ACE Models of Endogenous Interactions

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).Endogenous interaction, Agent-based Computational Economics (ACE)

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented

Altruistically Inclined?: The Behavioral Sciences, Evolutionary Theory, and the Origins of Reciprocity

Altruistically Inclined? examines the implications of recent research in the natural sciences for two important social scientific approaches to individual behavior: the economic/rational choice approach and the sociological/anthropological. It considers jointly two controversial and related ideas: the operation of group selection within early human evolutionary processes and the likelihood of modularity—domain-specific adaptations in our cognitive mechanisms and behavioral predispositions. Experimental research shows that people will often cooperate in one-shot prisoner\u27s dilemma (PD) games and reject positive offers in ultimatum games, contradicting commonly accepted notions of rationality. Upon first appearance, predispositions to behave in this fashion could not have been favored by natural selection operating only at the level of the individual organism. Emphasizing universal and variable features of human culture, developing research on how the brain functions, and refinements of thinking about levels of selection in evolutionary processes, Alexander J. Field argues that humans are born with the rudiments of a PD solution module—and differentially prepared to learn norms supportive of it. His emphasis on failure to harm, as opposed to the provision of affirmative assistance, as the empirically dominant form of altruistic behavior is also novel. The point of departure and principal point of reference is economics. But Altruistically Inclined? will interest a broad range of scholars in the social and behavioral sciences, natural scientists concerned with the implications of research and debates within their fields for the conduct of work elsewhere, and educated lay readers curious about essential features of human nature.https://scholarcommons.scu.edu/faculty_books/1325/thumbnail.jp

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (catneuro) To The Deep Learning Of Game Controller

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises about whether KD mechanisms that also incorporate cooperation can perform better in such environments than purely competitive ones? Borrowing from game theory, game-based KD mechanisms are implemented and tested against the default competitive mechanism – Weighted Majority (WTD). Two different concepts of complexity are addressed – numerical optimization under dynamic environments and hierarchal, multi-objective optimization for evolving deep learning models. The former is addressed with the CATGame software system and the later with CATNeuro. CATGame implements three types of games that span both cooperation and competition for knowledge distribution, namely: Iterated Prisoner\u27s Dilemma (IPD), Stag-Hunt and Stackelberg. The performance of the three game mechanisms is compared with the aid of a dynamic problem generator called Cones World. Weighted Majority, aka “wisdom of the crowd”, the default CA competitive KD mechanism is used as the benchmark. It is shown that games that support both cooperation and competition do indeed perform better but not in all cases. The results shed light on what kinds of games are suited to problem solving in complex, dynamic environments. Specifically, games that balance exploration and exploitation using the local signal of ‘social’ rank – Stag-Hunt and IPD – perform better. Stag-Hunt which is also the most cooperative of the games tested, performed the best overall. Dynamic analysis of the ‘social’ aspects of the CA test runs shows that Stag-Hunt allocates compute resources more consistently than the others in response to environmental complexity changes. Stackelberg where the allocation decisions are centralized, like in a centrally planned economic system, is found to be the least adaptive. CATNeuro is for solving neural architecture search (NAS) problems. Contemporary ‘deep learning’ neural network models are proven effective. However, the network topologies may be complex and not immediately obvious for the problem at hand. This has given rise to the secondary field of neural architecture search. It is still nascent with many frameworks and approaches now becoming available. This paper describes a NAS method based on graph evolution pioneered by NEAT (Neuroevolution of Augmenting Topologies) but driven by the evolutionary mechanisms under Cultural Algorithms. Here CATNeuro is applied to find optimal network topologies to play a 2D fighting game called FightingICE (derived from “The Rumble Fish” video game). A policy-based, reinforcement learning method is used to create the training data for network optimization. CATNeuro is still evolving. To inform the development of CATNeuro, in this primary foray into NAS, we contrast the performance of CATNeuro with two different knowledge distribution mechanisms – the stalwart Weighted Majority and a new one based on the Stag-Hunt game from evolutionary game theory that performed the best in CATGame. The research shows that Stag-Hunt has a distinct edge over WTD in terms of game performance, model accuracy, and model size. It is therefore deemed to be the preferred mechanism for complex, hierarchical optimization tasks such as NAS and is planned to be used as the default KD mechanism in CATNeuro going forward

Game theoretic modeling and analysis : A co-evolutionary, agent-based approach

Ph.DDOCTOR OF PHILOSOPH

Exertion interfaces : sports over a distance for social bonding and fun

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 141-148).Social interaction is an essential part of human communication, however the participants are often miles apart. Technological advances strive to bridge the physical distances between people, but generally lack the social potential offered through activities such as sports and games. An Exertion Interface combines the strength of both: the ability of sports to connect people socially, and the ability of telecommunications to connect people over a distance. By requiring intense physical effort from the participants, an Exertion Interface creates a better social bonding experience than traditional computer interfaces. This assertion is tested through the creation of a system in which two remotely-located participants play a physically demanding ball game against each other while communicating through an overlaid life-size video-conference. A study with 56 participants showed that players who used the Exertion Interface played longer, said they got to know the other player better, had more fun, became better friends, and were happier with the transmitted audio and video quality in comparison to a control group playing the game with the same video-conferencing setup, but using a traditional keyboard interface. Exertion Interfaces, which are easy to learn, but hard to master, open a door to a new world of interfaces that facilitate social interaction between remote individuals.by Florian Mueller.S.M

Distributed multi-agent based traffic management system

Ph.DDOCTOR OF PHILOSOPH

Defining the mechanisms of a cooperative computer system based on theories of cooperation

There is a growing interest in the development of computer systems that are actively involved in the tasks of the users and serve to augment the users' creativity. Cooperative computing is a major contribution to this research field. A survey of current developments in knowledge based systems led to the conclusion that there has hitherto been an absence of a formal definition of the mechanisms of cooperative computer systems based on theories of cooperation. The work in this thesis seeks to provide a full definition of cooperation derived from the behaviours of living cooperative systems. Studies on human cooperation and cooperation in the animal kingdom, established that cooperation is a dynamic behaviour; in that the interaction processes between the cooperative partners serve to facilitate the achievement of a common goal, or a set of goals that are mutually desired by the partners. Partners in cooperation are interdependent: one member's actions are contingent on another. Therefore, the underlying processes which induce and maintain cooperation were identified. These are: communication between the partners; emergence of norms and roles governing the behaviour of the cooperating members; resolution of conflicts; distributed and coordinated activities. These factors were further elucidated within the context of small problem solving groups. A model of cooperationw hich encapsulatedth esef actors was produced. From the discussionso f the advantageso f cooperationw ithin different contexts, the potential for synergy was found to be the main benefit of cooperation. The potential for achieving this synergy between a human and a computer is the main motivation for the work undertaken in this research. From the theoretical analysis of cooperation, the underlying mechanisms of a cooperative computer were successfully defined. A conceptual model of human-computer cooperation was presented. It was established that the quality of cooperation is closely associated with the nature of the task. Therefore, it is not practicable to produce a general purpose cooperative system. A specific task must be used. Creative tasks of a problem identifying and solving nature, were found to be more suitable to cooperative behaviour than others. Typical of these, and the one selected, was computer screen design. Current screen design practice was analysed, and the functional requirements and knowledge base needs of the systems were established. The underlying mechanisms of cooperation were formalised and successfully implemented within a software exemplar, named COSY. COSY exhibits the behavioural characteristics of cooperation, and utilises the knowledge of screen design to support users in the task of formatting computer screens. COSY successfully demonstrated the synergistic relationship in its cooperation with the users. It is concluded that the approach undertaken in this thesis has lead to a successful definition and implementation of the formal mechanisms of cooperation in a computer system, one which potentially enhances the innovative and creative aspects of design work