CSM-401 - Population based Incremental Learning vesus Genetic Algorithms: Iterated Prisoners Dilemma

Axelrod?s originally experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that while PBIL performs well, GA in general does slightly better although more experiments should be conducted

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

On Suicidal punishment among "Acromyrmex Versicolor" Cofoundresses: the Disadvantage in Personal Advantage

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

Ph.DDOCTOR OF PHILOSOPH

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

Exploring Hopes And Fears From Supply Chain Innovations: An Analysis Of Antecedents And Consequences Of Supply Chain Knowledge Exchanges

This dissertation sheds light on severalhopes and fears from supply chain innovation in three distinct papers. Paper one introduces the concept of Process Innovation Propagation as an appropriation technique helping to extract the most returns out of a process innovation by exporting to supply chain partners. Paper two devises and empirically tests knowledge properties that best lead to radical and incremental supply chain innovative capabilities. Lastly, paper three conducts an exploratory study that introduces factors affecting a firm’s optimum supply chain innovation strategy. The dissertation makes a strong argument that supply chain innovation is most prominently governed by power asymmetry that may either help or hurt innovative performance

A Common Protocol for Agent-Based Social Simulation

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.Agent-Based, Simulations, Methodology, Calibration, Validation, Sensitivity Analysis

A complexidade da cooperação climática internacional

Although there are several collective efforts to address the problem of climate change, the main initiatives, such as the Kyoto Protocol and the Paris Agreement, have not shown satisfactory results so far. The difficulty in engaging states into effective coordinated cooperative practices can be explained as a consequence of neoclassical rationality, given that the characterization of states as rationality-endowed entities bound them to situations like the Prisoners' Dilemma (PD) game and its related collective action dilemmas. There are models that provide ways to circumvent PD and foster cooperation among selfish rational agents, such as the application of strategies based on reciprocity (Tit-for-Tat) in iterated games. However, these approaches do not avoid the short-sighted neoclassical rationality that lies at the root of the problem. Thus, in order to develop more productive approaches to the development of global climate change policies, I present a characterization of the international political system as a complex adaptive system (CAS) and argue that this perspective, along with models based on evolutionary games rather than iterated games, provide a more promising approach.Embora existam vários esforços coletivos para enfrentar o problema das mudanças climáticas, as principais iniciativas, como o Protocolo de Quioto e o Acordo de Paris, não têm apresentado resultados satisfatórios até o momento. A dificuldade em envolver os Estados em práticas cooperativas coordenadas efetivas pode ser explicada como consequência da racionalidade neoclássica, uma vez que a caracterização dos Estados como entidades dotadas de racionalidade os vincul a situações como o jogo do Dilema do Prisioneiro (DP), bem como os dilemas da ação coletiva relacionados a esse jogo. Existem modelos que fornecem maneiras de contornar o PD e promover a cooperação entre agentes racionais egoístas, como por exemplo a aplicação de estratégias baseadas na reciprocidade (Tit-for-Tat) em jogos iterados. No entanto, essas abordagens não evitam a racionalidade neoclássica de curto prazo, que está na raiz do problema. Assim, para desenvolver abordagens mais produtivas para o desenvolvimento de políticas globais para lidar com a mudança climática, apresento uma caracterização do sistema político internacional como um sistema adaptativo complexo (CAS) e argumento que essa perspectiva, acompanhada de modelos baseados em jogos evolutivos em vez de jogos iterados, fornece uma abordagem mais promissora