Causal Analysis of an Agent-Based Model of Human Behaviour

This article investigates causal relationships leading to emergence in an agent-based model of human behaviour. A new method based on nonlinear structural causality is formulated and practically demonstrated. The method is based on the concept of acausal partitionof a model variable which quantifies the contribution of various factors to its numerical value. Causal partitions make it possible to judge the relative importance of contributing factors over crucial early periods in which the emergent behaviour of a system begins to form. They can also serve as the predictors of emergence. The time-evolution of their predictive power and its distribution among their components hint at the deeper causes of emergence and the possibilities to control it

Causal Analysis of an Agent-Based Model of Human Behaviour

This article investigates causal relationships leading to emergence in an agent-based model of human behaviour. A new method based on nonlinear structural causality is formulated and practically demonstrated. The method is based on the concept of a causal partition of a model variable which quantifies the contribution of various factors to its numerical value. Causal partitions make it possible to judge the relative importance of contributing factors over crucial early periods in which the emergent behaviour of a system begins to form. They can also serve as the predictors of emergence. The time-evolution of their predictive power and its distribution among their components hint at the deeper causes of emergence and the possibilities to control it

The Explanatory Potential of Artificial Societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Instead, artificial societies may sometimes provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from the standard kind of potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds of potential explanations differ, and how possible functional analyses can be appraised

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

Building machines that learn and think about morality

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss how work in embodied and situated cognition could provide a valu- able perspective on future research

Quantifying Morphological Computation

The field of embodied intelligence emphasises the importance of the morphology and environment with respect to the behaviour of a cognitive system. The contribution of the morphology to the behaviour, commonly known as morphological computation, is well-recognised in this community. We believe that the field would benefit from a formalisation of this concept as we would like to ask how much the morphology and the environment contribute to an embodied agent's behaviour, or how an embodied agent can maximise the exploitation of its morphology within its environment. In this work we derive two concepts of measuring morphological computation, and we discuss their relation to the Information Bottleneck Method. The first concepts asks how much the world contributes to the overall behaviour and the second concept asks how much the agent's action contributes to a behaviour. Various measures are derived from the concepts and validated in two experiments which highlight their strengths and weaknesses