply disruption. Many of the populations under threat of these effects are social animals, and thus their collective behaviour is an important part of their overall response. In particular, the RSPB, the UK's largest conservation charity, manages conservations efforts for several vulnerable seabird bird populations exposed to changes in North Sea fish stocks. In addition to the RSPB, other organisations are likely to find the research findings from this fellowship useful. Many fish species live and move in large schools which are exploited by the fishing industry. Understanding the collective response of key fisheries such as the cod population to changes in their own food supply is crucial to sustainable management of these resources by UK, EU and other international authorities. Validated mathematical and computational models of such populations will allow us to test putative interventions to ameliorate the damage of climate change with lower cost and risk than real-world tests.

Social: A long-term goal of this fellowship is to develop models of collective behaviour that extend to human groups. Accurate prediction of human collective behaviour and responses to external stimuli would have a great many potential applications. These include predicting and ameliorating dangerous crowd responses, identifying potential triggers for social disorder and design of collective spaces such as town centres. Some of these are addressed by existing literature, but this fellowship will have particular relevance for collective behaviour that is cognitively involved. Of special interest is the effect on the behaviour of each individual of changing how easily or comprehensively they can observe the actions of others. Increasing the salience of other's actions can potentially be used to induce greater uptake of socially desirable behaviours. For example, individuals may conserve more energy or recycle more if they observe their neighbours or peers doing so. But people may also habituate to new information sources and learn to disregard them. With the rapid increase of social interactions and communication on the web and via smartphones still ongoing, predicting how this will change societies and how it can be harnessed to improve collective efficacy is an important potential impact of this fellowship.

Engineering: This fellowship will investigate how individuals should strategically search for resources and information both in isolation and in groups. This can be to further both individual or collective goal. Models for optimal resource discovery and acquisition by individuals or collectives operating under physical constraints could improve physically-embodied artificial intelligences that face the same constraints. For example, optimising teams of search and rescue drones that must search collectively while obeying physical constraints on motion. Similarly, intelligence agents seeking to identify and investigate potential threats need to optimise their strategy knowing that they are part of a group whose collective goal is to identify as many real threats as possible, without wasting resources chasing the same information. As this suggests, applications of this research are potentially found in the defence industry and governmental bodies.

This fellowship also has scope for significant public engagement, through the media and public outreach events.