Altruism is fundamental to human societies, fostering cooperation and social cohesion. Recent studies suggest that large language models (LLMs) can display human-like prosocial behavior, but the internal computations that produce such behavior remain poorly understood. We investigate the mechanisms underlying LLM altruism using sparse autoencoders (SAEs). In a standard Dictator Game, minimal-pair prompts that differ only in social stance (generous versus selfish) induce large, economically meaningful shifts in allocations. Leveraging this contrast, we identify a set of SAE features (0.024% of all features across the model's layers) whose activations are strongly associated with the behavioral shift. To interpret these features, we use benchmark tasks motivated by dual-process theories to classify a subset as primarily heuristic (System 1) or primarily deliberative (System 2). Causal interventions validate their functional role: activation patching and continuous steering of this feature direction reliably shift allocation distributions, with System 2 features exerting a more proximal influence on the model's final output than System 1 features. The same steering direction generalizes across multiple social-preference games. Together, these results enhance our understanding of artificial cognition by translating altruistic behaviors into identifiable network states and provide a framework for aligning LLM behavior with human values, thereby informing more transparent and value-aligned deployment.

Advancements in large language models (LLMs) have sparked a growing interest in measuring and understanding their behavior through experimental economics. However, there is still a lack of established guidelines for designing economic experiments for LLMs. Inspired by principles from experimental economics with insights from LLM research in artificial intelligence, we outline key considerations in the experimental design and implementation stage, and perform two sets of experiments to assess the impact of these considerations on LLMs' responses. Based on our findings, we discuss seven practical tactics for conducting experiments with LLMs. Our study enhances the design, replicability, and generalizability of LLM experiments, and broadens the scope of experimental economics in the digital age.

Choice consistency with utility maximization, as a key assumption in economics, has been extensively used to evaluate decision quality of individuals and to predict real-world outcomes across different contexts. Here we investigate the generalizability of consistency measures derived from budgetary decisions in the lab-in-the-field experiment and purchasing decisions using supermarket scanner data. In the first study, we observe a lack of correlation between consistency scores derived from risky decisions in the experiment and those from supermarket food purchasing decisions. In the second study, we observe moderate correlations between experimental tasks and low to moderate correlations across purchasing categories and over time periods within the supermarket. Moreover, consistency in the two settings exhibits distinct predictive validity in predicting consumer behavior. These results suggest that choice consistency, as a measure of decision quality, may be better characterized as a multidimensional skill set rather than a single-dimensional ability.

Robust aggregation integrates predictions from multiple experts without knowledge of experts' information structures. Prior work assumes experts are Bayesian, providing predictions as perfect posteriors based on their signals. However, real-world experts often deviate systematically from Bayesian reasoning. Our work considers experts who tend to ignore the base rate and reveals that a certain degree of base rate neglect helps robust forecast aggregation. Specifically, we consider a two-expert forecast aggregation problem with a binary world state. Experts exhibit base rate neglect, incorporating the base rate information to degree \( \lambda \in [0,1] \). Aggregators' performance is measured by the worst-case regret, which is the maximum regret across the set of considered information structures compared to an omniscient benchmark. Our results reveal the surprising V-shape of regret as a function of experts' base rate consideration degree \( \lambda \), meaning that predictions with intermediate base rate neglect can counter-intuitively lead to better aggregated predictions than perfect Bayesian posteriors.

Background: Asynchronous telemedicine is a crucial component of multichannel health care, where effective communication drives satisfaction. However, the effectiveness of communication features remains poorly understood. Prior research relied on subjective surveys or small-scale simulations, failing to link features to objective outcomes. Understanding these features is critical for optimizing physician engagement and establishing quality indicators to enhance the patient experience.
Objective: This study aimed to bridge this gap by leveraging a large-scale real-world dataset to quantify the association between physicians’ communication features—including response modalities, length, and sequence—and patient repurchase behavior, as well as review scores, within a high-autonomy health care setting.
Methods: This retrospective cross-sectional study analyzed 304,337 paid, patient-initiated virtual visits from a Chinese academic medical center (2021‐2023), which included 823,135 physician responses. The sample was selected after applying a series of exclusion criteria, such as free consultations, team-based visits, and outlier data. The key exposures were the modality of physician responses, response length, and response sequence. Outcomes included patient loyalty and satisfaction. Loyalty was operationalized as follow-up visits within 6 months, with a 30-day exclusion period applied to same-physician (fv1) and same-department (fv2) revisits to filter out clinical necessity, but not to hospital-wide revisits (fv3). Satisfaction was measured by the review scores. We used probit and ordinary least squares regressions to examine the relationships between communication features and patient outcomes.
Results: Regarding loyalty, audio-only visits were associated with the lowest fv1, with an average marginal effect (AME) of −0.030 (95% CI −0.043 to −0.016, P<.001), translating to a 30.9% (0.030/0.097) reduction compared to text-only visits. Regarding satisfaction, audio messages were associated with a significantly increased likelihood of patients providing reviews, with an AME of 0.041 (95% CI 0.006‐0.076, P=.02), but they did not affect review scores after adjusting for inverse Mills ratios. Increased numbers of text and audio replies were (marginally) associated with improved fv1, with AMEs of 0.009 (95% CI 0.006‐0.011, P<.001) and 0.007 (95% CI −0.000 to 0.016, P=.06), respectively. Visits beginning with a sub-5-second audio response and ending with text had significantly higher fv1 than text-only visits, with an AME of 0.069 (95% CI 0.018‐0.120, P=.008). The same patterns hold for fv2 and fv3. Based on the Bonferroni test, coefficients with a P value smaller than α=.050/3=.017 or α=.50/2=.025 were regarded as significant when evaluating the association with patient loyalty or satisfaction, respectively.
Conclusions: Physician communication practices were significantly associated with patient loyalty and satisfaction. This study is innovative in leveraging large-scale real-world data to systematically examine physician communication. It differs from existing studies by transcending prior survey-based research limitations. It introduces an effective hybrid approach, balancing human connection with text clarity in the field. Its implication in the real world is providing data-driven evidence to guide clinicians and policymakers in designing high-quality telemedicine services.

在线教育平台普遍面临由于用户缺乏学习动力而导致的低参与率和低结课率的挑战。针对这一问题,本研究通过与某慕课平台合作,设计了基于沉没成本效应的积分兑换课程机制,并进行了1个学期、覆盖1426门课程的大规模田野实验。课程被随机分为兑换组和非兑换组,以检验积分兑换课程机制对课程平均表现的影响。研究结果表明：兑换组深度用户的课程学习表现显著提升,说明其受到了沉没成本效应的影响;但该机制产生的准入成本同时挤出了部分用户,从而导致课程平均表现下降。因此,平台应合理平衡积分兑换课程机制的正向效果及潜在的负向影响,在优化课程资源配置的同时促进深度用户的学习投入。

改革开放40年来,中国外资并购频繁发生,对经济增长做出了积极的贡献。然而伴随中国经济的高速发展,产能过剩问题日益凸显。本文基于1998—2007年中国企业数据,采用PSM-DID方法系统地研究了外资并购对中国企业产能利用率的影响及作用机制。实证结果表明,外资并购显著促进了企业产能利用率的提高;影响机制检验发现,出口扩张、研发创新和生产效率是外资并购提升企业产能利用率的重要途径;进一步的动态效应估计显示,外资并购后五年内对企业产能利用率具有显著的促进作用,且该效应呈"倒U型"动态变化特征。此外,本文还考察了外资并购对企业产能利用率的异质性影响,发现外资并购对内陆地区企业、高资本密集度企业和高融资约束企业产能利用率提高的促进作用更大。本文最后采用面板分位数回归方法检验了外资并购的产能利用率效应在不同产能水平企业之间的差异性,发现外资并购在更大程度上提升了落后产能企业的产能利用率。以上研究意味着,外资并购有助于缩小企业间产能利用率差距,这对于减少社会资源浪费、提高资源配置效率以及推动产业结构转型升级具有重要的作用。

改革开放四十年来,中国创造了出口贸易发展的奇迹,文章从地方金融自由化视角对中国企业出口扩张提供一种可能的解释。以城市商业银行的设立为研究背景,文章采用1998～2013年中国工业企业数据进行实证研究发现,地方金融自由化不仅显著提高了企业出口概率,而且对企业出口规模扩张具有明显的促进作用,这一结论在处理潜在的样本选择偏差和内生性问题后依旧成立。异质性分析表明,地方金融自由化的出口扩张效应对于民营企业、一般贸易企业和外部融资依赖度高的企业而言更加明显。此外,文章还将城商行的设立作为准自然实验,采用倍差法进行估计,结果也表明地方金融自由化显著促进了企业出口规模的扩张,并且这一促进效应随着城市商业银行设立时间的延长而增强。进一步的影响机制检验表明,地方金融自由化通过缓解企业面临的融资约束问题,促进了企业出口规模扩张。文章的研究结论有助于深入理解地方金融发展与企业出口的内在联系,同时为客观评估地方性金融机构的设立以及企业的出口效应提供了经验证据。此外,文章对地方金融性机构的政策调整也具有重要的启示意义。