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Conference on Agent-Based Modeling in Philosophy, Scott Page (Michigan) gives a talk at the Conference on Agent-Based Modeling in Philosophy (11-13 December, 2014) titled "Collective Accuracy: Agent Based & Emergent vs Statistical and Assumed". Abstract: In this talk, I describe two broad classes of models that can explain collective accuracy, what is more commonly referred to as the wisdom of crowds. The first model is based on statistical/law of large numbers logic. Accuracy emerges from the cancellation of random errors. The second model has roots in computer science and psychology. It assumes that predictions come from models. Different predictions arise because of different model. I then describe how in agent based models the amount model diversity, and therefore the accuracy of the collective emerges. It is possible to write difference equations that explain average diversity levels. The talk will summarize papers written with Lu Hong, Maria Riolo, PJ Lamberson, and Evan Economo. [LMUcast ID: ]

Conference Agent-Based Modeling in Philosophy, Michael Weisberg (Pennsylvania) gives a talk at the Conference on Agent-Based Modeling in Philosophy (11-13 December, 2014) titled "Agent-based Models and Confirmation Theory". Abstract: Is it possible to develop a confirmation theory for agent-based models? The are good reasons to be skeptical: Classical confirmation theory explains how empirical evidence bears on the truth of hypotheses and theories, while agent-based models are almost always idealized and hence known to be false. Moreover, classical ideas about confirmation have been developed for relatively simple hypotheses, while even the simplest agent-based models have thousands of variables. Nevertheless, we can draw on ideas from confirmation theory in order to develop an account of agent-based model confirmation. Theorists can confirm hypotheses about model/world relations, and they can also use a variety of techniques to investigate the reliability of model results. This paper is an exploration of these possibilities. [LMUcast ID: ]

Conference Agent-Based Modeling in Philosophy, Rosaria Conte (Institute for Cognitive Science and Technology/Rome) gives a talk at the Conference on Agent-Based Modeling in Philosophy (11-13 December, 2014) titled "Minding Norms. Mechanisms and Dynamics of Social Order in Agent Societies". Abstract: Despite ubiquity and universality, norms are still awaiting for a general comprehensive theory. In the presentation, a conceptual, theoretical, and computational framework will be proposed to provide a general account of norms, enabling us to investigate: (a) differences and commonalities among social, moral, and legal norms; (b) norm emergence and change; (c) the individual properties involved or responsible for bringing about norms. The main thesis is that observable conformity is only the tip of the normative iceberg, and that norms cannot emerge in society if they do not previously immerge in the mind, i.e. if they are not first converted into mental representations of some sort. [LMUcast ID: ]

Conference on Agent-Based Modeling in Philosophy, Jason Alexander (LSE) gives a talk at the Conference on Agent-Based Modeling in Philosophy (11-13 December, 2014) titled "The Structural Evolution of Morality". Abstract: One general problem faced by attempts to explain the origins of morality using traditional rational choice theory is that the demands of rationality and the demands of morality often fail to coincide. This can happen in at least three different ways. Sometimes our moral intuitions recommend actions which are identified as irrational (such as cooperating in the prisoner's dilemma or in the centipede game, or rejecting unfair offers in the ultimatum game). Sometimes our moral intuitions recommend an act which is only one of several recognised as rational (as can happen in games having multiple Nash equilibria). And sometimes we have multiple competing moral intuitions in cases where rationality recommends a unique act (such as in asymmetric bargaining games, in contrast to the Nash solution). In this talk, I present a number of results drawn from agent-based models of imitative learning on social networks, showing how this single framework manages to explain many of our moral intuitions across a wide variety of diverse cases. [LMUcast ID: ]