We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments—active user modelling with preferences, and hierarchical reinforcement learning— and a discussion of the pros and cons of Bayesian optimization based on our experiences. 1

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments. Attitudes to risk are one of the primitives of economics. Individual preferences over risky prospects are taken as given and subjective in all standard economic theory. Turning to the characterization of risk in applied work, however, one observes many restrictive assumptions being used. In many cases individuals are simply assumed to be risk neutral;1 or perhaps to have the same constant absolute or relative aversion to risk.2 Assumptions buy tractability, of course, but at a cost. How plausible are the restrictive assumptions about risk attitudes that are popularly used? If they are not plausible, perhaps there is some way in which one can characterize the distribution of risk attitudes so that it can be used to analyze the implications of relaxing these assumptions. If so, such characterizations will condition inferences about choice behavior under uncertainty, bidding in auctions, and behavior in games.

User-Generated Content (UGC) on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers ’ multi-dimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide on average the best value for the consumer’s money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique dataset of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geo-mapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking

Individuals must often choose among discrete alternatives with imperfect information about their values. Before choosing, they may have an opportunity to study the options, but doing so is costly. This costly information acquisition creates new choices such as the number of and types of questions to ask. We model these situations using the rational inattention approach to information frictions. We find that the decision maker’s optimal strategy results in choosing probabilistically in line with a modified multinomial logit model. The modification arises because the decision maker’s prior knowledge and attention allocation strategy affect his evaluation of the alternatives. When the options are a priori homogeneous, the standard logit model emerges.

Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by binary features and associated weights. For instance, when choosing which mobile phone to buy the features to consider may be: long lasting battery, color screen, etc. Existing methods for inferring the parameters of the model assume pre-specified features. However, the features that lead to the observed choices are not always known. Here, we present a non-parametric Bayesian model to infer the features of the options and the corresponding weights from choice data. We use the Indian buffet process (IBP) as a prior over the features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described. The main contribution of this paper is an MCMC algorithm for the EBA model that can also be used in inference for other non-conjugate IBP models—this may broaden the use of IBP priors considerably. 1.

1Transition challenges for alternative fuel vehicle and transportation systems

Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-ofwords models for documents), exchangeability is sometimes assumed simply for computational reasons; non-exchangeable models might be a better choice for applications based on subject matter. Drawing on ideas from graphical models and phylogenetics, we describe a non-exchangeable prior for a class of nonparametric latent feature models that is nearly as efficient computationally as its exchangeable counterpart. Our model is applicable to the general setting in which the dependencies between objects can be expressed using a tree, where edge lengths indicate the strength of relationships. We demonstrate an application to modeling probabilistic choice. 1

To increase their revenue from electronic commerce, more and more Internet businesses are soliciting personal information from consumers so as to target products and services at the right consumers. But when deciding whether to disclose their personal information to Internet businesses, consumers may weigh the concerns of giving up information privacy against the benefits of information disclosure. This paper examines how Internet businesses can motivate consumers to disclose their personal information. Based on a synthesis of the literature, it identifies seven types of extrinsic or intrinsic benefits that Internet businesses can provide when soliciting personal information from consumers. Through comprehensive conceptual and empirical validation processes, it develops an instrument that allows Internet businesses to gauge the preference of consumers for the various types of benefits. By testing a set of nomological networks, it offers some ideas to Internet businesses about what types of benefits may be more effective given the personality traits of consumer populations. Besides providing a foundation for efforts at developing theories on information privacy and information disclosure, the results of this research provide useful suggestions to Internet businesses on how best to solicit personal information from consumers. Implications for research and practice are discussed. Key words: privacy, Internet business, information disclosure, extrinsic benefit, intrinsic benefit, personality, confirmatory factor analysis. We gratefully thank all participants in the 2002 NUS summer research workshop and the 2002 International Conference on Information Systems (ICIS). We also thank Ee-Cheah Tam for her contributions to a previous version of this manuscript, and Juan-Juan Han for her research

Aim of this paper is to give an extensive treatment of bipartite mean field spin systems, ordered and disordered: at first, bipartite ferromagnets are investigated, achieving an explicit expression for the free energy trough a new minimax variational principle. Furthermore via the Hamilton-Jacobi technique the same free energy structure is obtained together with the existence of its thermodynamic limit and the minimax principle is connected to a standard max one. The same is investigated for bipartite spin-glasses: By the Borel-Cantelli lemma a control of the high temperature regime is obtained, while via the double stochastic stability technique we get also the explicit expression of the free energy at the replica symmetric level, uniquely defined by a minimax variational principle again. A general results that states that the free energies of these systems are convex linear combinations of their independent one party model counterparts is achieved too. For the sake of completeness we show further that at zero temperature the replica symmetric entropy becomes negative and, consequently, such a symmetry must be broken. The treatment of the fully broken replica symmetry case is deferred to a forthcoming paper. As a first step in this direction, we start deriving the linear and quadratic constraints to overlap fluctuations.

The computer graphics and animation fields are filled with applications that require the setting of tricky parameters. In many cases, the models are complex and the parameters unintuitive for non-experts. In this paper, we present an optimization method for setting parameters of a procedural fluid animation system by showing the user examples of different parametrized animations and asking for feedback. Our method employs the Bayesian technique of bringing in “prior ” belief based on previous runs of the system and/or expert knowledge, to assist users in finding good parameter settings in as few steps as possible. To do this, we introduce novel extensions to Bayesian optimization, which permit effective learning for parameter-based procedural animation applications. We show that even when users are trying to find a variety of different target animations, the system can learn and improve. We demonstrate the effectiveness of our method compared to related active learning methods. We also present a working application for assisting animators in the challenging task of designing curl-based velocity fields, even with minimal domain knowledge other than identifying when a simulation “looks right”.