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We formulate an advertiser's bidding problem as a profit maximization problem in which the advertiser has to decide on how much to bid on each keyword subject to a budget, given that she can only observe her bid, her rank, the search frequency and the click frequency. We assume that the other advertisers' bids are i.i.d. random variables. We discuss how to determine a bid that will maximize expected profits when the search traffic for each keyword is generated by an independent Poisson process. We provide a formulation and discuss several properties of the problem. [ABSTRACT FROM AUTHOR]

The metro-cities in developing countries (like India) generate enormous amount of municipal solid waste (MSW). The use of waste derived material (containing biogenic fraction) as co-fuel in energy intensive industries (like cement kilns) is receiving attention to reduce the cost of fossil fuel and greenhouse gas emissions. In the present work, the impacts of using refuse derived fuel (RDF) (from MSW generated in Mumbai, India) in a cement kiln as co-fuel were assessed. The expected RDF composition was derived from the information available in the literature. To predict its impact on the environment and overall production, a mass flow analysis was conducted. From the analysis, it was predicted that around 2200 t/d of RDF can be generated from the total MSW quantities in Mumbai (~ 9700 t/d) which is predicted to have ~ 35% biogenic fraction. To estimate the environmental impacts, three scenarios were modelled using a share of RDF from 25 - 40% in the total fuel feed. The emissions from transportation were also considered in the analysis. The maximum reductions of 13, 20 and 24% in CO2, SOx and NOx emissions, respectively could be achieved by using RDF as co-fuel. Besides, CO, hydrocarbon and particulate emissions caused during transportation of the waste derived fuel were also reduced. [ABSTRACT FROM AUTHOR]

The past decade has witnessed a financial technology (Fintech) revolution. With the advent of Fintech in trading markets, many technology startups are using social media to gauge investors' sentiment, as well as to detect events quickly, which in turn could impact stock prices and affect the efficiency of financial markets. Another example of Fintech that is enabled by the aggregation of opinions is social trading platforms. These platforms are financial counterparts of social networks where people can create an online profile and share information about investments and trading in financial instruments such as stocks and cryptocurrencies with other members of the platform. Online platforms like Sharewise and Estimize specialize in aggregating the opinions and predictions of all its users to come up with a target price for a stock, thus enabling the users to gain market insights from public opinion. This shift in trading has led to the emergence of a new class of investors who trade not only based on their own knowledge (or beliefs) about the market but also on that of the crowd's opinion. Investors who follow public information about the fundamental price and their own private information are classified as firstorder- beliefs traders while investors who use private information along with information gained from using such Fintech product are classified as higher-order-beliefs traders. The Fintech product captures the sentiment of other investors and experts by aggregating their opinions and predictions expressed on various social media platforms. Following the insight of Keynes (1936) on financial markets being akin to a beauty contest, where some people grade contestants based on who they think will be attractive to others, we model the market where a certain fraction of traders is employing the beauty-contest paradigm, using the services offered by Fintech firms, to form higher-order beliefs. We then establish the equilibrium in the market and examine its different properties. We also analyze how higher-order beliefs affect market efficiency and social welfare of investors. In the process, we answer the following research questions: is this shift beneficial for the market (in terms of efficiency) and the individual investors? And, if so, under what conditions and circumstances? Further, is there any limit to the number of Fintech investors (as a fraction of all investors) in the market so that social welfare is maximized? We find that higher-order beliefs tend to reduce market efficiency because public information is overweighted. Increased precision of private information always enhances market efficiency; however, when public information is relatively noisy, increased precision of public information is detrimental to market efficiency. We also examine the effect of relative precision of public information to private information and fraction of Fintech investors in the market on ex-ante wealth of investors. Since accounting disclosure is a main source of public information, our results highlight that the use of Fintech in financial trading can dramatically affect the optimal level of accounting disclosure (i.e., transparency). [ABSTRACT FROM AUTHOR]