Contributions in Merlin

This paper studies the evolution of wealth shares of portfolio rules in incomplete markets with short-lived assets. Prices are determined endogenously. The performance of a portfolio rule in the process of repeated reinvestment of wealth is determined by the wealth share eventually conquered in competition with other portfolio rules. Using random dynamical systems theory, we derive necessary and sufficient conditions for the evolutionary stability of portfolio rules. In the case of Markov (in particular i.i.d.) payoffs these local stability conditions lead to a simple portfolio rule that is the unique evolutionary stable strategy. This rule possesses an explicit representation. Moreover, it is demonstrated that mean–variance optimization is not evolutionary stable while the CAPM-rule always imitates the best portfolio rule and survives.

We study the effect on loan conditions of geographical distance between firms, the lending bank, and all other banks in the vicinity. For our study, we employ detailed contract information from more than 15,000 bank loans to small firms comprising the entire loan portfolio of a large Belgian bank. We report the first comprehensive evidence on the occurrence of spatial price discrimination in bank lending. Loan rates decrease with the distance between the firm and the lending bank and increase with the distance between the firm and competing banks. Transportation costs cause the spatial price discrimination we observe.

In this paper we introduce the approach of direct interruptability inference from accelerometer and audio data and show that it provides highly accurate and robust predictions. Furthermore, we argue that scenarios are central for evaluating the performance of interruptability predicting devices and prove it on our setup. We also demonstrate that scenarios provide the foundation for avoiding misleading results, assessing the results’ generalizability, and provide the basis for a stratified scenario-based learning model, which greatly speeds-up the training of such devices.

The new generation of commodity graphics cards with significant on-board video memory has become widely popular and provides high-performance rendering and flexibility. One of the features to be exploited with this hardware is the use of the on-board video memory to store geometry information. This strategy significantly reduces the data transfer overhead from sending geometry data over the (AGP) bus interface from main memory to the graphics card. However, taking advantage of cached geometry is not a trivial task because the data models often exceed the memory size of the graphics card. In this paper we present a dynamic Cached Geometry Manager (CGM) to address this issue. We show how this technique improves the performance of real-time view-dependent level-of-detail (LOD) selection and rendering algorithms of large data sets. Alternative caching approaches have been analyzed over two different view-dependent progressive mesh (VDPM) frameworks: one for rendering of arbitrary manifold 3D meshes, and one for terrain visualization.

We consider economies with additively separable utility functions and give conditions for the two-agents case under which the existence of sunspot equilibria is equivalent to the occurrence of the transfer paradox. This equivalence enables us to show that sunspots cannot matter if the initial economy has a unique spot market equilibrium and there are only two commodities or if the economy has a unique equilibrium for all distributions of endowments induced by asset trade. For more than two agents the equivalence breaks and we give an example for sunspot equilibria even though the economy has a unique equilibrium for all distributions of endowments induced by asset trade.

A newly developed paradigm for studying spontaneous trait inferences (STI) was applied in 3 experiments. The authors primed dyadic stimulus behaviors involving a subject (S) and an object (O) person through degraded pictures or movies. An encoding task called for the verification of either a graphical feature or a semantic interpretation, which either fit or did not fit the primed behavior. Next, participants had to identify a trait word that appeared gradually behind a mask and that either matched or did not match the primed behavior. STI effects, defined as shorter identification latencies for matching than nonmatching traits, were stronger for S than for O traits, after graphical rather than semantic encoding decisions and after encoding failures. These findings can be explained by assuming that trait inferences are facilitated by open versus closed mindsets supposed to result from distracting (graphical) encoding tasks or encoding failures (involving nonfitting interpretations).

We propose a network description of large market investments, where both stocks and shareholders are represented as vertices connected by weighted links corresponding to shareholdings. In this framework, the in-degree ($k_{in}$) and the sum of incoming link weights (v) of an investor correspond to the number of assets held (portfolio diversification) and to the invested wealth (portfolio volume), respectively. An empirical analysis of three different real markets reveals that the distributions of both $k_{in}$ and v display power-law tails with exponents y and a. Moreover, we find that $k_{in}$ scales as a power-law function of v with an exponent b. Remarkably, despite the values of a, b and y differ across the three markets, they are always governed by the scaling relation b = (1-a)/(1-y). We show that these empirical findings can be reproduced by a recent model relating the emergence of scale-free networks to an underlying Paretian distribution of ‘hidden’ vertex properties.