Investment, Resolution of Risk, and the Role of Affect

This experimental study is concerned with the impact of the timing of the resolution of risk on people’s willingness to take risks, with a special focus on the role of affect. While the importance of anticipatory emotions has so far been only inferred from decisions regarding hypothetical choice problems, we had participants put their own money at risk in a real investment task. Moreover, emotions were explicitly measured, including anticipatory emotions experienced during the waiting period under delayed resolution (which involved two days). Affective traits and risk attitudes were measured through a web-based questionnaire before the experiment and participants’ preferences for resolution timing, risk, and time were incentive compatibly measured during the experiment. Main findings are that delayed resolution can affect investment, that the effect depends on the risk involved, and that (among all the measures considered) only emotions can explain our results, albeit in ways that are not captured by existing models

How to Adapt to Changing Markets: Experience and Personality in a Repeated Investment Game

Investment behavior is traditionally investigated with the assumption that it is on average advantageous to invest. However, this may not always be the case. In this paper, we experimentally studied investment choices made by students and financial professionals facing alternately an advantageous and disadvantageous environment in a multi-round investment game. Expected returns from investment in the advantageous environment were higher than a safe alternative, while expected returns were lower in the disadvantageous environment. We investigate how experience and personality are related to choices. Investment behavior does not differ dependent on expected returns and professionals do not significantly differ from students. Personality predicts behavior and in particular we observe that openness to experience was an asset in unfavorable markets, leading to reduced risk taking

The ORFEUS II Echelle Spectrometer: Instrument description, performance and data reduction

During the second flight of the ORFEUS-SPAS mission in November/December 1996, the Echelle spectrometer was used extensively by the Principal and Guest Investigator teams as one of the two focal plane instruments of the ORFEUS telescope. We present the in-flight performance and the principles of the data reduction for this instrument. The wavelength range is 90 nm to 140 nm, the spectral resolution is significantly better than lambda/(Delta lambda) = 10000, where Delta lambda is measured as FWHM of the instrumental profile. The effective area peaks at 1.3 cm^2 near 110 nm. The background is dominated by straylight from the Echelle grating and is about 15% in an extracted spectrum for spectra with a rather flat continuum. The internal accuracy of the wavelength calibration is better than +/- 0.005 nm.Comment: 8 pages, 8 figure

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/

The Effects of Social Ties on Coordination: Conceptual Foundations for an Empirical Analysis

International audienceThis paper investigates the influence that social ties can have on behavior. After defining the concept of social ties that we consider, we introduce an original model of social ties. The impact of such ties on social preferences is studied in a coordination game with outside option. We provide a detailed game theoretical analysis of this game while considering various types of players, i.e., self-interest maximizing, inequity averse, and fair agents. In addition to these approaches that require strategic reasoning in order to reach some equilibrium, we also present an alternative hypothesis that relies on the concept of team reasoning. After having discussed the differences between the latter and our model of social ties, we show how an experiment can be designed so as to discriminate among the models presented in the paper

Dispositional free riders do not free ride on punishment

Strong reciprocity explains prosocial cooperation by the presence of individuals who incur costs to help those who helped them (‘strong positive reciprocity’) and to punish those who wronged them (‘strong negative reciprocity’). Theories of social preferences predict that in contrast to ‘strong reciprocators’, self-regarding people cooperate and punish only if there are sufficient future benefits. Here, we test this prediction in a two-stage design. First, participants are classified according to their disposition towards strong positive reciprocity as either dispositional conditional cooperators (DCC) or dispositional free riders (DFR). Participants then play a one-shot public goods game, either with or without punishment. As expected, DFR cooperate only when punishment is possible, whereas DCC cooperate without punishment. Surprisingly, dispositions towards strong positive reciprocity are unrelated to strong negative reciprocity: punishment by DCC and DFR is practically identical. The ‘burden of cooperation’ is thus carried by a larger set of individuals than previously assumed