Weather: Creating the Perfect Storm

Tap ◽  
2017 ◽  
Author(s):  
Anindya Ghose
Keyword(s):  
Decision Making ◽  
Scientific Literature ◽  
Business Success ◽  
Purchase Decisions ◽  
Mobile Advertising ◽  
Medium Term ◽  
Short Term ◽  
Success Stories ◽  
Perfect Storm

This chapter examines one of nine critical forces behind purchase decisions that make mobile advertising so powerful: weather. Weather is ubiquitous and omnipresent. Scientific literature tells us just how strongly weather can influence our behavior, our moods, and our short-term, medium-term, and long-term decision making. One study showed that when our moods change, weather can account for as much as 40 percent of that change. Weather also offers that special ingredient that helps improve our understanding and find ways to make advertising a lucrative win-win for businesses and customers: lots and lots of data. The chapter introduces a mix of academic studies and business success stories, which show that the value of weather as a very influential driver of behavior. It discusses how weather affects mobile purchases and drives sales of big-ticket items.

scholarly journals Conflict, Chaos, and the Art of Institutional Design

2020 ◽  
Author(s):  
Scott Ganz
Keyword(s):  
Decision Making ◽  
Computational Model ◽  
Organizational Design ◽  
Institutional Design ◽  
The Political ◽  
Medium Term ◽  
Short Term ◽  
Agent Based ◽  
Garbage Can Model

The metaphor of the organization as a ``garbage can'' is intended as a playful insult. The Garbage Can Model concludes that ``organized anarchies,'' organizations characterized by unclear technology, problematic preferences, and fluid participation, make bad and unreliable decisions. However, management theorists in the 1970s and 1980s also saw a glimmer of hope through the Garbage Can Model's gloomy predictions. Moch and Pondy (1977), for example, propose that garbage can decision making could be robust to environmental ruggedness, ``the organizational equivalent of an all-terrain vehicle'' (360). In this paper, I explore the hypothesis that garbage cans can be adaptively rational organizational design. Using an agent-based computational model, I demonstrate how preference conflict and fluid participation in decision making promote effective search in uncertain task environments. I show that the political gridlock and unstable outcomes that emerge as a result of garbage can decision making -- the very features of garbage cans that make them perceived to be dysfunctional -- can facilitate short-term exploitation and long-term exploration of uncertain technical landscapes. In the medium-term, however, conflict stands in the way of the speedy ascent of local performance peaks, leading to degraded performance.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Intralymphatic immunotherapy for allergic rhinoconjunctivitis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
M.P. Hoang ◽  
K. Seresirikachorn ◽  
W. Chitsuthipakorn ◽  
K. Snidvongs
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Specific Ige ◽  
Allergic Rhinoconjunctivitis ◽  
Medium Term ◽  
Short Term ◽  
Intralymphatic Immunotherapy ◽  
Specific Igg4 ◽  
Igg4 Level

BACKGROUND: Intralymphatic immunotherapy (ILIT) is a new route of allergen-specific immunotherapy. Data confirming its effect is restricted to a small number of studies. METHODOLOGY: A systematic review with meta-analysis was conducted. The short-term (less than 24 weeks), medium-term (24-52 weeks), and long-term (more than 52 weeks) effects of ILIT in patients with allergic rhinoconjunctivitis (ARC) were assessed. The outcomes were combined symptom and medication scores (CSMS), symptoms visual analog scale (VAS), disease-specific quality of life (QOL), specific IgG4 level, specific IgE level, and adverse events. RESULTS: Eleven randomized controlled trials and 2 cohorts (483 participants) were included. Compared with placebo, short term benefits of ILIT for seasonal ARC improved CSMS, improved VAS and increased specific IgG4 level but did not change QOL or specific IgE level. Medium-term effect improved VAS. Data on the long-term benefit of ILIT remain unavailable and require longer term follow-up studies. There were no clinical benefits of ILIT for perennial ARC. ILIT was safe and well-tolerated. CONCLUSION: ILIT showed short-term benefits for seasonal ARC. The sustained effects of ILIT were inconclusive. It was well tolerated.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms

2021 ◽  
Author(s):  
Can Küçükgül ◽  
Özalp Özer ◽  
Shouqiang Wang
Keyword(s):  
Information Design ◽  
Information Policy ◽  
Purchase Decision ◽  
Purchase Decisions ◽  
Short Term ◽  
Online Platforms ◽  
Fixed Prices ◽  
Cumulative Difference ◽  
Heuristic Policy

Many online platforms offer time-locked sales campaigns, whereby products are sold at fixed prices for prespecified lengths of time. Platforms often display some information about previous customers’ purchase decisions during campaigns. Using a dynamic Bayesian persuasion framework, we study how a revenue-maximizing platform should optimize its information policy for such a setting. We reformulate the platform’s problem equivalently by reducing the dimensionality of its message space and proprietary history. Specifically, three messages suffice: a neutral recommendation that induces a customer to make her purchase decision according to her private signal about the product and a positive (respectively (resp.), negative) recommendation that induces her to purchase (resp., not purchase) by ignoring her signal. The platform’s proprietary history can be represented by the net purchase position, a single-dimensional summary statistic that computes the cumulative difference between purchases and nonpurchases made by customers having received the neutral recommendation. Subsequently, we establish structural properties of the optimal policy and uncover the platform’s fundamental trade-off: long-term information (and revenue) generation versus short-term revenue extraction. Further, we propose and optimize over a class of heuristic policies. The optimal heuristic policy provides only neutral recommendations up to a cutoff customer and provides only positive or negative recommendations afterward, with the recommendation being positive if and only if the net purchase position after the cutoff customer exceeds a threshold. This policy is easy to implement and numerically shown to perform well. Finally, we demonstrate the generality of our methodology and the robustness of our findings by relaxing some informational assumptions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Long-Term Decision-Making: Biological and Psychological Evidence

2009 ◽  
Vol 9 (3) ◽  
pp. 9-19 ◽  
Author(s):  
Thomas Princen
Keyword(s):  
Decision Making ◽  
Time Horizon ◽  
Cultural Factors ◽  
Structural Factors ◽  
Short Term ◽  
Environmental Decision Making ◽  
Human Decision ◽  
Psychological Evidence ◽  
Widespread Belief

A central conundrum in the need to infuse a long-term perspective into climate policy and other environmental decision-making is the widespread belief that humans are inherently short-term thinkers. An analysis of human decision-making informed by evolved adaptations—biological, psychological and cultural—suggests that humans actually have a long-term thinking capacity. In fact, the human time horizon encompasses both the immediate and the future (near and far term). And yet this very temporal duality makes people susceptible to manipulation; it carries its own politics, a politics of the short term. A “legacy politics” would extend the prevailing time horizon by identifying structural factors that build on evolved biological and cultural factors.

Sign in / Sign up

Export Citation Format

Share Document