Group Selection: The quest for social preferences

This paper surveys the literature on group selection. I describe the early contributions and the group selection controversy. I also describe the main approaches to group selection in the recent literature; fixation, assortative group formation, and reproductive externalities.Altruism; spite; externalities; conformity; fixation; signalling

Accuracy, Certainty and Surprise - A Prediction Market on the Outcome of the 2002 FIFA World Cup

In this chapter, we present our empirical investigation of the forecasting accuracy of a prediction market experiment drawn on the outcome of the World Cup 2002. We analyse the predictive accuracy of 64 markets and compare to bookmakers’ quotes and chance as benchmarks. We revisit the evaluation of Schmidt and Werwatz (Chapter 16) and compare our results directly to their findings. In addition, we propose a new method for testing predictive accuracy by means of a non-parametric test for the similarity of probability distributions and we evaluate the incorporation of information in market prices by comparing pre-match and half-time price data. We find a reversed favourite-longshot bias when analysing market prices before the start of the match and this bias does not disappear with the inflow of new information until half-time. Unlike the market based predictions bookmakers appear to be perfectly calibrated. Since there were substantial deviations in outcome between the 2000 European Championship and our data, we offer possible explanations for the much worse performance of the 2002 World Cup prediction market. Consistent with Schmidt and Werwatz (Chapter 16) prediction markets do assign relatively higher probabilities to the favourite when compared to the odds-setters. Together with a long streak of surprising outcomes this fact appears most likely to be responsible for the predictive inaccuracy.

Strategic Voting in Multiparty Systems: A Group Experiment

The paper tests the theory of strategic voting for multiparty systems with proportional representation and coalition governments at the micro-level. The study focuses in particular on the question whether participation in repeated elections allows voters to learn from experience and enables them to optimize their decision behavior. An economic group experiment with decision scenarios of varying degrees of difficulty was used to test decision making at both the individual and group level. The results suggest that a majority of voters were able to pursue successful decision strategies and that the difficulty of the decision scenarios affected the voting performance of the participants as expected. However, a learning effect is not supported by the data.

A Computational Model of Creative Design as a Sociocultural Process Involving the Evolution of Language

The aim of this research is to investigate the mechanisms of creative design within the context of an evolving language through computational modelling. Computational Creativity is a subfield of Artificial Intelligence that focuses on modelling creative behaviours. Typically, research in Computational Creativity has treated language as a medium, e.g., poetry, rather than an active component of the creative process. Previous research studying the role of language in creative design has relied on interviewing human participants, limiting opportunities for computational modelling. This thesis explores the potential for language to play an active role in computational creativity by connecting computational models of the evolution of artificial languages and creative design processes. Multi-agent simulations based on the Domain-Individual-Field-Interaction framework are employed to evolve artificial languages with features that may support creative designing including ambiguity, incongruity, exaggeration and elaboration. The simulation process consists of three steps: (1) constructing representations associating topics, meanings and utterances; (2) structured communication of utterances and meanings through the playing of “language games”; and (3) evaluation of design briefs and works. The use of individual agents with different evaluation criteria, preferences and roles enriches the scope and diversity of the simulations. The results of the experiments conducted with artificial creative language systems demonstrate the expansion of design spaces by generating compositional utterances representing novel concepts among design agents using language features and weighted context free grammars. They can be used to computationally explore the roles of language in creative design, and possibly point to computational applications. Understanding the evolution of artificial languages may provide insights into human languages, especially those features that support creativity

Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Summarizing knowledge from animals and human beings inspires robotic innovations. In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments. Inspired by large pre-trained models witnessed with impressive performance in language and image understanding, we introduce the power of advanced deep generative models to produce motor control signals stimulating legged robots to act like real animals. Unlike conventional controllers and end-to-end RL methods that are task-specific, we propose to pre-train generative models over animal motion datasets to preserve expressive knowledge of animal behavior. The pre-trained model holds sufficient primitive-level knowledge yet is environment-agnostic. It is then reused for a successive stage of learning to align with the environments by traversing a number of challenging obstacles that are rarely considered in previous approaches, including creeping through narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc. Finally, a task-specific controller is trained to solve complex downstream tasks by reusing the knowledge from previous stages. Enriching the knowledge regarding each stage does not affect the usage of other levels of knowledge. This flexible framework offers the possibility of continual knowledge accumulation at different levels. We successfully apply the trained multi-level controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles, and play in a designed challenging multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the robots. The present research pushes the frontier of robot control with new insights on reusing multi-level pre-trained knowledge and solving highly complex downstream tasks in the real world

Developmental learning of internal models for robotics

Abstract: Robots that operate in human environments can learn motor skills asocially, from selfexploration, or socially, from imitating their peers. A robot capable of doing both can be more ~daptiveand autonomous. Learning by imitation, however, requires the ability to understand the actions ofothers in terms ofyour own motor system: this information can come from a robot's own exploration. This thesis investigates the minimal requirements for a robotic system than learns from both self-exploration and imitation of others. .Through self.exploration and computer vision techniques, a robot can develop forward 'models: internal mo'dels of its own motor system that enable it to predict the consequences of its actions. Multiple forward models are learnt that give the robot a distributed, causal representation of its motor system. It is demon~trated how a controlled increase in the complexity of these forward models speeds up the robot's learning. The robot can determine the uncertainty of its forward models, enabling it to explore so as to improve the accuracy of its???????predictions. Paying attention fO the forward models according to how their uncertainty is changing leads to a development in the robot's exploration: its interventions focus on increasingly difficult situations, adapting to the complexity of its motor system. A robot can invert forward models, creating inverse models, in order to estimate the actions that will achieve a desired goal. Switching to socialleaming. the robot uses these inverse model~ to imitate both a demonstrator's gestures and the underlying goals of their movement.Imperial Users onl

Accuracy, certainty and surprise : a prediction market on the outcome of the 2002 FIFA World Cup

In this chapter, we present our empirical investigation of the forecasting accuracy of a prediction market experiment drawn on the outcome of the World Cup 2002. We analyse the predictive accuracy of 64 markets and compare to bookmakers’ quotes and chance as benchmarks. We revisit the evaluation of Schmidt and Werwatz (Chapter 16) and compare our results directly to their findings. In addition, we propose a new method for testing predictive accuracy by means of a non-parametric test for the similarity of probability distributions and we evaluate the incorporation of information in market prices by comparing pre-match and half-time price data. We find a reversed favourite-longshot bias when analysing market prices before the start of the match and this bias does not disappear with the inflow of new information until half-time. Unlike the market based predictions bookmakers appear to be perfectly calibrated. Since there were substantial deviations in outcome between the 2000 European Championship and our data, we offer possible explanations for the much worse performance of the 2002 World Cup prediction market. Consistent with Schmidt and Werwatz (Chapter 16) prediction markets do assign relatively higher probabilities to the favourite when compared to the odds-setters. Together with a long streak of surprising outcomes this fact appears most likely to be responsible for the predictive inaccuracy

Structured Memetic Automation for Online Human-like Social Behavior Learning

Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior