Past Issues
Volume 1, Issue 4
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31 Oct 2026
This paper evaluates the economic and welfare implications of algorithmic personalized pricing. While advanced data collection and machine learning allow firms to approximate first-degree price discrimination, this study analyzes the resulting welfare effects and comes to the conclusion that they are heavily mediated by market structure and firm power. By comparing the "monopoly-like" algorithmic pricing strategies Uber partakes in with the highly competitive, elastic constraints faced by an oligopoly like American Airlines, this paper demonstrates the extent to which market power determines the effects of personalized pricing. In highly concentrated markets, as seen in our Uber case study, personalized pricing reduces consumer surplus, deepens information asymmetry, and exacerbates economic inequality. On the other hand, firms with competitive constraints, as seen with the industrial oligopoly American Airlines, frequently rely on segmented second- and third-degree price discrimination, such as discount seat allocation, which expands output and minimizes deadweight loss. As a result, the firm shifts closer to allocative efficiency, reflecting benefits for society. Ultimately, this paper discusses the fundamental efficiency-equity trade-off in welfare economics. It concludes that while personalized pricing can expand total welfare and the volume of surplus through the absorption of deadweight loss, the inevitable costs (privacy erosion, economic inequality, digitally illiterate consumers ending up worse off, etc.) outweigh the total sum of efficiency gains(total welfare), resulting in weakened overall social welfare.
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As artificial intelligence becomes increasingly prominent within financial markets, regulators lack the frameworks necessary to assess whether AI-driven investment strategies comply with fiduciary standards, manage systemic risk responsibly, or produce returns through legitimate means. This gap is particularly prevalent among leading quantitative trading and investment firms, which deploy sophisticated machine learning systems while simultaneously disclosing little about how those systems are built or monitored. This paper analyzes the publicly available AI disclosure practices and performance of five major quantitative trading and investment firms (Renaissance Technologies, Two Sigma, D. E. Shaw, Citadel, and Jane Street) from Q1 2023 to Q1 2026, comparing returns from their publicly disclosed top-20 equity holdings against the S&P 500 using data from the website HedgeFollow. The findings reveal that all five firms score below 40 out of 100 on a disclosure rubric created for this study, with the largest gaps appearing in model governance, retraining procedures, risk limits, stress-response behavior, and incident-reporting protocols. At the same time, three firms (Renaissance Technologies, D. E. Shaw, and Jane Street) display peak cumulative return gaps ranging from approximately 40 to over 100 percentage points above the S&P 500 during the study period, demonstrating that strong performance can coexist with limited public visibility into the systems and controls behind that performance. The paper concludes with four targeted regulatory proposals: mandatory AI disclosure standards, explainability requirements, systemic risk monitoring, and human oversight attribution rules, all of which must be implemented while preserving market innovation.
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Self-determination theory links support for autonomy, competence, and relatedness to intrinsic motivation and autonomous forms of extrinsic motivation (Ryan and Deci 2020). Drawing on this perspective, the present study explores student communication needs at Skolkovo International Gymnasium, a private school in Moscow, Russia. This study examines how student segmentation can inform communication about educational and social activities. An online questionnaire combining 14 closed-ended and 12 open-ended questions was distributed through school chats. Responses from 45 students in Grades 5 to 11 were analyzed descriptively. Seven grade-based student segments were considered alongside reported interests, awareness, participation, and communication preferences. The findings indicate uneven awareness of school opportunities, recurring time constraints, and demand for business, financial-literacy, and debate activities. Career guidance emerged as a priority because many respondents remained uncertain about their educational direction, while reported awareness of Career Centre opportunities was low. Mentors were an important information source across grades, although Telegram and peer communication became more prominent in some older groups. The study provides an exploratory basis for improving school communication, rather than evidence that a digital platform increases participation. A pilot should test whether consolidated information improves awareness and helps students select feasible activities.
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24 Oct 2026
Pakistan consumes an estimated 500 million disposable pens annually, generating roughly 2,300 tonnes of unrecovered plastic waste. No domestic manufacturer currently produces a pen made from zero virgin plastic. Biodegradable pen alternatives, though growing globally, are commercially non-viable in Pakistan because the binding agents required for their manufacture are not available in the country. This paper presents the case for a pen that is feasible and sustainable. A ballpoint pen made from plastic scrap generated during the manufacturing and moulding processes, with zero virgin plastic content. Primary research conducted in May 2026 surveyed 54 respondents, predominantly students aged 15 to 18 based in Lahore (a non-random convenience sample). Among this sample, 79.6% stated they would be interested in using sustainable recycled-plastic pens, and 66.6% stated a willingness to pay Rs. 100 or more per pen, and 94.4% indicated they would expect to buy from stationery shops. These are stated preferences rather than observed purchases, so they indicate potential demand and a likely retail channel among young urban students, not proven commercial viability. The paper argues that this pen occupies a market position that no existing domestic product holds, and that rising sustainability awareness among urban youth makes it a promising candidate for further market testing, including a pilot launch, across a broader and more representative sample of Pakistani consumers.
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This paper describes results of a psychological research experiment on High School students. The Rubinstein game is a theoretical game where two players go back and forth to determine how to split a reward that is continuously decreasing in size. Although the Rubinstein game has been extensively studied and researched, very little has been done about alterations, especially when turning it into a communicative game. I conducted separate tests with three different trial groups. In the first test, participants were given the standard Rubinstein game to play. In the second and third, participants were given alterations of the game, in which the second had a small reward if they could not reach an agreement by the end of the game and in the third participants were allowed limited online communication before starting the game. Similar to Güth's findings, participants generally had a more even allocation of money in all trials than predicted, in which the communication group was most even (Güth 1982). Therefore, communication has a bigger impact on initial split than outside options. However, there were limitations to this study, and future research is needed to determine the impact of factors such as average income, age, and location of study.
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Organized youth sports participation depends on families' ability to meet financial, transportation, and time costs. This study examines socioeconomic inequalities in U.S. youth sports participation using publicly available national survey estimates. The primary analysis uses the 2023 to 2024 National Survey of Children's Health (NSCH), covering youth ages 6 to 17. Supplementary analyses use the 2020 to 2021 NSCH and the 2020 National Health Interview Survey. In 2023 to 2024, 56.7% of youth participated in sports teams or lessons. Participation increased from 36.7% among families below the federal poverty threshold to 74.1% among those at or above 400% of that threshold, a 37.4-percentage-point gap and prevalence ratio of 2.02. State participation ranged from 46.3% to 70.9%. Using the 2020 to 2021 NSCH, Monte Carlo propagation of published uncertainty yielded a slope index of inequality of 46.6 percentage points (95% simulation interval: 43.5 to 49.6) and a relative inequality index of 2.59 (2.41 to 2.79). Comparing lower-income groups with the highest-income group's participation rate implied a descriptive shortfall of 8.23 million participants (7.66 to 8.80 million). Independent national survey estimates showed a similarly large income gap. These findings establish substantial economic stratification in youth sports access but do not identify causal effects of income or subsidies. They support considering policies that address participation fees alongside equipment, transportation, and program accessibility.
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21 Oct 2026
This paper focuses on how mathematics supports investment analysis, specifically on portfolio optimization and risk management under Modern Portfolio Theory. We begin with compound interest, then develop the probabilistic foundations of quantitative finance, including expected value, variance, covariance, the Law of Large Numbers, the Central Limit Theorem, and Bayes' Theorem. Building on these, we study diversification, the Sharpe ratio, Markowitz optimization, the efficient frontier, and the Capital Allocation Line. Risk is assessed through Value at Risk and the distinction between systematic and unsystematic risk, illustrated by major historical market crashes. Following the Capital Asset Pricing Model (CAPM), we relate an asset's expected return to the expected market return and beta. We also review common technical indicators, including moving averages, the relative strength index, and the VIX fear index. Finally, we apply linear regression and random forest models to stock return prediction and algorithmic trading, and discuss their advantages and limitations.
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20 Oct 2026
This paper examines whether US households that overestimate their financial knowledge trade more actively and hold less diversified portfolios. Using microdata from the 2022 Survey of Consumer Finances, a nationally representative sample of 4,595 US households, an overconfidence gap is constructed by subtracting the z-scored financial literacy score (FINLIT) from the z-scored self-rated financial knowledge score (KNOWL). OLS regression models are estimated separately across five imputed datasets and combined using Rubin's rules. Results consistently support the first hypothesis: a one-unit increase in the overconfidence gap is associated with a 12.3% increase in trading activity among brokerage-account holders, robust to demographic, wealth, and risk controls. The second hypothesis is not supported; more overconfident households hold significantly more (1.52 unique stocks) rather than fewer distinct stocks. This finding is interpreted as reflecting a higher willingness to participate in individual equity markets rather than true risk-reducing portfolio diversification. These findings extend prior literature by testing overconfidence within a representative population sample rather than single-brokerage customer data, and suggest that policy aimed at improving financial self-calibration, not just financial knowledge, may reduce the welfare costs of overconfident investing.
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This paper compares two live music growth models: fixed domestic venues (MSG Entertainment) and global touring networks (Live Nation Entertainment). Using FY2021 to FY2025 financial data and Spotify streaming metrics, the study evaluates how physical capacity limits and geographic scale impact revenue growth. Findings show that while Madison Square Garden achieves high revenue per show, its long-term growth is capped by physical seat and calendar limits. In contrast, Live Nation's International segment grew at a 37.86% CAGR, outpacing U.S. Domestic operations (13.62% CAGR) by nearly three times. A regression of U.S. Domestic revenue at Madison Square Garden shows that cumulative streaming data from Spotify most accurately determines per-show revenue at a U.S. domestic venue such as Madison Square Garden. By analyzing artists such as Billy Joel, and Lady Gaga, this paper demonstrates how real-time streaming analytics de-risk international tour routing, making global networks the main engine of live music growth.