THE GEOPOLITICAL DYNAMICS OF GOOGLE

US-based transnational corporation Alphabet-Google controls a critical entry point to the Internet, dominating over 90% of the global search market. Google’s global Internet dominance and expansion seem to reaffirm a long-established US-led global communication order. However, this paper argues that the US position is far from secure, as geopolitical rivalries continue to animate and reshape the global communication network.

Access to big data as a remedy in big tech

‘Big data’ and ‘big tech’ have become central topics in recent antitrust debate and regulation. For example, the Competition and Markets Authority (CMA) recently published a report on online platforms, expressing concerns that the major platforms like Google are now protected from competition by such strong incumbency advantages. Underlying the CMA's theory of harm is the essential facility theory of antitrust, under which Google's ability to control access to its click-and-query data is seen as preventing its rivals from competing effectively. EU jurisprudence has identified three criteria to determine whether data are an essential facility and whether access should be mandated. First, the data must be indispensable to compete in the market. Secondly, absent data sharing, technical improvements by competitors must be hampered or precluded. Thirdly, there must be no objective justification to refuse competitors access to the data. It is difficult to reconcile the authorities’ concerns with Google's click-and-query data with these criteria, however. Actual and potential alternatives exist; Google's competitors have been innovating in the search market for more than a decade; and there are objective reasons to limit data access, including threats to innovation and privacy concerns.

Tractable Equilibria in Sponsored Search with Endogenous Budgets

Given the scale of the sponsored search market, it is practically important yet technically difficult to understand the interplay between bidders and the ad network and its effect on the long-run state of the market. Although typical equilibrium models account for bidders strategizing over the individual bids they submit to the auctions, they ignore that bidders also strategically set their campaign budgets. In “Tractable Equilibria in Sponsored Search with Endogenous Budgets,” F. Ciocan and K. Iyer ask how this additional strategic layer affects market operation and prove that endogenizing budgets surprisingly yields simple and interpretable equilibria. Namely, these equilibria generate quasi-truthful bidding strategies guaranteeing bidders an ROI exceeding their cost per dollar of committed budget. Additionally, the ad network’s optimal allocation policy becomes greedy with high probability. Thus, in this equilibrium, the ad network need not solve computationally challenging, large-scale linear optimization problems typically required under exogenous budgets.

An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison to academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably under-performed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises.

Short-Term and Mid-Term Load Forecasting in Distribution System Using Copula

Short-term and (Mid\Long)-term load prediction are needed for the efficient management of distribution system. An accurate load forecasting could help distribution operators to achieve a great control and flexibility on energy. In addition, a reliable prediction would have significant impact on load power flow problems, operation management, planning, automation, and search market. This paper proposes a new method based on copula for Short-term, and (Mid/Long)-term load forecasting. Proposed method was used less historical data to predict the load consumption then the other methods, so it is useful in distribution system due to scarce installed meters. Copula is a recently developed statistical theory for multivariate probability analysis that has been proposed by Sklar in 1958. Copulas are used in popularity for financial and reliability application. Numerical tests results using database of Iran system’s operation (ISO) shows that this method provides a much better prediction performance in comparison with other methods employing the same data.

Beliefs and Consumer Search in a Vertical Industry

This paper studies vertical relations in a search market. As the wholesale arrangement between a manufacturer and its retailers is typically unobserved by consumers, their beliefs about who is to be blamed for a price deviation play a crucial role in determining wholesale and retail prices. The common assumption in the consumer search literature is that consumers exclusively blame an individual retailer for a price deviation. We show that in the vertical relations context, predictions based on this assumption are not robust in the sense that if consumers hold the upstream manufacturer at least partially responsible for the deviation, equilibrium predictions are qualitatively different. For robust beliefs, the vertical model can explain a variety of observations, such as retail price rigidity (or, alternatively, low cost pass-through), nonmonotonicity of retail prices in search costs, and (seemingly) collusive retail behavior. The model can be used to study a monopoly online platform that sells access to final consumers.