Leave and let leave: A sufficient condition to explain the evolutionary emergence of cooperation

The option to leave your current partner in response to his behavior, also known as conditional dissociation, is a mechanism that has been shown to promote the emergence and stability of cooperation in many social interactions. This mechanism, nevertheless, has always been studied in combination with other factors that are known to support cooperation by themselves. In this paper, we isolate the effect of conditional dissociation on the evolution of cooperation and show that this mechanism is enough to sustain a significant level of cooperation if the expected lifetime of individuals is sufficiently longACCESS (EU, 12-120610), SIMULPAST (MICINN, CSD2010-00034) and SPPORT (JCyL, VA056A12-2). L.R.I. Spanish Ministry of Education for grant JC2009-0026

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

Ph.DDOCTOR OF PHILOSOPH

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

Reciprocity and Prejudice: An Experiment of Hindu-Muslim Cooperation in the Slums of Mumbai

The dissertation develops and tests a new theory to explain intergroup cooperation and outgroup discrimination. The theoretical part specifies under what conditions ethnic differences undermine public goods provision and exacerbate ethnic discrimination. It posits that people cooperate more with and discriminate less against the groups expected to reciprocate cooperative behavior. Conditional cooperators rationally update their group stereotypes based on their experience with the groups' individual members. This change in turn reduces prejudice and discrimination. I tested observable implications of the model on a representative sample of more than 400 slum-dwellers in Mumbai. The field research in India combined laboratory experiments, an original survey, and interviews. Once I manipulated expectations of reciprocity, ethnically heterogeneous groups produced as much public goods as the homogeneous ones. The experimental treatment also radically increased trust and reduced ethnic discrimination of the generally mistrusted Muslim minority. The survey analysis compared the real-life effect of reciprocity with prominent alternative explanations from the literature. Compared to other factors, positive reciprocity provides a powerful explanation of why people choose to discriminate against some, but not other ethnic groups. The cross-national chapter of the dissertation extends the analysis beyond India. Using surveys from 87 countries, it shows that generalized trust moderates the negative effect of ethnic diversity on people's willingness to contribute to public goods

Dynamical Evolutionary Psychology: Individual Decision Rules and Emergent Social Norms

A new theory integrating evolutionary and dynamical approaches is proposed. Following evolutionary models, psychological mechanisms are conceived as conditional decision rules designed to address fundamental problems confronted by human ancestors, with qualitatively different decision rules serving different problem domains and individual differences in decision rules as a function of adaptive and random variation. Following dynamical models, decision mechanisms within individuals are assumed to unfold in dynamic interplay with decision mechanisms of others in social networks. Decision mecha-nisms in different domains have different dynamic outcomes and lead to different sociospatial geome-tries. Three series of simulations examining trade-offs in cooperation and mating decisions illustrate how individual decision mechanisms and group dynamics mutually constrain one another, and offer insights about gene–culture interactions. Evolutionary psychology and dynamical systems theory have both been proposed as antidotes to the theoretical fragmentation that long characterized the field of psychology. Evolutionary psy-chologists have proposed that isolated psychological research top-ics such as aggression, taste aversion, language acquisition, mate selection, and spatial cognition can be connected to research on cultural anthropology, ecology, zoology, genetics, and physiology via principles of modern Darwinian theory (e.g., Buss, 1995; Kenrick, 1994; Lumsden & Wilson, 1981; Tooby & Cosmides, 1992). Dynamical systems theorists have searched for even more fundamental principles: general rules capable of linking informa-tion processing in the human brain with processes found in eco-nomic markets, biological ecosystems, and worldwide weathe

Policy Implications of Economic Complexity and Complexity Economics

Complexity economics has developed into a promising cutting-edge research program for a more realistic economics in the last three or four decades. Also some convergent micro- and macro-foundations across heterodox schools have been attained with it. With some time lag, boosted by the financial crisis 2008ff., a surge to explore economic complexity’s (EC) policy implications emerged. It demonstrated flaws of “neoliberal” policy prescriptions mostly derived from the neoclassical mainstream and its relatively simple and teleological equilibrium models. However, most of the complexity-policy literature still remains rather general. Therefore, policy implications of EC are reinvestigated here. EC usually is specified by “Complex Adaptive (Economic) Systems” [CA(E)S], characterized by mechanisms, dynamic and statistical properties such as capacities of “self-organization” of their components (agents), structural “emergence”, and some statistical distributions in their topologies and movements. For agent-based systems, some underlying “intentionality” of agents, under bounded rationality, includes improving their benefits and reducing the perceived complexity of their decision situations, in an evolutionary process of a population. This includes emergent social institutions. Thus, EC has manifold affinities with long-standing issues of economic heterodoxies, such as uncertainty or path- dependent and idiosyncratic process. We envisage a subset of CA(E)S, with heterogeneous agents interacting, in the “evolution-of-cooperation” tradition. We exemplarily derive some more specific policy orientations, in a “framework” approach, embedded in a modern “meritorics”, that we call Interactive Policy

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included