Information Theoretic Models of Social Interaction

This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of \maximisation of relevant information" and \information parsimony" can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this di erentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one's own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting e ects, speci cally how the digested information of an agent is in uenced by several factors, such as the agent's performance and the integrated information of other agents. Two speci c phenomena based on information maximisation arise in this thesis. One is ocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent's actions via Bayes' Theorem. The other is an e ect where integrating information from too many agents becomes detrimental to an agent's performance, for which several explanations are provided

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario

Toward Computational Motivation for Multi-Agent Systems and Swarms

Motivation is a crucial part of animal and human mental development, fostering competence, autonomy, and open-ended development. Motivational constructs have proved to be an integral part of explaining human and animal behavior. Computer scientists have proposed various computational models of motivation for artificial agents, with the aim of building artificial agents capable of autonomous goal generation. Multi-agent systems and swarm intelligence are natural extensions to the individual agent setting. However, there are only a few works that focus on motivation theories in multi-agent or swarm settings. In this study, we review current computational models of motivation settings, mechanisms, functions and evaluation methods and discuss how we can produce systems with new kinds of functions not possible using individual agents. We describe in detail this open area of research and the major research challenges it holds

Top-down and bottom-up models of collective motion

Active matter is an expanding field of physics covering a diverse range of complex and beautiful phenomena. From examples we see in our everyday lives, such as the flight of birds and organisation of insects, to more esoteric bacteria and other micro-scale biological systems. What we can learn about the physical rules that pin these diverse systems together is important not just for our understanding of physics but our ability to utilise the natural world around us. The core of our understanding of Active matter spans between out-of-equilibrium analogues of wellknown thermodynamics to the realm of complex intelligent decision-making. From a top-down view point, we observe phenomena such as aggregation, ordered motion, dynamic pattern formation, leader-follower relationships, long range interactions, collisions avoidance, and coordinated motion to name a few, and model these directly within a mathematical formalism. From a bottom-up perspective we attempt to explain the generation of these phenomena from intrinsic process driving individual agents. In this thesis we consider a data-driven analysis of collective motion in an insect system, a top-down approach, as well as developing a model of individual decision making based upon future path entropy, a bottom-up approach. The latter results in the spontaneous emergence of some basic features of collective motion seen in real world examples, lending explanatory power

Group behaviour in financial markets

This thesis aims to revise the current understanding of the behaviour of different groups of traders in financial markets. Research involves statistical analysis of historic 'Commitment of Traders' reports, a U.S government dataset providing the long and short positions of core groups of traders, reported at weekly intervals over 17 years. Empirical work identifies a surprising level of consistency amongst different groups across 31 markets. A specific pattern is identified: speculators are found to increase their buying interest when prices are rising whilst commercial traders (or 'hedgers') increase their selling; the opposite pattern of behaviour occurs when prices are falling. The thesis explores the implications of this behaviour for existing models of financial markets by referencing a number of peer-reviewed studies. The agent-based computational model of Alfarano, Lux, and Wagner (2005) is implemented and analysed. A lack of validity is demonstrated in the interactions between the different types of traders in this model. These theoretical components are further shown to be typical of much of the literature in this area. An objective for the thesis is to correct this oversight by incorporating genuine patterns of trading behaviour into an existing computational model. The approach of Mike and Farmer (2008) is used for this purpose, being currently unique in that core components are calibrated from real-world data and no group-level representations are assumed. This model is extended to observe groups of traders with different levels of order-aggression: speculators are found to rely on market orders whereas commercial traders rely on limit orders. These preferences, in the absence of deeper theoretical considerations, are sufficient to account for the identified behaviour. A discussion is offered on the relevance of this finding for financial market regulators, who have typically focused on regulating types of traders, specifically speculators, rather than on types of trades

Collective photo-thermophoresis

The stationary distributions of thermally interacting thermophoretic colloids under illumination are studied by mathematical analysis of mean field models. This work generalises the model of Golestanian by explicitly modelling the non-local and non-pairwise effect of shading on the collective dynamics. Golestanian's solutions are recovered in the transparent limit, and the effect of shading is revealed to be two-fold: in the opaque limit in 1D all the heating occurs in the edges of the swarm which means the shaded centre of the colloid distribution is uniform and confined by exponentially decaying tails where the heating occurs; in 2D, hysteresis occurs of discontinuous transitions between dense, opaque distributions and diffuse, transparent distributions as the incident power is tuned. These results provide insight into systems governed by non-local line-of-sight based interactions which may illuminate other active matter systems, such as phototaxis of micro-organisms or flocking in birds

Armed actors and the fallacy of the state: non-state dichotomy: State-mediated networks of coercive power in Mindanao

C

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Agent based modelling and simulation: An examination of customer retention in the UK mobile market

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.This work is funded by the Brunel Department of Information Systems and Computing (DISC