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The Invention of New Strategies in Bargaining Games
Full text linkBargaining games have played a prominent role in modeling the evolution of social conventions. Previous models generally assumed that agents must choose from a predetermined, finite set of strategy options. Here, I present a new model of two agents learning in bargaining games in which new strategies must be invented and reinforced. I use simulations to study the dynamics of the model and to test the extent to which it leads to outcomes that are fair or efficient. Mean demands peak a little below the fair solution, with a moderate variation around this. Mean rewards are a little lower than mean demands. The outcomes are somewhat efficient, but a significant part of the resource is wasted nonetheless. I investigate several modifications of the model, by implementing two forms of forgetting, and restricting the set of strategies that can be invented. One form of forgetting increases the average fairness and decreases the variation and improves the efficiency, a second form widens the variation, with little change to the efficiency. I test one restriction of the possible strategies, which has little overall effect on the fairness and efficiency
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Polarization and Factionalization for Agents with Multiple, Related Beliefs
Full text linkEpistemic polarization arises when the statistical dispersion of a population's beliefs increases, especially when all agents update on exactly the same evidence. Factionalization arises when not just one belief, but many different beliefs become correlated across a population. Polarization and factionalization have generally been viewed as examples of human irrationality. In this dissertation I study the phenomena of epistemic convergence, polarization and factionalization for ideally rational agents, with multiple, probabilistically related beliefs. I demonstrate that rational belief polarization arises very naturally for such agents, even when they update on identical evidence. I demonstrate that probabilistic relations between beliefs can drive various kinds of belief convergence, polarization, and factionalization. Importantly, polarization and factionalization arise generically, without needing specific initial conditions. Under certain circumstances, polarization can even be rationally anticipated in advance. Furthermore, I show that a population of rational agents, should always expect their beliefs to either converge or factionalize under certain conditions
Effective Theory Building and Manifold Learning
Full text linkManifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains
Efficiency and fairness trade-offs in two player bargaining games
Full text linkRecent work on the evolution of social contracts and conventions has often used models of bargaining games, with reinforcement learning. A recent innovation is the requirement that every strategy must be invented either through through learning or reinforcement. However, agents frequently get stuck in highly-reinforced “traps” that prevent them from arriving at outcomes that are efficient or fair to the both players. Agents face a trade-off between exploration and exploitation, i.e. between continuing to invent new strategies and reinforcing strategies that have already become highly reinforced by yielding high rewards. In this paper I systematically study the relationship between rates of invention and the efficiency and fairness of outcomes in two-player, repeated bargaining games. I use a basic reinforcement learning model with invention, and five variations of this model, designed introduce various forms of forgetting, to prioritize more recent reinforcement, or to maintain a higher rate of invention. I use computer simulations to investigate the outcomes of each model. Each models shows qualitative similarities in the relationship between the efficiency and fairness of outcomes, and the relative amount of exploration or exploitation that takes place. Surprisingly, there are often trade-offs between the efficiency and the fairness of the outcomes
Effective Theory Building and Manifold Learning
Full text linkManifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains
Rational Factionalization for Agents with Probabilistically Related Beliefs
Full text linkGeneral epistemic polarization arises when the beliefs of a population grow further apart, in particular when all agents update on the same evidence. Epistemic factionalization arises when the beliefs grow further apart, but different beliefs also become correlated across the population. I present a model of how factionalization can emerge in a population of ideally rational agents. This kind of factionalization is driven by probabilistic relations between beliefs, with background beliefs shaping how the agents' beliefs evolve in the light of new evidence. Moreover, I show that in such a model, the only possible outcomes from updating on identical evidence are general convergence or factionalization. Beliefs cannot spread out in all directions: if the beliefs overall polarize, then it must result in factionalization
Effective Theory Building and Manifold Learning
No full textManifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains.33 page
Effective Theory Building and Manifold Learning
Full text linkManifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains
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