Economic and Societal Impacts of Technology, Data, and Algorithms
Permanent URI for this collectionhttps://hdl.handle.net/10125/107511
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Item type: Item , How Machine-Generated Ratings and Social Exposure Affect Human Reviewers: Evidence from Initial Coin Offerings(2024-01-03) Zhou, Yingxin; Kim, Keongtae; Xue, LingWhile machine-generated information is increasingly prevalent, how it is used as a basis for human ratings is not well-explored. Using the context of online professional ratings of initial coin offerings (ICOs) projects, this study examines how increased social exposure of human ratings and different experiences impact human experts’ ratings relative to machine-generated ratings (MGRs). Leveraging an interface design change on an ICO rating platform, we find that increased social exposure leads experts with advisor experiences to lower ratings and rate below and closer to MGRs. Additionally, increased social exposure leads human experts with team member experiences to rate closer to MGRs, without significantly affecting their rating levels. These suggest that human experts with advisor experiences may strategically rate above MGRs to overrate and impress project teams, while those with team member experiences do not. Overall, increased social exposure drives human experts to conform to MGRs, possibly correcting humans’ rating biases.Item type: Item , GPT in the Loop: Evidence from the Field.(2024-01-03) Yang, Cathy; Allen, Leo; Restrepo-Amariles, David; Troussel, AuroreGenerative Pre-trained Transformers (GPTs) are highly effective in generating content and increasing productivity, but companies have reservations about their use in a professional setting. OpenAI and policymakers suggest that disclosing the use of GPT is necessary, but there is little empirical evidence to understand its consequence. Our experiment found that managers from a leading consulting firm were unable to distinguish Human-GPT generated content when the content generation source was not disclosed and disclosing the use of GPT improved the content's evaluation. We explored the effects of applying the GPT disclosure policy in the workplace. Managers prefer analysts to disclose their use of GPT, but their preferences regarding how junior analysts should use GPT may differ from that of the analysts, leading to potential conflicts over disclosure.Item type: Item , AlphaVC: A Reinforcement Learning-based Venture Capital Investment Strategy(2024-01-03) Zhong, Hao; Yuan, Zixuan; Zhang, Denghui; Jiang, Yi; Zhang, Shengming; Xiong, HuiVenture capital investments play a powerful role in fueling the emergence and growth of early-stage startups. However, only a small fraction of venture-backed startups can survive and exit successfully. Prior data-driven prediction-based or recommendation-based solutions are incapable of providing effective and actionable strategies on proper investment timing and amounts for startups across different investment rounds. In this paper, we develop a novel reinforcement learning-based method, AlphaVC, to facilitate venture capitalists’ decision-making. Our policy-based reinforcement learning agents can dynamically identify the best candidates and sequentially place the optimal investment amounts at proper rounds to maximize financial returns for a given portfolio. We retrieve company demographics and investment activity data from Crunchbase. Our methodology demonstrates its efficacy and superiority in ranking and portfolio-based performance metrics in comparison with various state-of-the-art baseline methods. Through sensitivity and ablation analyses, our research highlights the significance of factoring in the distal outcome and acknowledging the learning effect when making decisions at different time points. Additionally, we observe that AlphaVC concentrates on a select number of high-potential companies, but distributes investments evenly across various stages of the investment process.Item type: Item , Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks(2024-01-03) Zhang, Shengming; Zhong, Hao; Ge, Yong; Xiong, HuiThe rapid acceleration of technology and the evolving global economy have led to a significant surge in high-potential startups, presenting immense opportunities for venture capital firms and investors to support and benefit from these innovative ventures. However, identifying startups with the highest likelihood of success remains a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics.Item type: Item , Introduction to the Minitrack on Economic and Societal Impacts of Technology, Data, and Algorithms(2024-01-03) Ge, Yong; Zhang, John