Full surplus extraction from colluding bidders

I consider a repeated auction setting with colluding buyers and a seller who adjusts reserve prices over time without long-term commitment. To model the seller's concern for collusion, I introduce a new equilibrium concept: collusive public perfect equilibrium. For every strategy of the seller I define the corresponding "buyer-game" in which the seller is replaced by Nature who chooses the reserve prices for the buyers in accordance with the seller's strategy. A public perfect equilibrium is collusive if the buyers cannot achieve a higher symmetric public perfect equilibrium payoff in the corresponding buyer-game. In a setting with symmetric buyers with private binary iid valuations and publicly revealed bids, I find collusive public perfect equilibria that allow the seller to extract the entire surplus from the buyers in the limit as the buyers' discount factor goes to 1. I therefore show that a non-committed seller can effectively fight collusion even when she faces patient buyers, can only set reserve prices, and has to satisfy stringent public disclosure requirements

Essays in mechanism design

This dissertation consists of three self-contained chapters which explore how designers of economic mechanisms can make them more robust to collusion and information acquisition by the participants

First Best Implementation with Costly Information Acquisition

We study a mechanism design model with flexible but costly information acquisition. There is a principal and I ≥ 4 agents. The principal and the agents share a common prior over the set of payoff-relevant states of the world. The principal proposes a mechanism to the agents who can then acquire information about the state of the world by privately designing a signal device. As long as it is costless for each agent to acquire a signal that is pairwise independent from the state of the world, we show that there exists a mecha-nism which allows the principal to implement any social choice rule at zero information acquisition cost to the agents

First best implementation with costly information acquisition

We study mechanism design with flexible but costly information acquisition. There is a principal and four or more agents, sharing a common prior over the set of payoff-relevant states. The principal proposes a mechanism to the agents who can then acquire information about the state of the world by privately designing a signal device. As long as it is costless for each agent to acquire a signal that is independent from the state, we show that there exists a mechanism which allows the principal to implement any social choice rule at zero information acquisition cost to the agents

Towards Computationally Feasible Deep Active Learning

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. One of such problems is the excessive computational resources required to train an acquisition model and estimate its uncertainty on instances in the unlabeled pool. We propose two techniques that tackle this issue for text classification and tagging tasks, offering a substantial reduction of AL iteration duration and the computational overhead introduced by deep acquisition models in AL. We also demonstrate that our algorithm that leverages pseudo-labeling and distilled models overcomes one of the essential obstacles revealed previously in the literature. Namely, it was shown that due to differences between an acquisition model used to select instances during AL and a successor model trained on the labeled data, the benefits of AL can diminish. We show that our algorithm, despite using a smaller and faster acquisition model, is capable of training a more expressive successor model with higher performance.Comment: Accepted at NAACL-2022 Finding

NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?

The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.Comment: Technical Repor

Full surplus extraction from colluding bidders

I consider a repeated auction setting with colluding buyers and a seller who adjusts reserve prices over time without long-term commitment. To model the seller's concern for collusion, I introduce a new equilibrium concept: collusive public perfect equilibrium. For every strategy of the seller I define the corresponding "buyer-game" in which the seller is replaced by Nature who chooses the reserve prices for the buyers in accordance with the seller's strategy. A public perfect equilibrium is collusive if the buyers cannot achieve a higher symmetric public perfect equilibrium payoff in the corresponding buyer-game. In a setting with symmetric buyers with private binary iid valuations and publicly revealed bids, I find collusive public perfect equilibria that allow the seller to extract the entire surplus from the buyers in the limit as the buyers' discount factor goes to 1. I therefore show that a non-committed seller can effectively fight collusion even when she faces patient buyers, can only set reserve prices, and has to satisfy stringent public disclosure requirements

ChatGPT: A meta-analysis after 2.5 months

ChatGPT, a chatbot developed by OpenAI, has gained widespread popularity and media attention since its release in November 2022. However, little hard evidence is available regarding its perception in various sources. In this paper, we analyze over 300,000 tweets and more than 150 scientific papers to investigate how ChatGPT is perceived and discussed. Our findings show that ChatGPT is generally viewed as of high quality, with positive sentiment and emotions of joy dominating social media. Its perception has slightly decreased since its debut, however, with joy decreasing and (negative) surprise on the rise, and it is perceived more negatively in languages other than English. In recent scientific papers, ChatGPT is characterized as a great opportunity across various fields including the medical domain, but also as a threat concerning ethics and receives mixed assessments for education. Our comprehensive meta-analysis of ChatGPT’s perception after 2.5 months since its release can contribute to shaping the public debate and informing its future development. We make our data available.11 https://github.com/NL2G/ChatGPTReview