Revisiting the size-R&D productivity relation: introducing the mediating role of decision-making style on the scale and quality of innovative output

We develop a mediation model in which firm size is proposed to affect the scale and quality of innovative output through the adoption of different decision styles during the R&D process. The aim of this study is to understand how the internal changes that firms undergo as they evolve from small to larger organizations affect R&D productivity. In so doing, we illuminate the underlying theoretical mechanism affecting two different dimensions of R&D productivity, namely the scale and quality of innovative output which have not received much attention in previous literature. Using longitudinal data of Spanish manufacturing firms we explore the validity of this mediation model. Our results show that as firms evolve in size, they increasingly emphasize analytical decision making, and consequently, large-sized firms aim for higher-quality innovations while small firms aim for a larger scale of innovative output.

Neuronal adaptation effects in decision making

Recently, there has been an increased interest on the neural mechanisms underlying perceptual decision making. However, the effect of neuronal adaptation in this context has not yet been studied. We begin our study by investigating how adaptation can bias perceptual decisions. We considered behavioral data from an experiment on high-level adaptation-related aftereffects in a perceptual decision task with ambiguous stimuli on humans. To understand the driving force behind the perceptual decision process, a biologically inspired cortical network model was used. Two theoretical scenarios arose for explaining the perceptual switch from the category of the adaptor stimulus to the opposite, nonadapted one. One is noise-driven transition due to the probabilistic spike times of neurons and the other is adaptation-driven transition due to afterhyperpolarization currents. With increasing levels of neural adaptation, the system shifts from a noise-driven to an adaptation-driven modus. The behavioral results show that the underlying model is not just a bistable model, as usual in the decision-making modeling literature, but that neuronal adaptation is high and therefore the working point of the model is in the oscillatory regime. Using the same model parameters, we studied the effect of neural adaptation in a perceptual decision-making task where the same ambiguous stimulus was presented with and without a preceding adaptor stimulus. We find that for different levels of sensory evidence favoring one of the two interpretations of the ambiguous stimulus, higher levels of neural adaptation lead to quicker decisions contributing to a speed–accuracy trade off.

Econometrics and decision making: Effects of presentation mode

Much of empirical economics involves regression analysis. However, does thepresentation of results affect economists ability to make inferences for decision makingpurposes? In a survey, 257 academic economists were asked to make probabilisticinferences on the basis of the outputs of a regression analysis presented in a standardformat. Questions concerned the distribution of the dependent variable conditional onknown values of the independent variable. However, many respondents underestimateduncertainty by failing to take into account the standard deviation of the estimatedresiduals. The addition of graphs did not substantially improve inferences. On the otherhand, when only graphs were provided (i.e., with no statistics), respondents weresubstantially more accurate. We discuss implications for improving practice in reportingresults of regression analyses.

The impact of gender composition on team performance and decision-making: Evidence from the field

We investigate whether the gender composition of teams affect theireconomic performance. We study a large business game, played in groups ofthree, where each group takes the role of a general manager. There are twoparallel competitions, one involving undergraduates and the other involvingMBAs. Our analysis shows that teams formed by three women aresignificantly outperformed by any other gender combination, both at theundergraduate and MBA levels. Looking across the performancedistribution, we find that for undergraduates, three women teams areoutperformed throughout, but by as much as 10pp at the bottom and by only1pp at the top. For MBAs, at the top, the best performing group is two menand one woman. The differences in performance are explained bydifferences in decision-making. We observe that three women teams are lessaggressive in their pricing strategies, invest less in R&D, and invest more insocial sustainability initiatives, than any other gender combination teams.Finally, we find support for the hypothesis that it is poor work dynamicsamong the three women teams that drives the results.

Social value orientation as a moral intuition: Decision-making in the dictator game

We studied the decision making process in the Dictator Game and showed that decisions are the result of a two-step process. In a first step, decision makers generate an automatic, intuitive proposal. Given sufficient motivation and cognitive resources, they adjust this in a second, more deliberated phase. In line with the social intuitionist model, we show that one s Social Value Orientation determines intuitive choice tendencies in the first step, and that this effect is mediated by the dictator s perceived interpersonal closeness with the receiver. Self-interested concerns subsequently leadto a reduction of donation size in step 2. Finally, we show that increasing interpersonal closeness can promote pro-social decision-making.

Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertainand changing environment, who has full information of the past, who discountsfuture payoffs, but who has no prior. We provide a learning algorithm thatperforms almost as well as the best of a given finite number of experts orbenchmark strategies and does so at any point in time, provided the agentis sufficiently patient. The key is to find the appropriate degree of forgettingdistant past. Standard learning algorithms that treat recent and distant pastequally do not have the sequential epsilon optimality property.

Improvement of a fully distributed decision making protocol for CRN

Abstract¿La deteción del espectro libre para las comunicaciones inalámbricas en un momento puntual es una tarea compleja cuyo desarrollo se simplica al realizarse de forma distribuida por una red de radio cognitiva. Sin embargo existes dificultades y vulnerabilidades de seguridad que han de ser tenidas en cuenta y solventadas a la hora de autenticar y validar los nodos de la red. Este artículo presenta una propuesta de mejora del protocolo fully distributed decision making protocol for CRN con el fin de llevar a cabo esta tarea de detección del espectro de una manera eficiente y segura.

Improvement of a fully distributed decision making protocol for CRN

Este artículo presenta una propuesta de mejora del protocolo fully distributed decision making protocol for CRN con el fin de llevar a cabo la tarea de detección del espectro libre para las comunicaciones inalámbricas de una manera eficiente y segura.

Weber's law in decision making: integrating behavioral data in humans with a neurophysiological model

Recent single-cell studies in monkeys (Romo et al., 2004) show that the activity of neurons in the ventral premotor cortex covaries with the animal's decisions in a perceptual comparison task regarding the frequency of vibrotactile events. The firing rate response of these neurons was dependent only on the frequency differences between the two applied vibrations, the sign of that difference being the determining factor for correct task performance. We present a biophysically realistic neurodynamical model that can account for the most relevant characteristics of this decision-making-related neural activity. One of the nontrivial predictions of this model is that Weber's law will underlie the perceptual discrimination behavior. We confirmed this prediction in behavioral tests of vibrotactile discrimination in humans and propose a computational explanation of perceptual discrimination that accounts naturally for the emergence of Weber's law. We conclude that the neurodynamical mechanisms and computational principles underlying the decision-making processes in this perceptual discrimination task are consistent with a fluctuation-driven scenario in a multistable regime.

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

Searching for consensus in ahp-group decision making. A bayesian perspective

This paper sets out to identify the initial positions of the different decisionmakers who intervene in a group decision making process with a reducednumber of actors, and to establish possible consensus paths between theseactors. As a methodological support, it employs one of the most widely-knownmulticriteria decision techniques, namely, the Analytic Hierarchy Process(AHP). Assuming that the judgements elicited by the decision makers follow theso-called multiplicative model (Crawford and Williams, 1985; Altuzarra et al.,1997; Laininen and Hämäläinen, 2003) with log-normal errors and unknownvariance, a Bayesian approach is used in the estimation of the relative prioritiesof the alternatives being compared. These priorities, estimated by way of themedian of the posterior distribution and normalised in a distributive manner(priorities add up to one), are a clear example of compositional data that will beused in the search for consensus between the actors involved in the resolution ofthe problem through the use of Multidimensional Scaling tools

Group preference structures in AHP–group decision making

This paper presents a procedure that allows us to determine the preference structures(PS) associated to each of the different groups of actors that can be identified in a groupdecision making problem with a large number of individuals. To that end, it makesuse of the Analytic Hierarchy Process (AHP) (Saaty, 1980) as the technique to solvediscrete multicriteria decision making problems. This technique permits the resolutionof multicriteria, multienvironment and multiactor problems in which subjective aspectsand uncertainty have been incorporated into the model, constructing ratio scales correspondingto the priorities relative to the elements being compared, normalised in adistributive manner (wi = 1). On the basis of the individuals’ priorities we identifydifferent clusters for the decision makers and, for each of these, the associated preferencestructure using, to that end, tools analogous to those of Multidimensional Scaling.The resulting PS will be employed to extract knowledge for the subsequent negotiationprocesses and, should it be necessary, to determine the relative importance of thealternatives being compared using anyone of the existing procedures