Enhancement of Multiobjective Hierarchical Bayesian Optimization Algorithm using Sporadic Model Building

This paper describes and analyzes the efficiency enhancement of Multiobjective hierarchical Bayesian Optimization Algorithm (mohBOA) by using Sporadic Model Building (SMB). Firstly, Multiobjective hierarchical Bayesian Optimization Algorithm is shortly described. Secondly, sporadic model building is presented. Using sporadic model building, the structure of a probabilistic model is updated once every few iterations, whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time of learning the structure of a model is much longer than the time of updating model parameters, sporadic model building decreases the total time complexity of model building. The results of experiments show that the theoretical predictions about using sporadic model building to the enhancement of mohBOA are true. Finally, short discussion about the results of experiments is added

Enhancement of Multiobjective Hierarchical Bayesian Optimization Algorithm using Sporadic Model Building

This paper describes and analyzes the efficiency enhancement of Multiobjective hierarchical Bayesian Optimization Algorithm (mohBOA) by using Sporadic Model Building (SMB). Firstly, Multiobjective hierarchical Bayesian Optimization Algorithm is shortly described. Secondly, sporadic model building is presented. Using sporadic model building, the structure of a probabilistic model is updated once every few iterations, whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time of learning the structure of a model is much longer than the time of updating model parameters, sporadic model building decreases the total time complexity of model building. The results of experiments show that the theoretical predictions about using sporadic model building to the enhancement of mohBOA are true. Finally, short discussion about the results of experiments is added

Epistemological Foundations for Neuroeconomics

Neuroeconomics is an emerging field crossing neuroscientific data, the use of brain-imaging tools, experimental and behavioral economics, and an attempt at a better understanding of the cognitive assumptions that underlie theoretical predictive economic models. In this paper the authors try two things: 1) To assess the epistemological biases that affect Neuroeconomics as it is currently done. A number of significant experiments are discussed in that perspective. 2) To imagine an original way - apart from what is already being done - to run experiments in brain-imaging that are relevant to the discussion of rationality assumptions at the core of economic theory.Neuroeconomics, Rationality Assumptions, Abduction

Unnatural pedagogy : a computational analysis of children\u27s learning to learn from other people.

Infants rely on others for much of what they learn. People are a ready source of quick information, but people produce data differently than the world. Data from a person are a result of that person\u27s knowledgeability and intentions. People may produce inaccurate or misleading data. On the other hand, if a person is knowledgeable about the world and intends to teach, that person may produce data that are more useful than simply accurate data: data that are pedagogical. This idea that people have special innate methods for efficient information transfer lies at the heart of recent proposals regarding what makes humans such powerful knowledge accumulators. These innate assumptions result in developmental patterns observed in epistemic trust research. This research seeks to create a computational account of the development of these abilities. We argue that pedagogy is not innate, but rather that people learn to learn from others. We employ novel computational models to show that there is sufficient data early on from which infants may learn that people choose data pedagogically, that the development of children\u27s epistemic trust is primarily a result of their decreasing beliefs that all informants are helpful, and that innate pedagogy would not lead to more rapid learning. We connect results from the pedagogy and epistemic trust literatures across tasks and development, showing that these are different manifestations of the same underlying abilities, and show that pedagogy need not be innate to have powerful implications for learning

The Federal Trade Commission and Online Consumer Contracts

Consumer contracts have long posed a challenge for traditional contract enforcement regimes. With the rise in quick online transactions involving clickwrap and browsewrap contracts, these challenges only become more pressing. This Note identifies the problems inherent in the current system and explores proposals and past attempts to improve online consumer contract interpretation and enforcement. Ultimately, this Note identifies the Federal Trade Commission (“FTC”) as an appropriate and effective agency to provide the much-needed change to online consumer contract enforcement. Based upon its authority under Section 5 of the Federal Trade Commission Act to regulate unfair business practices, the broad discretion that Congress has afforded the FTC, and its successful incursion into the related field of online privacy law, the FTC is uniquely situated to promulgate a new online consumer contracting regime. This Note illustrates the basis and precedent for such a step and explores the form and effects of FTC involvement in online consumer contracts

Monte Carlo Planning method estimates planning horizons during interactive social exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference