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The thesis examines the dynamics of intraguild predation models incorporating adaptive behaviour. The top predator varies its relative consumption of the intermediate consumer and the basal resource. The consumer alters its activity level in response to the threat of predation. Incorporating these adaptive behaviours facilitated three species coexistence, but restricted omnivory by promoting the formation of three species food chains. Model modifications which promoted omnivory also tended to reduce three species coexistence. It is predicted that omnivory should be most common when the intermediate and the basal species are similarly profitable to the predator. The model also predicts two types of omnivory. Strong omnivory occurs when the predator always consumes intermediate amounts of both prey items. Weak omnivory occurs when the predator preys almost exclusively on one prey at most times, but rapidly switches to include the other when the relative abundance of that second prey exceeds a certain level.

The thesis examines the dynamics of intraguild predation models incorporating adaptive behaviour. The top predator varies its relative consumption of the intermediate consumer and the basal resource. The consumer alters its activity level in response to the threat of predation. Incorporating these adaptive behaviours facilitated three species coexistence, but restricted omnivory by promoting the formation of three species food chains. Model modifications which promoted omnivory also tended to reduce three species coexistence. It is predicted that omnivory should be most common when the intermediate and the basal species are similarly profitable to the predator. The model also predicts two types of omnivory. Strong omnivory occurs when the predator always consumes intermediate amounts of both prey items. Weak omnivory occurs when the predator preys almost exclusively on one prey at most times, but rapidly switches to include the other when the relative abundance of that second prey exceeds a certain level.

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The idea that humans are prone to widespread and systematic biases has dominated the psychological study of thinking and decision-making. The conclusion that has often been drawn is that people are irrational. In recent decades, however, a number of psychologists have started to call into question key claims and findings in research on human biases. In particular, a body of research has come together under the heading of adaptive rationality (henceforth AR). AR theorists argue that people should not be assessed against formal principles of rationality but rather against the goals they entertain. Moreover, AR theorists maintain that the conclusion that people are irrational is unsupported: people are often remarkably successful once assessed against their goals and given the cognitive and external constraints imposed by the environment. The growth of literature around AR is what motivates the present investigation, and assessing the plausibility of the AR challenge to research on human biases is the goal of this thesis. My enquiry analyses several aspects of this suggested turn in the empirical study of rationality and provides one of the first philosophically-informed appraisals of the prospects of AR. First and foremost, my thesis seeks to provide a qualified defence of the AR project. On the one hand, I agree with AR theorists that there is room for a conceptual revolution in the study of thinking and decision-making: while it is commonly argued that behaviour and cognition should be assessed against formal principles of rationality, I stress the importance of assessing behaviour against the goals that people entertain. However, I also contend that AR theorists have hitherto failed to provide compelling evidence in support of their most ambitious and optimistic theses about people’s rationality. In particular, I present a great deal of evidence suggesting that people are often unsuccessful at achieving prudential and epistemic goals and I argue that AR theorists have not made clear how, in light of this evidence, optimistic claims about human rationality could be defended.

When making decisions, individuals rarely possess all the facts. This can be forgiven in a world where action is time sensitive; life rarely affords the luxury of comprehending all the nuances of an environment. However, individuals do not just ignore valuable information when it is costly to acquire, individuals often ignore veridical information even when it is freely available. Instead of employing an accurate understanding of a situation, individuals frequently make decisions with the aid of ignorance and misunderstanding. This dissertation attempts to examine why. I argue against the notion that such behaviour is always caused by cognitive limitations. Instead, I demonstrate that ignoring veridical information can be advantageous in a variety of contexts. Throughout this work, I examine several settings where research has shown that individuals consistently ignore freely available information. Using a combination of formal analysis and simulations, I demonstrate that such behaviour can be advantageous. Lacking veridical knowledge can be functional in order to navigate cooperative societies (Chapter 3), unpredictable environments (Chapter 4), investment markets (Chapter 5-7), and inefficient institutions (Chapter 8). Not only does this work contribute to explaining previously confusing human behaviour, it offers insight into the potential advantages of self-deception (Chapter 2).

Thesis (Ph.D.) -- University of Adelaide, Dept. of Psychology, 1982. / Typescript (photocopy).

The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that can not be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. In order to keep individual robots simple, a control algorithm has to keep expensive communication to a minimum and has to be able to act on little information to keep the amount of sensors down. The number of sensors and actuators can be reduced even more when necessary capabilities are spread out over different agents that then combine them by cooperating. Self-organised differentiation within these heterogeneous groups has to take the individual abilities of agents into account to improve group performance. In this thesis it is shown that a homogeneous group of versatile agents can not be easily replaced by a heterogeneous group, by separating the abilities of the versatile agents into several specialists. It is shown that no composition of those specialists produces the same outcome as a homogeneous group on a clustering task. In the second part of this work, an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is analysed in more detail. It does not require communication and needs only the information on individual success or failure. The algorithm leads to self-organised regulation of group activity depending on object availability in the environment by adjusting resting times in a base. A possible variation of this algorithm is introduced which replaces the probabilistic mechanism with which agents determine to leave the base. It is demonstrated that a direct calculation of the resting times does not lead to differences in terms of differentiation and speed of adaptation. After investigating effects of different parameters on the system, it is shown that there is no efficiency increase in static environments with constant object density when using a homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic environments. The algorithm was also reported to lead to higher activity of agents which have higher performance. It is shown that this leads to efficiency gains in heterogeneous groups in static and dynamic environments.

The central goal of this thesis is to provide additional criteria towards implementing open-ended evolution in an artificial system. Methods inspired by biological evolution are frequently applied to generate autonomous agents too complex to design by hand. Despite substantial progress in the area of evolutionary computation, additional efforts are needed to identify a coherent set of requirements for a system capable of exhibiting open-ended evolutionary dynamics. The thesis provides an extensive discussion of existing models and of the major considerations for designing a computational model of evolution by natural selection. Thus, the work in this thesis constitutes a further step towards determining the requirements for such a system and introduces a concrete implementation of an artificial evolution system to evaluate the developed suggestions. The proposed system improves upon existing models with respect to easy interpretability of agent behaviour, high structural freedom, and a low-level sensor and effector model to allow numerous long-term evolutionary gradients. In a series of experiments, the evolutionary dynamics of the system are examined against the set objectives and, where appropriate, compared with existing systems. Typical agent behaviours are introduced to convey a general overview of the system dynamics. These behaviours are related to properties of the respective agent populations and their evolved morphologies. It is shown that an intuitive classification of observed behaviours coincides with a more formal classification based on morphology. The evolutionary dynamics of the system are evaluated and shown to be unbounded according to the classification provided by Bedau and Packard’s measures of evolutionary activity. Further, it is analysed how observed behavioural complexity relates to the complexity of the agent-side mechanisms subserving these behaviours. It is shown that for the concrete definition of complexity applied, the average complexity continually increases for extended periods of evolutionary time. In combination, these two findings show how the observed behaviours are the result of an ongoing and lasting adaptive evolutionary process as opposed to being artifacts of the seeding process. Finally, the effect of variation in the system on the diversity of evolved behaviour is investigated. It is shown that coupling individual survival and reproductive success can restrict the available evolutionary trajectories in more than the trivial sense of removing another dimension, and conversely, decoupling individual survival from reproductive success can increase the number of evolutionary trajectories. The effect of different reproductive mechanisms is contrasted with that of variation in environmental conditions. The diversity of evolved strategies turns out to be sensitive to the reproductive mechanism while being remarkably robust to the variation of environmental conditions. These findings emphasize the importance of being explicit about the abstractions and assumptions underlying an artificial evolution system, particularly if the system is intended to model aspects of biological evolution.

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks.