Internet media planning : an optimization model

Of the various media vehicles available for advertising, the internet is the latest and the most rapidly growing, emerging as the ideal medium to promote products and services in the global market. In this article, the authors propose an internet media planning model whose main objective is to help advertisers determine the return they obtain from spending on internet advertising. Using available data such as internet page view and advertising performance data, the model contributes to attempts not only to optimize the internet advertising schedule but also to fix the right price for internet advertisements on the basis of the characteristics of the exposure distribution of sites. The authors test the model with data provided by KoreanClick, a Korean market research company that specializes in internet audience measurement. The optimal durations for the subject sites provide some useful insights. The findings contrast with current web media planning practices, and the authors demonstrate the potential savings that could be achieved if their approach were applied.media planning; optimization; advertising repeat exposure; probability distribution; internet

The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale

In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity---a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns---the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale---the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale---measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201

Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle

Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve AUPR. By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid

Nonparametric estimation of concave production technologies by entropic methods

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, bases on the priciple of maximum likelihood, uses entropic distance and concvex programming techniques to estimate production functions.convex programming, production functions, entropy

Diminishing returns of inoculum size on the rate of a plant RNA virus evolution

[EN] Understanding how genetic drift, mutation and selection interplay in determining the evolutionary fate of populations is one of the central themes of Evolutionary Biology. Theory predicts that by increasing the number of coexisting beneficial alleles in a population beyond some point does not necessarily translates into an acceleration in the rate of evolution. This diminishing-returns effect of beneficial genetic variability in microbial asexual populations is known as clonal interference. Clonal interference has been shown to operate in experimental populations of animal RNA viruses replicating in cell cultures. Here we carried out experiments to test whether a similar diminishing-returns of population size on the rate of adaptation exists for a plant RNA virus infecting real multicellular hosts. We have performed evolution experiments with tobacco etch potyvirus in two hosts, the natural and a novel one, at different inoculation sizes and estimated the rates of evolution for two phenotypic fitness-related traits. Firstly, we found that evolution proceeds faster in the novel than in the original host. Secondly, results were compatible with a diminishing-returns effect of inoculum size on the rate of evolution for one of the fitness traits, but not for the other, which suggests that selection operates differently on each trait.We thank F. DE LA IGLESIA and P. AGUDO for excellent technical support and J. A. CUESTA for critical reading and insightful suggestions. This work was supported by grant BFU2015-65037-P from Spain's Ministry of Economy, Industry and Competitiveness and by the Santa Fe Institute.Navarro, R.; Ambros Palaguerri, S.; Martinez, F.; Elena Fito, SF. (2017). Diminishing returns of inoculum size on the rate of a plant RNA virus evolution. EPL (Europhysics Letters). 120(38001):1-6. https://doi.org/10.1209/0295-5075/120/38001S161203800

No Spearman’s Law of Diminishing Returns for the working memory and intelligence relationship

Spearman’s Law of Diminishing Returns (SLODR) holds that correlation between general (g)/fluid (Gf) intelligence factor and other cognitive abilities weakens with increasing ability level. Thus, cognitive processing in low ability people is most strongly saturated by g/Gf, whereas processing in high ability people depends less on g/Gf. Numerous studies demonstrated that low g is more strongly correlated with crystallized intelligence/creativity/processing speed than is high g, however no study tested an analogous effect in the case of working memory (WM). Our aim was to investigate SLODR for the relationship between Gf and WM capacity, using a large data set from our own previous studies. We tested alternative regression models separately for three types of WM tasks that tapped short-term memory storage, attention control, and relational integration, respectively. No significant SLODR effect was found for any of these tasks. Each task shared with Gf virtually the same amount of variance in the case of low- and high-ability people. This result suggests that Gf and WM rely on one and the same (neuro)cognitive mechanism

Effects of social interactions on scientists' productivity

Recent economic research has focused on the economic effects of the social environment. In the economic literature, important phenomena are considered, at least in part, as results of the individual's social environment. There is a similar revival of interest among economists who analyse the world of science and basic research. In this case as well, the environment plays a key role in the agent's behaviour. This paper makes an an empirical analysis of the influence of social interactions on scientists' productivity. In the econometric analysis we investigate the aggregate importance of this phenomenon through the analysis of data on publications in four scientific fields of seven advanced countries. We find that social interactions among researchers have positive effects on a scientist's productivity and that there is a U-shaped relation between the size of a scientific network and individual productivity. We interpret this result as providing evidence for threshold externalities and increasing returns to scale.Keywords: scientists' productivity, increasing returns in science, social interactions