pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating and Aligning Human Economic Risk Preferences in LLMs
Authors: Jiaxin Liu, Yi Yang, Kar Yan Tam
Date: 2025-03-09 00:00:00
Tasks: Decision Making

Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual's persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we propose an alignment method designed to enhance LLM adherence to persona-specific risk preferences. Our approach improves the economic rationality of LLMs in risk-related applications, offering a step toward more human-aligned AI decision-making.
The impact of external uncertainties on the extreme return connectedness between food, fossil energy, and clean energy markets
Authors: Ting Zhang, Hai-Chuan Xu, Wei-Xing Zhou
Date: 2025-03-09 00:00:00
Tasks: CPU, GPR, quantile regression

We investigate the extreme return connectedness between the food, fossil energy, and clean energy markets using the quantile connectedness approach, which combines the traditional spillover index with quantile regression. Our results show that return connectedness at the tails (57.91% for the right tail and 61.47% for the left tail) is significantly higher than at the median (23.02%). Further-more, dynamic analysis reveals that connectedness fluctuates over time, with notable increases during extreme events. Among these markets, fossil energy market consistently acts as the net receiver, while clean energy market primarily serves as the net transmitter. Additionally, we use linear and nonlinear ARDL models to examine the role of external uncertainties on return connectedness. We find that climate policy uncertainty (CPU), geopolitical risk (GPR), and the COVID-19pandemic significantly impact median connectedness, while economic policy uncertainty (EPU),GPR, and trade policy uncertainty (TPU) are crucial drivers of extreme connectedness. Our findings provide valuable insights for investors and policymakers on risk spillover effects between food and energy markets under both normal and extreme market conditions.
Spillover effects between climate policy uncertainty, energy markets, and food markets: A time-frequency analysis
Authors: Ting Zhang, Peng-Fei Li, Wei-Xing Zhou
Date: 2025-03-09 00:00:00
Tasks: CPU

The study examines the return connectedness between climate policy uncertainty (CPU), clean energy, fossil energy, and food markets. Using the time-domain method of Diebold and Yilmaz (2012) and frequency-domain methods of Barun{\'{i}}k and K{\v{r}}hl{\'{i}}k (2018), we find substantial spillover effects between these markets. Furthermore, high frequency domain is the primary driver of overall connectedness. In addition, CPU is a net contributor of return shocks in the short term, whereas it turns to be a net recipient in the medium and long terms. Across all frequencies, clean energy and oils are consistent net recipients, while meat is a dominant net contributor.
Hate in the Time of Algorithms: Evidence on Online Behavior from a Large-Scale Experiment
Authors: Aarushi Kalra
Date: 2025-03-08 00:00:00

Social media algorithms are thought to amplify variation in user beliefs, thus contributing to radicalization. However, quantitative evidence on how algorithms and user preferences jointly shape harmful online engagement is limited. I conduct an individually randomized experiment with 8 million users of an Indian TikTok-like platform, replacing algorithmic ranking with random content delivery. Exposure to "toxic" posts decreases by 27%, mainly due to reduced platform usage by users with higher interest in such content. Strikingly, these users increase engagement with toxic posts they find. Survey evidence indicates shifts to other platforms. Model-based counterfactuals highlight the limitations of blanket algorithmic regulation.
Maine's Forestry and Logging Industry: Building a Model for Forecasting
Authors: Andrew Crawley, Adam Daigneault, Jonathan Gendron
Date: 2025-03-08 00:00:00

From 2000 to 2017, 64% of Maine's pulp and paper processing mills shut down; these closures resulted in harmful effects to communities in Maine and beyond. One question this research asks is how will key macroeconomic and related variables for Maine's forestry and logging industry change in the future? To answer this, we forecast key macroeconomic and related variables with a vector error correction (VEC model) to assess past and predict future economic contributions from Maine's forestry and logging industry. The forecasting results imply that although the contribution of the industry in Maine would likely remain stable due to level prices and a slight increase in output, local Maine communities could be worse off due to decreases in employment and firms. We then incorporated these forecasts into a 3-stage modeling process to analyze how a negative shock to exchange rates from an increase in tariffs could affect Maine's employment and output. Our results suggest that increased tariffs will reduce output and increase employment volatility in Maine. Rising uncertainty and costs of business operations suggest care should be taken when changing tariffs and trade restrictions, especially when changes to business operations can harm markets and communities.
Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones
Authors: Adamson Bryant
Date: 2025-03-07 00:00:00

Despite growing evidence that neighborhoods play a critical role in shaping economic mobility and well-being, effective policies to address neighborhood disadvantage remain elusive. This study evaluates the impact of the Promise Zone program, which aims to revitalize disadvantaged neighborhoods through streamlined federal support and grant incentives. I use an event study framework with newly obtained data on the location of failed finalist applications as a comparison group to estimate the effects of the program. The results reveal significant improvements in poverty, household incomes, and employment in Promise Zone neighborhoods, particularly in later-designated zones and initially low-status neighborhoods. I also find that effects are driven partly by changes in residential composition, and that Promise Zones appear to induce positive spillovers in adjacent areas.
Maximum Hallucination Standards for Domain-Specific Large Language Models
Authors: Tingmingke Lu
Date: 2025-03-07 00:00:00
Tasks: Attribute, Hallucination, Misinformation

Large language models (LLMs) often generate inaccurate yet credible-sounding content, known as hallucinations. This inherent feature of LLMs poses significant risks, especially in critical domains. I analyze LLMs as a new class of engineering products, treating hallucinations as a product attribute. I demonstrate that, in the presence of imperfect awareness of LLM hallucinations and misinformation externalities, net welfare improves when the maximum acceptable level of LLM hallucinations is designed to vary with two domain-specific factors: the willingness to pay for reduced LLM hallucinations and the marginal damage associated with misinformation.
Skill and spatial mismatches for sustainable development in Brazil
Authors: Anna K. Berryman, Joris Bücker, Fernanda Senra de Moura, Pete Barbrook-Johnson, Marek Hanusch, Penny Mealy, J. Doyne Farmer, R. Maria del Rio-Chanona
Date: 2025-03-07 00:00:00

Structural change is necessary for all countries transitioning to a more environmentally sustainable economy, but what are the likely impacts on workers? Studies often find that green transition scenarios result in net positive job creation numbers overall but rarely provide insights into the more granular dynamics of the labour market. This paper combines a dynamic labour market simulation model with development scenarios focused on agriculture and green manufacturing. We study how, within the context of a green transition, productivity shifts in different sectors and regions, with differing environmental impacts, may affect and be constrained by the labour market in Brazil. By accounting for labour market frictions associated with skill and spatial mismatches, we find that productivity shocks, if not well managed, can exacerbate inequality. Agricultural workers tend to be the most negatively affected as they are less occupationally and geographically mobile. Our results highlight the importance of well-targeted labour market policies to ensure the green transition is just and equitable.
GATE: An Integrated Assessment Model for AI Automation
Authors: Ege Erdil, Andrei Potlogea, Tamay Besiroglu, Edu Roldan, Anson Ho, Jaime Sevilla, Matthew Barnett, Matej Vrzla, Robert Sandler
Date: 2025-03-06 00:00:00
Tasks: model

Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation. GATE combines three key ingredients that have not been brought together in previous work: (1) a compute-based model of AI development, (2) an AI automation framework, and (3) a semi-endogenous growth model featuring endogenous investment and adjustment costs. The model allows users to simulate the economic effects of the transition to advanced AI across a range of potential scenarios. GATE captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms. This paper explains the model's structure and functionality, emphasizing AI development for economists and economic modeling for the AI community. The model is implemented in an interactive sandbox, enabling users to explore the impact of AI under different parameter choices and policy interventions. The modeling sandbox is available at: www.epoch.ai/GATE.
Welfare Effects of Self-Preferencing by a Platform: Empirical Evidence from Airbnb
Authors: Kaede Hanazawa
Date: 2025-03-06 00:00:00
Tasks: counterfactual

This paper studies the welfare effects of self-preferencing by Airbnb, a practice where Airbnb utilizes its pricing algorithm to prioritize maximizing platform-wide commission revenue rather than optimizing individual host revenues. To examine this welfare implication, I construct a Bertrand competition model with differentiated products between Airbnb hosts and hotels. Using unique data from Tokyo's 23 wards, I estimate the model and conduct counterfactual simulations to evaluate the welfare effects of self-preferencing. Counterfactual simulations reveal that self-preferencing reduces social welfare by 5.08% on average, equivalent to an annual loss of about 14.90% of Tokyo's vacation rental market size in 2023 while increasing Airbnb's commission revenue by an average of 37.73%. These findings highlight the significant trade-offs between platform-driven revenue optimization and market efficiency, emphasizing the urgent need for competition policy reforms and greater transparency and accountability in platform practices.
Optimizing Economic Complexity
Authors: Viktor Stojkoski, César A. Hidalgo
Date: 2025-03-06 00:00:00

Efforts to apply economic complexity to identify diversification opportunities often rely on diagrams comparing the relatedness and complexity of products, technologies, or industries. Yet, the use of these diagrams, is not based on empirical or theoretical evidence supporting some notion of optimality. Here, we introduce a method to identify diversification opportunities based on the minimization of a cost function that captures the constraints imposed by an economy's pattern of specialization and show that this ECI optimization algorithm produces recommendations that are substantially different from those obtained using relatedness-complexity diagrams. This method advances the use of economic complexity methods to explore questions of strategic diversification.
Persistent gender attitudes and women entrepreneurship
Authors: Ulrich Kaiser, Jose Mata
Date: 2025-03-06 00:00:00

How do persistent gender norms affect women's current startup activity? We investigate whether historical gender norms - measured by Switzerland's 1981 public referendum on enshrining gender equality as a constitutional right - influence female startup activity today. Using data from all 2,308 Swiss municipalities from 2016 to 2023, we show that gender norms have persisted over the past 42 years. Municipalities with a higher share of votes in favor of gender equality have a statistically and economically significantly higher ratio of women-founded to men-founded startups. The average elasticity of this ratio with respect to the 1981 vote is 0.2. These findings indicate that gender norms have persisted in Switzerland, despite mass immigration and the unprecedented economic growth the country has experienced over the past four decades. This result remains robust even after controlling for a later referendum on gender roles and a wide range of municipality-specific control variables, and the effect is stronger for non-growth-oriented firms than for growth-oriented startups.
Aggregation Model and Market Mechanism for Virtual Power Plant Participation in Inertia and Primary Frequency Response
Authors: Changsen Feng, Zhongliang Huang, Jun Lin, Licheng Wang, Youbing Zhang, Fushuan Wen
Date: 2025-03-06 00:00:00

The declining inertia provision from synchronous generators in modern power systems necessitates aggregating distributed ener-gy resources (DERs) into virtual power plants (VPPs) to unlock their potential in delivering inertia and primary frequency re-sponse (IPFR) through ancillary service markets. To facilitate DER participation in the IPFR market, this paper proposes a DER aggregation model and market mechanism for VPP partici-pating in IPFR. First, an energy-IPFR market framework is de-veloped, in which a VPP acts as an intermediary to coordinate heterogeneous DERs. Second, by taking into account the delay associated with inertia, an optimization-based VPP aggregation method is introduced to encapsulate the IPFR process involving a variety of DERs. Third, an energy-IPFR market mechanism with VPP participation is introduced, aiming to minimize social costs while considering the frequency response delay characteristics of the participants. Finally, the performance of the proposed ap-proaches is verified by case studies on a modified IEEE 30-bus system.
Managing Procurement Auction Failure: Bid Requirements or Reserve Prices
Authors: Jun Ma, Vadim Marmer, Pai Xu
Date: 2025-03-06 00:00:00

This paper examines bid requirements, where the government may cancel a procurement contract unless two or more bids are received. Using a first-price auction model with endogenous entry, we compare the bid requirement and reserve price mechanisms in terms of auction failure and procurement costs. We find that reserve prices result in lower procurement costs and substantially lower failure probabilities, especially when entry costs are high, or signals are sufficiently informative. Bid requirements are more likely to result in zero entry, while reserve prices can sustain positive entry under broader conditions.
Paying and Persuading
Authors: Daniel Luo
Date: 2025-03-08 00:00:00
Tasks: Informativeness

I study the joint design of information and transfers when an informed Sender can motivate Receiver by both paying and (Bayesian) persuading. I introduce a generalized concavification method to characterize Sender's value function when they can flexibly influence Receiver incentives directly. This characterization implies empirically verifiable conditions under which Sender strictly prefers access to the joint design of information and transfers. When Sender and Receiver interact repeatedly, Sender pays Receiver only when informational incentives are exhausted, highlighting a stark substitution effect between payments and persuasion as dynamic incentive tools. I conclude by discussing implications of my results for project selection in organizations, pricing for advertisements, and rideshare design.
Addressing the Subsumption Thesis: A Formal Bridge between Microeconomics and Active Inference
Authors: Noe Kuhn
Date: 2025-03-06 00:00:00

As a unified theory of sentient behaviour, active inference is formally intertwined with multiple normative theories of optimal behaviour. Specifically, we address what we call the subsumption thesis: The claim that expected utility from economics, as an account of agency, is subsumed by active inference. To investigate this claim, we present multiple examples that challenge the subsumption thesis. To formally compare these two accounts of agency, we analyze the objective functions for MDPs and POMDPs. By imposing information-theoretic rationality bounds (ITBR) on the expected utility agent, we find that the resultant agency is equivalent to that of active inference in MDPs, but slightly different in POMDPs. Rather than being strictly resolved, the subsumption thesis motivates the construction of a formal bridge between active inference and expected utility. This highlights the necessary formal assumptions and frameworks to make these disparate accounts of agency commensurable.
Market-based Architectures in RL and Beyond
Authors: Abhimanyu Pallavi Sudhir, Long Tran-Thanh
Date: 2025-03-05 00:00:00

Market-based agents refer to reinforcement learning agents which determine their actions based on an internal market of sub-agents. We introduce a new type of market-based algorithm where the state itself is factored into several axes called ``goods'', which allows for greater specialization and parallelism than existing market-based RL algorithms. Furthermore, we argue that market-based algorithms have the potential to address many current challenges in AI, such as search, dynamic scaling and complete feedback, and demonstrate that they may be seen to generalize neural networks; finally, we list some novel ways that market algorithms may be applied in conjunction with Large Language Models for immediate practical applicability.
Kuhn's Theorem for Games of the Extensive Form with Unawareness
Authors: Ki Vin Foo, Burkhard C. Schipper
Date: 2025-03-05 00:00:00
Tasks: Form

We extend Kuhn's Theorem to games of the extensive form with unawareness. This extension is not obvious: First, games of the extensive form with non-trivial unawareness involve a forest of partially ordered game trees rather than just one game tree. An information set at a node in one tree may consist of nodes in a less expressive tree. Consequently, perfect recall takes a more complicated form as players may also become aware of new actions during the play. Second, strategies can only be partially an object of ex ante choice in games with unawareness. Finally, nodes that a player may expect to reach with a strategy profile may not be the nodes that actually occur with this strategy profile, requiring us to define appropriate notions of equivalence of strategies. We show if a game of the extensive form with unawareness has perfect recall, then for each mixed strategy there is an equivalent behavior strategy but the converse does not hold under unawareness.
Building English ASR model with regional language support
Authors: Purvi Agrawal, Vikas Joshi, Bharati Patidar, Ankur Gupta, Rupesh Kumar Mehta
Date: 2025-03-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

In this paper, we present a novel approach to developing an English Automatic Speech Recognition (ASR) system that can effectively handle Hindi queries, without compromising its performance on English. We propose a novel acoustic model (AM), referred to as SplitHead with Attention (SHA) model, features shared hidden layers across languages and language-specific projection layers combined via a self-attention mechanism. This mechanism estimates the weight for each language based on input data and weighs the corresponding language-specific projection layers accordingly. Additionally, we propose a language modeling approach that interpolates n-gram models from both English and transliterated Hindi text corpora. Our results demonstrate the effectiveness of our approach, with a 69.3% and 5.7% relative reduction in word error rate on Hindi and English test sets respectively when compared to a monolingual English model.
Impact of Microphone Array Mismatches to Learning-based Replay Speech Detection
Authors: Michael Neri, Tuomas Virtanen
Date: 2025-03-10 00:00:00
Tasks: Speaker Verification

In this work, we investigate the generalization of a multi-channel learning-based replay speech detector, which employs adaptive beamforming and detection, across different microphone arrays. In general, deep neural network-based microphone array processing techniques generalize poorly to unseen array types, i.e., showing a significant training-test mismatch of performance. We employ the ReMASC dataset to analyze performance degradation due to inter- and intra-device mismatches, assessing both single- and multi-channel configurations. Furthermore, we explore fine-tuning to mitigate the performance loss when transitioning to unseen microphone arrays. Our findings reveal that array mismatches significantly decrease detection accuracy, with intra-device generalization being more robust than inter-device. However, fine-tuning with as little as ten minutes of target data can effectively recover performance, providing insights for practical deployment of replay detection systems in heterogeneous automatic speaker verification environments.
Linguistic Knowledge Transfer Learning for Speech Enhancement
Authors: Kuo-Hsuan Hung, Xugang Lu, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Yi Lin, Chii-Wann Lin, Yu Tsao
Date: 2025-03-10 00:00:00
Tasks: Speech Enhancement, Transfer Learning

Linguistic knowledge plays a crucial role in spoken language comprehension. It provides essential semantic and syntactic context for speech perception in noisy environments. However, most speech enhancement (SE) methods predominantly rely on acoustic features to learn the mapping relationship between noisy and clean speech, with limited exploration of linguistic integration. While text-informed SE approaches have been investigated, they often require explicit speech-text alignment or externally provided textual data, constraining their practicality in real-world scenarios. Additionally, using text as input poses challenges in aligning linguistic and acoustic representations due to their inherent differences. In this study, we propose the Cross-Modality Knowledge Transfer (CMKT) learning framework, which leverages pre-trained large language models (LLMs) to infuse linguistic knowledge into SE models without requiring text input or LLMs during inference. Furthermore, we introduce a misalignment strategy to improve knowledge transfer. This strategy applies controlled temporal shifts, encouraging the model to learn more robust representations. Experimental evaluations demonstrate that CMKT consistently outperforms baseline models across various SE architectures and LLM embeddings, highlighting its adaptability to different configurations. Additionally, results on Mandarin and English datasets confirm its effectiveness across diverse linguistic conditions, further validating its robustness. Moreover, CMKT remains effective even in scenarios without textual data, underscoring its practicality for real-world applications. By bridging the gap between linguistic and acoustic modalities, CMKT offers a scalable and innovative solution for integrating linguistic knowledge into SE models, leading to substantial improvements in both intelligibility and enhancement performance.