pwc-archive/papers-with-abstracts huggingface dataset:
A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options
Authors: Peilong Wang, Jason Holmes, Zhengliang Liu, Dequan Chen, Tianming Liu, Jiajian Shen, Wei Liu
Date: 2024-12-14 00:00:00
Tasks: Multiple-choice

Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models. Methods: A set of 100 multiple-choice radiation oncology physics questions, previously created by a well-experienced physicist, was used for this study. The answer options of the questions were randomly shuffled to create "new" exam sets. Five LLMs -- OpenAI o1-preview, GPT-4o, LLaMA 3.1 (405B), Gemini 1.5 Pro, and Claude 3.5 Sonnet -- with the versions released before September 30, 2024, were queried using these new exam sets. To evaluate their deductive reasoning ability, the correct answer options in the questions were replaced with "None of the above." Then, the explain-first and step-by-step instruction prompts were used to test if this strategy improved their reasoning ability. The performance of the LLMs was compared with the answers from medical physicists. Results: All models demonstrated expert-level performance on these questions, with o1-preview even surpassing medical physicists with a majority vote. When replacing the correct answer options with 'None of the above', all models exhibited a considerable decline in performance, suggesting room for improvement. The explain-first and step-by-step instruction prompts helped enhance the reasoning ability of the LLaMA 3.1 (405B), Gemini 1.5 Pro, and Claude 3.5 Sonnet models. Conclusion: These recently released LLMs demonstrated expert-level performance in answering radiation oncology physics questions, exhibiting great potential to assist in radiation oncology physics education and training.
Exbodiment: The Mind Made Matter
Authors: David C. Krakauer
Date: 2024-12-14 00:00:00
Tasks: Decision Making

Exbodiment describes mind outsourced to engineered matter and how matter reeducates mind. The constraints of exbodied matter encode elements of thought, channel decision-making, and constitute an important part of an extended computational phenotype. Here I provide an introduction and brief cultural history of exbodiment in music, natural history, cognition, and astrobiology. The "Helix of Exbodiment" is introduced to illustrate continuous feedback between mind and matter.
Multiplexing in Networks and Diffusion
Authors: Arun G. Chandrasekhar, Vasu Chaudhary, Benjamin Golub, Matthew O. Jackson
Date: 2024-12-16 00:00:00

Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make three contributions to the study of multiplexing. First, we document empirical multiplexing patterns in Indian village data: relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. Second, we examine how these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers (more multiplexing) experience less overall diffusion. This leads to our third contribution: developing a model and theoretical results about diffusion in multiplex networks. Multiplexing slows the spread of simple contagions, such as diseases or basic information, but can either impede or enhance the spread of complex contagions, such as new technologies, depending on their virality. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms.
Observing Micromotives and Macrobehavior of Large Language Models
Authors: Yuyang Cheng, Xingwei Qu, Tomas Goldsack, Chenghua Lin, Chung-Chi Chen
Date: 2024-12-10 00:00:00

Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs') micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs' micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society's macrobehavior. In this paper, we follow the design of Schelling's model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs' suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs' micromotives and encourage a reevaluation of how LLMs may influence users and society.
Moderating the Mediation Bootstrap for Causal Inference
Authors: Kees Jan van Garderen, Noud van Giersbergen
Date: 2024-12-15 00:00:00
Tasks: Causal Inference

Mediation analysis is a form of causal inference that investigates indirect effects and causal mechanisms. Confidence intervals for indirect effects play a central role in conducting inference. The problem is non-standard leading to coverage rates that deviate considerably from their nominal level. The default inference method in the mediation model is the paired bootstrap, which resamples directly from the observed data. However, a residual bootstrap that explicitly exploits the assumed causal structure (X->M->Y) could also be applied. There is also a debate whether the bias-corrected (BC) bootstrap method is superior to the percentile method, with the former showing liberal behavior (actual coverage too low) in certain circumstances. Moreover, bootstrap methods tend to be very conservative (coverage higher than required) when mediation effects are small. Finally, iterated bootstrap methods like the double bootstrap have not been considered due to their high computational demands. We investigate the issues mentioned in the simple mediation model by a large-scale simulation. Results are explained using graphical methods and the newly derived finite-sample distribution. The main findings are: (i) conservative behavior of the bootstrap is caused by extreme dependence of the bootstrap distribution's shape on the estimated coefficients (ii) this dependence leads to counterproductive correction of the the double bootstrap. The added randomness of the BC method inflates the coverage in the absence of mediation, but still leads to (invalid) liberal inference when the mediation effect is small.
VAR models with an index structure: A survey with new results
Authors: Gianluca Cubadda
Date: 2024-12-15 00:00:00
Tasks: Time Series

The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI], originally proposed by <cite>reinsel1983some</cite>, and their applications to economic and financial time series. MAI has recently gained momentum because it can be seen as a link between two popular but distinct multivariate time series approaches: vector autoregressive modeling [VAR] and the dynamic factor model [DFM]. Indeed, on the one hand, the MAI is a VAR model with a peculiar reduced-rank structure; on the other hand, it allows for identification of common components and common shocks in a similar way as the DFM. The focus is on recent developments of the MAI, which include extending the original model with individual autoregressive structures, stochastic volatility, time-varying parameters, high-dimensionality, and cointegration. In addition, new insights on previous contributions and a novel model are also provided.
Treatment Evaluation at the Intensive and Extensive Margins
Authors: Phillip Heiler, Asbjørn Kaufmann, Bezirgen Veliyev
Date: 2024-12-15 00:00:00

This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to non-compliance. The existence of such a population is crucially tied to the regularity of sharp population bounds and thus conventional asymptotic inference for methods such as Lee bounds can be misleading. It can be solved using smoothed outer identification regions for inference. We provide semiparametrically efficient debiased machine learning estimators for both regular and smooth bounds that can accommodate high-dimensional covariates and flexible functional forms. Our study of active labor market policy reveals the empirical prevalence of the aforementioned indifference population and supports results from previous impact analysis under much weaker assumptions.
Forecasting realized covariances using HAR-type models
Authors: Matias Quiroz, Laleh Tafakori, Hans Manner
Date: 2024-12-14 00:00:00
Tasks: State Space Models

We investigate methods for forecasting multivariate realized covariances matrices applied to a set of 30 assets that were included in the DJ30 index at some point, including two novel methods that use existing (univariate) log of realized variance models that account for attenuation bias and time-varying parameters. We consider the implications of some modeling choices within the class of heterogeneous autoregressive models. The following are our key findings. First, modeling the logs of the marginal volatilities is strongly preferred over direct modeling of marginal volatility. Thus, our proposed model that accounts for attenuation bias (for the log-response) provides superior one-step-ahead forecasts over existing multivariate realized covariance approaches. Second, accounting for measurement errors in marginal realized variances generally improves multivariate forecasting performance, but to a lesser degree than previously found in the literature. Third, time-varying parameter models based on state-space models perform almost equally well. Fourth, statistical and economic criteria for comparing the forecasting performance lead to some differences in the models' rankings, which can partially be explained by the turbulent post-pandemic data in our out-of-sample validation dataset using sub-sample analyses.
Do LLMs Act as Repositories of Causal Knowledge?
Authors: Nick Huntington-Klein, Eleanor J. Murray
Date: 2024-12-14 00:00:00
Tasks: Causal Inference, Multiple-choice

Large language models (LLMs) offer the potential to automate a large number of tasks that previously have not been possible to automate, including some in science. There is considerable interest in whether LLMs can automate the process of causal inference by providing the information about causal links necessary to build a structural model. We use the case of confounding in the Coronary Drug Project (CDP), for which there are several studies listing expert-selected confounders that can serve as a ground truth. LLMs exhibit mediocre performance in identifying confounders in this setting, even though text about the ground truth is in their training data. Variables that experts identify as confounders are only slightly more likely to be labeled as confounders by LLMs compared to variables that experts consider non-confounders. Further, LLM judgment on confounder status is highly inconsistent across models, prompts, and irrelevant concerns like multiple-choice option ordering. LLMs do not yet have the ability to automate the reporting of causal links.
An overview of meta-analytic methods for economic research
Authors: Amin Haghnejad, Mahboobeh Farahati
Date: 2024-12-13 00:00:00

Meta-analysis employs statistical techniques to synthesize the results of individual studies, providing an estimate of the overall effect size for a specific outcome of interest. The direction and magnitude of this estimate, along with its confidence interval, offer valuable insights into the underlying phenomenon or relationship. As an extension of standard meta-analysis, meta-regression analysis incorporates multiple moderators -- capturing key study characteristics -- into the model to explain heterogeneity in true effect sizes across studies. This study provides a comprehensive overview of meta-analytic procedures tailored to economic research, addressing key challenges such as between-study heterogeneity, publication bias, and effect size dependence. It equips researchers with essential tools and insights to conduct rigorous and informative meta-analyses in economics and related fields.
A Neyman-Orthogonalization Approach to the Incidental Parameter Problem
Authors: Stéphane Bonhomme, Koen Jochmans, Martin Weidner
Date: 2024-12-13 00:00:00
Tasks: counterfactual

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading examples where this is the case are models for panel and network data that feature fixed effects. In this paper, we show how, in the conditional-likelihood setting, estimating equations can be constructed that are orthogonal to any chosen order. Combining these equations with sample splitting yields higher-order bias-corrected estimators of target parameters. In an empirical application we apply our method to a fixed-effect model of team production and obtain estimates of complementarity in production and impacts of counterfactual re-allocations.
Transition dynamics of electricity asset-owning firms
Authors: Anton Pichler
Date: 2024-12-16 00:00:00

Despite dramatic growth and cost improvements in renewables, existing energy companies exhibit significant inertia in adapting to the evolving technological landscape. This study examines technology transition patterns by analyzing over 140,000 investments in power assets over more than two decades, focusing on how firms expand existing technology holdings and adopt new technologies. Building on our comprehensive micro-level dataset, we provide a number of quantitative metrics on global investment dynamism and the evolution of technology portfolios. We find that only about 10\% of firms experience capacity changes in a given year, and that technology portfolios of firms are highly concentrated and persistent in time. We also identify a small subset of frequently investing firms that tend to be large and are key drivers of global technology-specific capacity expansion. Technology transitions within companies are extremely rare. Less than 3% of the more than 8,400 fossil fuel-dominated firms have substantially transformed their portfolios to a renewable focus and firms fully transitioning to renewables are, up-to-date, virtually non-existent. Notably, firms divesting into renewables do not exhibit very characteristic technology-transition patterns but rather follow idiosyncratic transition pathways. Our results quantify the complex technology diffusion dynamics and the diverse corporate responses to a changing technology landscape, highlighting the challenge of designing general policies aimed at fostering technological transitions at the level of firms.
Binary or nonbinary? An evolutionary learning approach to gender identity
Authors: Hung Truong
Date: 2024-12-14 00:00:00
Tasks: Diversity

Is gender identity binary or nonbinary? My analysis shows that while both are possible, the latter is a more attracting equilibrium under an adaptive learning perspective. I frame the gender identity problem as a modified \textit{battle of the sexes} game, where individuals define their gender identity under pairwise matching motives. From a baseline game-theoretical standpoint, I demonstrate that the binary-only world and the nonbinary-only world are both Nash equilibria in the stage game and are locally stable in the infinitely repeated game. Thus, any state of gender identity could theoretically persist. I then adopt a genetic learning algorithm as an equilibrium selection criterion to investigate evolutionary dynamics further and provide a rationale for the transition from binary to nonbinary gender identity. Specifically, in a binary-origin world, divergence occurs as individuals identifying as nonbinary gender evolve to become the majority due to their higher flexibility in matching outcomes. My framework captures how adaptive learning drives identity evolution, offering a parsimonious tool to analyze how diversity and exclusivity emerge in varying economic environments.
On Prior Confidence and Belief Updating
Authors: Kenneth Chan, Gary Charness, Chetan Dave, J. Lucas Reddinger
Date: 2024-12-14 00:00:00

We experimentally investigate how confidence over multiple priors affects belief updating. Theory predicts that the average Bayesian posterior is unaffected by confidence over multiple priors if average priors are the same. We manipulate confidence by varying the time subjects view a black-and-white grid, the proportion representing the prior in a Bernoulli distribution. We find that when subjects view the grid for a longer duration, they have more confidence, under-update more, and place more (less) weight on priors (signals). Overall, confidence over multiple priors matters when it should not, while confidence in prior beliefs does not matter when it should.
Strategically Acting on Information
Authors: Xiaoming Wang
Date: 2024-12-13 00:00:00

In many non-cooperative settings, agents often possess useful information that provide an advantage over their opponent(s), but acting on such information too frequently can lead to detection. I develop a simple framework to analyze such a trade-off and characterize the optimal way in which to act on information.
Re-examining the social impact of silver monetization in the Ming Dynasty from the perspective of supply and demand
Authors: Tianwei Chang
Date: 2024-12-13 00:00:00

Existing studies have shown that the monetization of silver in the Ming Dynasty effectively promoted the prosperity of trade in the Ming Dynasty, while the prices of labor, handicraft products and grain were long suppressed by the deformed economic structure. With the expansion of silver application, the fluctuation of silver supply and demand exacerbated the above contradictions. Capital accumulation that should have been obtained through the marketization of labor was easily plundered by the landlord gentry class through silver. This article re-discusses the issue from the perspective of supply and demand. Compared with the increase and then decrease of silver supply, the evolution of silver demand is more complicated: at the tax level, the widespread use of silver leads to a huge difference in the elasticity of production and trade taxes. When government spending surges, the increase in tax burden will be mainly borne by agriculture and handicrafts. At the production level, the high liquidity of silver makes the concentration of social wealth more convenient, while the reduction in silver supply and the expansion of demand have rapidly expanded deflation, further exacerbating the gap between the rich and the poor. Such combined effect of supply and demand factors has caused the monetization of silver to become an accelerator of the economic collapse of the Ming Dynasty.
Quantifying Educational Competition: A Game-Theoretic Model with Policy Implications
Authors: Siyuan He
Date: 2024-12-14 00:00:00

The competitive pressures in China's primary and secondary education system have persisted despite decades of policy interventions aimed at reducing academic burdens and alleviating parental anxiety. This paper develops a game-theoretic model to analyze the strategic interactions among families in this system, revealing how competition escalates into a socially irrational "education arms race." Through equilibrium analysis and simulations, the study demonstrates the inherent trade-offs between education equity and social welfare, alongside the policy failures arising from biased social cognition. The model is further extended using Spence's signaling framework to explore the inefficiencies of the current system and propose policy solutions that address these issues.
Strategic Attribute Learning
Authors: Jean-Michel Benkert, Ludmila Matyskova, Egor Starkov
Date: 2024-12-13 00:00:00
Tasks: Attribute

A researcher allocates a budget of informative tests across multiple unknown attributes to influence a decision-maker. We derive the researcher's equilibrium learning strategy by solving an auxiliary single-player problem. The attribute weights in this problem depend on how much the researcher and the decision-maker disagree. If the researcher expects an excessive response to new information, she forgoes learning altogether. In an organizational context, we show that a manager favors more diverse analysts as the hierarchical distance grows. In another application, we show how an appropriately opposed advisor can constrain a discriminatory politician, and identify the welfare-inequality Pareto frontier of researchers.
Ant Nest Detection Using Underground P-Band TomoSAR
Authors: Gian Oré, Alexandre Santos, Daniele Ukan, Ronald Zanetti, Mariane Camargo, Luciano P. Oliveira, Guillermo Kemper, Alonso Sanchez, Aldo Diaz, Jorge Gonzalez, Ruth Rubio-Noriega, Levy Boccato, Hugo E. Hernandez-Figueroa
Date: 2024-12-16 00:00:00

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry.