How many consumers are rational?

Rationality places strong restrictions on individual consumer behavior. This paper is concerned with assessing the validity of the integrability constraints imposed by standard utility maximization, arising in classical consumer demand analysis. More specifically, we characterize the testable implications of negative semidefiniteness and symmetry of the Slutsky matrix across a heterogeneous population without assuming anything on the functional form of individual preferences. In the same spirit, homogeneity of degree zero is being considered. Our approach employs nonseparable models and is centered around a conditional independence assumption, which is sufficiently general to allow for endogenous regressors. It is the only substantial assumption a researcher has to specify in this model, and has to be evaluated with particular care. Finally, we apply all concepts to British household data: We show that rationality is an acceptable description for large parts of the population, regardless of whether we test on single or composite households.

QoS-Aware Middleware for Web Services Composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming

Bethe Projections for Non-Local Inference

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference objectives, capturing powerful non-local statistics of the latent variables, while maintaining tractable and provably fast inference using non-Euclidean projected gradient descent with a distance-generating function given by the Bethe entropy. We demonstrate the performance and flexibility of our method by (1) extracting structured citations from research papers by learning soft global constraints, (2) achieving state-of-the-art results on a widely-used handwriting recognition task using a novel learned non-convex inference procedure, and (3) providing a fast and highly scalable algorithm for the challenging problem of inference in a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201

When bonds matter: home bias in goods and assets

Recent models of international equity portfolios exhibit two potential weaknesses: 1) the structure of equilibrium equity portfolios is determined by the correlation of equity returns with real exchange rates; yet empirically equities don't appear to be a good hedge against real exchange rate risk; 2) Equity portfolios are highly sensitive to preference parameters. This paper solves both problems. It first shows that in more general and realistic environments, the hedging of real exchange rate risks occurs through international bond holdings since relative bond returns are strongly correlated with real exchange rate fluctuations. Equilibrium equity positions are then optimally determined by the correlation of equity returns with the return on non-financial wealth, conditional on the bond returns. The model delivers equilibrium portfolios that are well-behaved as a function of the underlying preference parameters. We find reasonable empirical support for the theory for G-7 countries. We are able to explain short positions in domestic currency bonds for all G-7 countries, as well as significant levels of home equity bias for the US, Japan and Canada.

The value of collective reputation for environmentally-friendly production methods: The case of Val di Gresta

In this paper we investigate consumers' preferences for various environment-friendly production systems for carrots. We use discrete-choice multi-attribute stated-preference data to explore the effect of collective reputations from growers of an Alpine valley known for its environment-friendly production: Val di Gresta 'the valley of organic orchards'. Data analysis of the panel of discrete responses identifies unobserved taste heterogeneity for organic, bio-dynamic and place of origin, while observed heterogeneity for income is addressed by a piece-wise linear function. The implied sample distributions of individual-specific WTP for each of these random attributes are then compared. Results indicate that Integrated Pest Management is preferred to biodynamic as an emerging method. The presence of a premium for Val di Gresta produce is confirmed. The use of an experimental design to identify the relevant second order effects reveals the presence of a reputation effect which can be decomposed into a generic effect from place of origin and a specific one for each EFPMs. Farmers operating in geographically limited marginal areas, such as mountain valleys, may find it useful to invest in collective reputation through high quality standard to achieve higher returns. This strategy may compensate for the dwindling public support to farm income from EU programmes

Rational Asset Prices

The mean, co-variability, and predictability of the return of different classes of financial assets challenge the rational economic model for an explanation. The unconditional mean aggregate equity premium is almost seven percent per year and remains high after adjusting downwards the sample mean premium by introducing prior beliefs about the stationarity of the price-dividend ratio and the (non) forecastability of the long-term dividend growth and price-dividend ratio. Recognition that idiosyncratic income shocks are uninsurable and concentrated in recessions contributes toward an explanation. Also borrowing constraints over the investors' life cycle that shift the stock market risk to the saving middle-aged consumers contribute toward an explanation.

Intertemporal choice and consumption mobility

The theory of intertemporal consumption choice makes sharp predictions about the evolution of the entire distribution of household consumption, not just about its conditional mean. In the paper, we study the empirical transition matrix of consumption using a panel drawn from the Bank of Italy Survey of Household Income and Wealth. We estimate the parameters that minimize the distance between the empirical and the theoretical transition matrix of the consumption distribution. The transition matrix generated by our estimates matches remarkably well the empirical matrix, both in the aggregate and in samples stratified by education. Our estimates strongly reject the consumption insurance model and suggest that households smooth income shocks to a lesser extent than implied by the permanent income hypothesis. Klassifikation: D52, D91, I3

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy